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e={1303:0,532:0};r.f.j=(a,d)=>{var f=r.o(e,a)?e[a]:void 0;if(0!==f)if(f)d.push(f[2]);else if(/^(1303|532)$/.test(a))e[a]=0;else{var b=new Promise(((d,b)=>f=e[a]=[d,b]));d.push(f[2]=b);var c=r.p+r.u(a),t=new Error;r.l(c,(d=>{if(r.o(e,a)&&(0!==(f=e[a])&&(e[a]=void 0),f)){var b=d&&("load"===d.type?"missing":d.type),c=d&&d.target&&d.target.src;t.message="Loading chunk "+a+" failed.\n("+b+": "+c+")",t.name="ChunkLoadError",t.type=b,t.request=c,f[1](t)}}),"chunk-"+a,a)}},r.O.j=a=>0===e[a];var a=(a,d)=>{var f,b,c=d[0],t=d[1],o=d[2],n=0;if(c.some((a=>0!==e[a]))){for(f in t)r.o(t,f)&&(r.m[f]=t[f]);if(o)var i=o(r)}for(a&&a(d);n APIs & integrations | Synmetrix Docs - +
- + \ No newline at end of file diff --git a/docs/architecture-and-design.html b/docs/architecture-and-design.html index 7332bd4..bb6d5ae 100644 --- a/docs/architecture-and-design.html +++ b/docs/architecture-and-design.html @@ -4,13 +4,13 @@ Architecture and Design | Synmetrix Docs - +
- + \ No newline at end of file diff --git a/docs/architecture-and-design/database-design.html b/docs/architecture-and-design/database-design.html index f58e23a..d5f3e37 100644 --- a/docs/architecture-and-design/database-design.html +++ b/docs/architecture-and-design/database-design.html @@ -4,13 +4,13 @@ Database Structure Design | Synmetrix Docs - +
Version: 1.0.0

Database Structure Design

Database Structure​

The "Synmetrix" database is organized according to the relational database model and includes the following tables:

  1. public.users: Stores information about registered users, including user ID (id), display name (display_name), avatar URL (avatar_url), as well as the creation time and last update time of the record.

  2. auth.account_providers: Describes the relationships between user accounts and their authentication providers.

  3. auth.accounts: Stores information about user accounts, including unique identifiers, email addresses, passwords, and other data.

  4. auth.providers: Provides a list of available authentication providers.

  5. auth.refresh_tokens: Contains information about refresh tokens for each user account.

  6. auth.roles: Manages user roles.

  7. auth.account_roles: Contains information about roles for each account.

  8. public.teams: Stores information about user teams.

  9. public.datasources: Contains information about data sources used by users.

  10. public.dataschemas: Describes data models used to define business metrics for data sources.

  11. public.explorations: Describes research tasks performed by users.

  12. public.members: Stores information about team members.

  13. public.team_roles: Manages user roles within teams.

  14. public.member_roles: Contains information about roles for each team member.

  15. public.reports: Contains information about the structure and schedule of reports based on metrics needed by users.

  16. public.sql_credentials: Manages SQL credentials used to access business metrics through the SQL interface.

  17. public.alerts: Stores information about alerts created by users.

Database Architecture Description​

Database Architecture Description

The architecture of the database, including relationships between tables, primary and foreign keys, and indexes, is represented in the Database Markup Language (DBML).

This database structure provides flexibility and scalability to the system, allowing convenient management of users, teams, data sources, reports, and other system elements. Each database table is designed for a specific purpose and can be extended or modified to meet evolving system requirements.

note

For the complete DBML representation of the database architecture, please refer to Database Markup Language (DBML).

- + \ No newline at end of file diff --git a/docs/architecture-and-design/system-architecture.html b/docs/architecture-and-design/system-architecture.html index 13cd503..05be4e4 100644 --- a/docs/architecture-and-design/system-architecture.html +++ b/docs/architecture-and-design/system-architecture.html @@ -4,13 +4,13 @@ System Architecture | Synmetrix Docs - +
Version: 1.0.0

Synmetrix System Architecture

The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:

Hasura​

Hasura plays a crucial role in the system by connecting to the database and providing a GraphQL API for external services and applications. Hasura simplifies working with the database, offering intuitive and powerful tools for quickly creating queries and mutations in GraphQL, thereby enhancing performance and optimizing data-related workflows.

PostgreSQL​

PostgreSQL has been chosen as the primary database management system due to its reliability, high performance, and flexibility. This allows for efficient handling of large volumes of data while ensuring excellent performance and stability.

Node.js​

Node.js is the server-side platform used to create the backend of the application. Node.js is known for its ability to easily and rapidly develop scalable networked applications, thanks to efficient handling of asynchronous operations and event processing.

React.js​

React.js is a library for developing user interfaces. It provides high performance and simplifies the development process through the use of a component-based architecture.

Cube.js​

Cube.js is an open-source analytics platform for building business analytics applications using JavaScript. In Synmetrix, Cube.js is used to manage business metrics, providing efficient tools for data processing.

CubeStore​

CubeStore is a distributed database optimized for analytical queries and integrated with Cube.js. It offers capabilities for fast and efficient processing of large volumes of data.

Redis​

Redis is a key-value database management system known for its high performance and flexibility. It offers unique features, including support for various data structures such as strings, lists, sets, hashes, and more.

Docker​

Docker is used for containerization and simplifying the application deployment process, ensuring consistent functionality in any environment.

Docker Swarm​

Docker Swarm is an orchestration and management tool for containers deployed using Docker. It allows for the management and scaling of applications across multiple servers, making infrastructure management easier.

Ubuntu​

Ubuntu has been chosen as the primary operating system for the servers hosting all the services. It is a stable and reliable system well-suited for server environments.

Interactions between architecture components​

Interactions between architecture components

Additionally, Synmetrix has been designed as a microservices-based system. Each microservice performs a specific function and can scale independently of others, providing flexibility and scalability to the system. This architecture allows for the independent updating and modernization of individual system components, simplifying the development and maintenance process of the system as a whole.

- + \ No newline at end of file diff --git a/docs/caching.html b/docs/caching.html index 8b58300..3a68e20 100644 --- a/docs/caching.html +++ b/docs/caching.html @@ -4,13 +4,13 @@ Caching | Synmetrix Docs - +
Version: 1.0.0

Overview

Synmetrix offers a robust caching system with two distinct layers to optimize query performance and minimize database load. This system enhances the efficiency of data retrieval and processing. The primary caching layer is an in-memory cache, which is active by default. The second layer is called "pre-aggregations" and requires explicit configuration to activate.

In-Memory Cache​

Synmetrix's in-memory cache serves as a temporary buffer for your database. It becomes especially valuable when multiple concurrent users request the same data simultaneously. While pre-aggregations are designed to strike a balance between query response time and performance, the in-memory cache helps smooth out spikes in data demand.

In development mode, you can reset the in-memory cache by simply restarting the server.

Note: It's generally not recommended to modify the default in-memory caching configuration unless it's absolutely necessary. To improve query performance, consider utilizing pre-aggregations.

Pre-Aggregations​

Pre-aggregations introduce an additional layer of aggregated data, constructed and updated by Synmetrix. They can significantly enhance query performance and concurrency.

To create pre-aggregations, Synmetrix might require write access to the pre-aggregations schema within the source database. In this scenario, Synmetrix initially builds pre-aggregations as tables in the source database and then exports them to the pre-aggregations storage. Refer to your specific driver's documentation for details on read-only support and pre-aggregation build strategies.

Pre-aggregations are defined within the data model. You can learn more about defining pre-aggregations in the data modeling reference.

  cubes:
      - name: orders
        sql_table: orders

        measures:
          - name: total_amount
            sql: amount
            type: sum

        dimensions:
          - name: created_at
            sql: created_at
            type: time

        pre_aggregations:
          - name: amount_by_created
            measures:
              - total_amount
            time_dimension: created_at
            granularity: month

In-Memory Cache Operation​

Synmetrix utilizes an in-memory cache to store the results of executed queries. The cache key is generated from the SQL statement, including any existing pre-aggregations that the query depends on.

Upon receiving an incoming request, Synmetrix first checks the cache using this key. If no match is found in the cache, the query is executed in the database. The result set is returned and simultaneously updates the cache.

If a cached value exists, and the refresh_key value for the query remains unchanged, the cached value is returned. Otherwise, an SQL query is executed either against the pre-aggregations storage or the source database to refresh the cache and retrieve the updated results.

Refresh Keys​

To avoid unnecessary database queries, Synmetrix defines a refresh_key for each cube. These refresh keys are evaluated to determine if data needs to be refreshed.

Here's an example of a refresh_key that instructs Synmetrix to refresh data every 5 minutes:

YAML

cubes:
  - name: orders
    # ...

    refresh_key:
      every: 5 minutes

JavaScript

cube(`orders`, {
  refresh_key: {
    every: `5 minute`,
  },
});

Alternatively, the following refresh_key only refreshes data when the value of MAX(created_at) changes. By default, Synmetrix checks this refresh key every 10 seconds:

YAML

cubes:
  - name: orders
    # ...

    refresh_key:
      sql: SELECT MAX(created_at) FROM orders

JavaScript

cube(`orders`, {
  // ...
 
  refresh_key: {
    sql: `SELECT MAX(created_at) FROM orders`,
  },
});

Default Refresh Keys​

By default, in development mode, Synmetrix will check and invalidate the cache in the background. For production environments, it's recommended to run a Refresh Worker as a separate instance.

We strongly recommend enabling background cache invalidation using a separate Synmetrix worker for production deployments. Please consult the Production Checklist for more information. If background refresh is disabled, Synmetrix will refresh the cache during query execution. To ensure optimal response times for end-users, it's advisable to always enable background refresh.

Inspecting Queries​

To gain insights into whether a query interacts with the in-memory cache, pre-aggregations, or the underlying data source, you can utilize the Synmetrix Playground or Synmetrix Cloud.

- + \ No newline at end of file diff --git a/docs/caching/getting-started-with-pre-aggregations.html b/docs/caching/getting-started-with-pre-aggregations.html index 198585e..d151962 100644 --- a/docs/caching/getting-started-with-pre-aggregations.html +++ b/docs/caching/getting-started-with-pre-aggregations.html @@ -4,7 +4,7 @@ Getting started with pre-aggregations | Synmetrix Docs - + @@ -27,7 +27,7 @@ database. We have a data model representing all our orders:

YAML 

cubes:
  - name: orders
    sql_table: orders

    measures:
      - name: count
        type: count

    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: status
        sql: status
        type: string

      - name: completed_at
        sql: completed_at
        type: time

JavaScript 

cube(`orders`, {
  sql_table: `orders`,

  measures: {
    count: {
      type: `count`,
    },
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primary_key: true,
    },

    status: {
      sql: `status`,
      type: `string`,
    },

    completed_at: {
      sql: `completed_at`,
      type: `time`,
    },
  },
});

Some sample data from this table might look like:

idstatuscompleted_at
1completed2021-02-15T12:21:11.290
2completed2021-02-25T18:15:12.369
3shipped2021-03-15T20:40:57.404
4processing2021-03-13T10:30:21.360
5completed2021-03-10T18:25:32.109

Our first requirement is to populate a dropdown in our front-end application which shows all possible statuses. The Cube query to retrieve this information might look something like:

JSON 

{
  "dimensions": ["orders.status"]
}

In that case, we can add the following pre-aggregation to the orders cube:

YAML 

cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: order_statuses
        dimensions:
          - status

JavaScript 

cube(`orders`, {
  // ...

  pre_aggregations: {
    order_statuses: {
      dimensions: [status],
    },
  },
});
- + \ No newline at end of file diff --git a/docs/development.html b/docs/development.html index a8dc491..283a32b 100644 --- a/docs/development.html +++ b/docs/development.html @@ -4,13 +4,13 @@ Development | Synmetrix Docs - +
- + \ No newline at end of file diff --git a/docs/development/contributing.html b/docs/development/contributing.html index 3f60b03..d6364bd 100644 --- a/docs/development/contributing.html +++ b/docs/development/contributing.html @@ -4,13 +4,13 @@ Contributing to Synmetrix | Synmetrix Docs - +
Version: 1.0.0

Contributing to Synmetrix

This guide provides information on how to contribute to Synmetrix, including code changes, helping other users, documentation updates, bug reports, bug fixes, new feature proposals, and coding standards.

Contributions to Synmetrix go beyond code development. Assisting new users, testing releases, improving documentation, and participating in discussions are all valuable ways to contribute to the community.

Helping Other Users​

One of the most valuable ways to contribute to Synmetrix is by helping answer user questions on the discussion forum or in the Slack channel. There are always new Synmetrix users seeking assistance, and providing answers is a valuable community service that showcases your expertise.

Contributing to Documentation​

To propose changes to Synmetrix documentation, edit the documentation source files located in the docs/ directory. The README file there explains how to build the documentation locally to test your changes. After making modifications, open a pull request to suggest the changes.

Reporting Bugs​

Reporting a bug is a simple and effective way to contribute to Synmetrix. It helps identify issues, enabling the team to provide patches and enhance the stability and usefulness of Synmetrix. Use the "New issue" button in the issues page to report a bug. Ensure your bug report includes a minimal executable test case to replicate the issue. Follow the instructions in the bug report template provided.

Contributing Bug Fixes​

Contributing bug fixes is an excellent way to build your experience and credibility within the community. If you are new to the Synmetrix codebase, start by looking at issues labeled with help wanted. If you need guidance on fixing an issue, seek assistance from experienced contributors by commenting on the issue page.

Proposing New Features​

Before contributing a new feature, submit a proposal in the issues page and discuss it with the community. This helps identify potential overlaps with planned features and prevents misunderstandings and conflicts.

Submitting Code Changes​

When submitting code contributions, you must sign a Developer Certificate of Origin (DCO) to certify your authorship of the source code or your right to submit it to the project. Sign your contributions by adding a Signed-off-by line to your commit message or using the -s option with the git commit command.

This is my commit message

Signed-off-by: Random J Developer <random@developer.example.org>
- + \ No newline at end of file diff --git a/docs/development/local-development.html b/docs/development/local-development.html index 5d37fbf..03117f7 100644 --- a/docs/development/local-development.html +++ b/docs/development/local-development.html @@ -4,7 +4,7 @@ Local Development | Synmetrix Docs - + @@ -14,7 +14,7 @@ A Less file containing the component's styles. Using module.less ensures that styles are scoped to the component and won't conflict with styles from other components.

  • index.stories.tsx: A file dedicated to Storybook stories for the component. Storybook stories help in visually testing and documenting the component's variations and use cases.

  • index.test.tsx: The file containing unit tests for the component using testing framework vitest. Writing tests ensures the reliability and correctness of the component's functionality.

    • Here's an example directory structure for a component named ExampleComponent:

    /src
      /components
        /ExampleComponent
          index.tsx
          index.module.less
          index.stories.tsx
          index.test.tsx

    Testing​

    Ensure the reliability and correctness of the Synmetrix client by utilizing testing functionalities powered by Vitest. Vitest provides a robust testing framework to validate the behavior of your components and functionalities.

    To run tests, use the following command:

    yarn test

    Executing this command will initiate the testing suite, allowing you to assess the client's performance and functionality. Any detected issues or failures will be highlighted, providing valuable insights for debugging and maintaining code quality.

    Build​

    Efficiently build and package the Synmetrix client with the following commands:

    • Build the Client:

      yarn build

      This command utilizes Vite to build the Synmetrix client.

    • Post-Build Tasks:

      yarn postbuild

      After the build, this command creates compressed archives (dist.tar.gz and dist.zip) of the generated distribution files.

    • Build Storybook:

      yarn build-storybook

      Use this command to build the Storybook for the client.

    • Serve the Built Client Locally:

      yarn serve

      This command previews the built client locally using Vite.

    Incorporate these commands into your workflow to streamline the build process and prepare the Synmetrix client for deployment.

    Conventional Commits​

    We adhere to the Conventional Commits specification to ensure consistent and meaningful commit messages. The structure of our commits follows the pattern:

    <type>[optional scope]: <description>

    [optional body]

    [optional footer(s)]

    For a detailed understanding of Conventional Commits, refer to the Conventional Commits specification.

    - + \ No newline at end of file diff --git a/docs/docker-swarm.html b/docs/docker-swarm.html index 1121ffe..3657ea6 100644 --- a/docs/docker-swarm.html +++ b/docs/docker-swarm.html @@ -4,13 +4,13 @@ Docker Swarm deployment guide | Synmetrix Docs - +
    -
    Version: 1.0.0

    Docker Swarm deployment guide

    For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.

    Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.

    Start Synmetrix on Docker Swarm​

    Go to the Synmetrix project directory and run the following commands:

    Step 1 - Run Docker Registry​

    ./scripts/run-registry.sh

    Step 2 - Run Synmetrix services​

    DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --env stage synmetrix

    Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.

    Step 3 - Run migrations​

    ./migrate.sh

    Destroy stack​

    ./cli.sh swarm destroy synmetrix

    Show logs​

    ./cli.sh swarm logs synmetrix_hasura

    Show stack tasks status​

    ./cli.sh swarm ps synmetrix

    For more information about the CLI tool, run the following command:

    ./cli.sh swarm --help
    - +
    Version: 1.0.0

    Docker Swarm deployment guide

    For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.

    Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.

    Start Synmetrix on Docker Swarm​

    Go to the Synmetrix project directory and run the following commands:

    Step 1 - Run Docker Registry​

    ./scripts/run-registry.sh

    Step 2 - Run Synmetrix services​

    DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --build --env stage synmetrix

    Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.

    Step 3 - Run migrations​

    ./migrate.sh

    Destroy stack​

    ./cli.sh swarm destroy synmetrix

    Show logs​

    ./cli.sh swarm logs synmetrix_hasura

    Show stack tasks status​

    ./cli.sh swarm ps synmetrix

    For more information about the CLI tool, run the following command:

    ./cli.sh swarm --help
    + \ No newline at end of file diff --git a/docs/integration-examples/excel.html b/docs/integration-examples/excel.html index 036cfbd..1f3cd3a 100644 --- a/docs/integration-examples/excel.html +++ b/docs/integration-examples/excel.html @@ -4,13 +4,13 @@ Excel | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/integration-examples/power-bi.html b/docs/integration-examples/power-bi.html index dceeebf..173a67f 100644 --- a/docs/integration-examples/power-bi.html +++ b/docs/integration-examples/power-bi.html @@ -4,13 +4,13 @@ Power BI | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/license.html b/docs/license.html index ec95851..2ae29a3 100644 --- a/docs/license.html +++ b/docs/license.html @@ -4,7 +4,7 @@ License | Synmetrix Docs - + @@ -16,7 +16,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

    - + \ No newline at end of file diff --git a/docs/license/license-community.html b/docs/license/license-community.html index c64c77c..5b83155 100644 --- a/docs/license/license-community.html +++ b/docs/license/license-community.html @@ -4,7 +4,7 @@ License and Community | Synmetrix Docs - + @@ -22,7 +22,7 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

    - + \ No newline at end of file diff --git a/docs/links-and-contacts.html b/docs/links-and-contacts.html index c4387e1..3e2cb82 100644 --- a/docs/links-and-contacts.html +++ b/docs/links-and-contacts.html @@ -4,13 +4,13 @@ Links and Contacts | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/apis-integrations.html b/docs/next/apis-integrations.html index ee56e4f..0f1a192 100644 --- a/docs/next/apis-integrations.html +++ b/docs/next/apis-integrations.html @@ -4,13 +4,13 @@ APIs & integrations | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/architecture-and-design.html b/docs/next/architecture-and-design.html index 47eafca..b3cdc02 100644 --- a/docs/next/architecture-and-design.html +++ b/docs/next/architecture-and-design.html @@ -4,13 +4,13 @@ Architecture and Design | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/architecture-and-design/database-design.html b/docs/next/architecture-and-design/database-design.html index 9e08a4c..d9b1f28 100644 --- a/docs/next/architecture-and-design/database-design.html +++ b/docs/next/architecture-and-design/database-design.html @@ -4,13 +4,13 @@ Database Structure Design | Synmetrix Docs - +
    Version: Next

    Database Structure Design

    Database Structure​

    The "Synmetrix" database is organized according to the relational database model and includes the following tables:

    1. public.users: Stores information about registered users, including user ID (id), display name (display_name), avatar URL (avatar_url), as well as the creation time and last update time of the record.

    2. auth.account_providers: Describes the relationships between user accounts and their authentication providers.

    3. auth.accounts: Stores information about user accounts, including unique identifiers, email addresses, passwords, and other data.

    4. auth.providers: Provides a list of available authentication providers.

    5. auth.refresh_tokens: Contains information about refresh tokens for each user account.

    6. auth.roles: Manages user roles.

    7. auth.account_roles: Contains information about roles for each account.

    8. public.teams: Stores information about user teams.

    9. public.datasources: Contains information about data sources used by users.

    10. public.dataschemas: Describes data models used to define business metrics for data sources.

    11. public.explorations: Describes research tasks performed by users.

    12. public.members: Stores information about team members.

    13. public.team_roles: Manages user roles within teams.

    14. public.member_roles: Contains information about roles for each team member.

    15. public.reports: Contains information about the structure and schedule of reports based on metrics needed by users.

    16. public.sql_credentials: Manages SQL credentials used to access business metrics through the SQL interface.

    17. public.alerts: Stores information about alerts created by users.

    Database Architecture Description​

    Database Architecture Description

    The architecture of the database, including relationships between tables, primary and foreign keys, and indexes, is represented in the Database Markup Language (DBML).

    This database structure provides flexibility and scalability to the system, allowing convenient management of users, teams, data sources, reports, and other system elements. Each database table is designed for a specific purpose and can be extended or modified to meet evolving system requirements.

    note

    For the complete DBML representation of the database architecture, please refer to Database Markup Language (DBML).

    - + \ No newline at end of file diff --git a/docs/next/architecture-and-design/system-architecture.html b/docs/next/architecture-and-design/system-architecture.html index 3ead7c4..6be795b 100644 --- a/docs/next/architecture-and-design/system-architecture.html +++ b/docs/next/architecture-and-design/system-architecture.html @@ -4,13 +4,13 @@ System Architecture | Synmetrix Docs - +
    Version: Next

    Synmetrix System Architecture

    The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:

    Hasura​

    Hasura plays a crucial role in the system by connecting to the database and providing a GraphQL API for external services and applications. Hasura simplifies working with the database, offering intuitive and powerful tools for quickly creating queries and mutations in GraphQL, thereby enhancing performance and optimizing data-related workflows.

    PostgreSQL​

    PostgreSQL has been chosen as the primary database management system due to its reliability, high performance, and flexibility. This allows for efficient handling of large volumes of data while ensuring excellent performance and stability.

    Node.js​

    Node.js is the server-side platform used to create the backend of the application. Node.js is known for its ability to easily and rapidly develop scalable networked applications, thanks to efficient handling of asynchronous operations and event processing.

    React.js​

    React.js is a library for developing user interfaces. It provides high performance and simplifies the development process through the use of a component-based architecture.

    Cube.js​

    Cube.js is an open-source analytics platform for building business analytics applications using JavaScript. In Synmetrix, Cube.js is used to manage business metrics, providing efficient tools for data processing.

    CubeStore​

    CubeStore is a distributed database optimized for analytical queries and integrated with Cube.js. It offers capabilities for fast and efficient processing of large volumes of data.

    Redis​

    Redis is a key-value database management system known for its high performance and flexibility. It offers unique features, including support for various data structures such as strings, lists, sets, hashes, and more.

    Docker​

    Docker is used for containerization and simplifying the application deployment process, ensuring consistent functionality in any environment.

    Docker Swarm​

    Docker Swarm is an orchestration and management tool for containers deployed using Docker. It allows for the management and scaling of applications across multiple servers, making infrastructure management easier.

    Ubuntu​

    Ubuntu has been chosen as the primary operating system for the servers hosting all the services. It is a stable and reliable system well-suited for server environments.

    Interactions between architecture components​

    Interactions between architecture components

    Additionally, Synmetrix has been designed as a microservices-based system. Each microservice performs a specific function and can scale independently of others, providing flexibility and scalability to the system. This architecture allows for the independent updating and modernization of individual system components, simplifying the development and maintenance process of the system as a whole.

    - + \ No newline at end of file diff --git a/docs/next/caching.html b/docs/next/caching.html index d46faaa..8646ec6 100644 --- a/docs/next/caching.html +++ b/docs/next/caching.html @@ -4,13 +4,13 @@ Caching | Synmetrix Docs - +
    Version: Next

    Overview

    Synmetrix offers a robust caching system with two distinct layers to optimize query performance and minimize database load. This system enhances the efficiency of data retrieval and processing. The primary caching layer is an in-memory cache, which is active by default. The second layer is called "pre-aggregations" and requires explicit configuration to activate.

    In-Memory Cache​

    Synmetrix's in-memory cache serves as a temporary buffer for your database. It becomes especially valuable when multiple concurrent users request the same data simultaneously. While pre-aggregations are designed to strike a balance between query response time and performance, the in-memory cache helps smooth out spikes in data demand.

    In development mode, you can reset the in-memory cache by simply restarting the server.

    Note: It's generally not recommended to modify the default in-memory caching configuration unless it's absolutely necessary. To improve query performance, consider utilizing pre-aggregations.

    Pre-Aggregations​

    Pre-aggregations introduce an additional layer of aggregated data, constructed and updated by Synmetrix. They can significantly enhance query performance and concurrency.

    To create pre-aggregations, Synmetrix might require write access to the pre-aggregations schema within the source database. In this scenario, Synmetrix initially builds pre-aggregations as tables in the source database and then exports them to the pre-aggregations storage. Refer to your specific driver's documentation for details on read-only support and pre-aggregation build strategies.

    Pre-aggregations are defined within the data model. You can learn more about defining pre-aggregations in the data modeling reference.

      cubes:
          - name: orders
            sql_table: orders

            measures:
              - name: total_amount
                sql: amount
                type: sum

            dimensions:
              - name: created_at
                sql: created_at
                type: time

            pre_aggregations:
              - name: amount_by_created
                measures:
                  - total_amount
                time_dimension: created_at
                granularity: month

    In-Memory Cache Operation​

    Synmetrix utilizes an in-memory cache to store the results of executed queries. The cache key is generated from the SQL statement, including any existing pre-aggregations that the query depends on.

    Upon receiving an incoming request, Synmetrix first checks the cache using this key. If no match is found in the cache, the query is executed in the database. The result set is returned and simultaneously updates the cache.

    If a cached value exists, and the refresh_key value for the query remains unchanged, the cached value is returned. Otherwise, an SQL query is executed either against the pre-aggregations storage or the source database to refresh the cache and retrieve the updated results.

    Refresh Keys​

    To avoid unnecessary database queries, Synmetrix defines a refresh_key for each cube. These refresh keys are evaluated to determine if data needs to be refreshed.

    Here's an example of a refresh_key that instructs Synmetrix to refresh data every 5 minutes:

    YAML

    cubes:
      - name: orders
        # ...

        refresh_key:
          every: 5 minutes

    JavaScript

    cube(`orders`, {
      refresh_key: {
        every: `5 minute`,
      },
    });

    Alternatively, the following refresh_key only refreshes data when the value of MAX(created_at) changes. By default, Synmetrix checks this refresh key every 10 seconds:

    YAML

    cubes:
      - name: orders
        # ...

        refresh_key:
          sql: SELECT MAX(created_at) FROM orders

    JavaScript

    cube(`orders`, {
      // ...
     
      refresh_key: {
        sql: `SELECT MAX(created_at) FROM orders`,
      },
    });

    Default Refresh Keys​

    By default, in development mode, Synmetrix will check and invalidate the cache in the background. For production environments, it's recommended to run a Refresh Worker as a separate instance.

    We strongly recommend enabling background cache invalidation using a separate Synmetrix worker for production deployments. Please consult the Production Checklist for more information. If background refresh is disabled, Synmetrix will refresh the cache during query execution. To ensure optimal response times for end-users, it's advisable to always enable background refresh.

    Inspecting Queries​

    To gain insights into whether a query interacts with the in-memory cache, pre-aggregations, or the underlying data source, you can utilize the Synmetrix Playground or Synmetrix Cloud.

    - + \ No newline at end of file diff --git a/docs/next/caching/getting-started-with-pre-aggregations.html b/docs/next/caching/getting-started-with-pre-aggregations.html index fe2c3b8..95a329a 100644 --- a/docs/next/caching/getting-started-with-pre-aggregations.html +++ b/docs/next/caching/getting-started-with-pre-aggregations.html @@ -4,7 +4,7 @@ Getting started with pre-aggregations | Synmetrix Docs - + @@ -27,7 +27,7 @@ database. We have a data model representing all our orders:

    YAML 

    cubes:
      - name: orders
        sql_table: orders

        measures:
          - name: count
            type: count

        dimensions:
          - name: id
            sql: id
            type: number
            primary_key: true

          - name: status
            sql: status
            type: string

          - name: completed_at
            sql: completed_at
            type: time

    JavaScript 

    cube(`orders`, {
      sql_table: `orders`,

      measures: {
        count: {
          type: `count`,
        },
      },

      dimensions: {
        id: {
          sql: `id`,
          type: `number`,
          primary_key: true,
        },

        status: {
          sql: `status`,
          type: `string`,
        },

        completed_at: {
          sql: `completed_at`,
          type: `time`,
        },
      },
    });

    Some sample data from this table might look like:

    idstatuscompleted_at
    1completed2021-02-15T12:21:11.290
    2completed2021-02-25T18:15:12.369
    3shipped2021-03-15T20:40:57.404
    4processing2021-03-13T10:30:21.360
    5completed2021-03-10T18:25:32.109

    Our first requirement is to populate a dropdown in our front-end application which shows all possible statuses. The Cube query to retrieve this information might look something like:

    JSON 

    {
      "dimensions": ["orders.status"]
    }

    In that case, we can add the following pre-aggregation to the orders cube:

    YAML 

    cubes:
      - name: orders
        # ...

        pre_aggregations:
          - name: order_statuses
            dimensions:
              - status

    JavaScript 

    cube(`orders`, {
      // ...

      pre_aggregations: {
        order_statuses: {
          dimensions: [status],
        },
      },
    });
    - + \ No newline at end of file diff --git a/docs/next/development.html b/docs/next/development.html index 36d64c7..31f4ac7 100644 --- a/docs/next/development.html +++ b/docs/next/development.html @@ -4,13 +4,13 @@ Development | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/development/contributing.html b/docs/next/development/contributing.html index 3e1f3f8..9a780c4 100644 --- a/docs/next/development/contributing.html +++ b/docs/next/development/contributing.html @@ -4,13 +4,13 @@ Contributing to Synmetrix | Synmetrix Docs - +
    Version: Next

    Contributing to Synmetrix

    This guide provides information on how to contribute to Synmetrix, including code changes, helping other users, documentation updates, bug reports, bug fixes, new feature proposals, and coding standards.

    Contributions to Synmetrix go beyond code development. Assisting new users, testing releases, improving documentation, and participating in discussions are all valuable ways to contribute to the community.

    Helping Other Users​

    One of the most valuable ways to contribute to Synmetrix is by helping answer user questions on the discussion forum or in the Slack channel. There are always new Synmetrix users seeking assistance, and providing answers is a valuable community service that showcases your expertise.

    Contributing to Documentation​

    To propose changes to Synmetrix documentation, edit the documentation source files located in the docs/ directory. The README file there explains how to build the documentation locally to test your changes. After making modifications, open a pull request to suggest the changes.

    Reporting Bugs​

    Reporting a bug is a simple and effective way to contribute to Synmetrix. It helps identify issues, enabling the team to provide patches and enhance the stability and usefulness of Synmetrix. Use the "New issue" button in the issues page to report a bug. Ensure your bug report includes a minimal executable test case to replicate the issue. Follow the instructions in the bug report template provided.

    Contributing Bug Fixes​

    Contributing bug fixes is an excellent way to build your experience and credibility within the community. If you are new to the Synmetrix codebase, start by looking at issues labeled with help wanted. If you need guidance on fixing an issue, seek assistance from experienced contributors by commenting on the issue page.

    Proposing New Features​

    Before contributing a new feature, submit a proposal in the issues page and discuss it with the community. This helps identify potential overlaps with planned features and prevents misunderstandings and conflicts.

    Submitting Code Changes​

    When submitting code contributions, you must sign a Developer Certificate of Origin (DCO) to certify your authorship of the source code or your right to submit it to the project. Sign your contributions by adding a Signed-off-by line to your commit message or using the -s option with the git commit command.

    This is my commit message

    Signed-off-by: Random J Developer <random@developer.example.org>
    - + \ No newline at end of file diff --git a/docs/next/development/local-development.html b/docs/next/development/local-development.html index fc49879..67fa5e6 100644 --- a/docs/next/development/local-development.html +++ b/docs/next/development/local-development.html @@ -4,7 +4,7 @@ Local Development | Synmetrix Docs - + @@ -14,7 +14,7 @@ A Less file containing the component's styles. Using module.less ensures that styles are scoped to the component and won't conflict with styles from other components.

  • index.stories.tsx: A file dedicated to Storybook stories for the component. Storybook stories help in visually testing and documenting the component's variations and use cases.

  • index.test.tsx: The file containing unit tests for the component using testing framework vitest. Writing tests ensures the reliability and correctness of the component's functionality.

    • Here's an example directory structure for a component named ExampleComponent:

    /src
      /components
        /ExampleComponent
          index.tsx
          index.module.less
          index.stories.tsx
          index.test.tsx

    Testing​

    Ensure the reliability and correctness of the Synmetrix client by utilizing testing functionalities powered by Vitest. Vitest provides a robust testing framework to validate the behavior of your components and functionalities.

    To run tests, use the following command:

    yarn test

    Executing this command will initiate the testing suite, allowing you to assess the client's performance and functionality. Any detected issues or failures will be highlighted, providing valuable insights for debugging and maintaining code quality.

    Build​

    Efficiently build and package the Synmetrix client with the following commands:

    • Build the Client:

      yarn build

      This command utilizes Vite to build the Synmetrix client.

    • Post-Build Tasks:

      yarn postbuild

      After the build, this command creates compressed archives (dist.tar.gz and dist.zip) of the generated distribution files.

    • Build Storybook:

      yarn build-storybook

      Use this command to build the Storybook for the client.

    • Serve the Built Client Locally:

      yarn serve

      This command previews the built client locally using Vite.

    Incorporate these commands into your workflow to streamline the build process and prepare the Synmetrix client for deployment.

    Conventional Commits​

    We adhere to the Conventional Commits specification to ensure consistent and meaningful commit messages. The structure of our commits follows the pattern:

    <type>[optional scope]: <description>

    [optional body]

    [optional footer(s)]

    For a detailed understanding of Conventional Commits, refer to the Conventional Commits specification.

    - + \ No newline at end of file diff --git a/docs/next/docker-swarm.html b/docs/next/docker-swarm.html index 164d2e3..5ae1648 100644 --- a/docs/next/docker-swarm.html +++ b/docs/next/docker-swarm.html @@ -4,13 +4,13 @@ Docker Swarm deployment guide | Synmetrix Docs - +
    -
    Version: Next

    Docker Swarm deployment guide

    For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.

    Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.

    Start Synmetrix on Docker Swarm​

    Go to the Synmetrix project directory and run the following commands:

    Step 1 - Run Docker Registry​

    ./scripts/run-registry.sh

    Step 2 - Run Synmetrix services​

    DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --env stage synmetrix

    Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.

    Step 3 - Run migrations​

    ./migrate.sh

    Destroy stack​

    ./cli.sh swarm destroy synmetrix

    Show logs​

    ./cli.sh swarm logs synmetrix_hasura

    Show stack tasks status​

    ./cli.sh swarm ps synmetrix

    For more information about the CLI tool, run the following command:

    ./cli.sh swarm --help
    - +
    Version: Next

    Docker Swarm deployment guide

    For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.

    Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.

    Start Synmetrix on Docker Swarm​

    Go to the Synmetrix project directory and run the following commands:

    Step 1 - Run Docker Registry​

    ./scripts/run-registry.sh

    Step 2 - Run Synmetrix services​

    DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --build --env stage synmetrix

    Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.

    Step 3 - Run migrations​

    ./migrate.sh

    Destroy stack​

    ./cli.sh swarm destroy synmetrix

    Show logs​

    ./cli.sh swarm logs synmetrix_hasura

    Show stack tasks status​

    ./cli.sh swarm ps synmetrix

    For more information about the CLI tool, run the following command:

    ./cli.sh swarm --help
    + \ No newline at end of file diff --git a/docs/next/integration-examples/excel.html b/docs/next/integration-examples/excel.html index f6de271..49b3176 100644 --- a/docs/next/integration-examples/excel.html +++ b/docs/next/integration-examples/excel.html @@ -4,13 +4,13 @@ Excel | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/integration-examples/power-bi.html b/docs/next/integration-examples/power-bi.html index f68f8f5..be6ffa6 100644 --- a/docs/next/integration-examples/power-bi.html +++ b/docs/next/integration-examples/power-bi.html @@ -4,13 +4,13 @@ Power BI | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/license.html b/docs/next/license.html index f1465a9..24f835b 100644 --- a/docs/next/license.html +++ b/docs/next/license.html @@ -4,7 +4,7 @@ License | Synmetrix Docs - + @@ -16,7 +16,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

    - + \ No newline at end of file diff --git a/docs/next/license/license-community.html b/docs/next/license/license-community.html index cb55826..5a746e1 100644 --- a/docs/next/license/license-community.html +++ b/docs/next/license/license-community.html @@ -4,7 +4,7 @@ License and Community | Synmetrix Docs - + @@ -22,7 +22,7 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

    - + \ No newline at end of file diff --git a/docs/next/links-and-contacts.html b/docs/next/links-and-contacts.html index ca0582a..c3e3aad 100644 --- a/docs/next/links-and-contacts.html +++ b/docs/next/links-and-contacts.html @@ -4,13 +4,13 @@ Links and Contacts | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/overview.html b/docs/next/overview.html index 43dc486..00a9471 100644 --- a/docs/next/overview.html +++ b/docs/next/overview.html @@ -4,13 +4,13 @@ Overview | Synmetrix Docs - +
    Version: Next

    Overview

    Welcome to Synmetrix, the ultimate data toolkit designed specifically for data engineers, analysts, and managers. At Synmetrix, we're on a mission to simplify data management, analysis, and transmission, so you can make informed decisions effortlessly.

    The Data Management Challenge​

    In today's data-driven world, the challenges of data management are ever-increasing, especially in fields that rely heavily on data processing, analysis, and transmission. Inefficient data management can result in lost productivity, missed opportunities, and even regulatory compliance issues. The need for a comprehensive solution to address these complexities has never been more pressing.

    Synmetrix is committed to tackling this challenge head-on and providing a robust solution to streamline data management, analysis, and transmission.

    Software Description and Purpose​

    With Synmetrix, you can effortlessly address the complexity and inefficiency in managing, analyzing, and transmitting data across various sectors. Our platform offers powerful features for creating and managing data models, tracking metric changes, and organizing data-related tasks. You can easily share your data with others through interfaces like REST, GraphQL, or SQL, making your data a single source of truth for your entire organization. Whether you're a data engineer, analyst, or manager, Synmetrix has everything you need to excel with big data.

    Tapping into a Vast Market​

    Synmetrix's versatile features find applications across a wide range of sectors, including finance, retail, the power industry, manufacturing, research and development, healthcare, and many more. As the global big data market continues to boom, with projections reaching $103 billion by 2027 and a compound annual growth rate of 10.9%, Synmetrix is poised to seize this massive opportunity. We're here to provide the solution to the complexity and inefficiency in data management, analysis, and transmission.

    Key Features​

    • Data Modeling (Semantic layer): Transform raw data into meaningful business indicators with our effective data modeling feature.
    • SQL Interface: Access metrics easily and manipulate data with our intuitive SQL interface.
    • Reporting System: Schedule reports and distribute them through various communication channels with our integrated reporting system.
    • Alerting System: Stay informed about significant data changes with our proactive alerting system.
    • Documentation Generation: Enhance the understanding and usability of your data models with our documentation generation feature.

    Programming Language​

    • JavaScript / Node.js

    How to Use This Documentation​

    To make the most of this documentation, you can explore the following sections:

    • Quick Start: Installation and initial setup of Synmetrix.

    • User Guide: In-depth guides on using Synmetrix.

    • Contributing: Information on how to contribute to the development of Synmetrix.

    Contributing​

    We welcome contributions from the community. If you'd like to get involved in the development of Synmetrix or have ideas for improvement, please check out our Contributing Guidelines.

    Contact Us​

    If you have questions, feedback, or need assistance, don't hesitate to reach out to us. Contact us. or by visiting our GitHub repository.

    Synmetrix is meticulously designed to streamline your data management, analysis, and transmission workflows, making it the ultimate tool for all your data needs. We look forward to your involvement and contributions!

    - + \ No newline at end of file diff --git a/docs/next/production-deployment.html b/docs/next/production-deployment.html index ea542c3..08dbc82 100644 --- a/docs/next/production-deployment.html +++ b/docs/next/production-deployment.html @@ -4,13 +4,13 @@ Production Deployment | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/production-deployment/requirements.html b/docs/next/production-deployment/requirements.html index a247236..3a21283 100644 --- a/docs/next/production-deployment/requirements.html +++ b/docs/next/production-deployment/requirements.html @@ -4,13 +4,13 @@ Requirements | Synmetrix Docs - +
    Version: Next

    Requirements

    Software Requirements​

    Hardware Requirements​

    Synmetrix is intended for installation and use on servers equipped with processors supporting x86-64 architecture and based on operating systems such as Ubuntu Server 18.04 and higher.

    Minimum Hardware Requirements:

    - Processor (CPU): 3.2 GHz or higher, modern processor with multi-threading and virtualization support.
    - RAM: 8 GB or more to handle computational tasks and data processing.
    - Disk Space: At least 30 GB of free space for software installation and storing working data.
    - Network: Internet connectivity is required for cloud services and software updates.
    note

    These requirements are minimum and may vary depending on specific use cases and data processing volumes. Scaling the solution may require more powerful servers or distributed systems.

    Source Code​

    You can access the source code of Synmetrix on GitHub.

    - + \ No newline at end of file diff --git a/docs/next/quickstart.html b/docs/next/quickstart.html index 1328624..32a6ecf 100644 --- a/docs/next/quickstart.html +++ b/docs/next/quickstart.html @@ -4,13 +4,13 @@ Quick Start | Synmetrix Docs - +
    -
    Version: Next

    Quick Start

    Quick Start

    Prerequisite Software​

    Ensure the following software is installed before proceeding:

    Step 1: Download the docker-compose file​

    The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository:

    Execute this in a new directory

    wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml

    Step 2: Launch Synmetrix​

    Execute the following command to start Synmetrix along with a Postgres database for data storage.

    $ docker-compose up -d

    Verify if the containers are operational:

    $ docker ps

    CONTAINER ID IMAGE                 ... CREATED STATUS PORTS          ...
    c8f342d086f3 synmetrix/stack       ... 1m ago  Up 1m  80->8888/tcp ...
    30ea14ddaa5e postgres:12           ... 1m ago  Up 1m  5432/tcp

    The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.

    Step 3: Explore Synmetrix​

    note

    1. Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment.

    2. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated.

    3. Default Seed Data: The project comes with default seed data. Use the following credentials to log in:

      • Email: demo@synmetrix.org
      • Password: demodemo

    If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.

    Demo online​

    Demo: app.synmetrix.org

    • Login: demo@synmetrix.org
    • Password: demodemo

    Demo credentials​

    Database typeHostPortDatabaseUserPasswordSSL
    ClickHousegh-api.clickhouse.tech443defaultplayno passwordtrue
    PostgreSQLdemo-db.cube.dev5432ecomcube12345false
    - +
    Version: Next

    Quick Start

    Quick Start

    Prerequisite Software​

    Ensure the following software is installed before proceeding:

    Step 1: Download the docker-compose file​

    The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository:

    Execute this in a new directory

    wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml

    Step 2: Launch Synmetrix​

    Execute the following command to start Synmetrix along with a Postgres database for data storage.

    $ docker-compose pull stack && docker-compose up -d

    Verify if the containers are operational:

    $ docker ps

    CONTAINER ID IMAGE                 ... CREATED STATUS PORTS          ...
    c8f342d086f3 synmetrix/stack       ... 1m ago  Up 1m  80->8888/tcp ...
    30ea14ddaa5e postgres:12           ... 1m ago  Up 1m  5432/tcp

    The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.

    Step 3: Explore Synmetrix​

    note

    1. Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment.

    2. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated.

    3. Preloaded Seed Data: The project is equipped with preloaded seed data. Use the credentials below to sign in:

      • Email: demo@synmetrix.org
      • Password: demodemo

      This account is pre-configured with two demo datasources and their respective SQL API access. For SQL operations, you can use the following credentials with any SQL client tool such as DBeaver or TablePlus:

      HostPortDatabaseUserPassword
      localhost15432dbdemo_pg_userdemo_pg_pass
      localhost15432dbdemo_clickhouse_userdemo_clickhouse_pass

    If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.

    Demo online​

    Demo: app.synmetrix.org

    • Login: demo@synmetrix.org
    • Password: demodemo

    Demo datasource credentials​

    Database typeHostPortDatabaseUserPasswordSSL
    ClickHousegh-api.clickhouse.tech443defaultplayno passwordtrue
    PostgreSQLdemo-db-examples.cube.dev5432ecomcube12345false
    + \ No newline at end of file diff --git a/docs/next/sql-interface.html b/docs/next/sql-interface.html index c3eba9d..26a6454 100644 --- a/docs/next/sql-interface.html +++ b/docs/next/sql-interface.html @@ -4,13 +4,13 @@ SQL Interface | Synmetrix Docs - +
    Version: Next

    Synmetrix SQL Interface

    Introduction​

    In the development of Synmetrix, the establishment of a robust SQL interface plays a pivotal role. This interface serves as a unified platform for accessing data sources, ensuring consistent metrics are available to all data consumers. It addresses the challenge of maintaining data integrity and precision across various tools employed by modern organizations.

    Problem Statement​

    Many modern organizations utilize numerous tools to process data from a shared storage but perform individual metric calculations. This often leads to discrepancies in calculations and disparities among different teams. The more tools an organization employs, the greater the potential for inconsistencies, making data-driven decision-making complex. Hence, it is essential to create a centralized hub for defining and maintaining metrics, ensuring that all tools operate with a single source of data.

    Solution: Leveraging Cube.js​

    To tackle this challenge, Synmetrix has chosen the Cube.js framework, successfully integrated into the Synmetrix platform. Cube.js offers a significant advantage in its ability to act as a data source proxy, transforming incoming queries—whether in JSON, GraphQL, or SQL format—into native queries to the underlying data store.

    Implementation of SQL Interface in Synmetrix​

    Creating the SQL interface within Synmetrix presented several challenges effectively addressed using the Cube.js framework and Apache Datafusion. This SQL interface enables Cube.js to provide data through a PostgreSQL-compatible protocol to various data-consuming applications, including but not limited to:

    • BI tools
    • Data exploration tools such as Jupyter or Hex
    • Reverse ETL tools like Census or Hightouch
    • Low-code development platforms such as Retool
    • Automated tasks

    The SQL interface has been thoroughly tested with a range of tools and platforms, including:

    The SQL API utilizes Apache Datafusion as its SQL execution engine, responsible for query planning and execution.

    Implementation Details​

    The implementation of the SQL interface in Synmetrix comprises several key steps, facilitated by the Cube.js framework and Apache Datafusion:

    • Request Retrieval: Incoming data retrieval requests arrive in SQL format to Cube.js, serving as the initial queries that require processing and execution.
    • Query Transformation: Cube.js transforms the incoming SQL queries into its own query format (JSON). This transformation optimizes and prepares the query for execution against the underlying data store.
    • Query Execution: The SQL query generated in the previous step is sent to the data source. This source can be any supported database to which Cube.js can connect.
    • Data Retrieval: The output is a dataset containing the required data in response to the original query.
    • Load into Apache Datafusion: The dataset is loaded into Apache Datafusion, where it undergoes further processing. Apache Datafusion serves as the SQL execution engine, responsible for query planning and execution.
    • Execution of the Original SQL Query: The original SQL query received by the server is executed within Apache Datafusion. This allows for the full utilization of SQL's capabilities for data analysis and processing while preserving the structure and semantics of the original query.
    • Data Return: Query results are returned through Cube.js, where they can be further processed or directly delivered to end-users.

    This process ensures high flexibility and power when working with queries while maintaining data consistency and coherence for all consumers. Each stage has been meticulously designed and optimized to ensure efficient query execution and overall system performance.

    Transformation of SQL Queries​

    A notable feature of the SQL interface in Synmetrix is how it handles and transforms SQL queries. As previously mentioned, Cube.js converts incoming SQL queries into its own query format (JSON), a process known as "Cube query rewrite." This transformation allows for optimization and query preparation for execution against the underlying data store.

    Let's illustrate this with an example query to the "orders" cube in our data model:

    Original SQL Query:

    SELECT
      city,
      SUM(amount)
    FROM orders
    WHERE status = 'shipped'
    GROUP BY 1

    This SQL query in the SELECT statement to the "orders" cube is transformed into Cube.js' internal query format:

    Transformed Cube.js Query:

    {
      "measures": ["Orders.amount"],
      "dimensions": ["Orders.city"],
      "filters": [
        {
          "member": "Orders.status",
          "operator": "equals",
          "values": ["shipped"]
        }
      ]
    }

    As a result of this transformation, not all functions and expressions are supported within query fragments that execute SELECT statements on cube tables. For example, the following query would not work because the SQL API cannot pass the CASE expression to Cube.js for processing, making it impossible to translate the CASE expression into a metric:

    Non-supported Query:

    SELECT
      city,
      CASE
        WHEN status = 'shipped' THEN 'done'
        ELSE 'in-progress'
      END real_status,
      SUM(number)
    FROM orders
      CROSS JOIN Users
    GROUP BY 1;

    However, in such cases, nested queries can be employed. You can encapsulate your SELECT query from a cube table within another SELECT query to perform calculations with expressions like CASE. This outer SELECT is not part of the rewritten SQL query, allowing you to use more SQL functions, operators, and expressions.

    Example Query:

    SELECT
      city,
      CASE
        WHEN status = 'shipped' THEN 'done'
        ELSE 'in-progress'
      END real_status,
      SUM(amount) AS total
    FROM (
      SELECT
        Users.city AS city,
        SUM(number) AS amount,
        orders.status
      FROM orders
        CROSS JOIN Users
      GROUP BY 1, 3
    ) AS inner
    GROUP BY 1, 2
    ORDER BY 1;

    The above query works because the CASE expression is supported in SELECT queries that do not reference cube tables. These nuances and query handling specifics should be considered when designing and utilizing the SQL interface in Synmetrix.

    Server Code​

    For a detailed analysis of the server code and its components, please refer to the Synmetrix SQL Interface Server Code.

    Conclusion​

    The creation of the SQL interface in Synmetrix has allowed for the development of a unified data access interface, ensuring metric consistency for all data tools. This has reduced the number of discrepancies and conflicts related to metric calculations, providing a more reliable foundation for data-driven decision-making.

    - + \ No newline at end of file diff --git a/docs/next/support-updates.html b/docs/next/support-updates.html index f2409c3..e959015 100644 --- a/docs/next/support-updates.html +++ b/docs/next/support-updates.html @@ -4,13 +4,13 @@ Support & Updates | Synmetrix Docs - +
    Version: Next

    Support and Updates

    Contact Information​

    For user support and inquiries, you can reach out to us through the following channels:

    Our support team is dedicated to assisting you with any questions or problems you might encounter while using Synmetrix.

    Upcoming Updates and Changes​

    We are continuously working to improve Synmetrix and provide you with the best possible experience. Here's a glimpse of our upcoming updates and changes: GitHub

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging.html b/docs/next/testing-and-debugging.html index 46bd309..6648278 100644 --- a/docs/next/testing-and-debugging.html +++ b/docs/next/testing-and-debugging.html @@ -4,13 +4,13 @@ Testing and Debugging | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/debugging.html b/docs/next/testing-and-debugging/debugging.html index be280da..91c38af 100644 --- a/docs/next/testing-and-debugging/debugging.html +++ b/docs/next/testing-and-debugging/debugging.html @@ -4,13 +4,13 @@ Debugging | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/test-structure.html b/docs/next/testing-and-debugging/test-structure.html index 5f121cb..3886b1d 100644 --- a/docs/next/testing-and-debugging/test-structure.html +++ b/docs/next/testing-and-debugging/test-structure.html @@ -4,13 +4,13 @@ Test Structure | Synmetrix Docs - +
    Version: Next

    Testing Structure for Synmetrix

    info

    In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI.

    StepCI is employed for writing and conducting integration tests, which verify the correctness of the system's operation when its various components interact with each other. This is particularly vital for projects like Synmetrix, which consist of numerous services and components working collaboratively.

    Test Structure​

    All test files are located in the test/stepci directory within the Synmetrix project folder. Each file represents an individual test scenario described in YAML format.

    - name: change_password
      http:
        url: ${{env.HASURA_PLUS_ENDPOINT}}/auth/change-password
        method: POST
        headers:
          Content-Type: application/json
          Authorization: Bearer ${{captures.accessToken}}
        body: |
          {
            "old_password": "pass321",
            "new_password": "pass123"
          }
        check:
          status: 204

    This YAML configuration represents a typical StepCI test scenario named "change_password." It performs a POST request with specific headers and a JSON request body to change a password. The response status is checked to ensure it's 204.

    Testing Algorithm​

    Testing on the StepCI platform typically involves the following steps:

    1. Request Creation: StepCI generates a request based on the input data defined in the test scenario.

    2. Request Sending: The request is sent to the tested endpoint in the Synmetrix system.

    3. Waiting for Response: StepCI waits for a response from the Synmetrix system. During this time, all necessary actions are performed, such as request processing, interaction with the database, and other services.

    4. Response Verification: After receiving the response, StepCI compares it with the expected result defined in the test scenario. If the response matches the expectations, the test is considered passed. Otherwise, StepCI logs an error, and the test is marked as failed.

    This testing structure ensures the reliability and stability of the Synmetrix system. Automated testing allows developers to quickly identify and rectify errors, as well as ensure that new changes do not disrupt the functionality of existing features.

    Upon completing a set of tests, StepCI provides a report with a brief description of the tests that were passed.

    PASS datasource_flow 9.458s 1 1620 bytes 1 16006 bytes
    Tests: 0 failed, 1 passed, 1 total
    Steps: 0 failed, 0 skipped, 18 passed, 18 total
    Time: 9,533s, estimated 10s
    CO2:
    0.04036g
    Workflow passed after 9.533s

    Test was passed.

    In the event of a test failure, StepCI records details, including the name of the failed testing step, input data and parameters, actual and expected results, and error messages obtained during the execution of the tested code. Subsequent test steps are halted upon encountering a failure.

    v create_team passed after 0.041s
    v invite_team_member passed after 0.597s 
    v create_datasource passed after 0.021s 
    v check_connection passed after 0.833s 
    v validate_datasource passed after 0.078s 
    v fetch_tables passed after 0.436s 
    × gen_dataschemas failed after 0.008s 
    A create_exploration skipped after 0s 
    A fetch_dataset skipped after Os 
    A export_data_models skipped after 0s 
    A fetch_meta skipped after 0s 
    A run_query skipped after Os


    • datasource_flow › gen_dataschemas

    Request HTTP
    POST http://hasura:8080/v1/graphql HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer eyJhbci01JIUzI1N1J9.eyJoYXN1cmEiOnsieC1oYXN1cmEtdXNlci1pZCI6IjU5Yjhj
    OTVjLTdIMmEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMi0\sidXNlciJdLCJ
    4LWhhc3VyYS1kzwZhdWx0LXJvbGUi0iJ1c2VyInosInN1Yi16IjU5YjhjOTVjLTdl™mEtNDc5Ny1hNjZjLWU1MTc3Nm
    NjZDM5ZSIsImlzcyI6Im5ob3N0IiwiaWF0Ijo×NjkwoDkoNzcOLCJleHAi0jE20TE1NDI3NzR9.DTtp9T†jNg6ABm1v
    V7eB0KXZw6k1d2iZx6C-DFicCw™
    ,x-hasura-user-id: 59b8c95c-7e2a-4797-a66c-e51776ccd39e
    {"query": "mutation (#datasource_id: uuid!, $tables: [SourceTable!]!) f\n gen_dataschemas (d atasource_id: $datasource_id, tables: $tables) f\n code\n
    messageln FInrIn"， "variabl
    es" :{"datasource_id":"9218058b-160c-4c43-8126-b944612c055f"
    "tables": K{"name": "default/cell
    towers"},{"name": "default/checks"}1}}
    Response
    HTTP/1.1 200 OK
    transfer-encoding: chunked
    date: Tue 01 Aug 2023 12:59:36 GMT
    server: Warp/3.3.23
    content-type: application/json; charset=utf-8
    {"errors": ["extensions":{"code": "validation-failed", "path":"$.selectionSet. gen_dataschemas args.branch_id"}, "message":"missing required field 'branch_id'"}1}
    Checks
    JSONPath
    × $. data.gen_dataschemas.code: undefined (expected ok)

    Test was failed.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/testing/test-sql-data-models.html b/docs/next/testing-and-debugging/testing/test-sql-data-models.html index 9411f7c..eb58c79 100644 --- a/docs/next/testing-and-debugging/testing/test-sql-data-models.html +++ b/docs/next/testing-and-debugging/testing/test-sql-data-models.html @@ -4,7 +4,7 @@ Test SQL Interface for Data Models | Synmetrix Docs - + @@ -18,7 +18,7 @@ Testing SQL Queries

    Query:

    SELECT MEASURE(count), status, DATE_TRUNC('month', created_at) date FROM orders GROUP BY date, status ORDER BY date ASC;

    Response: Testing SQL Queries

    Query:

    SELECT
      city,
      CASE
        WHEN status = 'shipped' THEN 'done'
        ELSE 'in-progress'
      END real_status,
      SUM(amount) AS total
    FROM (
      SELECT
        users.city AS city,
        SUM(number) AS amount,
        orders.status
      FROM orders
        CROSS JOIN users
      GROUP BY 1, 3
    ) AS inner
    GROUP BY 1, 2
    ORDER BY 1;

    Response: Testing SQL Queries

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source.html b/docs/next/testing-and-debugging/tests/data-source.html index 0a17bb4..7e41b93 100644 --- a/docs/next/testing-and-debugging/tests/data-source.html +++ b/docs/next/testing-and-debugging/tests/data-source.html @@ -4,13 +4,13 @@ Test a Data Source | Synmetrix Docs - +
    Version: Next

    Test a Data Source

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/crate.html b/docs/next/testing-and-debugging/tests/data-source/crate.html index 53066da..53f099b 100644 --- a/docs/next/testing-and-debugging/tests/data-source/crate.html +++ b/docs/next/testing-and-debugging/tests/data-source/crate.html @@ -4,13 +4,13 @@ Crate | Synmetrix Docs - +
    Version: Next

    Crate

    Tested on the crate:5.3.0 image.

    Step 1: Navigate to the CrateDB example directory​

    Navigate to the CrateDB example directory located at /docs/examples/crate in the root of the project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/crate

    Step 2: Run the CrateDB service using the script​

    In the CrateDB example directory, there is a script called run_crate.sh. Make sure the script is executable by running:

    chmod +x run_crate.sh

    Then, start the CrateDB service by running the script:

    ./run_crate.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the UI​

    Go to the UI and add a new Crate data source with the following parameters:

    • Name: Crate
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 4200
    • User: crate

    Step 4: Test the connection​

    Click the "Check connection" button in the UI to verify the connection to the CrateDB data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/dremio.html b/docs/next/testing-and-debugging/tests/data-source/dremio.html index 4e3a04a..c4d9614 100644 --- a/docs/next/testing-and-debugging/tests/data-source/dremio.html +++ b/docs/next/testing-and-debugging/tests/data-source/dremio.html @@ -4,13 +4,13 @@ Dremio | Synmetrix Docs - +
    Version: Next

    Dremio

    Tested on the dremio/dremio-oss:latest image.

    Step 1: Navigate to the Dremio example directory​

    Navigate to the Dremio example directory located at /docs/examples/dremio in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/dremio

    Step 2: Run the Dremio service using the script​

    In the Dremio example directory, there is a script called run_dremio.sh. Make sure the script is executable by running:

    chmod +x run_dremio.sh

    Then, start the Dremio service by running the script:

    ./run_dremio.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Create an administrator account and add a data source in Dremio​

    1. Open your web browser and go to http://localhost:9047.
    2. Follow the prompts to create an administrator account.
    3. Log in to the Dremio UI with your new account.
    4. Click on "Add Source" and select "PostgreSQL" from the list of available data sources.
    5. Use the credentials provided at https://uibakery.io/sql-playground to configure the PostgreSQL data source.

    Step 4: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Host: host_ip (obtained from the script in Step 2)
    • Port: 9047
    • Database name: Name given to the connection in Dremio
    • User: Dremio administrator username
    • Password: Dremio administrator password

    Step 5: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the Dremio data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/druid.html b/docs/next/testing-and-debugging/tests/data-source/druid.html index 939d4b7..d645016 100644 --- a/docs/next/testing-and-debugging/tests/data-source/druid.html +++ b/docs/next/testing-and-debugging/tests/data-source/druid.html @@ -4,14 +4,14 @@ Druid | Synmetrix Docs - +
    Version: Next

    Druid Setup Guide

    Step 1: Navigate to the Druid example directory​

    Navigate to the Druid example directory located at /docs/examples/druid in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/druid

    Step 2: Run the Druid services using the script​

    In the Druid example directory, there is a script called run_druid.sh. Make sure the script is executable by running:

    chmod +x run_druid.sh

    Then, start the Druid services by running the script:

    ./run_druid.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Wait for the cluster to start and go to http://localhost:8889.

    Step 3: Check the services​

    Go to the Services tab and make sure all services are online, as shown in the screenshot below.

    All services online

    Step 4: Load example data​

    Select the "Load data" tab, then choose "Batch - classic" from the dropdown menu.

    Load data -&gt; Batch - classic

    Next, select the "Example data" tile and click "Load example".

    Load example data

    Click the button with the text "Next: ..." in the bottom right corner until the data starts loading (about 9 times). When the data starts loading, you will be redirected to the Ingestion tab and see the message "Task submitted successfully. Going to task view...".

    Next button

    After the data has finished loading, the task status will change to SUCCESS.

    Step 5: Create a new Druid datasource in Synmetrix​

    Fill out the form with the following information:

    • Name: Druid test datasource
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8082

    Currently, the Test Connection feature is not implemented and always returns "OK". To ensure functionality, generate a schema and request any data in the Explore tab.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/elasticsearch.html b/docs/next/testing-and-debugging/tests/data-source/elasticsearch.html index 4321460..26cd5b1 100644 --- a/docs/next/testing-and-debugging/tests/data-source/elasticsearch.html +++ b/docs/next/testing-and-debugging/tests/data-source/elasticsearch.html @@ -4,13 +4,13 @@ Elasticsearch | Synmetrix Docs - +
    Version: Next

    Elasticsearch

    Step 1: Navigate to the Elasticsearch Example Directory​

    Navigate to the Elasticsearch example directory located at /docs/examples/elasticsearch in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/elasticsearch

    Step 2: Run the Elasticsearch Service Using the Script​

    In the Elasticsearch example directory, there is a script called run_elasticsearch.sh. Make sure the script is executable by running:

    chmod +x run_elasticsearch.sh

    Then, start the Elasticsearch service by running the script:

    ./run_elasticsearch.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Wait for the Service to Be Ready and Add a User​

    Wait for the Elasticsearch service to be ready to accept requests. Then run:

    curl -X POST -u elastic:password "http://localhost:9200/_security/user/user" -H 'Content-Type: application/json' -d'
    {
      "password": "password",
      "roles": ["kibana", "kibana_system"],
      "full_name": "Test user"
    }'

    Step 4: Access Kibana and Add Sample Data​

    Wait for the Kibana service to start, then go to http://localhost:5601 and log in with the credentials:

    User: elastic
    Password: password

    Click on the Try sample data link:

    Try sample data screenshot

    At the bottom of the page, click Other sample data sets:

    Other sample data sets screenshot

    Choose a dataset and click Add data.

    Step 5: Create a Datasource in Synmetrix​

    Now you can create a datasource in Synmetrix using the following information:

    • Name: Elasticsearch test datasource
    • Url: http://<host_ip>:9200
    • User: user
    • Password: password

    Save the datasource and click Test connection. You should see a message indicating that the connection is OK.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/firebolt.html b/docs/next/testing-and-debugging/tests/data-source/firebolt.html index a7e1236..028b5e5 100644 --- a/docs/next/testing-and-debugging/tests/data-source/firebolt.html +++ b/docs/next/testing-and-debugging/tests/data-source/firebolt.html @@ -4,13 +4,13 @@ Firebolt | Synmetrix Docs - +
    Version: Next

    Firebolt

    Connecting to Firebolt datasource​

    To connect to a Firebolt datasource, you will need the following information:

    • Database Name - the name of the database to connect to.
    • Username - the username used to connect to the database.
    • Password - the password used to connect to the database.
    • Engine Name - the name of the engine to connect to.
    • API Endpoint - the Firebolt API endpoint used for authentication, such as api.dev.firebolt.io or api.app.firebolt.io.
    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/ksql.html b/docs/next/testing-and-debugging/tests/data-source/ksql.html index accf3d7..f4d4a9b 100644 --- a/docs/next/testing-and-debugging/tests/data-source/ksql.html +++ b/docs/next/testing-and-debugging/tests/data-source/ksql.html @@ -4,13 +4,13 @@ KSQL | Synmetrix Docs - +
    Version: Next

    KSQL

    Tested on the confluentinc/cp-ksqldb-server:7.0.9 image.

    Step 1: Navigate to the ksql example directory​

    Navigate to the ksql example directory located at /docs/examples/ksql in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/ksql

    Step 2: Run the ksql service using the script​

    In the ksql example directory, there is a script called run_ksql.sh. Make sure the script is executable by running:

    chmod +x run_ksql.sh

    Then, start the ksql service by running the script:

    ./run_ksql.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a datasource in the Synmetrix UI​

    Go to the Synmetrix UI and add a new datasource with the following parameters:

    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8088

    Step 4: Test the connection​

    Click the "Test connection" button in the Synmetrix UI to verify the connection to the ksql datasource. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/materialize.html b/docs/next/testing-and-debugging/tests/data-source/materialize.html index 6edcfd9..e756e11 100644 --- a/docs/next/testing-and-debugging/tests/data-source/materialize.html +++ b/docs/next/testing-and-debugging/tests/data-source/materialize.html @@ -4,13 +4,13 @@ Materialize | Synmetrix Docs - +
    Version: Next

    Materialize

    Tested on the materialize/materialized:latest image.

    Step 1: Navigate to the Materialize example directory​

    Navigate to the Materialize example directory located at /docs/examples/materialize in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/materialize

    Step 2: Run the Materialize service using the script​

    In the Materialize example directory, there is a script called run_materialize.sh. Make sure the script is executable by running:

    chmod +x run_materialize.sh

    Then, start the Materialize service by running the script:

    ./run_materialize.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Host: Host IP (obtained from the script in Step 2)
    • Port: 6875
    • Database name: Test
    • User: user

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the Materialize data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/presto.html b/docs/next/testing-and-debugging/tests/data-source/presto.html index 18e5931..25b359a 100644 --- a/docs/next/testing-and-debugging/tests/data-source/presto.html +++ b/docs/next/testing-and-debugging/tests/data-source/presto.html @@ -4,13 +4,13 @@ Presto | Synmetrix Docs - +
    Version: Next

    PrestoDB

    Tested on the starburstdata/presto image.

    Step 1: Navigate to the PrestoDB example directory​

    Navigate to the PrestoDB example directory located at /docs/examples/prestodb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/prestodb

    Step 2: Run the PrestoDB service using the script​

    In the PrestoDB example directory, there is a script called run_prestodb.sh. Make sure the script is executable by running:

    chmod +x run_prestodb.sh

    Then, start the PrestoDB service by running the script:

    ./run_prestodb.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Name: PrestoDB Test
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8050
    • Catalog: tpch
    • User: user

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the PrestoDB data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/questdb.html b/docs/next/testing-and-debugging/tests/data-source/questdb.html index 27e8c97..fbb060a 100644 --- a/docs/next/testing-and-debugging/tests/data-source/questdb.html +++ b/docs/next/testing-and-debugging/tests/data-source/questdb.html @@ -4,13 +4,13 @@ QuestDB | Synmetrix Docs - +
    Version: Next

    QuestDB

    Tested on the questdb/questdb:7.1.1 image.

    Step 1: Navigate to the QuestDB example directory​

    Navigate to the QuestDB example directory located at /docs/examples/questdb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/questdb

    Step 2: Run the QuestDB service using the script​

    In the QuestDB example directory, there is a script called run_questdb.sh. Make sure the script is executable by running:

    chmod +x run_questdb.sh

    Then, start the QuestDB service by running the script:

    ./run_questdb.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Name: QuestDB Test
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8812
    • User: quest
    • Password: quest

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the QuestDB data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/snowflake.html b/docs/next/testing-and-debugging/tests/data-source/snowflake.html index 19b813e..4d15936 100644 --- a/docs/next/testing-and-debugging/tests/data-source/snowflake.html +++ b/docs/next/testing-and-debugging/tests/data-source/snowflake.html @@ -4,13 +4,13 @@ Snowflake | Synmetrix Docs - +
    Version: Next

    Snowflake

    This guide provides step-by-step instructions on how to connect Snowflake to Synmetrix.

    Step 1: Gather Snowflake connection information​

    Before proceeding, ensure you have the following Snowflake connection details:

    • Database Name
    • Warehouse
    • Organization ID
    • Account ID
    • Username
    • Password
    • Role

    You can find this information in your Snowflake account.

    Organization and Account IDs

    Step 2: Add a new data source in the Synmetrix UI​

    1. Go to the Synmetrix UI and navigate to the "Data Sources" section.
    2. Click on the "+ Connect" button.
    3. Select "Snowflake" from the list of available data sources.

    Step 3: Fill in the connection details​

    Fill in the Snowflake connection details in the form with the following fields:

    • Name: A custom name for your data source (e.g., "My Snowflake")
    • Database Name: The name of your Snowflake database
    • Warehouse: The name of your Snowflake warehouse
    • Organization ID: The ID of your Snowflake organization
    • Account ID: The ID of your Snowflake account
    • Username: Your Snowflake username
    • Password: Your Snowflake password
    • Role: Your Snowflake role

    Step 4: Test the connection​

    Click the "Test connection" button in the Synmetrix UI to verify the connection to your Snowflake data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing-and-debugging/tests/data-source/trino.html b/docs/next/testing-and-debugging/tests/data-source/trino.html index 70bce3f..6ef6cda 100644 --- a/docs/next/testing-and-debugging/tests/data-source/trino.html +++ b/docs/next/testing-and-debugging/tests/data-source/trino.html @@ -4,13 +4,13 @@ Trino | Synmetrix Docs - +
    Version: Next

    Trino

    Tested on the trinodb/trino:latest image.

    Step 1: Navigate to the Trino example directory​

    Navigate to the Trino example directory located at /docs/examples/trino in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/trino

    Step 2: Run the Trino service using the script​

    In the Trino example directory, there is a script called run_trino.sh. Make sure the script is executable by running:

    chmod +x run_trino.sh

    Then, start the Trino service by running the script:

    ./run_trino.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Name: Trino Test
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8050
    • Catalog: tpch
    • User: user

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the Trino data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/next/testing.html b/docs/next/testing.html index dc09587..5d70fba 100644 --- a/docs/next/testing.html +++ b/docs/next/testing.html @@ -4,13 +4,13 @@ Testing Guides | Synmetrix Docs - +
    Version: Next

    Testing Guides

    - + \ No newline at end of file diff --git a/docs/next/usage/user-guide/data-source/databricks.html b/docs/next/usage/user-guide/data-source/databricks.html index c7fe47e..fc36e83 100644 --- a/docs/next/usage/user-guide/data-source/databricks.html +++ b/docs/next/usage/user-guide/data-source/databricks.html @@ -4,13 +4,13 @@ Databricks | Synmetrix Docs - +
    Version: Next

    Connecting to Databricks Datasource via JDBC

    To connect to a Databricks datasource via JDBC, you will need the following information:

    • Access Token: A personal access token used to authenticate the Databricks connection.

    • Databricks JDBC URL: The URL for a JDBC connection. It should be in the format:

      jdbc:databricks://{'<Databricks address>'}:443/default;transportMode=http;ssl=1;

    • Database Name: The name of the database to connect to.

    - + \ No newline at end of file diff --git a/docs/next/user-guide.html b/docs/next/user-guide.html index 8686fc3..5b83b15 100644 --- a/docs/next/user-guide.html +++ b/docs/next/user-guide.html @@ -4,13 +4,13 @@ User Guide | Synmetrix Docs - +
    Version: Next

    User Guide

    User Guide

    This documentation provides an in-depth guide to the various pages and features of the Synmetrix interface. Here, you will find detailed information and instructions on how to utilize each page effectively.

    The interface documentation is organized into separate pages, each focusing on a specific aspect of the Synmetrix interface. Below are the links to the documentation for each page:

    Table of Contents​

    📄️ Models

    The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page:

    Feel free to explore the documentation for each page to gain a comprehensive understanding of the Synmetrix interface and its functionalities.

    Happy exploring and analyzing data with Synmetrix!

    - + \ No newline at end of file diff --git a/docs/next/user-guide/explore.html b/docs/next/user-guide/explore.html index ab2c2e4..bd6b967 100644 --- a/docs/next/user-guide/explore.html +++ b/docs/next/user-guide/explore.html @@ -4,7 +4,7 @@ Explore Page | Synmetrix Docs - + @@ -13,7 +13,7 @@ To switch to a different data source:

    1. Find the Data Source in the sidebar.

    2. Pick a New Data Source: Click on the list there and choose the one you want.

    3. It's Changed!: Your data source will change right away.

    Switching Branches in Explorer​

    Switching Branches in Explorer To change the branch you're working on:

    1. Look for Branch Section in the sidebar.

    2. Choose Your Branch: Click on the list and pick the branch you need.

    3. Branch Changed: Now you're working in the new branch.

    Selecting Dimensions and Measures​

    Selecting Dimensions and Measures

    Picking the right dimensions and measures is super important for looking at your data the right way. Here's what to do:

    1. Go to the Sidebar: First, find the sidebar.

    2. Click on Desired Model: Click it and you'll see a list.

    3. See All the Options: This list shows all the measures, dimensions, time dimensions and segments you can use.

    4. Pick What You Need: Choose the ones that you're willing to analyze.

    5. Run Your Query: After picking, run your query to see your data.

    tip
    • Quick Find: Use the search box to find dimensions or measures fast.

    Adding Filters​

    Guide to Adding Filters Incorporating filters into your analysis is a crucial step for refining and focusing your data exploration. Here’s a professional approach to adding filters through the Explorer Sidebar:

    1. Navigate to Explorer Sidebar: Begin by accessing the sidebar options within the Explorer.

    2. Hover Over Measures or Dimensions: Move your cursor over either the Measures or Dimensions sections in the sidebar.

    3. Initiate Filter Selection: Look for a filter icon on the specific measure or dimension you are interested in. Click on this icon to start setting up a filter.

    4. Configure Filter in Result Tab: After clicking the filter button, switch to the "Result" tab. Here, you'll find options to customize your filter according to your specific data requirements.

      Process of Adding Filters

    Explore Toolbar Functionality Overview​

    Toolbar in Action

    The toolbar in your data analysis tool is equipped with a variety of features to enhance your data exploration and reporting capabilities. Here's a breakdown of the key actions available:

    Run Query​

    • Function: Runs the analysis based on the selected dimensions, measures, and filters.
    • Purpose: To dynamically generate and view data results tailored to your current selections.

    Set Up Alerts​

    • Function: Allows for the creation of alerts based on specific data conditions.
    • Usage: Configure alert parameters to monitor data changes and receive notifications.

    Generate Reports​

    • Function: Creates reports reflecting your current data view.
    • Customization: Offers options to define report parameters and settings for targeted reporting.

    Export to CSV​

    • Function: Enables the export of data into a CSV file.
    • Application: Useful for offline data analysis or for sharing data externally.

    Work with data​

    Results Tab​

    Results Tab

    • Display: Shows the outcome of your executed queries.
    • Adjustments: Allows configuration of row limits, offsets, and view of applied filters.

    Generated SQL Tab​

    Generated SQL Tab

    • Function: Displays the automatically generated SQL query post-execution.
    • Utility: Useful for understanding the query structure or for manual SQL query replication.

    REST API Tab​

    REST API Tab

    • Access Details: Provides necessary API information for data retrieval.
    • Components: Includes headers and the API endpoint URL.

    Example API Utilization​

    Example API Utilization

    • Headers:
      • Authorization: Bearer <token>
      • x-hasura-datasource-id: <datasource-id>
      • x-hasura-branch-id: <branch-id>
    • URL: https://cube.synmetrix.org/api/v1/load
    • Body: Follow the API documentation for accurate setup.

    Execute API Request: Enables sending an API request directly from the interface.

    - + \ No newline at end of file diff --git a/docs/next/user-guide/explore/alerts.html b/docs/next/user-guide/explore/alerts.html index 05b3db4..0503a49 100644 --- a/docs/next/user-guide/explore/alerts.html +++ b/docs/next/user-guide/explore/alerts.html @@ -4,7 +4,7 @@ Alerts Page | Synmetrix Docs - + @@ -13,7 +13,7 @@ Alerts Page Creation

  • Choose your notification delivery method. Options include Webhook, Slack, or Email, depending on your system integrations and preferences. Delivery Method Selection

  • Complete the alert form with the necessary information: Alert Configuration Form

    • Detailed Description of Form Fields on the Alerts Page:​

    Alert Name: Provide a descriptive name that clearly identifies the alert's purpose and content.

    Preview Section:​

    This section offers a comprehensive overview of the Alert or Report setup. Key features include:

    Query Key: This is the primary query that your alert or report is based on. It's a critical element in the monitoring process.

    Measures: Select the quantitative data points you wish to track or display. These are crucial for your analysis and will be highlighted in the Preview.

    Dimensions: These provide context and categorization for your data, allowing a more detailed and segmented analysis.

    info

    The Preview amalgamates all these components, offering a clear snapshot of the data and insights being monitored. This ensures that your analysis, whether for an Alert or Report, is accurate and relevant.

    Trigger Settings:​

    Customize how and when your alert is triggered with these settings:

    • Schedule: Set how often the alert conditions are checked (e.g., hourly, daily, weekly).
    • Lower Bound: Define the minimum threshold that triggers the alert.
    • Upper Bound: Specify the maximum threshold for triggering the alert.
    • Request Timeout (minutes): Set the maximum time allowed for the data request to process.
    • Timeout On Fire (minutes): Determine how long before an ongoing alert condition is considered "fired."

    Delivery Settings:​

    Options here vary based on your chosen alert delivery method:

    • Webhook: Input the URL for the webhook endpoint.
    • Slack: Enter the Slack channel or user ID.
    • Email: Specify the email addresses of the alert recipients.
    note

    After configuring your settings, click "Save" to activate the alert. This will commence the monitoring of your specified conditions.

    Enhancements for Clarity and Efficiency:

    1. Clarified instructions for setting up alerts.
    2. Detailed descriptions for each section, ensuring comprehensive understanding.
    3. Streamlined language for ease of reading and comprehension.
    4. Added informative notes and info sections for additional clarity.
    - + \ No newline at end of file diff --git a/docs/next/user-guide/explore/reports.html b/docs/next/user-guide/explore/reports.html index 4af3eda..f462508 100644 --- a/docs/next/user-guide/explore/reports.html +++ b/docs/next/user-guide/explore/reports.html @@ -4,7 +4,7 @@ Reports Page | Synmetrix Docs - + @@ -13,7 +13,7 @@ Reports Page

  • Select the destination where the screenshot will be sent. You can choose between Webhook, Slack, or Email based on your preferences and integrations. Reports Destination

  • Fill out the report form with the following details: Reports Form

    • Form fields description for the Reports page:​

    Report Name: Enter a name that easily identifies the purpose and content of the report.

    Preview:​

    In the "Preview" section, you will find crucial details for the Alert or Report, including the user-selected metric referred to as the "Query Key." This metric is at the core of your monitoring process and plays a pivotal role in alerting or report generation.

    Here, you'll also discover various other essential components such as:

    Measures: These represent the quantitative data that you want to track or display. Users can choose specific measures that matter the most to their analysis, and these will be prominently featured in the Preview.

    Dimensions: Dimensions help to provide context and categorize your data. They allow you to break down the information based on various attributes, providing a more comprehensive view of your metrics.

    info

    In the Preview, all these elements come together to give you a snapshot of the data and insights you're tracking, ensuring that you're monitoring the right information for your analysis, whether it's for an Alert or Report.

    Trigger Settings:​

    • Schedule: Configure the frequency at which the report will be executed by setting a schedule (e.g., daily, weekly, monthly).

    Delivery Settings:​

    The available fields in this section depend on the chosen delivery method for the report. Here are some common options:

    • Webhook: Enter the URL of the webhook endpoint where the report will be sent.
    • Slack: Provide the Slack channel or user ID to which the report will be delivered.
    • Email: Enter the email address(es) of the recipient(s) who will receive the report.
    note

    Once you have completed the settings, click the "Save" button.

    - + \ No newline at end of file diff --git a/docs/next/user-guide/logs.html b/docs/next/user-guide/logs.html index b6b7fb0..988750d 100644 --- a/docs/next/user-guide/logs.html +++ b/docs/next/user-guide/logs.html @@ -4,13 +4,13 @@ Logs Page | Synmetrix Docs - +
    Version: Next

    Logs Page

    Overview​

    Alert Logs

    The Logs page is designed to provide control over the internal workings of the Cube.js service. It allows you to review the requests and the events that occur during the processing of these events. You can access information such as the execution time, start and end time, the actual query, and additional technical details. This enables you to monitor and analyze the performance and behavior of the Cube.js service. Follow the instructions below to utilize the Logs page:

    1. Access the Logs page within the project interface.
    2. Review the list of logs, which includes details such as the request information, execution time, start and end timestamps, and the actual query being processed.
    3. Click on a log entry to expand it and access additional technical information, if available.
    4. Use the logs as a resource for troubleshooting, performance optimization, and gaining insights into the inner workings of the Cube.js service.

    Query Logs filters​

    Query Logs

    - + \ No newline at end of file diff --git a/docs/next/user-guide/models.html b/docs/next/user-guide/models.html index b12eff2..10d40b7 100644 --- a/docs/next/user-guide/models.html +++ b/docs/next/user-guide/models.html @@ -4,7 +4,7 @@ Models Page | Synmetrix Docs - + @@ -17,7 +17,7 @@ New branch

    This will create a new branch with the current state. To work with the new branch, you need to switch to it and then make it the default branch by clicking the "Set as default" button (shown in the screenshot). New branch

    Viewing Version Docs​

    Viewing Version Docs

    1. Go to Sidebar: Click on the sidebar in Synmetrix.
    2. Click 'Docs': Find and click the 'Docs' button.
    3. Get the Docs: The file with the version's documentation will be generated for you. SQL Runner

    SQL Runner in Models​

    SQL Runner

    Accessing the SQL Runner in Synmetrix​

    Opening the SQL Runner in Synmetrix for running custom SQL queries is a straightforward process. Here's how to do it:

    1. Navigate to Model Page: First, go to the model page in the Synmetrix interface. This is where all your data models are listed.

    2. Open SQL Runner Tab: Look for a tab or section labeled "SQL Runner" and click on it. This action will open the SQL Runner interface.

    3. Compose Your SQL Query: In the provided space, you can write your SQL query. For example, you might write a query to select data from a specific table or to perform a more complex data manipulation.

      SELECT * FROM your_table_name WHERE condition;

    4. Execute the Query: Once you've written your query, click the "Run" button. This will execute the SQL query against your data models.

    5. View Results: After running the query, the results will be displayed in a table format directly in the SQL Runner. This allows you to review and analyze the data returned by your query.

    SQL Runner Result

    note

    The Model SQL Runner in Synmetrix is a feature that allows you to run SQL queries directly against your data models. This tool is particularly useful for users who need to perform complex data analysis or want to test and validate their model configurations. Here’s a brief overview:

    • Functionality: The SQL Runner enables you to write and execute SQL queries on the fly. It interacts directly with the data models you've set up in Synmetrix, allowing for real-time data retrieval and analysis.

    • Use Cases:

      • Data Exploration: Quickly run queries to explore data patterns or validate data integrity.
      • Testing Models: Test how new or modified data models perform with actual SQL queries.
      • Advanced Analysis: Perform complex data analysis that might not be possible through standard model exploration.

    • Real-Time Results: Queries executed in the SQL Runner provide immediate feedback, enabling you to rapidly iterate and refine your analysis.

    Editing Models in Synmetrix​

    Editing Models

    1. Open a Model: In sidebar, select and open the model file you wish to edit.
    2. Begin Editing: Utilize the Visual Model Editor to make changes to the model. You can adjust various aspects such as dimensions, measures, and relationships.
    3. Save Changes or close tab: After completing your edits, ensure to save the model to apply the modifications.
    note

    The Visual Model Editor in Synmetrix, enhanced with YAML support, offers an intuitive and user-friendly way to manage data models. YAML's readable format simplifies the process of defining and configuring models, making it accessible even to those with limited coding experience. This combination of a graphical interface and YAML/JS editing provides a flexible and efficient model editing experience, catering to both beginner and advanced users in data model management.

    Importing or Exporting Data Models​

    Importing or Exporting Data Models

    • You can upload data model files from your computer to Synmetrix.
    • Easily save and download your data model files from Synmetrix.

    Using the Visual Model Editor​

    • Edit and tweak your data models right in Synmetrix with a user-friendly editor.

    Deleting a Model​

    Deleting a Model

    1. Open Sidebar: Start in the sidebar.
    2. Find Your Model: Look for the model you want to delete and hover over it.
    3. Click the Trash Icon: Hit the trash can icon next to the model.
    4. Confirm Delete: Click "Remove" to confirm.
    5. Done: The model is now deleted.

    Changing Model Name and File Format​

    Changing Model Name and File Format

    1. Head to Sidebar: Go to the sidebar in Synmetrix.
    2. Choose a Model: Hover over the model you want to change.
    3. Edit: Click the pen icon.
    4. Make Changes: Change the name and file format (JS or YAML).
    5. Finished: Your changes are saved.

    These steps are a simple way to handle your data models in Synmetrix, whether you're adding, editing, or removing them.

    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings.html b/docs/next/user-guide/settings.html index c807061..7953b5c 100644 --- a/docs/next/user-guide/settings.html +++ b/docs/next/user-guide/settings.html @@ -4,13 +4,13 @@ Settings | Synmetrix Docs - +
    Version: Next

    Settings

    Table of Contents​

    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings/data-sources.html b/docs/next/user-guide/settings/data-sources.html index 4ab83de..ed07868 100644 --- a/docs/next/user-guide/settings/data-sources.html +++ b/docs/next/user-guide/settings/data-sources.html @@ -4,7 +4,7 @@ Data Sources | Synmetrix Docs - + @@ -13,7 +13,7 @@ Datasource Connect button

    Step 2​

    A list of available datasource types will be displayed. Select the desired type from the options presented. Datasource Creation Form

    Step 3​

    In the opened modal window, fill in all the required fields in the form. These fields may include connection details, authentication credentials, and other relevant information.

    On the screenshot below, you can see a modal window with the datasource creation form. Datasource Creation Form

    Test Connection

    To test the connection to the datasource, click the "Test connection" button. To save the entered data and create the datasource, click the "Save" button.

    Fast Switching Data Sources in Synmetrix

    In Synmetrix, switching between data sources is a straightforward process. You can find the data models dropdown in the sidebar menu of both the "Models" and "Explore" pages. Simply access this menu to view and select from the available data sources. This dropdown is conveniently placed to ensure ease of access, enhancing your experience by allowing quick and efficient transitions between various data repositories within Synmetrix.

    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings/members.html b/docs/next/user-guide/settings/members.html index caf1ae1..bae3bcf 100644 --- a/docs/next/user-guide/settings/members.html +++ b/docs/next/user-guide/settings/members.html @@ -4,13 +4,13 @@ Members | Synmetrix Docs - +
    Version: Next

    Managing Members

    Managing Members

    Overview​

    The Members section in your settings allows you to manage the team members who have access to your workspace. This guide provides step-by-step instructions on how to add new members, as well as how to remove existing members.

    Member Types​

    There are three types of members you can add to your account:

    1. Owner: The primary account holder with full access and privileges.
    2. Admin: Users with administrative rights, including the ability to manage settings and members.
    3. Member: Regular users with access to the account but limited control over settings and member management.

    Adding a New Member​

    Adding a New Member

    To add a new member to your account, follow these steps:

    1. Navigate to Settings > Members in your account.
    2. Click the Create Now button.
    3. In the popup window, enter the email address of the new member.
    4. Select the user type for the new member (either Member or Admin).
    5. Click the Invite button.
    6. The new member will receive an email with a magic link to sign in to their account.

    Removing a Member​

    To remove a member from your account, follow these steps:

    1. Go to Settings > Members.
    2. Locate the member you wish to remove and find the gear icon on their member card.
    3. Click on Delete.
    4. Confirm the deletion. After confirming, the member will be removed from your account.
    5. The process is complete.

    This documentation provides a clear and concise guide for managing members within your account settings.

    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings/personal-info.html b/docs/next/user-guide/settings/personal-info.html index cbf75a4..b42e279 100644 --- a/docs/next/user-guide/settings/personal-info.html +++ b/docs/next/user-guide/settings/personal-info.html @@ -4,13 +4,13 @@ Personal Information | Synmetrix Docs - +
    Version: Next

    Personal Info

    Overview​

    The "Personal Info" section is designed to provide you with full control over your personal settings and security. Here, you can easily update your username, email address, and password, ensuring your account remains secure and personalized. Additionally, you have the option to manage your active sessions, allowing you to logout from all for enhanced security.

    How to Change Your Username?​

    To change your username:

    1. Go to "Settings" in the sidebar menu.
    2. Navigate to the "Personal Info" tab.
    3. Locate the "Full Name" field under the "General Information" section.
    4. Click on the "Full Name" input field and enter your new username.
    5. Click the "Update" button.
    6. You're all set!

    How to Change Your Email?​

    To update your email address:

    1. Access "Settings" from the sidebar menu.
    2. Select the "Personal Info" tab.
    3. Find the "Email" field in the "General Information" section.
    4. Enter your new email address in the "Email" input field.
    5. Click the "Update" button.
    6. Your email is now updated!

    How to Change Your Password?​

    To change your password:

    1. Visit "Settings" from the sidebar menu.
    2. Click on the "Personal Info" tab.
    3. Scroll down to the "Security" section.
    4. Enter your old password and your desired new password.
    5. Click the "Update Password" button.
    6. Your password has been changed!

    How to Logout from Active Sessions?​

    To logout from all active sessions:

    1. Go to "Settings" in the sidebar menu.
    2. Choose the "Personal Info" tab.
    3. Scroll to the "Active Sessions" section.
    4. Click the "Logout from all sessions" button.
    5. All sessions have been logged out!
    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings/role-access.html b/docs/next/user-guide/settings/role-access.html index 5358bae..c12646e 100644 --- a/docs/next/user-guide/settings/role-access.html +++ b/docs/next/user-guide/settings/role-access.html @@ -4,7 +4,7 @@ Role Based Access | Synmetrix Docs - + @@ -12,7 +12,7 @@
    Version: Next

    Role Based Access Control

    Role Based Access Control

    Overview​

    Role Based Access Control (RBAC) is a system for regulating access to resources within your organization based on the roles of individual users. This document outlines how to manage roles and the types of access they grant.

    Access Types​

    Access Types

    • Partial Access: Grants access to specific Data Sources, Data Models, and various Measures, Dimensions, or Segments.
    • Full Access: Provides unrestricted access to all resources.
    • No Access: Denies access to all resources.

    Adding a New Role​

    To add a new role, follow these steps:

    1. Navigate to Settings > Roles and Access.
    2. Click on the Create Now button.
    3. In the popup window: Role Name
      • Enter the role name.

    Role Popup

    • Set access for data source resources by selecting one or multiple data sources.
    • Choose Data Models.
    • Select Measures, Dimensions, and/or Segments.
      tip

      Use the "Select All" checkbox to select all Measures, Dimensions, and Segments in the current Data Model.

    1. Click Create.
    info

    Roles display information including the creation date, last update, and data sources along with their respective access types.

    Deleting a Role​

    Role Edit

    To delete a role, follow these steps:

    1. Go to Settings > Roles and Access.
    2. Find the role to be deleted and click the gear icon on their card.
    3. Choose Delete from the dropdown list and confirm the deletion.
    4. The deletion process is complete.

    Editing a Role​

    Role Edit

    To edit an existing role, follow these steps:

    1. Navigate to Settings > Roles and Access.

    2. Locate the role to edit and click the gear icon on their card.

    3. Choose Edit from the dropdown list. Role Popup

    4. In the popup window, make the desired changes:

      • Update the role name.
      • Adjust access to data source resources.
      • Select or change Data Models.
      • Choose Measures, Dimensions, and/or Segments.
        tip

        Utilize the "Select All" checkbox for selecting all items in the current Data Model.

    5. Click Save to apply the changes.

    This document provides clear guidelines for managing roles within the Role Based Access Control system, ensuring efficient and secure access management.

    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings/sql-api.html b/docs/next/user-guide/settings/sql-api.html index 40cb1f4..6dfbd23 100644 --- a/docs/next/user-guide/settings/sql-api.html +++ b/docs/next/user-guide/settings/sql-api.html @@ -4,13 +4,13 @@ SQL API | Synmetrix Docs - +
    Version: Next

    SQL API

    SQL API Page

    Effortless Data Management and Uninterrupted Collaboration​

    Engage in powerful and flexible data retrieval and manipulation with our SQL API, designed for effortless data management and seamless collaboration.

    Simplified Data Sharing​

    Synmetrix revolutionizes data sharing with its versatile protocol, ensuring smooth integration with a variety of data tools. Our platform seamlessly connects with business intelligence and data exploration tools (Superset, Tableau), data notebooks (Jupyter, Hex), reverse ETL tools (Census, Hightouch), low-code platforms (Retool), and more, enhancing efficiency and simplicity in data-driven tasks.

    One-Stop Metrics Solution​

    Synmetrix serves as the ultimate repository for all your data needs, centralizing metrics in an organized manner. Regardless of your preferred tools or platforms for data analysis or visualization, Synmetrix provides easy access to essential data.

    Key Benefits​

    1. Integrations: Seamlessly integrate into your workflow, including Excel compatibility.
    2. Flexibility: Encourage collaborative efforts with shared models in a harmonious team environment.
    3. Security: Rely on our robust and reliable security measures.
    4. Effortless: Tailor SQL queries to meet your unique requirements.

    Acquiring SQL Credentials​

    Obtain your SQL API credentials through two distinct methods:

    First Method: From Onboarding​

    1. Initiate a new account creation.
    2. Add your data source.
    3. Opt to generate models (optional).
    4. Synmetrix generates SQL API access during this step.
    5. A pop-up allows you to choose between PSQL or MYSQL for connection, along with credential copying.
    6. Connect using PSQL or MYSQL, or opt to "Download Credentials" for later use.
    7. Finish the onboarding.

    Second Method: From Settings​

    Second Method: From Settings

    1. Navigate to Settings > SQL API.

    2. Click the gear icon on the database card and select "Edit".

    3. The pop-up window provides PSQL or MYSQL connection options and credential copying.

      Example Credentials
      • Host/URL: cube.synmetrix.org:15432
      • Database: db
      • Login: 4x4qg8ei76
      • Password: **********
      • PSQL Client Connection: psql --host=cube.synmetrix.org --port 15432 --username=4x4qg8ei76 --dbname=db

    4. Connect using PSQL or MYSQL, or "Download Credentials" for later use.

    Managing SQL API Credentials​

    Easily create, edit, or delete SQL API credentials via Settings > SQL API.

    Deleting Credentials​

    Delete SQL API

    1. Click the gear icon and select "Delete" from the dropdown.
    2. Confirm deletion.

    Creating Credentials​

    Creating Credentials

    1. Select the "Create Now" button on the SQL API page.
    2. In the pop-up, specify the Team member and Data source.

    Editing Credentials​

    Editing SQL API

    1. Click the gear icon and select "Edit".
    2. Modify the credentials as needed.
    3. Confirm by clicking "Finish".
    - + \ No newline at end of file diff --git a/docs/next/user-guide/settings/teams.html b/docs/next/user-guide/settings/teams.html index cb97eca..df8cce9 100644 --- a/docs/next/user-guide/settings/teams.html +++ b/docs/next/user-guide/settings/teams.html @@ -4,13 +4,13 @@ Teams | Synmetrix Docs - +
    Version: Next

    Managing Your Team

    Introduction​

    The "Teams" section of Synmetrix is a dedicated space for creating, managing, and customizing teams. This feature is essential for collaborative work, allowing you to form new teams, set a focus on a current team, remove teams that are no longer needed, and update team names to align with evolving projects or goals.

    Accessing the Team Page​

    To navigate to the Team page:

    1. Hover over your account profile picture at the top-right corner of the page.
    2. Select "Team" from the dropdown menu.

    Accessing the Team Page

    Team Page Functions​

    On the Team page, you can perform various actions:

    1. Inviting New Users​

    You can invite new members to join your project by entering their email addresses. This step is crucial for expanding your team and fostering collaboration.

    2. Assigning Roles and Access Levels​

    Based on your access management system, assign appropriate roles and access levels to team members, ensuring they have the necessary permissions to contribute effectively.

    3. Managing Access​

    Control access to specific features and resources for each team member, tailoring their permissions to suit their role and needs in the project.

    4. Viewing and Managing Team Members​

    The Team page provides a comprehensive view of all team members, including their roles and responsibilities, allowing for efficient team management.

    How to Create a New Team?​

    To create a new team:

    1. Open "Settings" from the sidebar.
    2. Click on the "Teams" tab.
    3. In "Manage Teams", view your existing teams.
    4. Select the gear icon ("Settings").
    5. Choose "Create Team" from the dropdown and follow the prompts.

    How to Set a Current Team?​

    To set an active team:

    1. Go to "Settings" in the sidebar.
    2. Choose the "Teams" tab.
    3. Find your team list in "Manage Teams".
    4. Click the gear icon next to the team you want to focus on.
    5. Select "Set Current" from the dropdown.

    How to Edit a Team Name?​

    To change a team's name:

    1. Navigate to "Settings" via the sidebar.
    2. Select the "Teams" tab.
    3. Find your team in "Manage Teams".
    4. Click the gear icon next to the team you want to rename.
    5. Choose "Edit" from the dropdown.
    6. Change the "Team Name" in the pop-up.
    7. Click "Save" to confirm the changes.

    How to Delete a Team?​

    To remove a team:

    1. Access "Settings" from the sidebar.
    2. Go to the "Teams" tab.
    3. Identify the team you want to delete in "Manage Teams".
    4. Click the gear icon next to the team.
    5. Choose "Delete" from the dropdown list.

    I've refined the language for professional tone and clarity, ensuring each step is clear and straightforward.

    - + \ No newline at end of file diff --git a/docs/overview.html b/docs/overview.html index 1f2bac1..5ac6e24 100644 --- a/docs/overview.html +++ b/docs/overview.html @@ -4,13 +4,13 @@ Overview | Synmetrix Docs - +
    Version: 1.0.0

    Overview

    Welcome to Synmetrix, the ultimate data toolkit designed specifically for data engineers, analysts, and managers. At Synmetrix, we're on a mission to simplify data management, analysis, and transmission, so you can make informed decisions effortlessly.

    The Data Management Challenge​

    In today's data-driven world, the challenges of data management are ever-increasing, especially in fields that rely heavily on data processing, analysis, and transmission. Inefficient data management can result in lost productivity, missed opportunities, and even regulatory compliance issues. The need for a comprehensive solution to address these complexities has never been more pressing.

    Synmetrix is committed to tackling this challenge head-on and providing a robust solution to streamline data management, analysis, and transmission.

    Software Description and Purpose​

    With Synmetrix, you can effortlessly address the complexity and inefficiency in managing, analyzing, and transmitting data across various sectors. Our platform offers powerful features for creating and managing data models, tracking metric changes, and organizing data-related tasks. You can easily share your data with others through interfaces like REST, GraphQL, or SQL, making your data a single source of truth for your entire organization. Whether you're a data engineer, analyst, or manager, Synmetrix has everything you need to excel with big data.

    Tapping into a Vast Market​

    Synmetrix's versatile features find applications across a wide range of sectors, including finance, retail, the power industry, manufacturing, research and development, healthcare, and many more. As the global big data market continues to boom, with projections reaching $103 billion by 2027 and a compound annual growth rate of 10.9%, Synmetrix is poised to seize this massive opportunity. We're here to provide the solution to the complexity and inefficiency in data management, analysis, and transmission.

    Key Features​

    • Data Modeling (Semantic layer): Transform raw data into meaningful business indicators with our effective data modeling feature.
    • SQL Interface: Access metrics easily and manipulate data with our intuitive SQL interface.
    • Reporting System: Schedule reports and distribute them through various communication channels with our integrated reporting system.
    • Alerting System: Stay informed about significant data changes with our proactive alerting system.
    • Documentation Generation: Enhance the understanding and usability of your data models with our documentation generation feature.

    Programming Language​

    • JavaScript / Node.js

    How to Use This Documentation​

    To make the most of this documentation, you can explore the following sections:

    • Quick Start: Installation and initial setup of Synmetrix.

    • User Guide: In-depth guides on using Synmetrix.

    • Contributing: Information on how to contribute to the development of Synmetrix.

    Contributing​

    We welcome contributions from the community. If you'd like to get involved in the development of Synmetrix or have ideas for improvement, please check out our Contributing Guidelines.

    Contact Us​

    If you have questions, feedback, or need assistance, don't hesitate to reach out to us. Contact us. or by visiting our GitHub repository.

    Synmetrix is meticulously designed to streamline your data management, analysis, and transmission workflows, making it the ultimate tool for all your data needs. We look forward to your involvement and contributions!

    - + \ No newline at end of file diff --git a/docs/production-deployment.html b/docs/production-deployment.html index 6b1fd7f..4917c4a 100644 --- a/docs/production-deployment.html +++ b/docs/production-deployment.html @@ -4,13 +4,13 @@ Production Deployment | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/production-deployment/requirements.html b/docs/production-deployment/requirements.html index 3f977b0..4b66630 100644 --- a/docs/production-deployment/requirements.html +++ b/docs/production-deployment/requirements.html @@ -4,13 +4,13 @@ Requirements | Synmetrix Docs - +
    Version: 1.0.0

    Requirements

    Software Requirements​

    Hardware Requirements​

    Synmetrix is intended for installation and use on servers equipped with processors supporting x86-64 architecture and based on operating systems such as Ubuntu Server 18.04 and higher.

    Minimum Hardware Requirements:

    - Processor (CPU): 3.2 GHz or higher, modern processor with multi-threading and virtualization support.
    - RAM: 8 GB or more to handle computational tasks and data processing.
    - Disk Space: At least 30 GB of free space for software installation and storing working data.
    - Network: Internet connectivity is required for cloud services and software updates.
    note

    These requirements are minimum and may vary depending on specific use cases and data processing volumes. Scaling the solution may require more powerful servers or distributed systems.

    Source Code​

    You can access the source code of Synmetrix on GitHub.

    - + \ No newline at end of file diff --git a/docs/quickstart.html b/docs/quickstart.html index 8bc59a7..44c3979 100644 --- a/docs/quickstart.html +++ b/docs/quickstart.html @@ -4,13 +4,13 @@ Quick Start | Synmetrix Docs - +
    -
    Version: 1.0.0

    Quick Start

    Quick Start

    Prerequisite Software​

    Ensure the following software is installed before proceeding:

    Step 1: Download the docker-compose file​

    The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository:

    Execute this in a new directory

    wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml

    Step 2: Launch Synmetrix​

    Execute the following command to start Synmetrix along with a Postgres database for data storage.

    $ docker-compose up -d

    Verify if the containers are operational:

    $ docker ps

    CONTAINER ID IMAGE                 ... CREATED STATUS PORTS          ...
    c8f342d086f3 synmetrix/stack       ... 1m ago  Up 1m  80->8888/tcp ...
    30ea14ddaa5e postgres:12           ... 1m ago  Up 1m  5432/tcp

    The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.

    Step 3: Explore Synmetrix​

    note

    1. Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment.

    2. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated.

    3. Default Seed Data: The project comes with default seed data. Use the following credentials to log in:

      • Email: demo@synmetrix.org
      • Password: demodemo

    If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.

    Demo online​

    Demo: app.synmetrix.org

    • Login: demo@synmetrix.org
    • Password: demodemo

    Demo credentials​

    Database typeHostPortDatabaseUserPasswordSSL
    ClickHousegh-api.clickhouse.tech443defaultplayno passwordtrue
    PostgreSQLdemo-db.cube.dev5432ecomcube12345false
    - +
    Version: 1.0.0

    Quick Start

    Quick Start

    Prerequisite Software​

    Ensure the following software is installed before proceeding:

    Step 1: Download the docker-compose file​

    The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository:

    Execute this in a new directory

    wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml

    Step 2: Launch Synmetrix​

    Execute the following command to start Synmetrix along with a Postgres database for data storage.

    $ docker-compose pull stack && docker-compose up -d

    Verify if the containers are operational:

    $ docker ps

    CONTAINER ID IMAGE                 ... CREATED STATUS PORTS          ...
    c8f342d086f3 synmetrix/stack       ... 1m ago  Up 1m  80->8888/tcp ...
    30ea14ddaa5e postgres:12           ... 1m ago  Up 1m  5432/tcp

    The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.

    Step 3: Explore Synmetrix​

    note

    1. Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment.

    2. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated.

    3. Preloaded Seed Data: The project is equipped with preloaded seed data. Use the credentials below to sign in:

      • Email: demo@synmetrix.org
      • Password: demodemo

      This account is pre-configured with two demo datasources and their respective SQL API access. For SQL operations, you can use the following credentials with any SQL client tool such as DBeaver or TablePlus:

      HostPortDatabaseUserPassword
      localhost15432dbdemo_pg_userdemo_pg_pass
      localhost15432dbdemo_clickhouse_userdemo_clickhouse_pass

    If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.

    Demo online​

    Demo: app.synmetrix.org

    • Login: demo@synmetrix.org
    • Password: demodemo

    Demo datasource credentials​

    Database typeHostPortDatabaseUserPasswordSSL
    ClickHousegh-api.clickhouse.tech443defaultplayno passwordtrue
    PostgreSQLdemo-db-examples.cube.dev5432ecomcube12345false
    + \ No newline at end of file diff --git a/docs/sql-interface.html b/docs/sql-interface.html index 374481c..ddd5bb8 100644 --- a/docs/sql-interface.html +++ b/docs/sql-interface.html @@ -4,13 +4,13 @@ SQL Interface | Synmetrix Docs - +
    Version: 1.0.0

    Synmetrix SQL Interface

    Introduction​

    In the development of Synmetrix, the establishment of a robust SQL interface plays a pivotal role. This interface serves as a unified platform for accessing data sources, ensuring consistent metrics are available to all data consumers. It addresses the challenge of maintaining data integrity and precision across various tools employed by modern organizations.

    Problem Statement​

    Many modern organizations utilize numerous tools to process data from a shared storage but perform individual metric calculations. This often leads to discrepancies in calculations and disparities among different teams. The more tools an organization employs, the greater the potential for inconsistencies, making data-driven decision-making complex. Hence, it is essential to create a centralized hub for defining and maintaining metrics, ensuring that all tools operate with a single source of data.

    Solution: Leveraging Cube.js​

    To tackle this challenge, Synmetrix has chosen the Cube.js framework, successfully integrated into the Synmetrix platform. Cube.js offers a significant advantage in its ability to act as a data source proxy, transforming incoming queries—whether in JSON, GraphQL, or SQL format—into native queries to the underlying data store.

    Implementation of SQL Interface in Synmetrix​

    Creating the SQL interface within Synmetrix presented several challenges effectively addressed using the Cube.js framework and Apache Datafusion. This SQL interface enables Cube.js to provide data through a PostgreSQL-compatible protocol to various data-consuming applications, including but not limited to:

    • BI tools
    • Data exploration tools such as Jupyter or Hex
    • Reverse ETL tools like Census or Hightouch
    • Low-code development platforms such as Retool
    • Automated tasks

    The SQL interface has been thoroughly tested with a range of tools and platforms, including:

    The SQL API utilizes Apache Datafusion as its SQL execution engine, responsible for query planning and execution.

    Implementation Details​

    The implementation of the SQL interface in Synmetrix comprises several key steps, facilitated by the Cube.js framework and Apache Datafusion:

    • Request Retrieval: Incoming data retrieval requests arrive in SQL format to Cube.js, serving as the initial queries that require processing and execution.
    • Query Transformation: Cube.js transforms the incoming SQL queries into its own query format (JSON). This transformation optimizes and prepares the query for execution against the underlying data store.
    • Query Execution: The SQL query generated in the previous step is sent to the data source. This source can be any supported database to which Cube.js can connect.
    • Data Retrieval: The output is a dataset containing the required data in response to the original query.
    • Load into Apache Datafusion: The dataset is loaded into Apache Datafusion, where it undergoes further processing. Apache Datafusion serves as the SQL execution engine, responsible for query planning and execution.
    • Execution of the Original SQL Query: The original SQL query received by the server is executed within Apache Datafusion. This allows for the full utilization of SQL's capabilities for data analysis and processing while preserving the structure and semantics of the original query.
    • Data Return: Query results are returned through Cube.js, where they can be further processed or directly delivered to end-users.

    This process ensures high flexibility and power when working with queries while maintaining data consistency and coherence for all consumers. Each stage has been meticulously designed and optimized to ensure efficient query execution and overall system performance.

    Transformation of SQL Queries​

    A notable feature of the SQL interface in Synmetrix is how it handles and transforms SQL queries. As previously mentioned, Cube.js converts incoming SQL queries into its own query format (JSON), a process known as "Cube query rewrite." This transformation allows for optimization and query preparation for execution against the underlying data store.

    Let's illustrate this with an example query to the "orders" cube in our data model:

    Original SQL Query:

    SELECT
      city,
      SUM(amount)
    FROM orders
    WHERE status = 'shipped'
    GROUP BY 1

    This SQL query in the SELECT statement to the "orders" cube is transformed into Cube.js' internal query format:

    Transformed Cube.js Query:

    {
      "measures": ["Orders.amount"],
      "dimensions": ["Orders.city"],
      "filters": [
        {
          "member": "Orders.status",
          "operator": "equals",
          "values": ["shipped"]
        }
      ]
    }

    As a result of this transformation, not all functions and expressions are supported within query fragments that execute SELECT statements on cube tables. For example, the following query would not work because the SQL API cannot pass the CASE expression to Cube.js for processing, making it impossible to translate the CASE expression into a metric:

    Non-supported Query:

    SELECT
      city,
      CASE
        WHEN status = 'shipped' THEN 'done'
        ELSE 'in-progress'
      END real_status,
      SUM(number)
    FROM orders
      CROSS JOIN Users
    GROUP BY 1;

    However, in such cases, nested queries can be employed. You can encapsulate your SELECT query from a cube table within another SELECT query to perform calculations with expressions like CASE. This outer SELECT is not part of the rewritten SQL query, allowing you to use more SQL functions, operators, and expressions.

    Example Query:

    SELECT
      city,
      CASE
        WHEN status = 'shipped' THEN 'done'
        ELSE 'in-progress'
      END real_status,
      SUM(amount) AS total
    FROM (
      SELECT
        Users.city AS city,
        SUM(number) AS amount,
        orders.status
      FROM orders
        CROSS JOIN Users
      GROUP BY 1, 3
    ) AS inner
    GROUP BY 1, 2
    ORDER BY 1;

    The above query works because the CASE expression is supported in SELECT queries that do not reference cube tables. These nuances and query handling specifics should be considered when designing and utilizing the SQL interface in Synmetrix.

    Server Code​

    For a detailed analysis of the server code and its components, please refer to the Synmetrix SQL Interface Server Code.

    Conclusion​

    The creation of the SQL interface in Synmetrix has allowed for the development of a unified data access interface, ensuring metric consistency for all data tools. This has reduced the number of discrepancies and conflicts related to metric calculations, providing a more reliable foundation for data-driven decision-making.

    - + \ No newline at end of file diff --git a/docs/support-updates.html b/docs/support-updates.html index 2e0c581..a6cc6f4 100644 --- a/docs/support-updates.html +++ b/docs/support-updates.html @@ -4,13 +4,13 @@ Support & Updates | Synmetrix Docs - +
    Version: 1.0.0

    Support and Updates

    Contact Information​

    For user support and inquiries, you can reach out to us through the following channels:

    Our support team is dedicated to assisting you with any questions or problems you might encounter while using Synmetrix.

    Upcoming Updates and Changes​

    We are continuously working to improve Synmetrix and provide you with the best possible experience. Here's a glimpse of our upcoming updates and changes: GitHub

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging.html b/docs/testing-and-debugging.html index 344bbdb..e691564 100644 --- a/docs/testing-and-debugging.html +++ b/docs/testing-and-debugging.html @@ -4,13 +4,13 @@ Testing and Debugging | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/debugging.html b/docs/testing-and-debugging/debugging.html index b97b57b..8af7fbd 100644 --- a/docs/testing-and-debugging/debugging.html +++ b/docs/testing-and-debugging/debugging.html @@ -4,13 +4,13 @@ Debugging | Synmetrix Docs - +
    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/test-structure.html b/docs/testing-and-debugging/test-structure.html index d47d189..2f1b76c 100644 --- a/docs/testing-and-debugging/test-structure.html +++ b/docs/testing-and-debugging/test-structure.html @@ -4,13 +4,13 @@ Test Structure | Synmetrix Docs - +
    Version: 1.0.0

    Testing Structure for Synmetrix

    info

    In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI.

    StepCI is employed for writing and conducting integration tests, which verify the correctness of the system's operation when its various components interact with each other. This is particularly vital for projects like Synmetrix, which consist of numerous services and components working collaboratively.

    Test Structure​

    All test files are located in the test/stepci directory within the Synmetrix project folder. Each file represents an individual test scenario described in YAML format.

    - name: change_password
      http:
        url: ${{env.HASURA_PLUS_ENDPOINT}}/auth/change-password
        method: POST
        headers:
          Content-Type: application/json
          Authorization: Bearer ${{captures.accessToken}}
        body: |
          {
            "old_password": "pass321",
            "new_password": "pass123"
          }
        check:
          status: 204

    This YAML configuration represents a typical StepCI test scenario named "change_password." It performs a POST request with specific headers and a JSON request body to change a password. The response status is checked to ensure it's 204.

    Testing Algorithm​

    Testing on the StepCI platform typically involves the following steps:

    1. Request Creation: StepCI generates a request based on the input data defined in the test scenario.

    2. Request Sending: The request is sent to the tested endpoint in the Synmetrix system.

    3. Waiting for Response: StepCI waits for a response from the Synmetrix system. During this time, all necessary actions are performed, such as request processing, interaction with the database, and other services.

    4. Response Verification: After receiving the response, StepCI compares it with the expected result defined in the test scenario. If the response matches the expectations, the test is considered passed. Otherwise, StepCI logs an error, and the test is marked as failed.

    This testing structure ensures the reliability and stability of the Synmetrix system. Automated testing allows developers to quickly identify and rectify errors, as well as ensure that new changes do not disrupt the functionality of existing features.

    Upon completing a set of tests, StepCI provides a report with a brief description of the tests that were passed.

    PASS datasource_flow 9.458s 1 1620 bytes 1 16006 bytes
    Tests: 0 failed, 1 passed, 1 total
    Steps: 0 failed, 0 skipped, 18 passed, 18 total
    Time: 9,533s, estimated 10s
    CO2:
    0.04036g
    Workflow passed after 9.533s

    Test was passed.

    In the event of a test failure, StepCI records details, including the name of the failed testing step, input data and parameters, actual and expected results, and error messages obtained during the execution of the tested code. Subsequent test steps are halted upon encountering a failure.

    v create_team passed after 0.041s
    v invite_team_member passed after 0.597s 
    v create_datasource passed after 0.021s 
    v check_connection passed after 0.833s 
    v validate_datasource passed after 0.078s 
    v fetch_tables passed after 0.436s 
    × gen_dataschemas failed after 0.008s 
    A create_exploration skipped after 0s 
    A fetch_dataset skipped after Os 
    A export_data_models skipped after 0s 
    A fetch_meta skipped after 0s 
    A run_query skipped after Os


    • datasource_flow › gen_dataschemas

    Request HTTP
    POST http://hasura:8080/v1/graphql HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer eyJhbci01JIUzI1N1J9.eyJoYXN1cmEiOnsieC1oYXN1cmEtdXNlci1pZCI6IjU5Yjhj
    OTVjLTdIMmEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMi0\sidXNlciJdLCJ
    4LWhhc3VyYS1kzwZhdWx0LXJvbGUi0iJ1c2VyInosInN1Yi16IjU5YjhjOTVjLTdl™mEtNDc5Ny1hNjZjLWU1MTc3Nm
    NjZDM5ZSIsImlzcyI6Im5ob3N0IiwiaWF0Ijo×NjkwoDkoNzcOLCJleHAi0jE20TE1NDI3NzR9.DTtp9T†jNg6ABm1v
    V7eB0KXZw6k1d2iZx6C-DFicCw™
    ,x-hasura-user-id: 59b8c95c-7e2a-4797-a66c-e51776ccd39e
    {"query": "mutation (#datasource_id: uuid!, $tables: [SourceTable!]!) f\n gen_dataschemas (d atasource_id: $datasource_id, tables: $tables) f\n code\n
    messageln FInrIn"， "variabl
    es" :{"datasource_id":"9218058b-160c-4c43-8126-b944612c055f"
    "tables": K{"name": "default/cell
    towers"},{"name": "default/checks"}1}}
    Response
    HTTP/1.1 200 OK
    transfer-encoding: chunked
    date: Tue 01 Aug 2023 12:59:36 GMT
    server: Warp/3.3.23
    content-type: application/json; charset=utf-8
    {"errors": ["extensions":{"code": "validation-failed", "path":"$.selectionSet. gen_dataschemas args.branch_id"}, "message":"missing required field 'branch_id'"}1}
    Checks
    JSONPath
    × $. data.gen_dataschemas.code: undefined (expected ok)

    Test was failed.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/testing/test-sql-data-models.html b/docs/testing-and-debugging/testing/test-sql-data-models.html index 52db581..770a7fe 100644 --- a/docs/testing-and-debugging/testing/test-sql-data-models.html +++ b/docs/testing-and-debugging/testing/test-sql-data-models.html @@ -4,7 +4,7 @@ Test SQL Interface for Data Models | Synmetrix Docs - + @@ -18,7 +18,7 @@ Testing SQL Queries

    Query:

    SELECT MEASURE(count), status, DATE_TRUNC('month', created_at) date FROM orders GROUP BY date, status ORDER BY date ASC;

    Response: Testing SQL Queries

    Query:

    SELECT
      city,
      CASE
        WHEN status = 'shipped' THEN 'done'
        ELSE 'in-progress'
      END real_status,
      SUM(amount) AS total
    FROM (
      SELECT
        users.city AS city,
        SUM(number) AS amount,
        orders.status
      FROM orders
        CROSS JOIN users
      GROUP BY 1, 3
    ) AS inner
    GROUP BY 1, 2
    ORDER BY 1;

    Response: Testing SQL Queries

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source.html b/docs/testing-and-debugging/tests/data-source.html index 7814be2..9517868 100644 --- a/docs/testing-and-debugging/tests/data-source.html +++ b/docs/testing-and-debugging/tests/data-source.html @@ -4,13 +4,13 @@ Test a Data Source | Synmetrix Docs - +
    Version: 1.0.0

    Test a Data Source

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/crate.html b/docs/testing-and-debugging/tests/data-source/crate.html index 428289f..e352137 100644 --- a/docs/testing-and-debugging/tests/data-source/crate.html +++ b/docs/testing-and-debugging/tests/data-source/crate.html @@ -4,13 +4,13 @@ Crate | Synmetrix Docs - +
    Version: 1.0.0

    Crate

    Tested on the crate:5.3.0 image.

    Step 1: Navigate to the CrateDB example directory​

    Navigate to the CrateDB example directory located at /docs/examples/crate in the root of the project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/crate

    Step 2: Run the CrateDB service using the script​

    In the CrateDB example directory, there is a script called run_crate.sh. Make sure the script is executable by running:

    chmod +x run_crate.sh

    Then, start the CrateDB service by running the script:

    ./run_crate.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the UI​

    Go to the UI and add a new Crate data source with the following parameters:

    • Name: Crate
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 4200
    • User: crate

    Step 4: Test the connection​

    Click the "Check connection" button in the UI to verify the connection to the CrateDB data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/dremio.html b/docs/testing-and-debugging/tests/data-source/dremio.html index 3264576..a90bdeb 100644 --- a/docs/testing-and-debugging/tests/data-source/dremio.html +++ b/docs/testing-and-debugging/tests/data-source/dremio.html @@ -4,13 +4,13 @@ Dremio | Synmetrix Docs - +
    Version: 1.0.0

    Dremio

    Tested on the dremio/dremio-oss:latest image.

    Step 1: Navigate to the Dremio example directory​

    Navigate to the Dremio example directory located at /docs/examples/dremio in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/dremio

    Step 2: Run the Dremio service using the script​

    In the Dremio example directory, there is a script called run_dremio.sh. Make sure the script is executable by running:

    chmod +x run_dremio.sh

    Then, start the Dremio service by running the script:

    ./run_dremio.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Create an administrator account and add a data source in Dremio​

    1. Open your web browser and go to http://localhost:9047.
    2. Follow the prompts to create an administrator account.
    3. Log in to the Dremio UI with your new account.
    4. Click on "Add Source" and select "PostgreSQL" from the list of available data sources.
    5. Use the credentials provided at https://uibakery.io/sql-playground to configure the PostgreSQL data source.

    Step 4: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Host: host_ip (obtained from the script in Step 2)
    • Port: 9047
    • Database name: Name given to the connection in Dremio
    • User: Dremio administrator username
    • Password: Dremio administrator password

    Step 5: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the Dremio data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/druid.html b/docs/testing-and-debugging/tests/data-source/druid.html index 086a20e..2f24ad0 100644 --- a/docs/testing-and-debugging/tests/data-source/druid.html +++ b/docs/testing-and-debugging/tests/data-source/druid.html @@ -4,14 +4,14 @@ Druid | Synmetrix Docs - +
    Version: 1.0.0

    Druid Setup Guide

    Step 1: Navigate to the Druid example directory​

    Navigate to the Druid example directory located at /docs/examples/druid in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/druid

    Step 2: Run the Druid services using the script​

    In the Druid example directory, there is a script called run_druid.sh. Make sure the script is executable by running:

    chmod +x run_druid.sh

    Then, start the Druid services by running the script:

    ./run_druid.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Wait for the cluster to start and go to http://localhost:8889.

    Step 3: Check the services​

    Go to the Services tab and make sure all services are online, as shown in the screenshot below.

    All services online

    Step 4: Load example data​

    Select the "Load data" tab, then choose "Batch - classic" from the dropdown menu.

    Load data -&gt; Batch - classic

    Next, select the "Example data" tile and click "Load example".

    Load example data

    Click the button with the text "Next: ..." in the bottom right corner until the data starts loading (about 9 times). When the data starts loading, you will be redirected to the Ingestion tab and see the message "Task submitted successfully. Going to task view...".

    Next button

    After the data has finished loading, the task status will change to SUCCESS.

    Step 5: Create a new Druid datasource in Synmetrix​

    Fill out the form with the following information:

    • Name: Druid test datasource
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8082

    Currently, the Test Connection feature is not implemented and always returns "OK". To ensure functionality, generate a schema and request any data in the Explore tab.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/elasticsearch.html b/docs/testing-and-debugging/tests/data-source/elasticsearch.html index 8883f24..5ee7825 100644 --- a/docs/testing-and-debugging/tests/data-source/elasticsearch.html +++ b/docs/testing-and-debugging/tests/data-source/elasticsearch.html @@ -4,13 +4,13 @@ Elasticsearch | Synmetrix Docs - +
    Version: 1.0.0

    Elasticsearch

    Step 1: Navigate to the Elasticsearch Example Directory​

    Navigate to the Elasticsearch example directory located at /docs/examples/elasticsearch in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/elasticsearch

    Step 2: Run the Elasticsearch Service Using the Script​

    In the Elasticsearch example directory, there is a script called run_elasticsearch.sh. Make sure the script is executable by running:

    chmod +x run_elasticsearch.sh

    Then, start the Elasticsearch service by running the script:

    ./run_elasticsearch.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Wait for the Service to Be Ready and Add a User​

    Wait for the Elasticsearch service to be ready to accept requests. Then run:

    curl -X POST -u elastic:password "http://localhost:9200/_security/user/user" -H 'Content-Type: application/json' -d'
    {
      "password": "password",
      "roles": ["kibana", "kibana_system"],
      "full_name": "Test user"
    }'

    Step 4: Access Kibana and Add Sample Data​

    Wait for the Kibana service to start, then go to http://localhost:5601 and log in with the credentials:

    User: elastic
    Password: password

    Click on the Try sample data link:

    Try sample data screenshot

    At the bottom of the page, click Other sample data sets:

    Other sample data sets screenshot

    Choose a dataset and click Add data.

    Step 5: Create a Datasource in Synmetrix​

    Now you can create a datasource in Synmetrix using the following information:

    • Name: Elasticsearch test datasource
    • Url: http://<host_ip>:9200
    • User: user
    • Password: password

    Save the datasource and click Test connection. You should see a message indicating that the connection is OK.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/firebolt.html b/docs/testing-and-debugging/tests/data-source/firebolt.html index ef8415a..ac482fe 100644 --- a/docs/testing-and-debugging/tests/data-source/firebolt.html +++ b/docs/testing-and-debugging/tests/data-source/firebolt.html @@ -4,13 +4,13 @@ Firebolt | Synmetrix Docs - +
    Version: 1.0.0

    Firebolt

    Connecting to Firebolt datasource​

    To connect to a Firebolt datasource, you will need the following information:

    • Database Name - the name of the database to connect to.
    • Username - the username used to connect to the database.
    • Password - the password used to connect to the database.
    • Engine Name - the name of the engine to connect to.
    • API Endpoint - the Firebolt API endpoint used for authentication, such as api.dev.firebolt.io or api.app.firebolt.io.
    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/ksql.html b/docs/testing-and-debugging/tests/data-source/ksql.html index c4d282d..6507639 100644 --- a/docs/testing-and-debugging/tests/data-source/ksql.html +++ b/docs/testing-and-debugging/tests/data-source/ksql.html @@ -4,13 +4,13 @@ KSQL | Synmetrix Docs - +
    Version: 1.0.0

    KSQL

    Tested on the confluentinc/cp-ksqldb-server:7.0.9 image.

    Step 1: Navigate to the ksql example directory​

    Navigate to the ksql example directory located at /docs/examples/ksql in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/ksql

    Step 2: Run the ksql service using the script​

    In the ksql example directory, there is a script called run_ksql.sh. Make sure the script is executable by running:

    chmod +x run_ksql.sh

    Then, start the ksql service by running the script:

    ./run_ksql.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a datasource in the Synmetrix UI​

    Go to the Synmetrix UI and add a new datasource with the following parameters:

    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8088

    Step 4: Test the connection​

    Click the "Test connection" button in the Synmetrix UI to verify the connection to the ksql datasource. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/materialize.html b/docs/testing-and-debugging/tests/data-source/materialize.html index 09a2e59..80057cb 100644 --- a/docs/testing-and-debugging/tests/data-source/materialize.html +++ b/docs/testing-and-debugging/tests/data-source/materialize.html @@ -4,13 +4,13 @@ Materialize | Synmetrix Docs - +
    Version: 1.0.0

    Materialize

    Tested on the materialize/materialized:latest image.

    Step 1: Navigate to the Materialize example directory​

    Navigate to the Materialize example directory located at /docs/examples/materialize in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/materialize

    Step 2: Run the Materialize service using the script​

    In the Materialize example directory, there is a script called run_materialize.sh. Make sure the script is executable by running:

    chmod +x run_materialize.sh

    Then, start the Materialize service by running the script:

    ./run_materialize.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Host: Host IP (obtained from the script in Step 2)
    • Port: 6875
    • Database name: Test
    • User: user

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the Materialize data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/presto.html b/docs/testing-and-debugging/tests/data-source/presto.html index 40ed8db..21f3d99 100644 --- a/docs/testing-and-debugging/tests/data-source/presto.html +++ b/docs/testing-and-debugging/tests/data-source/presto.html @@ -4,13 +4,13 @@ Presto | Synmetrix Docs - +
    Version: 1.0.0

    PrestoDB

    Tested on the starburstdata/presto image.

    Step 1: Navigate to the PrestoDB example directory​

    Navigate to the PrestoDB example directory located at /docs/examples/prestodb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/prestodb

    Step 2: Run the PrestoDB service using the script​

    In the PrestoDB example directory, there is a script called run_prestodb.sh. Make sure the script is executable by running:

    chmod +x run_prestodb.sh

    Then, start the PrestoDB service by running the script:

    ./run_prestodb.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Name: PrestoDB Test
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8050
    • Catalog: tpch
    • User: user

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the PrestoDB data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/questdb.html b/docs/testing-and-debugging/tests/data-source/questdb.html index 3bf99bc..98f834e 100644 --- a/docs/testing-and-debugging/tests/data-source/questdb.html +++ b/docs/testing-and-debugging/tests/data-source/questdb.html @@ -4,13 +4,13 @@ QuestDB | Synmetrix Docs - +
    Version: 1.0.0

    QuestDB

    Tested on the questdb/questdb:7.1.1 image.

    Step 1: Navigate to the QuestDB example directory​

    Navigate to the QuestDB example directory located at /docs/examples/questdb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/questdb

    Step 2: Run the QuestDB service using the script​

    In the QuestDB example directory, there is a script called run_questdb.sh. Make sure the script is executable by running:

    chmod +x run_questdb.sh

    Then, start the QuestDB service by running the script:

    ./run_questdb.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Name: QuestDB Test
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8812
    • User: quest
    • Password: quest

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the QuestDB data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/snowflake.html b/docs/testing-and-debugging/tests/data-source/snowflake.html index 0085c5f..3b7a22a 100644 --- a/docs/testing-and-debugging/tests/data-source/snowflake.html +++ b/docs/testing-and-debugging/tests/data-source/snowflake.html @@ -4,13 +4,13 @@ Snowflake | Synmetrix Docs - +
    Version: 1.0.0

    Snowflake

    This guide provides step-by-step instructions on how to connect Snowflake to Synmetrix.

    Step 1: Gather Snowflake connection information​

    Before proceeding, ensure you have the following Snowflake connection details:

    • Database Name
    • Warehouse
    • Organization ID
    • Account ID
    • Username
    • Password
    • Role

    You can find this information in your Snowflake account.

    Organization and Account IDs

    Step 2: Add a new data source in the Synmetrix UI​

    1. Go to the Synmetrix UI and navigate to the "Data Sources" section.
    2. Click on the "+ Connect" button.
    3. Select "Snowflake" from the list of available data sources.

    Step 3: Fill in the connection details​

    Fill in the Snowflake connection details in the form with the following fields:

    • Name: A custom name for your data source (e.g., "My Snowflake")
    • Database Name: The name of your Snowflake database
    • Warehouse: The name of your Snowflake warehouse
    • Organization ID: The ID of your Snowflake organization
    • Account ID: The ID of your Snowflake account
    • Username: Your Snowflake username
    • Password: Your Snowflake password
    • Role: Your Snowflake role

    Step 4: Test the connection​

    Click the "Test connection" button in the Synmetrix UI to verify the connection to your Snowflake data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing-and-debugging/tests/data-source/trino.html b/docs/testing-and-debugging/tests/data-source/trino.html index bf30c90..79a0d4c 100644 --- a/docs/testing-and-debugging/tests/data-source/trino.html +++ b/docs/testing-and-debugging/tests/data-source/trino.html @@ -4,13 +4,13 @@ Trino | Synmetrix Docs - +
    Version: 1.0.0

    Trino

    Tested on the trinodb/trino:latest image.

    Step 1: Navigate to the Trino example directory​

    Navigate to the Trino example directory located at /docs/examples/trino in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command:

    cd ./docs/examples/trino

    Step 2: Run the Trino service using the script​

    In the Trino example directory, there is a script called run_trino.sh. Make sure the script is executable by running:

    chmod +x run_trino.sh

    Then, start the Trino service by running the script:

    ./run_trino.sh

    After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.

    Step 3: Add a data source in the Synmetrix UI​

    Go to the Synmetrix UI and add a new data source with the following parameters:

    • Name: Trino Test
    • Host: Host IP (obtained from the script in Step 2)
    • Port: 8050
    • Catalog: tpch
    • User: user

    Step 4: Test the connection​

    Click the "Check connection" button in the Synmetrix UI to verify the connection to the Trino data source. If the connection is successful, you will see a confirmation message.

    - + \ No newline at end of file diff --git a/docs/testing.html b/docs/testing.html index 6ff57a4..fd09f31 100644 --- a/docs/testing.html +++ b/docs/testing.html @@ -4,13 +4,13 @@ Testing Guides | Synmetrix Docs - +
    Version: 1.0.0

    Testing Guides

    - + \ No newline at end of file diff --git a/docs/usage/user-guide/data-source/databricks.html b/docs/usage/user-guide/data-source/databricks.html index 4315934..34b1307 100644 --- a/docs/usage/user-guide/data-source/databricks.html +++ b/docs/usage/user-guide/data-source/databricks.html @@ -4,13 +4,13 @@ Databricks | Synmetrix Docs - +
    Version: 1.0.0

    Connecting to Databricks Datasource via JDBC

    To connect to a Databricks datasource via JDBC, you will need the following information:

    • Access Token: A personal access token used to authenticate the Databricks connection.

    • Databricks JDBC URL: The URL for a JDBC connection. It should be in the format:

      jdbc:databricks://{'<Databricks address>'}:443/default;transportMode=http;ssl=1;

    • Database Name: The name of the database to connect to.

    - + \ No newline at end of file diff --git a/docs/user-guide.html b/docs/user-guide.html index 0fa6b4d..a6c0770 100644 --- a/docs/user-guide.html +++ b/docs/user-guide.html @@ -4,13 +4,13 @@ User Guide | Synmetrix Docs - +
    Version: 1.0.0

    User Guide

    User Guide

    This documentation provides an in-depth guide to the various pages and features of the Synmetrix interface. Here, you will find detailed information and instructions on how to utilize each page effectively.

    The interface documentation is organized into separate pages, each focusing on a specific aspect of the Synmetrix interface. Below are the links to the documentation for each page:

    Table of Contents​

    📄️ Models

    The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page:

    Feel free to explore the documentation for each page to gain a comprehensive understanding of the Synmetrix interface and its functionalities.

    Happy exploring and analyzing data with Synmetrix!

    - + \ No newline at end of file diff --git a/docs/user-guide/explore.html b/docs/user-guide/explore.html index 3efa543..4358669 100644 --- a/docs/user-guide/explore.html +++ b/docs/user-guide/explore.html @@ -4,7 +4,7 @@ Explore Page | Synmetrix Docs - + @@ -13,7 +13,7 @@ To switch to a different data source:

    1. Find the Data Source in the sidebar.

    2. Pick a New Data Source: Click on the list there and choose the one you want.

    3. It's Changed!: Your data source will change right away.

    Switching Branches in Explorer​

    Switching Branches in Explorer To change the branch you're working on:

    1. Look for Branch Section in the sidebar.

    2. Choose Your Branch: Click on the list and pick the branch you need.

    3. Branch Changed: Now you're working in the new branch.

    Selecting Dimensions and Measures​

    Selecting Dimensions and Measures

    Picking the right dimensions and measures is super important for looking at your data the right way. Here's what to do:

    1. Go to the Sidebar: First, find the sidebar.

    2. Click on Desired Model: Click it and you'll see a list.

    3. See All the Options: This list shows all the measures, dimensions, time dimensions and segments you can use.

    4. Pick What You Need: Choose the ones that you're willing to analyze.

    5. Run Your Query: After picking, run your query to see your data.

    tip
    • Quick Find: Use the search box to find dimensions or measures fast.

    Adding Filters​

    Guide to Adding Filters Incorporating filters into your analysis is a crucial step for refining and focusing your data exploration. Here’s a professional approach to adding filters through the Explorer Sidebar:

    1. Navigate to Explorer Sidebar: Begin by accessing the sidebar options within the Explorer.

    2. Hover Over Measures or Dimensions: Move your cursor over either the Measures or Dimensions sections in the sidebar.

    3. Initiate Filter Selection: Look for a filter icon on the specific measure or dimension you are interested in. Click on this icon to start setting up a filter.

    4. Configure Filter in Result Tab: After clicking the filter button, switch to the "Result" tab. Here, you'll find options to customize your filter according to your specific data requirements.

      Process of Adding Filters

    Explore Toolbar Functionality Overview​

    Toolbar in Action

    The toolbar in your data analysis tool is equipped with a variety of features to enhance your data exploration and reporting capabilities. Here's a breakdown of the key actions available:

    Run Query​

    • Function: Runs the analysis based on the selected dimensions, measures, and filters.
    • Purpose: To dynamically generate and view data results tailored to your current selections.

    Set Up Alerts​

    • Function: Allows for the creation of alerts based on specific data conditions.
    • Usage: Configure alert parameters to monitor data changes and receive notifications.

    Generate Reports​

    • Function: Creates reports reflecting your current data view.
    • Customization: Offers options to define report parameters and settings for targeted reporting.

    Export to CSV​

    • Function: Enables the export of data into a CSV file.
    • Application: Useful for offline data analysis or for sharing data externally.

    Work with data​

    Results Tab​

    Results Tab

    • Display: Shows the outcome of your executed queries.
    • Adjustments: Allows configuration of row limits, offsets, and view of applied filters.

    Generated SQL Tab​

    Generated SQL Tab

    • Function: Displays the automatically generated SQL query post-execution.
    • Utility: Useful for understanding the query structure or for manual SQL query replication.

    REST API Tab​

    REST API Tab

    • Access Details: Provides necessary API information for data retrieval.
    • Components: Includes headers and the API endpoint URL.

    Example API Utilization​

    Example API Utilization

    • Headers:
      • Authorization: Bearer <token>
      • x-hasura-datasource-id: <datasource-id>
      • x-hasura-branch-id: <branch-id>
    • URL: https://cube.synmetrix.org/api/v1/load
    • Body: Follow the API documentation for accurate setup.

    Execute API Request: Enables sending an API request directly from the interface.

    - + \ No newline at end of file diff --git a/docs/user-guide/explore/alerts.html b/docs/user-guide/explore/alerts.html index 0989fab..9d66096 100644 --- a/docs/user-guide/explore/alerts.html +++ b/docs/user-guide/explore/alerts.html @@ -4,7 +4,7 @@ Alerts Page | Synmetrix Docs - + @@ -13,7 +13,7 @@ Alerts Page Creation

  • Choose your notification delivery method. Options include Webhook, Slack, or Email, depending on your system integrations and preferences. Delivery Method Selection

  • Complete the alert form with the necessary information: Alert Configuration Form

    • Detailed Description of Form Fields on the Alerts Page:​

    Alert Name: Provide a descriptive name that clearly identifies the alert's purpose and content.

    Preview Section:​

    This section offers a comprehensive overview of the Alert or Report setup. Key features include:

    Query Key: This is the primary query that your alert or report is based on. It's a critical element in the monitoring process.

    Measures: Select the quantitative data points you wish to track or display. These are crucial for your analysis and will be highlighted in the Preview.

    Dimensions: These provide context and categorization for your data, allowing a more detailed and segmented analysis.

    info

    The Preview amalgamates all these components, offering a clear snapshot of the data and insights being monitored. This ensures that your analysis, whether for an Alert or Report, is accurate and relevant.

    Trigger Settings:​

    Customize how and when your alert is triggered with these settings:

    • Schedule: Set how often the alert conditions are checked (e.g., hourly, daily, weekly).
    • Lower Bound: Define the minimum threshold that triggers the alert.
    • Upper Bound: Specify the maximum threshold for triggering the alert.
    • Request Timeout (minutes): Set the maximum time allowed for the data request to process.
    • Timeout On Fire (minutes): Determine how long before an ongoing alert condition is considered "fired."

    Delivery Settings:​

    Options here vary based on your chosen alert delivery method:

    • Webhook: Input the URL for the webhook endpoint.
    • Slack: Enter the Slack channel or user ID.
    • Email: Specify the email addresses of the alert recipients.
    note

    After configuring your settings, click "Save" to activate the alert. This will commence the monitoring of your specified conditions.

    Enhancements for Clarity and Efficiency:

    1. Clarified instructions for setting up alerts.
    2. Detailed descriptions for each section, ensuring comprehensive understanding.
    3. Streamlined language for ease of reading and comprehension.
    4. Added informative notes and info sections for additional clarity.
    - + \ No newline at end of file diff --git a/docs/user-guide/explore/reports.html b/docs/user-guide/explore/reports.html index d21e7b8..96b68c4 100644 --- a/docs/user-guide/explore/reports.html +++ b/docs/user-guide/explore/reports.html @@ -4,7 +4,7 @@ Reports Page | Synmetrix Docs - + @@ -13,7 +13,7 @@ Reports Page

  • Select the destination where the screenshot will be sent. You can choose between Webhook, Slack, or Email based on your preferences and integrations. Reports Destination

  • Fill out the report form with the following details: Reports Form

    • Form fields description for the Reports page:​

    Report Name: Enter a name that easily identifies the purpose and content of the report.

    Preview:​

    In the "Preview" section, you will find crucial details for the Alert or Report, including the user-selected metric referred to as the "Query Key." This metric is at the core of your monitoring process and plays a pivotal role in alerting or report generation.

    Here, you'll also discover various other essential components such as:

    Measures: These represent the quantitative data that you want to track or display. Users can choose specific measures that matter the most to their analysis, and these will be prominently featured in the Preview.

    Dimensions: Dimensions help to provide context and categorize your data. They allow you to break down the information based on various attributes, providing a more comprehensive view of your metrics.

    info

    In the Preview, all these elements come together to give you a snapshot of the data and insights you're tracking, ensuring that you're monitoring the right information for your analysis, whether it's for an Alert or Report.

    Trigger Settings:​

    • Schedule: Configure the frequency at which the report will be executed by setting a schedule (e.g., daily, weekly, monthly).

    Delivery Settings:​

    The available fields in this section depend on the chosen delivery method for the report. Here are some common options:

    • Webhook: Enter the URL of the webhook endpoint where the report will be sent.
    • Slack: Provide the Slack channel or user ID to which the report will be delivered.
    • Email: Enter the email address(es) of the recipient(s) who will receive the report.
    note

    Once you have completed the settings, click the "Save" button.

    - + \ No newline at end of file diff --git a/docs/user-guide/logs.html b/docs/user-guide/logs.html index b0ce992..5c5dc42 100644 --- a/docs/user-guide/logs.html +++ b/docs/user-guide/logs.html @@ -4,13 +4,13 @@ Logs Page | Synmetrix Docs - +
    Version: 1.0.0

    Logs Page

    Overview​

    Alert Logs

    The Logs page is designed to provide control over the internal workings of the Cube.js service. It allows you to review the requests and the events that occur during the processing of these events. You can access information such as the execution time, start and end time, the actual query, and additional technical details. This enables you to monitor and analyze the performance and behavior of the Cube.js service. Follow the instructions below to utilize the Logs page:

    1. Access the Logs page within the project interface.
    2. Review the list of logs, which includes details such as the request information, execution time, start and end timestamps, and the actual query being processed.
    3. Click on a log entry to expand it and access additional technical information, if available.
    4. Use the logs as a resource for troubleshooting, performance optimization, and gaining insights into the inner workings of the Cube.js service.

    Query Logs filters​

    Query Logs

    - + \ No newline at end of file diff --git a/docs/user-guide/models.html b/docs/user-guide/models.html index 4a5287b..c5963ac 100644 --- a/docs/user-guide/models.html +++ b/docs/user-guide/models.html @@ -4,7 +4,7 @@ Models Page | Synmetrix Docs - + @@ -17,7 +17,7 @@ New branch

    This will create a new branch with the current state. To work with the new branch, you need to switch to it and then make it the default branch by clicking the "Set as default" button (shown in the screenshot). New branch

    Viewing Version Docs​

    Viewing Version Docs

    1. Go to Sidebar: Click on the sidebar in Synmetrix.
    2. Click 'Docs': Find and click the 'Docs' button.
    3. Get the Docs: The file with the version's documentation will be generated for you. SQL Runner

    SQL Runner in Models​

    SQL Runner

    Accessing the SQL Runner in Synmetrix​

    Opening the SQL Runner in Synmetrix for running custom SQL queries is a straightforward process. Here's how to do it:

    1. Navigate to Model Page: First, go to the model page in the Synmetrix interface. This is where all your data models are listed.

    2. Open SQL Runner Tab: Look for a tab or section labeled "SQL Runner" and click on it. This action will open the SQL Runner interface.

    3. Compose Your SQL Query: In the provided space, you can write your SQL query. For example, you might write a query to select data from a specific table or to perform a more complex data manipulation.

      SELECT * FROM your_table_name WHERE condition;

    4. Execute the Query: Once you've written your query, click the "Run" button. This will execute the SQL query against your data models.

    5. View Results: After running the query, the results will be displayed in a table format directly in the SQL Runner. This allows you to review and analyze the data returned by your query.

    SQL Runner Result

    note

    The Model SQL Runner in Synmetrix is a feature that allows you to run SQL queries directly against your data models. This tool is particularly useful for users who need to perform complex data analysis or want to test and validate their model configurations. Here’s a brief overview:

    • Functionality: The SQL Runner enables you to write and execute SQL queries on the fly. It interacts directly with the data models you've set up in Synmetrix, allowing for real-time data retrieval and analysis.

    • Use Cases:

      • Data Exploration: Quickly run queries to explore data patterns or validate data integrity.
      • Testing Models: Test how new or modified data models perform with actual SQL queries.
      • Advanced Analysis: Perform complex data analysis that might not be possible through standard model exploration.

    • Real-Time Results: Queries executed in the SQL Runner provide immediate feedback, enabling you to rapidly iterate and refine your analysis.

    Editing Models in Synmetrix​

    Editing Models

    1. Open a Model: In sidebar, select and open the model file you wish to edit.
    2. Begin Editing: Utilize the Visual Model Editor to make changes to the model. You can adjust various aspects such as dimensions, measures, and relationships.
    3. Save Changes or close tab: After completing your edits, ensure to save the model to apply the modifications.
    note

    The Visual Model Editor in Synmetrix, enhanced with YAML support, offers an intuitive and user-friendly way to manage data models. YAML's readable format simplifies the process of defining and configuring models, making it accessible even to those with limited coding experience. This combination of a graphical interface and YAML/JS editing provides a flexible and efficient model editing experience, catering to both beginner and advanced users in data model management.

    Importing or Exporting Data Models​

    Importing or Exporting Data Models

    • You can upload data model files from your computer to Synmetrix.
    • Easily save and download your data model files from Synmetrix.

    Using the Visual Model Editor​

    • Edit and tweak your data models right in Synmetrix with a user-friendly editor.

    Deleting a Model​

    Deleting a Model

    1. Open Sidebar: Start in the sidebar.
    2. Find Your Model: Look for the model you want to delete and hover over it.
    3. Click the Trash Icon: Hit the trash can icon next to the model.
    4. Confirm Delete: Click "Remove" to confirm.
    5. Done: The model is now deleted.

    Changing Model Name and File Format​

    Changing Model Name and File Format

    1. Head to Sidebar: Go to the sidebar in Synmetrix.
    2. Choose a Model: Hover over the model you want to change.
    3. Edit: Click the pen icon.
    4. Make Changes: Change the name and file format (JS or YAML).
    5. Finished: Your changes are saved.

    These steps are a simple way to handle your data models in Synmetrix, whether you're adding, editing, or removing them.

    - + \ No newline at end of file diff --git a/docs/user-guide/settings.html b/docs/user-guide/settings.html index 6dd46ee..d8dece5 100644 --- a/docs/user-guide/settings.html +++ b/docs/user-guide/settings.html @@ -4,13 +4,13 @@ Settings | Synmetrix Docs - +
    Version: 1.0.0

    Settings

    Table of Contents​

    - + \ No newline at end of file diff --git a/docs/user-guide/settings/data-sources.html b/docs/user-guide/settings/data-sources.html index 656a138..c61e3a2 100644 --- a/docs/user-guide/settings/data-sources.html +++ b/docs/user-guide/settings/data-sources.html @@ -4,7 +4,7 @@ Data Sources | Synmetrix Docs - + @@ -13,7 +13,7 @@ Datasource Connect button

    Step 2​

    A list of available datasource types will be displayed. Select the desired type from the options presented. Datasource Creation Form

    Step 3​

    In the opened modal window, fill in all the required fields in the form. These fields may include connection details, authentication credentials, and other relevant information.

    On the screenshot below, you can see a modal window with the datasource creation form. Datasource Creation Form

    Test Connection

    To test the connection to the datasource, click the "Test connection" button. To save the entered data and create the datasource, click the "Save" button.

    Fast Switching Data Sources in Synmetrix

    In Synmetrix, switching between data sources is a straightforward process. You can find the data models dropdown in the sidebar menu of both the "Models" and "Explore" pages. Simply access this menu to view and select from the available data sources. This dropdown is conveniently placed to ensure ease of access, enhancing your experience by allowing quick and efficient transitions between various data repositories within Synmetrix.

    - + \ No newline at end of file diff --git a/docs/user-guide/settings/members.html b/docs/user-guide/settings/members.html index f6b80e2..716d7d7 100644 --- a/docs/user-guide/settings/members.html +++ b/docs/user-guide/settings/members.html @@ -4,13 +4,13 @@ Members | Synmetrix Docs - +
    Version: 1.0.0

    Managing Members

    Managing Members

    Overview​

    The Members section in your settings allows you to manage the team members who have access to your workspace. This guide provides step-by-step instructions on how to add new members, as well as how to remove existing members.

    Member Types​

    There are three types of members you can add to your account:

    1. Owner: The primary account holder with full access and privileges.
    2. Admin: Users with administrative rights, including the ability to manage settings and members.
    3. Member: Regular users with access to the account but limited control over settings and member management.

    Adding a New Member​

    Adding a New Member

    To add a new member to your account, follow these steps:

    1. Navigate to Settings > Members in your account.
    2. Click the Create Now button.
    3. In the popup window, enter the email address of the new member.
    4. Select the user type for the new member (either Member or Admin).
    5. Click the Invite button.
    6. The new member will receive an email with a magic link to sign in to their account.

    Removing a Member​

    To remove a member from your account, follow these steps:

    1. Go to Settings > Members.
    2. Locate the member you wish to remove and find the gear icon on their member card.
    3. Click on Delete.
    4. Confirm the deletion. After confirming, the member will be removed from your account.
    5. The process is complete.

    This documentation provides a clear and concise guide for managing members within your account settings.

    - + \ No newline at end of file diff --git a/docs/user-guide/settings/personal-info.html b/docs/user-guide/settings/personal-info.html index 98c747a..a9b9de5 100644 --- a/docs/user-guide/settings/personal-info.html +++ b/docs/user-guide/settings/personal-info.html @@ -4,13 +4,13 @@ Personal Information | Synmetrix Docs - +
    Version: 1.0.0

    Personal Info

    Overview​

    The "Personal Info" section is designed to provide you with full control over your personal settings and security. Here, you can easily update your username, email address, and password, ensuring your account remains secure and personalized. Additionally, you have the option to manage your active sessions, allowing you to logout from all for enhanced security.

    How to Change Your Username?​

    To change your username:

    1. Go to "Settings" in the sidebar menu.
    2. Navigate to the "Personal Info" tab.
    3. Locate the "Full Name" field under the "General Information" section.
    4. Click on the "Full Name" input field and enter your new username.
    5. Click the "Update" button.
    6. You're all set!

    How to Change Your Email?​

    To update your email address:

    1. Access "Settings" from the sidebar menu.
    2. Select the "Personal Info" tab.
    3. Find the "Email" field in the "General Information" section.
    4. Enter your new email address in the "Email" input field.
    5. Click the "Update" button.
    6. Your email is now updated!

    How to Change Your Password?​

    To change your password:

    1. Visit "Settings" from the sidebar menu.
    2. Click on the "Personal Info" tab.
    3. Scroll down to the "Security" section.
    4. Enter your old password and your desired new password.
    5. Click the "Update Password" button.
    6. Your password has been changed!

    How to Logout from Active Sessions?​

    To logout from all active sessions:

    1. Go to "Settings" in the sidebar menu.
    2. Choose the "Personal Info" tab.
    3. Scroll to the "Active Sessions" section.
    4. Click the "Logout from all sessions" button.
    5. All sessions have been logged out!
    - + \ No newline at end of file diff --git a/docs/user-guide/settings/role-access.html b/docs/user-guide/settings/role-access.html index 0a95c81..585d388 100644 --- a/docs/user-guide/settings/role-access.html +++ b/docs/user-guide/settings/role-access.html @@ -4,7 +4,7 @@ Role Based Access | Synmetrix Docs - + @@ -12,7 +12,7 @@
    Version: 1.0.0

    Role Based Access Control

    Role Based Access Control

    Overview​

    Role Based Access Control (RBAC) is a system for regulating access to resources within your organization based on the roles of individual users. This document outlines how to manage roles and the types of access they grant.

    Access Types​

    Access Types

    • Partial Access: Grants access to specific Data Sources, Data Models, and various Measures, Dimensions, or Segments.
    • Full Access: Provides unrestricted access to all resources.
    • No Access: Denies access to all resources.

    Adding a New Role​

    To add a new role, follow these steps:

    1. Navigate to Settings > Roles and Access.
    2. Click on the Create Now button.
    3. In the popup window: Role Name
      • Enter the role name.

    Role Popup

    • Set access for data source resources by selecting one or multiple data sources.
    • Choose Data Models.
    • Select Measures, Dimensions, and/or Segments.
      tip

      Use the "Select All" checkbox to select all Measures, Dimensions, and Segments in the current Data Model.

    1. Click Create.
    info

    Roles display information including the creation date, last update, and data sources along with their respective access types.

    Deleting a Role​

    Role Edit

    To delete a role, follow these steps:

    1. Go to Settings > Roles and Access.
    2. Find the role to be deleted and click the gear icon on their card.
    3. Choose Delete from the dropdown list and confirm the deletion.
    4. The deletion process is complete.

    Editing a Role​

    Role Edit

    To edit an existing role, follow these steps:

    1. Navigate to Settings > Roles and Access.

    2. Locate the role to edit and click the gear icon on their card.

    3. Choose Edit from the dropdown list. Role Popup

    4. In the popup window, make the desired changes:

      • Update the role name.
      • Adjust access to data source resources.
      • Select or change Data Models.
      • Choose Measures, Dimensions, and/or Segments.
        tip

        Utilize the "Select All" checkbox for selecting all items in the current Data Model.

    5. Click Save to apply the changes.

    This document provides clear guidelines for managing roles within the Role Based Access Control system, ensuring efficient and secure access management.

    - + \ No newline at end of file diff --git a/docs/user-guide/settings/sql-api.html b/docs/user-guide/settings/sql-api.html index 5944d77..f669c3a 100644 --- a/docs/user-guide/settings/sql-api.html +++ b/docs/user-guide/settings/sql-api.html @@ -4,13 +4,13 @@ SQL API | Synmetrix Docs - +
    Version: 1.0.0

    SQL API

    SQL API Page

    Effortless Data Management and Uninterrupted Collaboration​

    Engage in powerful and flexible data retrieval and manipulation with our SQL API, designed for effortless data management and seamless collaboration.

    Simplified Data Sharing​

    Synmetrix revolutionizes data sharing with its versatile protocol, ensuring smooth integration with a variety of data tools. Our platform seamlessly connects with business intelligence and data exploration tools (Superset, Tableau), data notebooks (Jupyter, Hex), reverse ETL tools (Census, Hightouch), low-code platforms (Retool), and more, enhancing efficiency and simplicity in data-driven tasks.

    One-Stop Metrics Solution​

    Synmetrix serves as the ultimate repository for all your data needs, centralizing metrics in an organized manner. Regardless of your preferred tools or platforms for data analysis or visualization, Synmetrix provides easy access to essential data.

    Key Benefits​

    1. Integrations: Seamlessly integrate into your workflow, including Excel compatibility.
    2. Flexibility: Encourage collaborative efforts with shared models in a harmonious team environment.
    3. Security: Rely on our robust and reliable security measures.
    4. Effortless: Tailor SQL queries to meet your unique requirements.

    Acquiring SQL Credentials​

    Obtain your SQL API credentials through two distinct methods:

    First Method: From Onboarding​

    1. Initiate a new account creation.
    2. Add your data source.
    3. Opt to generate models (optional).
    4. Synmetrix generates SQL API access during this step.
    5. A pop-up allows you to choose between PSQL or MYSQL for connection, along with credential copying.
    6. Connect using PSQL or MYSQL, or opt to "Download Credentials" for later use.
    7. Finish the onboarding.

    Second Method: From Settings​

    Second Method: From Settings

    1. Navigate to Settings > SQL API.

    2. Click the gear icon on the database card and select "Edit".

    3. The pop-up window provides PSQL or MYSQL connection options and credential copying.

      Example Credentials
      • Host/URL: cube.synmetrix.org:15432
      • Database: db
      • Login: 4x4qg8ei76
      • Password: **********
      • PSQL Client Connection: psql --host=cube.synmetrix.org --port 15432 --username=4x4qg8ei76 --dbname=db

    4. Connect using PSQL or MYSQL, or "Download Credentials" for later use.

    Managing SQL API Credentials​

    Easily create, edit, or delete SQL API credentials via Settings > SQL API.

    Deleting Credentials​

    Delete SQL API

    1. Click the gear icon and select "Delete" from the dropdown.
    2. Confirm deletion.

    Creating Credentials​

    Creating Credentials

    1. Select the "Create Now" button on the SQL API page.
    2. In the pop-up, specify the Team member and Data source.

    Editing Credentials​

    Editing SQL API

    1. Click the gear icon and select "Edit".
    2. Modify the credentials as needed.
    3. Confirm by clicking "Finish".
    - + \ No newline at end of file diff --git a/docs/user-guide/settings/teams.html b/docs/user-guide/settings/teams.html index dbf2973..d65a5d7 100644 --- a/docs/user-guide/settings/teams.html +++ b/docs/user-guide/settings/teams.html @@ -4,13 +4,13 @@ Teams | Synmetrix Docs - +
    Version: 1.0.0

    Managing Your Team

    Introduction​

    The "Teams" section of Synmetrix is a dedicated space for creating, managing, and customizing teams. This feature is essential for collaborative work, allowing you to form new teams, set a focus on a current team, remove teams that are no longer needed, and update team names to align with evolving projects or goals.

    Accessing the Team Page​

    To navigate to the Team page:

    1. Hover over your account profile picture at the top-right corner of the page.
    2. Select "Team" from the dropdown menu.

    Accessing the Team Page

    Team Page Functions​

    On the Team page, you can perform various actions:

    1. Inviting New Users​

    You can invite new members to join your project by entering their email addresses. This step is crucial for expanding your team and fostering collaboration.

    2. Assigning Roles and Access Levels​

    Based on your access management system, assign appropriate roles and access levels to team members, ensuring they have the necessary permissions to contribute effectively.

    3. Managing Access​

    Control access to specific features and resources for each team member, tailoring their permissions to suit their role and needs in the project.

    4. Viewing and Managing Team Members​

    The Team page provides a comprehensive view of all team members, including their roles and responsibilities, allowing for efficient team management.

    How to Create a New Team?​

    To create a new team:

    1. Open "Settings" from the sidebar.
    2. Click on the "Teams" tab.
    3. In "Manage Teams", view your existing teams.
    4. Select the gear icon ("Settings").
    5. Choose "Create Team" from the dropdown and follow the prompts.

    How to Set a Current Team?​

    To set an active team:

    1. Go to "Settings" in the sidebar.
    2. Choose the "Teams" tab.
    3. Find your team list in "Manage Teams".
    4. Click the gear icon next to the team you want to focus on.
    5. Select "Set Current" from the dropdown.

    How to Edit a Team Name?​

    To change a team's name:

    1. Navigate to "Settings" via the sidebar.
    2. Select the "Teams" tab.
    3. Find your team in "Manage Teams".
    4. Click the gear icon next to the team you want to rename.
    5. Choose "Edit" from the dropdown.
    6. Change the "Team Name" in the pop-up.
    7. Click "Save" to confirm the changes.

    How to Delete a Team?​

    To remove a team:

    1. Access "Settings" from the sidebar.
    2. Go to the "Teams" tab.
    3. Identify the team you want to delete in "Manage Teams".
    4. Click the gear icon next to the team.
    5. Choose "Delete" from the dropdown list.

    I've refined the language for professional tone and clarity, ensuring each step is clear and straightforward.

    - + \ No newline at end of file diff --git a/index.html b/index.html index 973cba1..4da88ee 100644 --- a/index.html +++ b/index.html @@ -4,13 +4,13 @@ Hello from Synmetrix Docs | Synmetrix Docs - +

    Synmetrix Docs

    Explore our comprehensive documentation to get started

    Quick Start
    Quick Start

    Quick Start

    Get started quickly with our quick start guide.

    Development

    Development

    Learn about development practices and guidelines.

    Requirements

    Requirements

    Check out the requirements for your project.

    Production Deployment

    Production Deployment

    Explore how to deploy your project in a production environment.

    Architecture and Design

    Architecture and Design

    Understand the architecture and design of our project.

    Caching

    Caching

    Learn about caching and its importance in our project.

    SQL Interface

    SQL Interface

    Explore the SQL interface in our project.

    User Guide

    User Guide

    A comprehensive user guide to help you get started.

    Testing and Debugging

    Testing and Debugging

    Tips and techniques for testing and debugging your project.

    - + \ No newline at end of file diff --git a/markdown-page.html b/markdown-page.html index 77db759..d2d3790 100644 --- a/markdown-page.html +++ b/markdown-page.html @@ -4,13 +4,13 @@ Markdown page example | Synmetrix Docs - +

    Markdown page example

    You don't need React to write simple standalone pages.

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When creating the architecture, modern technologies were utilized, including the following key components:","s":"📄️ System Architecture","u":"/docs/next/architecture-and-design","h":"","p":5},{"i":12,"t":"Markdown page example You don't need React to write simple standalone pages.","s":"Markdown page example","u":"/markdown-page","h":"","p":11},{"i":14,"t":"Architecture and design Database Design","s":"Database Structure Design","u":"/docs/next/architecture-and-design/database-design","h":"","p":13},{"i":16,"t":"The \"Synmetrix\" database is organized according to the relational database model and includes the following tables: public.users: Stores information about registered users, including user ID (id), display name (display_name), avatar URL (avatar_url), as well as the creation time and last update time of the record. auth.account_providers: Describes the relationships between user accounts and their authentication providers. auth.accounts: Stores information about user accounts, including unique identifiers, email addresses, passwords, and other data. auth.providers: Provides a list of available authentication providers. auth.refresh_tokens: Contains information about refresh tokens for each user account. auth.roles: Manages user roles. auth.account_roles: Contains information about roles for each account. public.teams: Stores information about user teams. public.datasources: Contains information about data sources used by users. public.dataschemas: Describes data models used to define business metrics for data sources. public.explorations: Describes research tasks performed by users. public.members: Stores information about team members. public.team_roles: Manages user roles within teams. public.member_roles: Contains information about roles for each team member. public.reports: Contains information about the structure and schedule of reports based on metrics needed by users. public.sql_credentials: Manages SQL credentials used to access business metrics through the SQL interface. public.alerts: Stores information about alerts created by users.","s":"Database Structure","u":"/docs/next/architecture-and-design/database-design","h":"#database-structure","p":13},{"i":18,"t":"The architecture of the database, including relationships between tables, primary and foreign keys, and indexes, is represented in the Database Markup Language (DBML). This database structure provides flexibility and scalability to the system, allowing convenient management of users, teams, data sources, reports, and other system elements. Each database table is designed for a specific purpose and can be extended or modified to meet evolving system requirements. note For the complete DBML representation of the database architecture, please refer to Database Markup Language (DBML).","s":"Database Architecture Description","u":"/docs/next/architecture-and-design/database-design","h":"#database-architecture-description","p":13},{"i":21,"t":"Synmetrix offers a robust caching system with two distinct layers to optimize query performance and minimize database load. This system enhances the efficiency of data retrieval and processing. The primary caching layer is an in-memory cache, which is active by default. The second layer is called \"pre-aggregations\" and requires explicit configuration to activate.","s":"Overview","u":"/docs/next/caching","h":"","p":19},{"i":23,"t":"Synmetrix's in-memory cache serves as a temporary buffer for your database. It becomes especially valuable when multiple concurrent users request the same data simultaneously. While pre-aggregations are designed to strike a balance between query response time and performance, the in-memory cache helps smooth out spikes in data demand. In development mode, you can reset the in-memory cache by simply restarting the server. Note: It's generally not recommended to modify the default in-memory caching configuration unless it's absolutely necessary. To improve query performance, consider utilizing pre-aggregations.","s":"In-Memory Cache","u":"/docs/next/caching","h":"#in-memory-cache","p":19},{"i":25,"t":"Pre-aggregations introduce an additional layer of aggregated data, constructed and updated by Synmetrix. They can significantly enhance query performance and concurrency. To create pre-aggregations, Synmetrix might require write access to the pre-aggregations schema within the source database. In this scenario, Synmetrix initially builds pre-aggregations as tables in the source database and then exports them to the pre-aggregations storage. Refer to your specific driver's documentation for details on read-only support and pre-aggregation build strategies. Pre-aggregations are defined within the data model. You can learn more about defining pre-aggregations in the data modeling reference. Using YAML Using JavaScript cubes: - name: orders sql_table: orders measures: - name: total_amount sql: amount type: sum dimensions: - name: created_at sql: created_at type: time pre_aggregations: - name: amount_by_created measures: - total_amount time_dimension: created_at granularity: month cube(`orders`, { sql_table: `orders`, measures: { total_amount: { sql: `amount`, type: `sum`, }, }, dimensions: { created_at: { sql: `created_at`, type: `time`, }, }, pre_aggregations: { amount_by_created: { measures: [total_amount], time_dimension: created_at, granularity: `month`, }, },","s":"Pre-Aggregations","u":"/docs/next/caching","h":"#pre-aggregations","p":19},{"i":27,"t":"Synmetrix utilizes an in-memory cache to store the results of executed queries. The cache key is generated from the SQL statement, including any existing pre-aggregations that the query depends on. Upon receiving an incoming request, Synmetrix first checks the cache using this key. If no match is found in the cache, the query is executed in the database. The result set is returned and simultaneously updates the cache. If a cached value exists, and the refresh_key value for the query remains unchanged, the cached value is returned. Otherwise, an SQL query is executed either against the pre-aggregations storage or the source database to refresh the cache and retrieve the updated results.","s":"In-Memory Cache Operation","u":"/docs/next/caching","h":"#in-memory-cache-operation","p":19},{"i":29,"t":"To avoid unnecessary database queries, Synmetrix defines a refresh_key for each cube. These refresh keys are evaluated to determine if data needs to be refreshed. Here's an example of a refresh_key that instructs Synmetrix to refresh data every 5 minutes: YAML cubes: - name: orders # ... refresh_key: every: 5 minutes JavaScript cube(`orders`, { refresh_key: { every: `5 minute`, },}); Alternatively, the following refresh_key only refreshes data when the value of MAX(created_at) changes. By default, Synmetrix checks this refresh key every 10 seconds: YAML cubes: - name: orders # ... refresh_key: sql: SELECT MAX(created_at) FROM orders JavaScript cube(`orders`, { // ... refresh_key: { sql: `SELECT MAX(created_at) FROM orders`, },});","s":"Refresh Keys","u":"/docs/next/caching","h":"#refresh-keys","p":19},{"i":31,"t":"By default, in development mode, Synmetrix will check and invalidate the cache in the background. For production environments, it's recommended to run a Refresh Worker as a separate instance. We strongly recommend enabling background cache invalidation using a separate Synmetrix worker for production deployments. Please consult the Production Checklist for more information. If background refresh is disabled, Synmetrix will refresh the cache during query execution. To ensure optimal response times for end-users, it's advisable to always enable background refresh.","s":"Default Refresh Keys","u":"/docs/next/caching","h":"#default-refresh-keys","p":19},{"i":33,"t":"To gain insights into whether a query interacts with the in-memory cache, pre-aggregations, or the underlying data source, you can utilize the Synmetrix Playground or Synmetrix Cloud.","s":"Inspecting Queries","u":"/docs/next/caching","h":"#inspecting-queries","p":19},{"i":36,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"Synmetrix System Architecture","u":"/docs/next/architecture-and-design/system-architecture","h":"","p":34},{"i":38,"t":"Hasura plays a crucial role in the system by connecting to the database and providing a GraphQL API for external services and applications. Hasura simplifies working with the database, offering intuitive and powerful tools for quickly creating queries and mutations in GraphQL, thereby enhancing performance and optimizing data-related workflows.","s":"Hasura","u":"/docs/next/architecture-and-design/system-architecture","h":"#hasura","p":34},{"i":40,"t":"PostgreSQL has been chosen as the primary database management system due to its reliability, high performance, and flexibility. This allows for efficient handling of large volumes of data while ensuring excellent performance and stability.","s":"PostgreSQL","u":"/docs/next/architecture-and-design/system-architecture","h":"#postgresql","p":34},{"i":42,"t":"Node.js is the server-side platform used to create the backend of the application. Node.js is known for its ability to easily and rapidly develop scalable networked applications, thanks to efficient handling of asynchronous operations and event processing.","s":"Node.js","u":"/docs/next/architecture-and-design/system-architecture","h":"#nodejs","p":34},{"i":44,"t":"React.js is a library for developing user interfaces. It provides high performance and simplifies the development process through the use of a component-based architecture.","s":"React.js","u":"/docs/next/architecture-and-design/system-architecture","h":"#reactjs","p":34},{"i":46,"t":"Cube.js is an open-source analytics platform for building business analytics applications using JavaScript. In Synmetrix, Cube.js is used to manage business metrics, providing efficient tools for data processing.","s":"Cube.js","u":"/docs/next/architecture-and-design/system-architecture","h":"#cubejs","p":34},{"i":48,"t":"CubeStore is a distributed database optimized for analytical queries and integrated with Cube.js. It offers capabilities for fast and efficient processing of large volumes of data.","s":"CubeStore","u":"/docs/next/architecture-and-design/system-architecture","h":"#cubestore","p":34},{"i":50,"t":"Redis is a key-value database management system known for its high performance and flexibility. It offers unique features, including support for various data structures such as strings, lists, sets, hashes, and more.","s":"Redis","u":"/docs/next/architecture-and-design/system-architecture","h":"#redis","p":34},{"i":52,"t":"Docker is used for containerization and simplifying the application deployment process, ensuring consistent functionality in any environment.","s":"Docker","u":"/docs/next/architecture-and-design/system-architecture","h":"#docker","p":34},{"i":54,"t":"Docker Swarm is an orchestration and management tool for containers deployed using Docker. It allows for the management and scaling of applications across multiple servers, making infrastructure management easier.","s":"Docker Swarm","u":"/docs/next/architecture-and-design/system-architecture","h":"#docker-swarm","p":34},{"i":56,"t":"Ubuntu has been chosen as the primary operating system for the servers hosting all the services. It is a stable and reliable system well-suited for server environments.","s":"Ubuntu","u":"/docs/next/architecture-and-design/system-architecture","h":"#ubuntu","p":34},{"i":58,"t":"Additionally, Synmetrix has been designed as a microservices-based system. Each microservice performs a specific function and can scale independently of others, providing flexibility and scalability to the system. This architecture allows for the independent updating and modernization of individual system components, simplifying the development and maintenance process of the system as a whole.","s":"Interactions between architecture components","u":"/docs/next/architecture-and-design/system-architecture","h":"#interactions-between-architecture-components","p":34},{"i":60,"t":"Development","s":"Development","u":"/docs/next/development","h":"","p":59},{"i":62,"t":"Requirements","s":"📄️ Local Development","u":"/docs/next/development","h":"","p":59},{"i":64,"t":"This guide provides information on how to contribute to Synmetrix, including code changes, helping other users, documentation updates, bug reports, bug fixes, new feature proposals, and coding standards.","s":"📄️ Contributing","u":"/docs/next/development","h":"","p":59},{"i":66,"t":"Caching Getting started with pre-aggregations","s":"Getting started with pre-aggregations","u":"/docs/next/caching/getting-started-with-pre-aggregations","h":"","p":65},{"i":68,"t":"To illustrate pre-aggregations with an example, let's use a sample e-commerce database. We have a data model representing all our orders: YAML cubes: - name: orders sql_table: orders measures: - name: count type: count dimensions: - name: id sql: id type: number primary_key: true - name: status sql: status type: string - name: completed_at sql: completed_at type: time JavaScript cube(`orders`, { sql_table: `orders`, measures: { count: { type: `count`, }, }, dimensions: { id: { sql: `id`, type: `number`, primary_key: true, }, status: { sql: `status`, type: `string`, }, completed_at: { sql: `completed_at`, type: `time`, }, },}); Some sample data from this table might look like: id status completed_at 1 completed 2021-02-15T12:21:11.290 2 completed 2021-02-25T18:15:12.369 3 shipped 2021-03-15T20:40:57.404 4 processing 2021-03-13T10:30:21.360 5 completed 2021-03-10T18:25:32.109 Our first requirement is to populate a dropdown in our front-end application which shows all possible statuses. The Cube query to retrieve this information might look something like: JSON { \"dimensions\": [\"orders.status\"]} In that case, we can add the following pre-aggregation to the orders cube: YAML cubes: - name: orders # ... pre_aggregations: - name: order_statuses dimensions: - status JavaScript cube(`orders`, { // ... pre_aggregations: { order_statuses: { dimensions: [status], }, },});","s":"Pre-Aggregations without Time Dimension","u":"/docs/next/caching/getting-started-with-pre-aggregations","h":"#pre-aggregations-without-time-dimension","p":65},{"i":70,"t":"Development Contributing","s":"Contributing to Synmetrix","u":"/docs/next/development/contributing","h":"","p":69},{"i":72,"t":"One of the most valuable ways to contribute to Synmetrix is by helping answer user questions on the discussion forum or in the Slack channel. There are always new Synmetrix users seeking assistance, and providing answers is a valuable community service that showcases your expertise.","s":"Helping Other Users","u":"/docs/next/development/contributing","h":"#helping-other-users","p":69},{"i":74,"t":"To propose changes to Synmetrix documentation, edit the documentation source files located in the docs/ directory. The README file there explains how to build the documentation locally to test your changes. After making modifications, open a pull request to suggest the changes.","s":"Contributing to Documentation","u":"/docs/next/development/contributing","h":"#contributing-to-documentation","p":69},{"i":76,"t":"Reporting a bug is a simple and effective way to contribute to Synmetrix. It helps identify issues, enabling the team to provide patches and enhance the stability and usefulness of Synmetrix. Use the \"New issue\" button in the issues page to report a bug. Ensure your bug report includes a minimal executable test case to replicate the issue. Follow the instructions in the bug report template provided.","s":"Reporting Bugs","u":"/docs/next/development/contributing","h":"#reporting-bugs","p":69},{"i":78,"t":"Contributing bug fixes is an excellent way to build your experience and credibility within the community. If you are new to the Synmetrix codebase, start by looking at issues labeled with help wanted. If you need guidance on fixing an issue, seek assistance from experienced contributors by commenting on the issue page.","s":"Contributing Bug Fixes","u":"/docs/next/development/contributing","h":"#contributing-bug-fixes","p":69},{"i":80,"t":"Before contributing a new feature, submit a proposal in the issues page and discuss it with the community. This helps identify potential overlaps with planned features and prevents misunderstandings and conflicts.","s":"Proposing New Features","u":"/docs/next/development/contributing","h":"#proposing-new-features","p":69},{"i":82,"t":"When submitting code contributions, you must sign a Developer Certificate of Origin (DCO) to certify your authorship of the source code or your right to submit it to the project. Sign your contributions by adding a Signed-off-by line to your commit message or using the -s option with the git commit command. This is my commit messageSigned-off-by: Random J Developer ","s":"Submitting Code Changes","u":"/docs/next/development/contributing","h":"#submitting-code-changes","p":69},{"i":85,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster. Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.","s":"Docker Swarm deployment guide","u":"/docs/next/docker-swarm","h":"","p":83},{"i":87,"t":"Go to the Synmetrix project directory and run the following commands:","s":"Start Synmetrix on Docker Swarm","u":"/docs/next/docker-swarm","h":"#start-synmetrix-on-docker-swarm","p":83},{"i":89,"t":"./scripts/run-registry.sh","s":"Step 1 - Run Docker Registry","u":"/docs/next/docker-swarm","h":"#step-1---run-docker-registry","p":83},{"i":91,"t":"DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --env stage synmetrix Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.","s":"Step 2 - Run Synmetrix services","u":"/docs/next/docker-swarm","h":"#step-2---run-synmetrix-services","p":83},{"i":93,"t":"./migrate.sh","s":"Step 3 - Run migrations","u":"/docs/next/docker-swarm","h":"#step-3---run-migrations","p":83},{"i":95,"t":"./cli.sh swarm destroy synmetrix","s":"Destroy stack","u":"/docs/next/docker-swarm","h":"#destroy-stack","p":83},{"i":97,"t":"./cli.sh swarm logs synmetrix_hasura","s":"Show logs","u":"/docs/next/docker-swarm","h":"#show-logs","p":83},{"i":99,"t":"./cli.sh swarm ps synmetrix For more information about the CLI tool, run the following command: ./cli.sh swarm --help","s":"Show stack tasks status","u":"/docs/next/docker-swarm","h":"#show-stack-tasks-status","p":83},{"i":101,"t":"APIs & integrations Excel","s":"Excel","u":"/docs/next/integration-examples/excel","h":"","p":100},{"i":103,"t":"Development Local Development","s":"Local Development","u":"/docs/next/development/local-development","h":"","p":102},{"i":105,"t":"Before using the backend-side of the project, ensure that you have the following components installed: Docker Node.js (Version 20.8.1 or above) Yarn","s":"Requirements","u":"/docs/next/development/local-development","h":"#requirements","p":102},{"i":108,"t":"Clone the Synmetrix repository: git clone https://github.com/mlcraft-io/mlcraft.gitcd mlcraft Run the initialization script: bash init.sh","s":"Getting Started","u":"/docs/next/development/local-development","h":"#getting-started","p":102},{"i":111,"t":"Before using the client-side of the project, ensure that you have the following components installed: Synmetrix Backend: Ensure the Synmetrix backend is properly set up and running.","s":"Requirements","u":"/docs/next/development/local-development","h":"#requirements-1","p":102},{"i":113,"t":"To install the client-side of the project, follow these steps: # Clone the Synmetrix Client repositorygit clone https://github.com/mlcraft-io/client-v2# Navigate to the project directorycd client-v2# Install required packages using Yarnyarn# Start the client-side applicationyarn start After completing these steps, open your web browser and go to to access the Synmetrix client.","s":"Getting Started","u":"/docs/next/development/local-development","h":"#getting-started-1","p":102},{"i":115,"t":"For detailed guidance on utilizing the Synmetrix client and exploring its features, please consult the Synmetrix Documentation.","s":"Usage","u":"/docs/next/development/local-development","h":"#usage","p":102},{"i":117,"t":"Explore the interactive UI components and functionalities of the Synmetrix client using Storybook. Storybook provides a convenient environment to showcase and test UI components in isolation. To launch Storybook, use the following command: yarn storybook Once the command is executed, navigate to","s":"Storybook Integration","u":"/docs/next/development/local-development","h":"#storybook-integration","p":102},{"i":119,"t":"Maintain code consistency and quality in the Synmetrix client by implementing linting. Follow these steps to configure linting in Visual Studio Code (VS Code): Requirements:​ ESLint Extension Prettier Extension Configuration:​ Install ESLint and Prettier VS Code extensions. Open your VS Code settings (settings.json) and add the following configuration: { \"eslint.enable\": true, \"eslint.format.enable\": true, \"editor.formatOnSave\": true} This configures VS Code to run ESLint and Prettier on file save.","s":"Code Linting","u":"/docs/next/development/local-development","h":"#code-linting","p":102},{"i":121,"t":"In the Synmetrix project, components follow a consistent structure to enhance maintainability and organization. Each component typically consists of the following files: index.tsx: The main file containing the component's implementation. This file includes the React component code. index.module.less: A Less file containing the component's styles. Using module.less ensures that styles are scoped to the component and won't conflict with styles from other components. index.stories.tsx: A file dedicated to Storybook stories for the component. Storybook stories help in visually testing and documenting the component's variations and use cases. index.test.tsx: The file containing unit tests for the component using testing framework vitest. Writing tests ensures the reliability and correctness of the component's functionality. Here's an example directory structure for a component named ExampleComponent: /src /components /ExampleComponent index.tsx index.module.less index.stories.tsx index.test.tsx","s":"Component Structure","u":"/docs/next/development/local-development","h":"#component-structure","p":102},{"i":123,"t":"Ensure the reliability and correctness of the Synmetrix client by utilizing testing functionalities powered by Vitest. Vitest provides a robust testing framework to validate the behavior of your components and functionalities. To run tests, use the following command: yarn test Executing this command will initiate the testing suite, allowing you to assess the client's performance and functionality. Any detected issues or failures will be highlighted, providing valuable insights for debugging and maintaining code quality.","s":"Testing","u":"/docs/next/development/local-development","h":"#testing","p":102},{"i":125,"t":"Efficiently build and package the Synmetrix client with the following commands: Build the Client: yarn build This command utilizes Vite to build the Synmetrix client. Post-Build Tasks: yarn postbuild After the build, this command creates compressed archives (dist.tar.gz and dist.zip) of the generated distribution files. Build Storybook: yarn build-storybook Use this command to build the Storybook for the client. Serve the Built Client Locally: yarn serve This command previews the built client locally using Vite. Incorporate these commands into your workflow to streamline the build process and prepare the Synmetrix client for deployment.","s":"Build","u":"/docs/next/development/local-development","h":"#build","p":102},{"i":127,"t":"We adhere to the Conventional Commits specification to ensure consistent and meaningful commit messages. The structure of our commits follows the pattern: [optional scope]: [optional body][optional footer(s)] For a detailed understanding of Conventional Commits, refer to the Conventional Commits specification.","s":"Conventional Commits","u":"/docs/next/development/local-development","h":"#conventional-commits","p":102},{"i":129,"t":"APIs & integrations Power BI","s":"Power BI","u":"/docs/next/integration-examples/power-bi","h":"","p":128},{"i":131,"t":"License","s":"License","u":"/docs/next/license","h":"","p":130},{"i":133,"t":"MIT License","s":"📄️ License & Community","u":"/docs/next/license","h":"","p":130},{"i":135,"t":"License License & Community","s":"License and Community","u":"/docs/next/license/license-community","h":"","p":134},{"i":137,"t":"Links and Contacts","s":"Links and Contacts","u":"/docs/next/links-and-contacts","h":"","p":136},{"i":139,"t":"GitHub Repository Slack Channel Docker Hub Meeting Calendar","s":"Useful Links and Contacts","u":"/docs/next/links-and-contacts","h":"#useful-links-and-contacts","p":136},{"i":141,"t":"Overview","s":"Overview","u":"/docs/next/overview","h":"","p":140},{"i":143,"t":"In today's data-driven world, the challenges of data management are ever-increasing, especially in fields that rely heavily on data processing, analysis, and transmission. Inefficient data management can result in lost productivity, missed opportunities, and even regulatory compliance issues. The need for a comprehensive solution to address these complexities has never been more pressing. Synmetrix is committed to tackling this challenge head-on and providing a robust solution to streamline data management, analysis, and transmission.","s":"The Data Management Challenge","u":"/docs/next/overview","h":"#the-data-management-challenge","p":140},{"i":145,"t":"With Synmetrix, you can effortlessly address the complexity and inefficiency in managing, analyzing, and transmitting data across various sectors. Our platform offers powerful features for creating and managing data models, tracking metric changes, and organizing data-related tasks. You can easily share your data with others through interfaces like REST, GraphQL, or SQL, making your data a single source of truth for your entire organization. Whether you're a data engineer, analyst, or manager, Synmetrix has everything you need to excel with big data.","s":"Software Description and Purpose","u":"/docs/next/overview","h":"#software-description-and-purpose","p":140},{"i":147,"t":"Synmetrix's versatile features find applications across a wide range of sectors, including finance, retail, the power industry, manufacturing, research and development, healthcare, and many more. As the global big data market continues to boom, with projections reaching $103 billion by 2027 and a compound annual growth rate of 10.9%, Synmetrix is poised to seize this massive opportunity. We're here to provide the solution to the complexity and inefficiency in data management, analysis, and transmission.","s":"Tapping into a Vast Market","u":"/docs/next/overview","h":"#tapping-into-a-vast-market","p":140},{"i":149,"t":"Data Modeling (Semantic layer): Transform raw data into meaningful business indicators with our effective data modeling feature. SQL Interface: Access metrics easily and manipulate data with our intuitive SQL interface. Reporting System: Schedule reports and distribute them through various communication channels with our integrated reporting system. Alerting System: Stay informed about significant data changes with our proactive alerting system. Documentation Generation: Enhance the understanding and usability of your data models with our documentation generation feature.","s":"Key Features","u":"/docs/next/overview","h":"#key-features","p":140},{"i":151,"t":"JavaScript / Node.js","s":"Programming Language","u":"/docs/next/overview","h":"#programming-language","p":140},{"i":153,"t":"To make the most of this documentation, you can explore the following sections: Quick Start: Installation and initial setup of Synmetrix. User Guide: In-depth guides on using Synmetrix. Contributing: Information on how to contribute to the development of Synmetrix.","s":"How to Use This Documentation","u":"/docs/next/overview","h":"#how-to-use-this-documentation","p":140},{"i":155,"t":"We welcome contributions from the community. If you'd like to get involved in the development of Synmetrix or have ideas for improvement, please check out our Contributing Guidelines.","s":"Contributing","u":"/docs/next/overview","h":"#contributing","p":140},{"i":157,"t":"If you have questions, feedback, or need assistance, don't hesitate to reach out to us. Contact us. or by visiting our GitHub repository. Synmetrix is meticulously designed to streamline your data management, analysis, and transmission workflows, making it the ultimate tool for all your data needs. We look forward to your involvement and contributions!","s":"Contact Us","u":"/docs/next/overview","h":"#contact-us","p":140},{"i":159,"t":"Production Deployment Requirements","s":"Requirements","u":"/docs/next/production-deployment/requirements","h":"","p":158},{"i":161,"t":"Docker Node.js (Version 20.8.1 or above) Yarn","s":"Software Requirements","u":"/docs/next/production-deployment/requirements","h":"#software-requirements","p":158},{"i":163,"t":"Synmetrix is intended for installation and use on servers equipped with processors supporting x86-64 architecture and based on operating systems such as Ubuntu Server 18.04 and higher. Minimum Hardware Requirements: - Processor (CPU): 3.2 GHz or higher, modern processor with multi-threading and virtualization support.- RAM: 8 GB or more to handle computational tasks and data processing.- Disk Space: At least 30 GB of free space for software installation and storing working data.- Network: Internet connectivity is required for cloud services and software updates. note These requirements are minimum and may vary depending on specific use cases and data processing volumes. Scaling the solution may require more powerful servers or distributed systems.","s":"Hardware Requirements","u":"/docs/next/production-deployment/requirements","h":"#hardware-requirements","p":158},{"i":165,"t":"You can access the source code of Synmetrix on GitHub.","s":"Source Code","u":"/docs/next/production-deployment/requirements","h":"#source-code","p":158},{"i":167,"t":"Production Deployment","s":"Production Deployment","u":"/docs/next/production-deployment","h":"","p":166},{"i":169,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.","s":"📄️ Docker Swarm","u":"/docs/next/production-deployment","h":"","p":166},{"i":171,"t":"Software Requirements","s":"📄️ Requirements","u":"/docs/next/production-deployment","h":"","p":166},{"i":173,"t":"Quick Start","s":"Quick Start","u":"/docs/next/quickstart","h":"","p":172},{"i":175,"t":"Ensure the following software is installed before proceeding: Docker Docker Compose","s":"Prerequisite Software","u":"/docs/next/quickstart","h":"#prerequisite-software","p":172},{"i":177,"t":"The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository: Execute this in a new directory Using wget Using curl wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml curl https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml -o docker-compose.yml","s":"Step 1: Download the docker-compose file","u":"/docs/next/quickstart","h":"#step-1-download-the-docker-compose-file","p":172},{"i":179,"t":"Execute the following command to start Synmetrix along with a Postgres database for data storage. $ docker-compose up -d Verify if the containers are operational: $ docker psCONTAINER ID IMAGE ... CREATED STATUS PORTS ...c8f342d086f3 synmetrix/stack ... 1m ago Up 1m 80->8888/tcp ...30ea14ddaa5e postgres:12 ... 1m ago Up 1m 5432/tcp The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.","s":"Step 2: Launch Synmetrix","u":"/docs/next/quickstart","h":"#step-2-launch-synmetrix","p":172},{"i":181,"t":"You can access Synmetrix at http://localhost/ The GraphQL endpoint is located at http://localhost/v1/graphql The Admin Console (Hasura Console) can be found at http://localhost/console The Cube Swagger API can be found at http://localhost:4000/docs note Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated. Default Seed Data: The project comes with default seed data. Use the following credentials to log in: Email: demo@synmetrix.org Password: demodemo If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.","s":"Step 3: Explore Synmetrix","u":"/docs/next/quickstart","h":"#step-3-explore-synmetrix","p":172},{"i":183,"t":"Demo: app.synmetrix.org Login: demo@synmetrix.org Password: demodemo","s":"Demo online","u":"/docs/next/quickstart","h":"#demo-online","p":172},{"i":185,"t":"Database type Host Port Database User Password SSL ClickHouse gh-api.clickhouse.tech 443 default play no password true PostgreSQL demo-db.cube.dev 5432 ecom cube 12345 false","s":"Demo credentials","u":"/docs/next/quickstart","h":"#demo-credentials","p":172},{"i":189,"t":"For user support and inquiries, you can reach out to us through the following channels: Email Support: hello@synmetrix.org Issue Tracker: GitHub Issues Our support team is dedicated to assisting you with any questions or problems you might encounter while using Synmetrix.","s":"Contact Information","u":"/docs/next/support-updates","h":"#contact-information","p":186},{"i":191,"t":"We are continuously working to improve Synmetrix and provide you with the best possible experience. Here's a glimpse of our upcoming updates and changes: GitHub","s":"Upcoming Updates and Changes","u":"/docs/next/support-updates","h":"#upcoming-updates-and-changes","p":186},{"i":193,"t":"Testing and Debugging Testing Guides","s":"Testing Guides","u":"/docs/next/testing","h":"","p":192},{"i":195,"t":"12 items","s":"🗃️ Test a Data Source","u":"/docs/next/testing","h":"","p":192},{"i":197,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"📄️ Test SQL Interface for Data Models","u":"/docs/next/testing","h":"","p":192},{"i":199,"t":"In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI.","s":"📄️ Test Structure","u":"/docs/next/testing","h":"","p":192},{"i":203,"t":"In the development of Synmetrix, the establishment of a robust SQL interface plays a pivotal role. This interface serves as a unified platform for accessing data sources, ensuring consistent metrics are available to all data consumers. It addresses the challenge of maintaining data integrity and precision across various tools employed by modern organizations.","s":"Introduction","u":"/docs/next/sql-interface","h":"#introduction","p":200},{"i":205,"t":"Many modern organizations utilize numerous tools to process data from a shared storage but perform individual metric calculations. This often leads to discrepancies in calculations and disparities among different teams. The more tools an organization employs, the greater the potential for inconsistencies, making data-driven decision-making complex. Hence, it is essential to create a centralized hub for defining and maintaining metrics, ensuring that all tools operate with a single source of data.","s":"Problem Statement","u":"/docs/next/sql-interface","h":"#problem-statement","p":200},{"i":207,"t":"To tackle this challenge, Synmetrix has chosen the Cube.js framework, successfully integrated into the Synmetrix platform. Cube.js offers a significant advantage in its ability to act as a data source proxy, transforming incoming queries—whether in JSON, GraphQL, or SQL format—into native queries to the underlying data store.","s":"Solution: Leveraging Cube.js","u":"/docs/next/sql-interface","h":"#solution-leveraging-cubejs","p":200},{"i":209,"t":"Creating the SQL interface within Synmetrix presented several challenges effectively addressed using the Cube.js framework and Apache Datafusion. This SQL interface enables Cube.js to provide data through a PostgreSQL-compatible protocol to various data-consuming applications, including but not limited to: BI tools Data exploration tools such as Jupyter or Hex Reverse ETL tools like Census or Hightouch Low-code development platforms such as Retool Automated tasks The SQL interface has been thoroughly tested with a range of tools and platforms, including: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch The SQL API utilizes Apache Datafusion as its SQL execution engine, responsible for query planning and execution.","s":"Implementation of SQL Interface in Synmetrix","u":"/docs/next/sql-interface","h":"#implementation-of-sql-interface-in-synmetrix","p":200},{"i":211,"t":"The implementation of the SQL interface in Synmetrix comprises several key steps, facilitated by the Cube.js framework and Apache Datafusion: Request Retrieval: Incoming data retrieval requests arrive in SQL format to Cube.js, serving as the initial queries that require processing and execution. Query Transformation: Cube.js transforms the incoming SQL queries into its own query format (JSON). This transformation optimizes and prepares the query for execution against the underlying data store. Query Execution: The SQL query generated in the previous step is sent to the data source. This source can be any supported database to which Cube.js can connect. Data Retrieval: The output is a dataset containing the required data in response to the original query. Load into Apache Datafusion: The dataset is loaded into Apache Datafusion, where it undergoes further processing. Apache Datafusion serves as the SQL execution engine, responsible for query planning and execution. Execution of the Original SQL Query: The original SQL query received by the server is executed within Apache Datafusion. This allows for the full utilization of SQL's capabilities for data analysis and processing while preserving the structure and semantics of the original query. Data Return: Query results are returned through Cube.js, where they can be further processed or directly delivered to end-users. This process ensures high flexibility and power when working with queries while maintaining data consistency and coherence for all consumers. Each stage has been meticulously designed and optimized to ensure efficient query execution and overall system performance.","s":"Implementation Details","u":"/docs/next/sql-interface","h":"#implementation-details","p":200},{"i":213,"t":"A notable feature of the SQL interface in Synmetrix is how it handles and transforms SQL queries. As previously mentioned, Cube.js converts incoming SQL queries into its own query format (JSON), a process known as \"Cube query rewrite.\" This transformation allows for optimization and query preparation for execution against the underlying data store. Let's illustrate this with an example query to the \"orders\" cube in our data model: Original SQL Query: SELECT city, SUM(amount)FROM ordersWHERE status = 'shipped'GROUP BY 1 This SQL query in the SELECT statement to the \"orders\" cube is transformed into Cube.js' internal query format: Transformed Cube.js Query: { \"measures\": [\"Orders.amount\"], \"dimensions\": [\"Orders.city\"], \"filters\": [ { \"member\": \"Orders.status\", \"operator\": \"equals\", \"values\": [\"shipped\"] } ]} As a result of this transformation, not all functions and expressions are supported within query fragments that execute SELECT statements on cube tables. For example, the following query would not work because the SQL API cannot pass the CASE expression to Cube.js for processing, making it impossible to translate the CASE expression into a metric: Non-supported Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(number)FROM orders CROSS JOIN UsersGROUP BY 1; However, in such cases, nested queries can be employed. You can encapsulate your SELECT query from a cube table within another SELECT query to perform calculations with expressions like CASE. This outer SELECT is not part of the rewritten SQL query, allowing you to use more SQL functions, operators, and expressions. Example Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT Users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN Users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; The above query works because the CASE expression is supported in SELECT queries that do not reference cube tables. These nuances and query handling specifics should be considered when designing and utilizing the SQL interface in Synmetrix.","s":"Transformation of SQL Queries","u":"/docs/next/sql-interface","h":"#transformation-of-sql-queries","p":200},{"i":215,"t":"For a detailed analysis of the server code and its components, please refer to the Synmetrix SQL Interface Server Code.","s":"Server Code","u":"/docs/next/sql-interface","h":"#server-code","p":200},{"i":217,"t":"The creation of the SQL interface in Synmetrix has allowed for the development of a unified data access interface, ensuring metric consistency for all data tools. This has reduced the number of discrepancies and conflicts related to metric calculations, providing a more reliable foundation for data-driven decision-making.","s":"Conclusion","u":"/docs/next/sql-interface","h":"#conclusion","p":200},{"i":219,"t":"Testing and Debugging","s":"Testing and Debugging","u":"/docs/next/testing-and-debugging","h":"","p":218},{"i":221,"t":"3 items","s":"🗃️ Testing Guides","u":"/docs/next/testing-and-debugging","h":"","p":218},{"i":225,"t":"info In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI. StepCI is employed for writing and conducting integration tests, which verify the correctness of the system's operation when its various components interact with each other. This is particularly vital for projects like Synmetrix, which consist of numerous services and components working collaboratively.","s":"Testing Structure for Synmetrix","u":"/docs/next/testing-and-debugging/test-structure","h":"","p":223},{"i":227,"t":"All test files are located in the test/stepci directory within the Synmetrix project folder. Each file represents an individual test scenario described in YAML format. - name: change_password http: url: ${{env.HASURA_PLUS_ENDPOINT}}/auth/change-password method: POST headers: Content-Type: application/json Authorization: Bearer ${{captures.accessToken}} body: | { \"old_password\": \"pass321\", \"new_password\": \"pass123\" } check: status: 204 This YAML configuration represents a typical StepCI test scenario named \"change_password.\" It performs a POST request with specific headers and a JSON request body to change a password. The response status is checked to ensure it's 204.","s":"Test Structure","u":"/docs/next/testing-and-debugging/test-structure","h":"#test-structure","p":223},{"i":229,"t":"Testing on the StepCI platform typically involves the following steps: Request Creation: StepCI generates a request based on the input data defined in the test scenario. Request Sending: The request is sent to the tested endpoint in the Synmetrix system. Waiting for Response: StepCI waits for a response from the Synmetrix system. During this time, all necessary actions are performed, such as request processing, interaction with the database, and other services. Response Verification: After receiving the response, StepCI compares it with the expected result defined in the test scenario. If the response matches the expectations, the test is considered passed. Otherwise, StepCI logs an error, and the test is marked as failed. This testing structure ensures the reliability and stability of the Synmetrix system. Automated testing allows developers to quickly identify and rectify errors, as well as ensure that new changes do not disrupt the functionality of existing features. Upon completing a set of tests, StepCI provides a report with a brief description of the tests that were passed. PASS datasource_flow 9.458s 1 1620 bytes 1 16006 bytesTests: 0 failed, 1 passed, 1 totalSteps: 0 failed, 0 skipped, 18 passed, 18 totalTime: 9,533s, estimated 10sCO2:0.04036gWorkflow passed after 9.533s Test was passed. In the event of a test failure, StepCI records details, including the name of the failed testing step, input data and parameters, actual and expected results, and error messages obtained during the execution of the tested code. Subsequent test steps are halted upon encountering a failure. v create_team passed after 0.041sv invite_team_member passed after 0.597s v create_datasource passed after 0.021s v check_connection passed after 0.833s v validate_datasource passed after 0.078s v fetch_tables passed after 0.436s × gen_dataschemas failed after 0.008s A create_exploration skipped after 0s A fetch_dataset skipped after Os A export_data_models skipped after 0s A fetch_meta skipped after 0s A run_query skipped after Os• datasource_flow › gen_dataschemasRequest HTTPPOST http://hasura:8080/v1/graphql HTTP/1.1Content-Type: application/jsonAuthorization: Bearer eyJhbci01JIUzI1N1J9.eyJoYXN1cmEiOnsieC1oYXN1cmEtdXNlci1pZCI6IjU5YjhjOTVjLTdIMmEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMi0\\sidXNlciJdLCJ4LWhhc3VyYS1kzwZhdWx0LXJvbGUi0iJ1c2VyInosInN1Yi16IjU5YjhjOTVjLTdl™mEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsImlzcyI6Im5ob3N0IiwiaWF0Ijo×NjkwoDkoNzcOLCJleHAi0jE20TE1NDI3NzR9.DTtp9T†jNg6ABm1vV7eB0KXZw6k1d2iZx6C-DFicCw™,x-hasura-user-id: 59b8c95c-7e2a-4797-a66c-e51776ccd39e{\"query\": \"mutation (#datasource_id: uuid!, $tables: [SourceTable!]!) f\\n gen_dataschemas (d atasource_id: $datasource_id, tables: $tables) f\\n code\\nmessageln FInrIn\"， \"variables\" :{\"datasource_id\":\"9218058b-160c-4c43-8126-b944612c055f\"\"tables\": K{\"name\": \"default/celltowers\"},{\"name\": \"default/checks\"}1}}ResponseHTTP/1.1 200 OKtransfer-encoding: chunkeddate: Tue 01 Aug 2023 12:59:36 GMTserver: Warp/3.3.23content-type: application/json; charset=utf-8{\"errors\": [\"extensions\":{\"code\": \"validation-failed\", \"path\":\"$.selectionSet. gen_dataschemas args.branch_id\"}, \"message\":\"missing required field 'branch_id'\"}1}ChecksJSONPath× $. data.gen_dataschemas.code: undefined (expected ok) Test was failed.","s":"Testing Algorithm","u":"/docs/next/testing-and-debugging/test-structure","h":"#testing-algorithm","p":223},{"i":234,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"SQL Interface Testing Guide for Data Models","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"","p":232},{"i":236,"t":"Before you begin testing, you'll need the following Postgres database credentials: Type: Postgres Host: demo-db.cube.dev Port: 5432 User: cube Password: 12345 Database Name: ecom","s":"Section 1: Test Database Credentials","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-1-test-database-credentials","p":232},{"i":238,"t":"To add an SQL interface, follow these steps: Go to the Settings > SQL API and Click to the \"Attach SQL API\" button. Select the data source for connection; in our case, it's \"Ecom (demo db).\" Enter your login and password or use the default ones (login: \"test\" and password: \"test\"). Click \"Connect new.\"","s":"Section 2: Adding SQL Interface","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-2-adding-sql-interface","p":232},{"i":240,"t":"Refer to the guide in Appendix V.2 for generating data models. For \"Ecom (demo db),\" you'll need to generate data models for the following tables: Line Items Line Items Count by States Orders Product Categories Products Suppliers Users This will result in the following list of data models: LineItems.yml LineItemsCountByStates.yml Orders.yml ProductCategories.yml Products.yml Suppliers.yml Users.yml Each script is labeled with its corresponding name, which is also the table name when accessing via the SQL interface.","s":"Section 3: Generating Data Models","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-3-generating-data-models","p":232},{"i":242,"t":"We recommend using DBeaver of any version (https://dbeaver.io/) for connecting. However, our connection has also been tested with the following tools: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch","s":"Section 4: Connecting via SQL Interface with a Postgres Client","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-4-connecting-via-sql-interface-with-a-postgres-client","p":232},{"i":244,"t":"Create a new PostgresSQL connection. Enter the credentials provided when setting up the SQL interface. In this example: Host: localhost Port: 15432 Database: ecom (you can specify any value) Username: test Password: test Click \"Test Connection\" to verify the connection. Click \"Finish\" and save the connection. Go to the SQL console to test SQL queries against the data models.","s":"4.1: Connecting via DBeaver","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#41-connecting-via-dbeaver","p":232},{"i":246,"t":"Start writing queries for the generated data models following the steps outlined in Appendix D.3. 4.2.1: Sample SQL Queries​ Query: SELECT * FROM orders ORDER BY created_at LIMIT 3; Response: Query: SELECT p.name, SUM(o.count) FROM orders o CROSS JOIN products p GROUP BY 1 LIMIT 5; Response: Query: SELECT MEASURE(count), status, DATE_TRUNC('month', created_at) date FROM orders GROUP BY date, status ORDER BY date ASC; Response: Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; Response:","s":"4.2: Testing SQL Queries","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#42-testing-sql-queries","p":232},{"i":248,"t":"Testing and Debugging Testing Guides Test a Data Source","s":"Test a Data Source","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":250,"t":"Tested on the crate:5.3.0 image.","s":"📄️ CrateDB","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":252,"t":"To connect to a Databricks datasource via JDBC, you will need the following information:","s":"📄️ Databricks","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":254,"t":"Tested on the dremio/dremio-oss:latest image.","s":"📄️ Dremio","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":256,"t":"Step 1: Navigate to the Druid example directory","s":"📄️ Druid","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":258,"t":"Step 1: Navigate to the Elasticsearch Example Directory","s":"📄️ Elasticsearch","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":260,"t":"Connecting to Firebolt datasource","s":"📄️ Firebolt","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":262,"t":"Tested on the confluentinc/cp-ksqldb-server:7.0.9 image.","s":"📄️ KSQL","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":264,"t":"Tested on the materialize/materialized:latest image.","s":"📄️ Materialize","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":266,"t":"Tested on the starburstdata/presto image.","s":"📄️ Presto","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":268,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"📄️ QuestDB","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":270,"t":"This guide provides step-by-step instructions on how to connect Snowflake to Synmetrix.","s":"📄️ Snowflake","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":272,"t":"Tested on the trinodb/trino:latest image.","s":"📄️ Trino","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":247},{"i":276,"t":"Navigate to the Druid example directory located at /docs/examples/druid in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/druid","s":"Step 1: Navigate to the Druid example directory","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-1-navigate-to-the-druid-example-directory","p":273},{"i":278,"t":"In the Druid example directory, there is a script called run_druid.sh. Make sure the script is executable by running: chmod +x run_druid.sh Then, start the Druid services by running the script: ./run_druid.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step. Wait for the cluster to start and go to http://localhost:8889.","s":"Step 2: Run the Druid services using the script","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-2-run-the-druid-services-using-the-script","p":273},{"i":280,"t":"Go to the Services tab and make sure all services are online, as shown in the screenshot below.","s":"Step 3: Check the services","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-3-check-the-services","p":273},{"i":282,"t":"Select the \"Load data\" tab, then choose \"Batch - classic\" from the dropdown menu. Next, select the \"Example data\" tile and click \"Load example\". Click the button with the text \"Next: ...\" in the bottom right corner until the data starts loading (about 9 times). When the data starts loading, you will be redirected to the Ingestion tab and see the message \"Task submitted successfully. Going to task view...\". After the data has finished loading, the task status will change to SUCCESS.","s":"Step 4: Load example data","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-4-load-example-data","p":273},{"i":284,"t":"Fill out the form with the following information: Name: Druid test datasource Host: Host IP (obtained from the script in Step 2) Port: 8082 Currently, the Test Connection feature is not implemented and always returns \"OK\". To ensure functionality, generate a schema and request any data in the Explore tab.","s":"Step 5: Create a new Druid datasource in Synmetrix","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-5-create-a-new-druid-datasource-in-synmetrix","p":273},{"i":287,"t":"Tested on the dremio/dremio-oss:latest image.","s":"Dremio","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"","p":285},{"i":289,"t":"Navigate to the Dremio example directory located at /docs/examples/dremio in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/dremio","s":"Step 1: Navigate to the Dremio example directory","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-1-navigate-to-the-dremio-example-directory","p":285},{"i":291,"t":"In the Dremio example directory, there is a script called run_dremio.sh. Make sure the script is executable by running: chmod +x run_dremio.sh Then, start the Dremio service by running the script: ./run_dremio.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Dremio service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-2-run-the-dremio-service-using-the-script","p":285},{"i":293,"t":"Open your web browser and go to http://localhost:9047. Follow the prompts to create an administrator account. Log in to the Dremio UI with your new account. Click on \"Add Source\" and select \"PostgreSQL\" from the list of available data sources. Use the credentials provided at https://uibakery.io/sql-playground to configure the PostgreSQL data source.","s":"Step 3: Create an administrator account and add a data source in Dremio","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-3-create-an-administrator-account-and-add-a-data-source-in-dremio","p":285},{"i":295,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: host_ip (obtained from the script in Step 2) Port: 9047 Database name: Name given to the connection in Dremio User: Dremio administrator username Password: Dremio administrator password","s":"Step 4: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-4-add-a-data-source-in-the-synmetrix-ui","p":285},{"i":297,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Dremio data source. If the connection is successful, you will see a confirmation message.","s":"Step 5: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-5-test-the-connection","p":285},{"i":299,"t":"Testing and Debugging Testing Guides Test a Data Source CrateDB","s":"Crate","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"","p":298},{"i":301,"t":"Navigate to the CrateDB example directory located at /docs/examples/crate in the root of the project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/crate","s":"Step 1: Navigate to the CrateDB example directory","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-1-navigate-to-the-cratedb-example-directory","p":298},{"i":303,"t":"In the CrateDB example directory, there is a script called run_crate.sh. Make sure the script is executable by running: chmod +x run_crate.sh Then, start the CrateDB service by running the script: ./run_crate.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the CrateDB service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-2-run-the-cratedb-service-using-the-script","p":298},{"i":305,"t":"Go to the UI and add a new Crate data source with the following parameters: Name: Crate Host: Host IP (obtained from the script in Step 2) Port: 4200 User: crate","s":"Step 3: Add a data source in the UI","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-3-add-a-data-source-in-the-ui","p":298},{"i":307,"t":"Click the \"Check connection\" button in the UI to verify the connection to the CrateDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-4-test-the-connection","p":298},{"i":309,"t":"Testing and Debugging Testing Guides Test a Data Source Elasticsearch","s":"Elasticsearch","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"","p":308},{"i":311,"t":"Navigate to the Elasticsearch example directory located at /docs/examples/elasticsearch in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/elasticsearch","s":"Step 1: Navigate to the Elasticsearch Example Directory","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-1-navigate-to-the-elasticsearch-example-directory","p":308},{"i":313,"t":"In the Elasticsearch example directory, there is a script called run_elasticsearch.sh. Make sure the script is executable by running: chmod +x run_elasticsearch.sh Then, start the Elasticsearch service by running the script: ./run_elasticsearch.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Elasticsearch Service Using the Script","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-2-run-the-elasticsearch-service-using-the-script","p":308},{"i":315,"t":"Wait for the Elasticsearch service to be ready to accept requests. Then run: curl -X POST -u elastic:password \"http://localhost:9200/_security/user/user\" -H 'Content-Type: application/json' -d'{ \"password\": \"password\", \"roles\": [\"kibana\", \"kibana_system\"], \"full_name\": \"Test user\"}'","s":"Step 3: Wait for the Service to Be Ready and Add a User","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-3-wait-for-the-service-to-be-ready-and-add-a-user","p":308},{"i":317,"t":"Wait for the Kibana service to start, then go to http://localhost:5601 and log in with the credentials: User: elasticPassword: password Click on the Try sample data link: At the bottom of the page, click Other sample data sets: Choose a dataset and click Add data.","s":"Step 4: Access Kibana and Add Sample Data","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-4-access-kibana-and-add-sample-data","p":308},{"i":319,"t":"Now you can create a datasource in Synmetrix using the following information: Name: Elasticsearch test datasource Url: http://:9200 User: user Password: password Save the datasource and click Test connection. You should see a message indicating that the connection is OK.","s":"Step 5: Create a Datasource in Synmetrix","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-5-create-a-datasource-in-synmetrix","p":308},{"i":321,"t":"Testing and Debugging Testing Guides Test a Data Source Firebolt","s":"Firebolt","u":"/docs/next/testing-and-debugging/tests/data-source/firebolt","h":"","p":320},{"i":323,"t":"To connect to a Firebolt datasource, you will need the following information: Database Name - the name of the database to connect to. Username - the username used to connect to the database. Password - the password used to connect to the database. Engine Name - the name of the engine to connect to. API Endpoint - the Firebolt API endpoint used for authentication, such as api.dev.firebolt.io or api.app.firebolt.io.","s":"Connecting to Firebolt datasource","u":"/docs/next/testing-and-debugging/tests/data-source/firebolt","h":"#connecting-to-firebolt-datasource","p":320},{"i":325,"t":"Testing and Debugging Testing Guides Test a Data Source KSQL","s":"KSQL","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"","p":324},{"i":327,"t":"Navigate to the ksql example directory located at /docs/examples/ksql in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/ksql","s":"Step 1: Navigate to the ksql example directory","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-1-navigate-to-the-ksql-example-directory","p":324},{"i":329,"t":"In the ksql example directory, there is a script called run_ksql.sh. Make sure the script is executable by running: chmod +x run_ksql.sh Then, start the ksql service by running the script: ./run_ksql.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the ksql service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-2-run-the-ksql-service-using-the-script","p":324},{"i":331,"t":"Go to the Synmetrix UI and add a new datasource with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 8088","s":"Step 3: Add a datasource in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-3-add-a-datasource-in-the-synmetrix-ui","p":324},{"i":333,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to the ksql datasource. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-4-test-the-connection","p":324},{"i":335,"t":"Testing and Debugging Testing Guides Test a Data Source Materialize","s":"Materialize","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"","p":334},{"i":337,"t":"Navigate to the Materialize example directory located at /docs/examples/materialize in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/materialize","s":"Step 1: Navigate to the Materialize example directory","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-1-navigate-to-the-materialize-example-directory","p":334},{"i":339,"t":"In the Materialize example directory, there is a script called run_materialize.sh. Make sure the script is executable by running: chmod +x run_materialize.sh Then, start the Materialize service by running the script: ./run_materialize.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Materialize service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-2-run-the-materialize-service-using-the-script","p":334},{"i":341,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 6875 Database name: Test User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":334},{"i":343,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Materialize data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-4-test-the-connection","p":334},{"i":345,"t":"Testing and Debugging Testing Guides Test a Data Source Trino","s":"Trino","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"","p":344},{"i":347,"t":"Navigate to the Trino example directory located at /docs/examples/trino in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/trino","s":"Step 1: Navigate to the Trino example directory","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-1-navigate-to-the-trino-example-directory","p":344},{"i":349,"t":"In the Trino example directory, there is a script called run_trino.sh. Make sure the script is executable by running: chmod +x run_trino.sh Then, start the Trino service by running the script: ./run_trino.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Trino service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-2-run-the-trino-service-using-the-script","p":344},{"i":351,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: Trino Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":344},{"i":353,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Trino data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-4-test-the-connection","p":344},{"i":356,"t":"Tested on the starburstdata/presto image.","s":"PrestoDB","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"","p":354},{"i":358,"t":"Navigate to the PrestoDB example directory located at /docs/examples/prestodb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/prestodb","s":"Step 1: Navigate to the PrestoDB example directory","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-1-navigate-to-the-prestodb-example-directory","p":354},{"i":360,"t":"In the PrestoDB example directory, there is a script called run_prestodb.sh. Make sure the script is executable by running: chmod +x run_prestodb.sh Then, start the PrestoDB service by running the script: ./run_prestodb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the PrestoDB service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-2-run-the-prestodb-service-using-the-script","p":354},{"i":362,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: PrestoDB Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":354},{"i":364,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the PrestoDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-4-test-the-connection","p":354},{"i":367,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"QuestDB","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"","p":365},{"i":369,"t":"Navigate to the QuestDB example directory located at /docs/examples/questdb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/questdb","s":"Step 1: Navigate to the QuestDB example directory","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-1-navigate-to-the-questdb-example-directory","p":365},{"i":371,"t":"In the QuestDB example directory, there is a script called run_questdb.sh. Make sure the script is executable by running: chmod +x run_questdb.sh Then, start the QuestDB service by running the script: ./run_questdb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the QuestDB service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-2-run-the-questdb-service-using-the-script","p":365},{"i":373,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: QuestDB Test Host: Host IP (obtained from the script in Step 2) Port: 8812 User: quest Password: quest","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":365},{"i":375,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the QuestDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-4-test-the-connection","p":365},{"i":377,"t":"Testing and Debugging Testing Guides Test a Data Source Snowflake","s":"Snowflake","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"","p":376},{"i":379,"t":"Before proceeding, ensure you have the following Snowflake connection details: Database Name Warehouse Organization ID Account ID Username Password Role You can find this information in your Snowflake account.","s":"Step 1: Gather Snowflake connection information","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-1-gather-snowflake-connection-information","p":376},{"i":381,"t":"Go to the Synmetrix UI and navigate to the \"Data Sources\" section. Click on the \"+ Connect\" button. Select \"Snowflake\" from the list of available data sources.","s":"Step 2: Add a new data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-2-add-a-new-data-source-in-the-synmetrix-ui","p":376},{"i":383,"t":"Fill in the Snowflake connection details in the form with the following fields: Name: A custom name for your data source (e.g., \"My Snowflake\") Database Name: The name of your Snowflake database Warehouse: The name of your Snowflake warehouse Organization ID: The ID of your Snowflake organization Account ID: The ID of your Snowflake account Username: Your Snowflake username Password: Your Snowflake password Role: Your Snowflake role","s":"Step 3: Fill in the connection details","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-3-fill-in-the-connection-details","p":376},{"i":385,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to your Snowflake data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-4-test-the-connection","p":376},{"i":388,"t":"To connect to a Databricks datasource via JDBC, you will need the following information: Access Token: A personal access token used to authenticate the Databricks connection. Databricks JDBC URL: The URL for a JDBC connection. It should be in the format: jdbc:databricks://{''}:443/default;transportMode=http;ssl=1; Database Name: The name of the database to connect to.","s":"Connecting to Databricks Datasource via JDBC","u":"/docs/next/usage/user-guide/data-source/databricks","h":"","p":386},{"i":390,"t":"User Guide Explore Page Reports","s":"Reports Page","u":"/docs/next/user-guide/explore/reports","h":"","p":389},{"i":392,"t":"Click the \"Add Report\" button on the Explore page. Select the destination where the screenshot will be sent. You can choose between Webhook, Slack, or Email based on your preferences and integrations. Fill out the report form with the following details:","s":"How to Create a Report","u":"/docs/next/user-guide/explore/reports","h":"#how-to-create-a-report","p":389},{"i":394,"t":"Report Name: Enter a name that easily identifies the purpose and content of the report.","s":"Form fields description for the Reports page:","u":"/docs/next/user-guide/explore/reports","h":"#form-fields-description-for-the-reports-page","p":389},{"i":396,"t":"In the \"Preview\" section, you will find crucial details for the Alert or Report, including the user-selected metric referred to as the \"Query Key.\" This metric is at the core of your monitoring process and plays a pivotal role in alerting or report generation. Here, you'll also discover various other essential components such as: Measures: These represent the quantitative data that you want to track or display. Users can choose specific measures that matter the most to their analysis, and these will be prominently featured in the Preview. Dimensions: Dimensions help to provide context and categorize your data. They allow you to break down the information based on various attributes, providing a more comprehensive view of your metrics. info In the Preview, all these elements come together to give you a snapshot of the data and insights you're tracking, ensuring that you're monitoring the right information for your analysis, whether it's for an Alert or Report.","s":"Preview:","u":"/docs/next/user-guide/explore/reports","h":"#preview","p":389},{"i":398,"t":"Schedule: Configure the frequency at which the report will be executed by setting a schedule (e.g., daily, weekly, monthly).","s":"Trigger Settings:","u":"/docs/next/user-guide/explore/reports","h":"#trigger-settings","p":389},{"i":400,"t":"The available fields in this section depend on the chosen delivery method for the report. Here are some common options: Webhook: Enter the URL of the webhook endpoint where the report will be sent. Slack: Provide the Slack channel or user ID to which the report will be delivered. Email: Enter the email address(es) of the recipient(s) who will receive the report. note Once you have completed the settings, click the \"Save\" button.","s":"Delivery Settings:","u":"/docs/next/user-guide/explore/reports","h":"#delivery-settings","p":389},{"i":402,"t":"User Guide","s":"User Guide","u":"/docs/next/user-guide","h":"","p":401},{"i":404,"t":"🗃️ Explore Page 2 items 📄️ Models The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page: 📄️ Logs Overview 🗃️ Settings 6 items Feel free to explore the documentation for each page to gain a comprehensive understanding of the Synmetrix interface and its functionalities. Happy exploring and analyzing data with Synmetrix!","s":"Table of Contents","u":"/docs/next/user-guide","h":"#table-of-contents","p":401},{"i":406,"t":"2 items","s":"🗃️ Explore Page","u":"/docs/next/user-guide","h":"","p":401},{"i":408,"t":"The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page:","s":"📄️ Models","u":"/docs/next/user-guide","h":"","p":401},{"i":410,"t":"Overview","s":"📄️ Logs","u":"/docs/next/user-guide","h":"","p":401},{"i":412,"t":"6 items","s":"🗃️ Settings","u":"/docs/next/user-guide","h":"","p":401},{"i":416,"t":"The Explore page serves as a dynamic and intuitive platform for users to engage with data, offering tools and options for efficient data exploration and analysis. This includes changing data sources, selecting branches, and manipulating data dimensions, measures, and filters. Additional functionalities allow users to run queries, set up alerts or reports, and export data.","s":"Overview","u":"/docs/next/user-guide/explore","h":"#overview","p":413},{"i":418,"t":"The sidebar in your data tool is where you can do a lot of important stuff. Let's go over how to change your data source, switch branches, and pick the right dimensions and measures.","s":"Sidebar Actions","u":"/docs/next/user-guide/explore","h":"#sidebar-actions","p":413},{"i":420,"t":"To switch to a different data source: Find the Data Source in the sidebar. Pick a New Data Source: Click on the list there and choose the one you want. It's Changed!: Your data source will change right away.","s":"Changing Data Source in Explorer","u":"/docs/next/user-guide/explore","h":"#changing-data-source-in-explorer","p":413},{"i":422,"t":"To change the branch you're working on: Look for Branch Section in the sidebar. Choose Your Branch: Click on the list and pick the branch you need. Branch Changed: Now you're working in the new branch.","s":"Switching Branches in Explorer","u":"/docs/next/user-guide/explore","h":"#switching-branches-in-explorer","p":413},{"i":424,"t":"Picking the right dimensions and measures is super important for looking at your data the right way. Here's what to do: Go to the Sidebar: First, find the sidebar. Click on Desired Model: Click it and you'll see a list. See All the Options: This list shows all the measures, dimensions, time dimensions and segments you can use. Pick What You Need: Choose the ones that you're willing to analyze. Run Your Query: After picking, run your query to see your data. tip Quick Find: Use the search box to find dimensions or measures fast.","s":"Selecting Dimensions and Measures","u":"/docs/next/user-guide/explore","h":"#selecting-dimensions-and-measures","p":413},{"i":426,"t":"Incorporating filters into your analysis is a crucial step for refining and focusing your data exploration. Here’s a professional approach to adding filters through the Explorer Sidebar: Navigate to Explorer Sidebar: Begin by accessing the sidebar options within the Explorer. Hover Over Measures or Dimensions: Move your cursor over either the Measures or Dimensions sections in the sidebar. Initiate Filter Selection: Look for a filter icon on the specific measure or dimension you are interested in. Click on this icon to start setting up a filter. Configure Filter in Result Tab: After clicking the filter button, switch to the \"Result\" tab. Here, you'll find options to customize your filter according to your specific data requirements.","s":"Adding Filters","u":"/docs/next/user-guide/explore","h":"#adding-filters","p":413},{"i":428,"t":"The toolbar in your data analysis tool is equipped with a variety of features to enhance your data exploration and reporting capabilities. Here's a breakdown of the key actions available:","s":"Explore Toolbar Functionality Overview","u":"/docs/next/user-guide/explore","h":"#explore-toolbar-functionality-overview","p":413},{"i":430,"t":"Function: Runs the analysis based on the selected dimensions, measures, and filters. Purpose: To dynamically generate and view data results tailored to your current selections.","s":"Run Query","u":"/docs/next/user-guide/explore","h":"#run-query","p":413},{"i":432,"t":"Function: Allows for the creation of alerts based on specific data conditions. Usage: Configure alert parameters to monitor data changes and receive notifications.","s":"Set Up Alerts","u":"/docs/next/user-guide/explore","h":"#set-up-alerts","p":413},{"i":434,"t":"Function: Creates reports reflecting your current data view. Customization: Offers options to define report parameters and settings for targeted reporting.","s":"Generate Reports","u":"/docs/next/user-guide/explore","h":"#generate-reports","p":413},{"i":436,"t":"Function: Enables the export of data into a CSV file. Application: Useful for offline data analysis or for sharing data externally.","s":"Export to CSV","u":"/docs/next/user-guide/explore","h":"#export-to-csv","p":413},{"i":439,"t":"Display: Shows the outcome of your executed queries. Adjustments: Allows configuration of row limits, offsets, and view of applied filters.","s":"Results Tab","u":"/docs/next/user-guide/explore","h":"#results-tab","p":413},{"i":441,"t":"Function: Displays the automatically generated SQL query post-execution. Utility: Useful for understanding the query structure or for manual SQL query replication.","s":"Generated SQL Tab","u":"/docs/next/user-guide/explore","h":"#generated-sql-tab","p":413},{"i":443,"t":"Access Details: Provides necessary API information for data retrieval. Components: Includes headers and the API endpoint URL. Example API Utilization​ Headers: Authorization: Bearer x-hasura-datasource-id: x-hasura-branch-id: URL: https://cube.synmetrix.org/api/v1/load Body: Follow the API documentation for accurate setup. Execute API Request: Enables sending an API request directly from the interface. 📄️ Alerts The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page: 📄️ Reports The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"REST API Tab","u":"/docs/next/user-guide/explore","h":"#rest-api-tab","p":413},{"i":445,"t":"The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page:","s":"📄️ Alerts","u":"/docs/next/user-guide/explore","h":"","p":413},{"i":447,"t":"The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"📄️ Reports","u":"/docs/next/user-guide/explore","h":"","p":413},{"i":449,"t":"User Guide Logs","s":"Logs Page","u":"/docs/next/user-guide/logs","h":"","p":448},{"i":451,"t":"The Logs page is designed to provide control over the internal workings of the Cube.js service. It allows you to review the requests and the events that occur during the processing of these events. You can access information such as the execution time, start and end time, the actual query, and additional technical details. This enables you to monitor and analyze the performance and behavior of the Cube.js service. Follow the instructions below to utilize the Logs page: Access the Logs page within the project interface. Review the list of logs, which includes details such as the request information, execution time, start and end timestamps, and the actual query being processed. Click on a log entry to expand it and access additional technical information, if available. Use the logs as a resource for troubleshooting, performance optimization, and gaining insights into the inner workings of the Cube.js service.","s":"Overview","u":"/docs/next/user-guide/logs","h":"#overview","p":448},{"i":454,"t":"User Guide Models","s":"Models Page","u":"/docs/next/user-guide/models","h":"","p":453},{"i":456,"t":"To get started, you need to generate data models. Select the \"Generate Model\" option from the menu (shown in the screenshot below). In the opened window, navigate to the required sections by opening the corresponding tabs and check the boxes next to the tables or metrics you want to analyze. Then, click the \"Generate\" button (indicated by the arrow in the screenshot below). Now you should see the generated data model files in the right column (as shown in the screenshot below). You can freely edit your models as needed, adding and removing measures and dimensions. All your changes will be tracked by the version control system, the details of which are described below.","s":"How to Get Started","u":"/docs/next/user-guide/models","h":"#how-to-get-started","p":453},{"i":458,"t":"The version control system tracks changes to the data models, allowing you to revert to previous states of the models. To view the saved versions, open the drop-down menu located at the top left of the page and select the \"Show versions\" option (as shown in the screenshot below). In the opened window, you will see all the saved versions of the data models (as shown in the screenshot below). By expanding the rows, you can view the files and their contents. To revert to a specific version, simply click the \"Restore\" button next to it, and it will become the current version. Additionally, the version control system allows you to create separate branches of versions, which can help you switch between versions more efficiently than restoring versions within a single branch. To create a branch, open the branch list located in the top left corner of the page, enter the name of the new branch in the field, and click the plus icon button. This will create a new branch with the current state. To work with the new branch, you need to switch to it and then make it the default branch by clicking the \"Set as default\" button (shown in the screenshot).","s":"Version Control System","u":"/docs/next/user-guide/models","h":"#version-control-system","p":453},{"i":460,"t":"Go to Sidebar: Click on the sidebar in Synmetrix. Click 'Docs': Find and click the 'Docs' button. Get the Docs: The file with the version's documentation will be generated for you.","s":"Viewing Version Docs","u":"/docs/next/user-guide/models","h":"#viewing-version-docs","p":453},{"i":463,"t":"Opening the SQL Runner in Synmetrix for running custom SQL queries is a straightforward process. Here's how to do it: Navigate to Model Page: First, go to the model page in the Synmetrix interface. This is where all your data models are listed. Open SQL Runner Tab: Look for a tab or section labeled \"SQL Runner\" and click on it. This action will open the SQL Runner interface. Compose Your SQL Query: In the provided space, you can write your SQL query. For example, you might write a query to select data from a specific table or to perform a more complex data manipulation. SELECT * FROM your_table_name WHERE condition; Execute the Query: Once you've written your query, click the \"Run\" button. This will execute the SQL query against your data models. View Results: After running the query, the results will be displayed in a table format directly in the SQL Runner. This allows you to review and analyze the data returned by your query. note The Model SQL Runner in Synmetrix is a feature that allows you to run SQL queries directly against your data models. This tool is particularly useful for users who need to perform complex data analysis or want to test and validate their model configurations. Here’s a brief overview: Functionality: The SQL Runner enables you to write and execute SQL queries on the fly. It interacts directly with the data models you've set up in Synmetrix, allowing for real-time data retrieval and analysis. Use Cases: Data Exploration: Quickly run queries to explore data patterns or validate data integrity. Testing Models: Test how new or modified data models perform with actual SQL queries. Advanced Analysis: Perform complex data analysis that might not be possible through standard model exploration. Real-Time Results: Queries executed in the SQL Runner provide immediate feedback, enabling you to rapidly iterate and refine your analysis.","s":"Accessing the SQL Runner in Synmetrix","u":"/docs/next/user-guide/models","h":"#accessing-the-sql-runner-in-synmetrix","p":453},{"i":465,"t":"Open a Model: In sidebar, select and open the model file you wish to edit. Begin Editing: Utilize the Visual Model Editor to make changes to the model. You can adjust various aspects such as dimensions, measures, and relationships. Save Changes or close tab: After completing your edits, ensure to save the model to apply the modifications. note The Visual Model Editor in Synmetrix, enhanced with YAML support, offers an intuitive and user-friendly way to manage data models. YAML's readable format simplifies the process of defining and configuring models, making it accessible even to those with limited coding experience. This combination of a graphical interface and YAML/JS editing provides a flexible and efficient model editing experience, catering to both beginner and advanced users in data model management.","s":"Editing Models in Synmetrix","u":"/docs/next/user-guide/models","h":"#editing-models-in-synmetrix","p":453},{"i":467,"t":"You can upload data model files from your computer to Synmetrix. Easily save and download your data model files from Synmetrix.","s":"Importing or Exporting Data Models","u":"/docs/next/user-guide/models","h":"#importing-or-exporting-data-models","p":453},{"i":469,"t":"Edit and tweak your data models right in Synmetrix with a user-friendly editor.","s":"Using the Visual Model Editor","u":"/docs/next/user-guide/models","h":"#using-the-visual-model-editor","p":453},{"i":471,"t":"Open Sidebar: Start in the sidebar. Find Your Model: Look for the model you want to delete and hover over it. Click the Trash Icon: Hit the trash can icon next to the model. Confirm Delete: Click \"Remove\" to confirm. Done: The model is now deleted.","s":"Deleting a Model","u":"/docs/next/user-guide/models","h":"#deleting-a-model","p":453},{"i":473,"t":"Head to Sidebar: Go to the sidebar in Synmetrix. Choose a Model: Hover over the model you want to change. Edit: Click the pen icon. Make Changes: Change the name and file format (JS or YAML). Finished: Your changes are saved. These steps are a simple way to handle your data models in Synmetrix, whether you're adding, editing, or removing them.","s":"Changing Model Name and File Format","u":"/docs/next/user-guide/models","h":"#changing-model-name-and-file-format","p":453},{"i":475,"t":"User Guide Settings","s":"Settings","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":477,"t":"📄️ Data Sources The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps: 📄️ Members Managing Members 📄️ Personal Information Overview 📄️ Role Based Access Role Based Access Control 📄️ SQL API SQL API Page 📄️ Teams Introduction","s":"Table of Contents","u":"/docs/next/user-guide/settings","h":"#table-of-contents","p":474},{"i":479,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"📄️ Data Sources","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":481,"t":"Managing Members","s":"📄️ Members","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":483,"t":"Overview","s":"📄️ Personal Information","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":485,"t":"Role Based Access Control","s":"📄️ Role Based Access","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":487,"t":"SQL API Page","s":"📄️ SQL API","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":489,"t":"Introduction","s":"📄️ Teams","u":"/docs/next/user-guide/settings","h":"","p":474},{"i":491,"t":"User Guide Explore Page Alerts","s":"Alerts Page","u":"/docs/next/user-guide/explore/alerts","h":"","p":490},{"i":493,"t":"Click the \"Add Alert\" button on the Explore page. Choose your notification delivery method. Options include Webhook, Slack, or Email, depending on your system integrations and preferences. Complete the alert form with the necessary information:","s":"How to Set Up an Alert","u":"/docs/next/user-guide/explore/alerts","h":"#how-to-set-up-an-alert","p":490},{"i":495,"t":"Alert Name: Provide a descriptive name that clearly identifies the alert's purpose and content.","s":"Detailed Description of Form Fields on the Alerts Page:","u":"/docs/next/user-guide/explore/alerts","h":"#detailed-description-of-form-fields-on-the-alerts-page","p":490},{"i":497,"t":"This section offers a comprehensive overview of the Alert or Report setup. Key features include: Query Key: This is the primary query that your alert or report is based on. It's a critical element in the monitoring process. Measures: Select the quantitative data points you wish to track or display. These are crucial for your analysis and will be highlighted in the Preview. Dimensions: These provide context and categorization for your data, allowing a more detailed and segmented analysis. info The Preview amalgamates all these components, offering a clear snapshot of the data and insights being monitored. This ensures that your analysis, whether for an Alert or Report, is accurate and relevant.","s":"Preview Section:","u":"/docs/next/user-guide/explore/alerts","h":"#preview-section","p":490},{"i":499,"t":"Customize how and when your alert is triggered with these settings: Schedule: Set how often the alert conditions are checked (e.g., hourly, daily, weekly). Lower Bound: Define the minimum threshold that triggers the alert. Upper Bound: Specify the maximum threshold for triggering the alert. Request Timeout (minutes): Set the maximum time allowed for the data request to process. Timeout On Fire (minutes): Determine how long before an ongoing alert condition is considered \"fired.\"","s":"Trigger Settings:","u":"/docs/next/user-guide/explore/alerts","h":"#trigger-settings","p":490},{"i":501,"t":"Options here vary based on your chosen alert delivery method: Webhook: Input the URL for the webhook endpoint. Slack: Enter the Slack channel or user ID. Email: Specify the email addresses of the alert recipients. note After configuring your settings, click \"Save\" to activate the alert. This will commence the monitoring of your specified conditions. Enhancements for Clarity and Efficiency: Clarified instructions for setting up alerts. Detailed descriptions for each section, ensuring comprehensive understanding. Streamlined language for ease of reading and comprehension. Added informative notes and info sections for additional clarity.","s":"Delivery Settings:","u":"/docs/next/user-guide/explore/alerts","h":"#delivery-settings","p":490},{"i":504,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"Data Sources Settings","u":"/docs/next/user-guide/settings/data-sources","h":"","p":502},{"i":507,"t":"Click on the \"Connect\" button.","s":"Step 1","u":"/docs/next/user-guide/settings/data-sources","h":"#step-1","p":502},{"i":509,"t":"A list of available datasource types will be displayed. Select the desired type from the options presented.","s":"Step 2","u":"/docs/next/user-guide/settings/data-sources","h":"#step-2","p":502},{"i":511,"t":"In the opened modal window, fill in all the required fields in the form. These fields may include connection details, authentication credentials, and other relevant information. On the screenshot below, you can see a modal window with the datasource creation form. Test Connection To test the connection to the datasource, click the \"Test connection\" button. To save the entered data and create the datasource, click the \"Save\" button. Fast Switching Data Sources in Synmetrix In Synmetrix, switching between data sources is a straightforward process. You can find the data models dropdown in the sidebar menu of both the \"Models\" and \"Explore\" pages. Simply access this menu to view and select from the available data sources. This dropdown is conveniently placed to ensure ease of access, enhancing your experience by allowing quick and efficient transitions between various data repositories within Synmetrix.","s":"Step 3","u":"/docs/next/user-guide/settings/data-sources","h":"#step-3","p":502},{"i":515,"t":"The Members section in your settings allows you to manage the team members who have access to your workspace. This guide provides step-by-step instructions on how to add new members, as well as how to remove existing members.","s":"Overview","u":"/docs/next/user-guide/settings/members","h":"#overview","p":512},{"i":517,"t":"There are three types of members you can add to your account: Owner: The primary account holder with full access and privileges. Admin: Users with administrative rights, including the ability to manage settings and members. Member: Regular users with access to the account but limited control over settings and member management.","s":"Member Types","u":"/docs/next/user-guide/settings/members","h":"#member-types","p":512},{"i":519,"t":"To add a new member to your account, follow these steps: Navigate to Settings > Members in your account. Click the Create Now button. In the popup window, enter the email address of the new member. Select the user type for the new member (either Member or Admin). Click the Invite button. The new member will receive an email with a magic link to sign in to their account.","s":"Adding a New Member","u":"/docs/next/user-guide/settings/members","h":"#adding-a-new-member","p":512},{"i":521,"t":"To remove a member from your account, follow these steps: Go to Settings > Members. Locate the member you wish to remove and find the gear icon on their member card. Click on Delete. Confirm the deletion. After confirming, the member will be removed from your account. The process is complete. This documentation provides a clear and concise guide for managing members within your account settings.","s":"Removing a Member","u":"/docs/next/user-guide/settings/members","h":"#removing-a-member","p":512},{"i":525,"t":"The \"Personal Info\" section is designed to provide you with full control over your personal settings and security. Here, you can easily update your username, email address, and password, ensuring your account remains secure and personalized. Additionally, you have the option to manage your active sessions, allowing you to logout from all for enhanced security.","s":"Overview","u":"/docs/next/user-guide/settings/personal-info","h":"#overview","p":522},{"i":527,"t":"To change your username: Go to \"Settings\" in the sidebar menu. Navigate to the \"Personal Info\" tab. Locate the \"Full Name\" field under the \"General Information\" section. Click on the \"Full Name\" input field and enter your new username. Click the \"Update\" button. You're all set!","s":"How to Change Your Username?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-change-your-username","p":522},{"i":529,"t":"To update your email address: Access \"Settings\" from the sidebar menu. Select the \"Personal Info\" tab. Find the \"Email\" field in the \"General Information\" section. Enter your new email address in the \"Email\" input field. Click the \"Update\" button. Your email is now updated!","s":"How to Change Your Email?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-change-your-email","p":522},{"i":531,"t":"To change your password: Visit \"Settings\" from the sidebar menu. Click on the \"Personal Info\" tab. Scroll down to the \"Security\" section. Enter your old password and your desired new password. Click the \"Update Password\" button. Your password has been changed!","s":"How to Change Your Password?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-change-your-password","p":522},{"i":533,"t":"To logout from all active sessions: Go to \"Settings\" in the sidebar menu. Choose the \"Personal Info\" tab. Scroll to the \"Active Sessions\" section. Click the \"Logout from all sessions\" button. All sessions have been logged out!","s":"How to Logout from Active Sessions?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-logout-from-active-sessions","p":522},{"i":537,"t":"Role Based Access Control (RBAC) is a system for regulating access to resources within your organization based on the roles of individual users. This document outlines how to manage roles and the types of access they grant.","s":"Overview","u":"/docs/next/user-guide/settings/role-access","h":"#overview","p":534},{"i":539,"t":"Partial Access: Grants access to specific Data Sources, Data Models, and various Measures, Dimensions, or Segments. Full Access: Provides unrestricted access to all resources. No Access: Denies access to all resources.","s":"Access Types","u":"/docs/next/user-guide/settings/role-access","h":"#access-types","p":534},{"i":541,"t":"To add a new role, follow these steps: Navigate to Settings > Roles and Access. Click on the Create Now button. In the popup window: Enter the role name. Set access for data source resources by selecting one or multiple data sources. Choose Data Models. Select Measures, Dimensions, and/or Segments. tip Use the \"Select All\" checkbox to select all Measures, Dimensions, and Segments in the current Data Model. Click Create. info Roles display information including the creation date, last update, and data sources along with their respective access types.","s":"Adding a New Role","u":"/docs/next/user-guide/settings/role-access","h":"#adding-a-new-role","p":534},{"i":543,"t":"To delete a role, follow these steps: Go to Settings > Roles and Access. Find the role to be deleted and click the gear icon on their card. Choose Delete from the dropdown list and confirm the deletion. The deletion process is complete.","s":"Deleting a Role","u":"/docs/next/user-guide/settings/role-access","h":"#deleting-a-role","p":534},{"i":545,"t":"To edit an existing role, follow these steps: Navigate to Settings > Roles and Access. Locate the role to edit and click the gear icon on their card. Choose Edit from the dropdown list. In the popup window, make the desired changes: Update the role name. Adjust access to data source resources. Select or change Data Models. Choose Measures, Dimensions, and/or Segments. tip Utilize the \"Select All\" checkbox for selecting all items in the current Data Model. Click Save to apply the changes. This document provides clear guidelines for managing roles within the Role Based Access Control system, ensuring efficient and secure access management.","s":"Editing a Role","u":"/docs/next/user-guide/settings/role-access","h":"#editing-a-role","p":534},{"i":547,"t":"APIs & integrations","s":"APIs & integrations","u":"/docs/apis-integrations","h":"","p":546},{"i":553,"t":"Engage in powerful and flexible data retrieval and manipulation with our SQL API, designed for effortless data management and seamless collaboration.","s":"Effortless Data Management and Uninterrupted Collaboration","u":"/docs/next/user-guide/settings/sql-api","h":"#effortless-data-management-and-uninterrupted-collaboration","p":550},{"i":555,"t":"Synmetrix revolutionizes data sharing with its versatile protocol, ensuring smooth integration with a variety of data tools. Our platform seamlessly connects with business intelligence and data exploration tools (Superset, Tableau), data notebooks (Jupyter, Hex), reverse ETL tools (Census, Hightouch), low-code platforms (Retool), and more, enhancing efficiency and simplicity in data-driven tasks.","s":"Simplified Data Sharing","u":"/docs/next/user-guide/settings/sql-api","h":"#simplified-data-sharing","p":550},{"i":557,"t":"Synmetrix serves as the ultimate repository for all your data needs, centralizing metrics in an organized manner. Regardless of your preferred tools or platforms for data analysis or visualization, Synmetrix provides easy access to essential data.","s":"One-Stop Metrics Solution","u":"/docs/next/user-guide/settings/sql-api","h":"#one-stop-metrics-solution","p":550},{"i":559,"t":"Integrations: Seamlessly integrate into your workflow, including Excel compatibility. Flexibility: Encourage collaborative efforts with shared models in a harmonious team environment. Security: Rely on our robust and reliable security measures. Effortless: Tailor SQL queries to meet your unique requirements.","s":"Key Benefits","u":"/docs/next/user-guide/settings/sql-api","h":"#key-benefits","p":550},{"i":561,"t":"Obtain your SQL API credentials through two distinct methods:","s":"Acquiring SQL Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#acquiring-sql-credentials","p":550},{"i":563,"t":"Initiate a new account creation. Add your data source. Opt to generate models (optional). Synmetrix generates SQL API access during this step. A pop-up allows you to choose between PSQL or MYSQL for connection, along with credential copying. Connect using PSQL or MYSQL, or opt to \"Download Credentials\" for later use. Finish the onboarding.","s":"First Method: From Onboarding","u":"/docs/next/user-guide/settings/sql-api","h":"#first-method-from-onboarding","p":550},{"i":565,"t":"Navigate to Settings > SQL API. Click the gear icon on the database card and select \"Edit\". The pop-up window provides PSQL or MYSQL connection options and credential copying. Example Credentials Host/URL: cube.synmetrix.org:15432 Database: db Login: 4x4qg8ei76 Password: ********** PSQL Client Connection: psql --host=cube.synmetrix.org --port 15432 --username=4x4qg8ei76 --dbname=db Connect using PSQL or MYSQL, or \"Download Credentials\" for later use.","s":"Second Method: From Settings","u":"/docs/next/user-guide/settings/sql-api","h":"#second-method-from-settings","p":550},{"i":567,"t":"Easily create, edit, or delete SQL API credentials via Settings > SQL API.","s":"Managing SQL API Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#managing-sql-api-credentials","p":550},{"i":569,"t":"Click the gear icon and select \"Delete\" from the dropdown. Confirm deletion.","s":"Deleting Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#deleting-credentials","p":550},{"i":571,"t":"Select the \"Create Now\" button on the SQL API page. In the pop-up, specify the Team member and Data source.","s":"Creating Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#creating-credentials","p":550},{"i":573,"t":"Click the gear icon and select \"Edit\". Modify the credentials as needed. Confirm by clicking \"Finish\".","s":"Editing Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#editing-credentials","p":550},{"i":577,"t":"The \"Teams\" section of Synmetrix is a dedicated space for creating, managing, and customizing teams. This feature is essential for collaborative work, allowing you to form new teams, set a focus on a current team, remove teams that are no longer needed, and update team names to align with evolving projects or goals.","s":"Introduction","u":"/docs/next/user-guide/settings/teams","h":"#introduction","p":574},{"i":579,"t":"To navigate to the Team page: Hover over your account profile picture at the top-right corner of the page. Select \"Team\" from the dropdown menu.","s":"Accessing the Team Page","u":"/docs/next/user-guide/settings/teams","h":"#accessing-the-team-page","p":574},{"i":581,"t":"On the Team page, you can perform various actions:","s":"Team Page Functions","u":"/docs/next/user-guide/settings/teams","h":"#team-page-functions","p":574},{"i":583,"t":"You can invite new members to join your project by entering their email addresses. This step is crucial for expanding your team and fostering collaboration.","s":"1. Inviting New Users","u":"/docs/next/user-guide/settings/teams","h":"#1-inviting-new-users","p":574},{"i":585,"t":"Based on your access management system, assign appropriate roles and access levels to team members, ensuring they have the necessary permissions to contribute effectively.","s":"2. Assigning Roles and Access Levels","u":"/docs/next/user-guide/settings/teams","h":"#2-assigning-roles-and-access-levels","p":574},{"i":587,"t":"Control access to specific features and resources for each team member, tailoring their permissions to suit their role and needs in the project.","s":"3. Managing Access","u":"/docs/next/user-guide/settings/teams","h":"#3-managing-access","p":574},{"i":589,"t":"The Team page provides a comprehensive view of all team members, including their roles and responsibilities, allowing for efficient team management.","s":"4. Viewing and Managing Team Members","u":"/docs/next/user-guide/settings/teams","h":"#4-viewing-and-managing-team-members","p":574},{"i":591,"t":"To create a new team: Open \"Settings\" from the sidebar. Click on the \"Teams\" tab. In \"Manage Teams\", view your existing teams. Select the gear icon (\"Settings\"). Choose \"Create Team\" from the dropdown and follow the prompts.","s":"How to Create a New Team?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-create-a-new-team","p":574},{"i":593,"t":"To set an active team: Go to \"Settings\" in the sidebar. Choose the \"Teams\" tab. Find your team list in \"Manage Teams\". Click the gear icon next to the team you want to focus on. Select \"Set Current\" from the dropdown.","s":"How to Set a Current Team?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-set-a-current-team","p":574},{"i":595,"t":"To change a team's name: Navigate to \"Settings\" via the sidebar. Select the \"Teams\" tab. Find your team in \"Manage Teams\". Click the gear icon next to the team you want to rename. Choose \"Edit\" from the dropdown. Change the \"Team Name\" in the pop-up. Click \"Save\" to confirm the changes.","s":"How to Edit a Team Name?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-edit-a-team-name","p":574},{"i":597,"t":"To remove a team: Access \"Settings\" from the sidebar. Go to the \"Teams\" tab. Identify the team you want to delete in \"Manage Teams\". Click the gear icon next to the team. Choose \"Delete\" from the dropdown list. I've refined the language for professional tone and clarity, ensuring each step is clear and straightforward.","s":"How to Delete a Team?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-delete-a-team","p":574},{"i":599,"t":"Architecture and design","s":"Architecture and Design","u":"/docs/architecture-and-design","h":"","p":598},{"i":601,"t":"Database Structure","s":"📄️ Database Design","u":"/docs/architecture-and-design","h":"","p":598},{"i":603,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"📄️ System Architecture","u":"/docs/architecture-and-design","h":"","p":598},{"i":605,"t":"Architecture and design Database Design","s":"Database Structure Design","u":"/docs/architecture-and-design/database-design","h":"","p":604},{"i":607,"t":"The \"Synmetrix\" database is organized according to the relational database model and includes the following tables: public.users: Stores information about registered users, including user ID (id), display name (display_name), avatar URL (avatar_url), as well as the creation time and last update time of the record. auth.account_providers: Describes the relationships between user accounts and their authentication providers. auth.accounts: Stores information about user accounts, including unique identifiers, email addresses, passwords, and other data. auth.providers: Provides a list of available authentication providers. auth.refresh_tokens: Contains information about refresh tokens for each user account. auth.roles: Manages user roles. auth.account_roles: Contains information about roles for each account. public.teams: Stores information about user teams. public.datasources: Contains information about data sources used by users. public.dataschemas: Describes data models used to define business metrics for data sources. public.explorations: Describes research tasks performed by users. public.members: Stores information about team members. public.team_roles: Manages user roles within teams. public.member_roles: Contains information about roles for each team member. public.reports: Contains information about the structure and schedule of reports based on metrics needed by users. public.sql_credentials: Manages SQL credentials used to access business metrics through the SQL interface. public.alerts: Stores information about alerts created by users.","s":"Database Structure","u":"/docs/architecture-and-design/database-design","h":"#database-structure","p":604},{"i":609,"t":"The architecture of the database, including relationships between tables, primary and foreign keys, and indexes, is represented in the Database Markup Language (DBML). This database structure provides flexibility and scalability to the system, allowing convenient management of users, teams, data sources, reports, and other system elements. Each database table is designed for a specific purpose and can be extended or modified to meet evolving system requirements. note For the complete DBML representation of the database architecture, please refer to Database Markup Language (DBML).","s":"Database Architecture Description","u":"/docs/architecture-and-design/database-design","h":"#database-architecture-description","p":604},{"i":612,"t":"Synmetrix offers a robust caching system with two distinct layers to optimize query performance and minimize database load. This system enhances the efficiency of data retrieval and processing. The primary caching layer is an in-memory cache, which is active by default. The second layer is called \"pre-aggregations\" and requires explicit configuration to activate.","s":"Overview","u":"/docs/caching","h":"","p":610},{"i":614,"t":"Synmetrix's in-memory cache serves as a temporary buffer for your database. It becomes especially valuable when multiple concurrent users request the same data simultaneously. While pre-aggregations are designed to strike a balance between query response time and performance, the in-memory cache helps smooth out spikes in data demand. In development mode, you can reset the in-memory cache by simply restarting the server. Note: It's generally not recommended to modify the default in-memory caching configuration unless it's absolutely necessary. To improve query performance, consider utilizing pre-aggregations.","s":"In-Memory Cache","u":"/docs/caching","h":"#in-memory-cache","p":610},{"i":616,"t":"Pre-aggregations introduce an additional layer of aggregated data, constructed and updated by Synmetrix. They can significantly enhance query performance and concurrency. To create pre-aggregations, Synmetrix might require write access to the pre-aggregations schema within the source database. In this scenario, Synmetrix initially builds pre-aggregations as tables in the source database and then exports them to the pre-aggregations storage. Refer to your specific driver's documentation for details on read-only support and pre-aggregation build strategies. Pre-aggregations are defined within the data model. You can learn more about defining pre-aggregations in the data modeling reference. Using YAML Using JavaScript cubes: - name: orders sql_table: orders measures: - name: total_amount sql: amount type: sum dimensions: - name: created_at sql: created_at type: time pre_aggregations: - name: amount_by_created measures: - total_amount time_dimension: created_at granularity: month cube(`orders`, { sql_table: `orders`, measures: { total_amount: { sql: `amount`, type: `sum`, }, }, dimensions: { created_at: { sql: `created_at`, type: `time`, }, }, pre_aggregations: { amount_by_created: { measures: [total_amount], time_dimension: created_at, granularity: `month`, }, },","s":"Pre-Aggregations","u":"/docs/caching","h":"#pre-aggregations","p":610},{"i":618,"t":"Synmetrix utilizes an in-memory cache to store the results of executed queries. The cache key is generated from the SQL statement, including any existing pre-aggregations that the query depends on. Upon receiving an incoming request, Synmetrix first checks the cache using this key. If no match is found in the cache, the query is executed in the database. The result set is returned and simultaneously updates the cache. If a cached value exists, and the refresh_key value for the query remains unchanged, the cached value is returned. Otherwise, an SQL query is executed either against the pre-aggregations storage or the source database to refresh the cache and retrieve the updated results.","s":"In-Memory Cache Operation","u":"/docs/caching","h":"#in-memory-cache-operation","p":610},{"i":620,"t":"To avoid unnecessary database queries, Synmetrix defines a refresh_key for each cube. These refresh keys are evaluated to determine if data needs to be refreshed. Here's an example of a refresh_key that instructs Synmetrix to refresh data every 5 minutes: YAML cubes: - name: orders # ... refresh_key: every: 5 minutes JavaScript cube(`orders`, { refresh_key: { every: `5 minute`, },}); Alternatively, the following refresh_key only refreshes data when the value of MAX(created_at) changes. By default, Synmetrix checks this refresh key every 10 seconds: YAML cubes: - name: orders # ... refresh_key: sql: SELECT MAX(created_at) FROM orders JavaScript cube(`orders`, { // ... refresh_key: { sql: `SELECT MAX(created_at) FROM orders`, },});","s":"Refresh Keys","u":"/docs/caching","h":"#refresh-keys","p":610},{"i":622,"t":"By default, in development mode, Synmetrix will check and invalidate the cache in the background. For production environments, it's recommended to run a Refresh Worker as a separate instance. We strongly recommend enabling background cache invalidation using a separate Synmetrix worker for production deployments. Please consult the Production Checklist for more information. If background refresh is disabled, Synmetrix will refresh the cache during query execution. To ensure optimal response times for end-users, it's advisable to always enable background refresh.","s":"Default Refresh Keys","u":"/docs/caching","h":"#default-refresh-keys","p":610},{"i":624,"t":"To gain insights into whether a query interacts with the in-memory cache, pre-aggregations, or the underlying data source, you can utilize the Synmetrix Playground or Synmetrix Cloud.","s":"Inspecting Queries","u":"/docs/caching","h":"#inspecting-queries","p":610},{"i":627,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"Synmetrix System Architecture","u":"/docs/architecture-and-design/system-architecture","h":"","p":625},{"i":629,"t":"Hasura plays a crucial role in the system by connecting to the database and providing a GraphQL API for external services and applications. Hasura simplifies working with the database, offering intuitive and powerful tools for quickly creating queries and mutations in GraphQL, thereby enhancing performance and optimizing data-related workflows.","s":"Hasura","u":"/docs/architecture-and-design/system-architecture","h":"#hasura","p":625},{"i":631,"t":"PostgreSQL has been chosen as the primary database management system due to its reliability, high performance, and flexibility. This allows for efficient handling of large volumes of data while ensuring excellent performance and stability.","s":"PostgreSQL","u":"/docs/architecture-and-design/system-architecture","h":"#postgresql","p":625},{"i":633,"t":"Node.js is the server-side platform used to create the backend of the application. Node.js is known for its ability to easily and rapidly develop scalable networked applications, thanks to efficient handling of asynchronous operations and event processing.","s":"Node.js","u":"/docs/architecture-and-design/system-architecture","h":"#nodejs","p":625},{"i":635,"t":"React.js is a library for developing user interfaces. It provides high performance and simplifies the development process through the use of a component-based architecture.","s":"React.js","u":"/docs/architecture-and-design/system-architecture","h":"#reactjs","p":625},{"i":637,"t":"Cube.js is an open-source analytics platform for building business analytics applications using JavaScript. In Synmetrix, Cube.js is used to manage business metrics, providing efficient tools for data processing.","s":"Cube.js","u":"/docs/architecture-and-design/system-architecture","h":"#cubejs","p":625},{"i":639,"t":"CubeStore is a distributed database optimized for analytical queries and integrated with Cube.js. It offers capabilities for fast and efficient processing of large volumes of data.","s":"CubeStore","u":"/docs/architecture-and-design/system-architecture","h":"#cubestore","p":625},{"i":641,"t":"Redis is a key-value database management system known for its high performance and flexibility. It offers unique features, including support for various data structures such as strings, lists, sets, hashes, and more.","s":"Redis","u":"/docs/architecture-and-design/system-architecture","h":"#redis","p":625},{"i":643,"t":"Docker is used for containerization and simplifying the application deployment process, ensuring consistent functionality in any environment.","s":"Docker","u":"/docs/architecture-and-design/system-architecture","h":"#docker","p":625},{"i":645,"t":"Docker Swarm is an orchestration and management tool for containers deployed using Docker. It allows for the management and scaling of applications across multiple servers, making infrastructure management easier.","s":"Docker Swarm","u":"/docs/architecture-and-design/system-architecture","h":"#docker-swarm","p":625},{"i":647,"t":"Ubuntu has been chosen as the primary operating system for the servers hosting all the services. It is a stable and reliable system well-suited for server environments.","s":"Ubuntu","u":"/docs/architecture-and-design/system-architecture","h":"#ubuntu","p":625},{"i":649,"t":"Additionally, Synmetrix has been designed as a microservices-based system. Each microservice performs a specific function and can scale independently of others, providing flexibility and scalability to the system. This architecture allows for the independent updating and modernization of individual system components, simplifying the development and maintenance process of the system as a whole.","s":"Interactions between architecture components","u":"/docs/architecture-and-design/system-architecture","h":"#interactions-between-architecture-components","p":625},{"i":651,"t":"Caching Getting started with pre-aggregations","s":"Getting started with pre-aggregations","u":"/docs/caching/getting-started-with-pre-aggregations","h":"","p":650},{"i":653,"t":"To illustrate pre-aggregations with an example, let's use a sample e-commerce database. We have a data model representing all our orders: YAML cubes: - name: orders sql_table: orders measures: - name: count type: count dimensions: - name: id sql: id type: number primary_key: true - name: status sql: status type: string - name: completed_at sql: completed_at type: time JavaScript cube(`orders`, { sql_table: `orders`, measures: { count: { type: `count`, }, }, dimensions: { id: { sql: `id`, type: `number`, primary_key: true, }, status: { sql: `status`, type: `string`, }, completed_at: { sql: `completed_at`, type: `time`, }, },}); Some sample data from this table might look like: id status completed_at 1 completed 2021-02-15T12:21:11.290 2 completed 2021-02-25T18:15:12.369 3 shipped 2021-03-15T20:40:57.404 4 processing 2021-03-13T10:30:21.360 5 completed 2021-03-10T18:25:32.109 Our first requirement is to populate a dropdown in our front-end application which shows all possible statuses. The Cube query to retrieve this information might look something like: JSON { \"dimensions\": [\"orders.status\"]} In that case, we can add the following pre-aggregation to the orders cube: YAML cubes: - name: orders # ... pre_aggregations: - name: order_statuses dimensions: - status JavaScript cube(`orders`, { // ... pre_aggregations: { order_statuses: { dimensions: [status], }, },});","s":"Pre-Aggregations without Time Dimension","u":"/docs/caching/getting-started-with-pre-aggregations","h":"#pre-aggregations-without-time-dimension","p":650},{"i":655,"t":"Development","s":"Development","u":"/docs/development","h":"","p":654},{"i":657,"t":"Requirements","s":"📄️ Local Development","u":"/docs/development","h":"","p":654},{"i":659,"t":"This guide provides information on how to contribute to Synmetrix, including code changes, helping other users, documentation updates, bug reports, bug fixes, new feature proposals, and coding standards.","s":"📄️ Contributing","u":"/docs/development","h":"","p":654},{"i":661,"t":"Development Contributing","s":"Contributing to Synmetrix","u":"/docs/development/contributing","h":"","p":660},{"i":663,"t":"One of the most valuable ways to contribute to Synmetrix is by helping answer user questions on the discussion forum or in the Slack channel. There are always new Synmetrix users seeking assistance, and providing answers is a valuable community service that showcases your expertise.","s":"Helping Other Users","u":"/docs/development/contributing","h":"#helping-other-users","p":660},{"i":665,"t":"To propose changes to Synmetrix documentation, edit the documentation source files located in the docs/ directory. The README file there explains how to build the documentation locally to test your changes. After making modifications, open a pull request to suggest the changes.","s":"Contributing to Documentation","u":"/docs/development/contributing","h":"#contributing-to-documentation","p":660},{"i":667,"t":"Reporting a bug is a simple and effective way to contribute to Synmetrix. It helps identify issues, enabling the team to provide patches and enhance the stability and usefulness of Synmetrix. Use the \"New issue\" button in the issues page to report a bug. Ensure your bug report includes a minimal executable test case to replicate the issue. Follow the instructions in the bug report template provided.","s":"Reporting Bugs","u":"/docs/development/contributing","h":"#reporting-bugs","p":660},{"i":669,"t":"Contributing bug fixes is an excellent way to build your experience and credibility within the community. If you are new to the Synmetrix codebase, start by looking at issues labeled with help wanted. If you need guidance on fixing an issue, seek assistance from experienced contributors by commenting on the issue page.","s":"Contributing Bug Fixes","u":"/docs/development/contributing","h":"#contributing-bug-fixes","p":660},{"i":671,"t":"Before contributing a new feature, submit a proposal in the issues page and discuss it with the community. This helps identify potential overlaps with planned features and prevents misunderstandings and conflicts.","s":"Proposing New Features","u":"/docs/development/contributing","h":"#proposing-new-features","p":660},{"i":673,"t":"When submitting code contributions, you must sign a Developer Certificate of Origin (DCO) to certify your authorship of the source code or your right to submit it to the project. Sign your contributions by adding a Signed-off-by line to your commit message or using the -s option with the git commit command. This is my commit messageSigned-off-by: Random J Developer ","s":"Submitting Code Changes","u":"/docs/development/contributing","h":"#submitting-code-changes","p":660},{"i":676,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster. Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.","s":"Docker Swarm deployment guide","u":"/docs/docker-swarm","h":"","p":674},{"i":678,"t":"Go to the Synmetrix project directory and run the following commands:","s":"Start Synmetrix on Docker Swarm","u":"/docs/docker-swarm","h":"#start-synmetrix-on-docker-swarm","p":674},{"i":680,"t":"./scripts/run-registry.sh","s":"Step 1 - Run Docker Registry","u":"/docs/docker-swarm","h":"#step-1---run-docker-registry","p":674},{"i":682,"t":"DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --env stage synmetrix Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.","s":"Step 2 - Run Synmetrix services","u":"/docs/docker-swarm","h":"#step-2---run-synmetrix-services","p":674},{"i":684,"t":"./migrate.sh","s":"Step 3 - Run migrations","u":"/docs/docker-swarm","h":"#step-3---run-migrations","p":674},{"i":686,"t":"./cli.sh swarm destroy synmetrix","s":"Destroy stack","u":"/docs/docker-swarm","h":"#destroy-stack","p":674},{"i":688,"t":"./cli.sh swarm logs synmetrix_hasura","s":"Show logs","u":"/docs/docker-swarm","h":"#show-logs","p":674},{"i":690,"t":"./cli.sh swarm ps synmetrix For more information about the CLI tool, run the following command: ./cli.sh swarm --help","s":"Show stack tasks status","u":"/docs/docker-swarm","h":"#show-stack-tasks-status","p":674},{"i":692,"t":"Development Local Development","s":"Local Development","u":"/docs/development/local-development","h":"","p":691},{"i":694,"t":"Before using the backend-side of the project, ensure that you have the following components installed: Docker Node.js (Version 20.8.1 or above) Yarn","s":"Requirements","u":"/docs/development/local-development","h":"#requirements","p":691},{"i":697,"t":"Clone the Synmetrix repository: git clone https://github.com/mlcraft-io/mlcraft.gitcd mlcraft Run the initialization script: bash init.sh","s":"Getting Started","u":"/docs/development/local-development","h":"#getting-started","p":691},{"i":700,"t":"Before using the client-side of the project, ensure that you have the following components installed: Synmetrix Backend: Ensure the Synmetrix backend is properly set up and running.","s":"Requirements","u":"/docs/development/local-development","h":"#requirements-1","p":691},{"i":702,"t":"To install the client-side of the project, follow these steps: # Clone the Synmetrix Client repositorygit clone https://github.com/mlcraft-io/client-v2# Navigate to the project directorycd client-v2# Install required packages using Yarnyarn# Start the client-side applicationyarn start After completing these steps, open your web browser and go to to access the Synmetrix client.","s":"Getting Started","u":"/docs/development/local-development","h":"#getting-started-1","p":691},{"i":704,"t":"For detailed guidance on utilizing the Synmetrix client and exploring its features, please consult the Synmetrix Documentation.","s":"Usage","u":"/docs/development/local-development","h":"#usage","p":691},{"i":706,"t":"Explore the interactive UI components and functionalities of the Synmetrix client using Storybook. Storybook provides a convenient environment to showcase and test UI components in isolation. To launch Storybook, use the following command: yarn storybook Once the command is executed, navigate to","s":"Storybook Integration","u":"/docs/development/local-development","h":"#storybook-integration","p":691},{"i":708,"t":"Maintain code consistency and quality in the Synmetrix client by implementing linting. Follow these steps to configure linting in Visual Studio Code (VS Code): Requirements:​ ESLint Extension Prettier Extension Configuration:​ Install ESLint and Prettier VS Code extensions. Open your VS Code settings (settings.json) and add the following configuration: { \"eslint.enable\": true, \"eslint.format.enable\": true, \"editor.formatOnSave\": true} This configures VS Code to run ESLint and Prettier on file save.","s":"Code Linting","u":"/docs/development/local-development","h":"#code-linting","p":691},{"i":710,"t":"In the Synmetrix project, components follow a consistent structure to enhance maintainability and organization. Each component typically consists of the following files: index.tsx: The main file containing the component's implementation. This file includes the React component code. index.module.less: A Less file containing the component's styles. Using module.less ensures that styles are scoped to the component and won't conflict with styles from other components. index.stories.tsx: A file dedicated to Storybook stories for the component. Storybook stories help in visually testing and documenting the component's variations and use cases. index.test.tsx: The file containing unit tests for the component using testing framework vitest. Writing tests ensures the reliability and correctness of the component's functionality. Here's an example directory structure for a component named ExampleComponent: /src /components /ExampleComponent index.tsx index.module.less index.stories.tsx index.test.tsx","s":"Component Structure","u":"/docs/development/local-development","h":"#component-structure","p":691},{"i":712,"t":"Ensure the reliability and correctness of the Synmetrix client by utilizing testing functionalities powered by Vitest. Vitest provides a robust testing framework to validate the behavior of your components and functionalities. To run tests, use the following command: yarn test Executing this command will initiate the testing suite, allowing you to assess the client's performance and functionality. Any detected issues or failures will be highlighted, providing valuable insights for debugging and maintaining code quality.","s":"Testing","u":"/docs/development/local-development","h":"#testing","p":691},{"i":714,"t":"Efficiently build and package the Synmetrix client with the following commands: Build the Client: yarn build This command utilizes Vite to build the Synmetrix client. Post-Build Tasks: yarn postbuild After the build, this command creates compressed archives (dist.tar.gz and dist.zip) of the generated distribution files. Build Storybook: yarn build-storybook Use this command to build the Storybook for the client. Serve the Built Client Locally: yarn serve This command previews the built client locally using Vite. Incorporate these commands into your workflow to streamline the build process and prepare the Synmetrix client for deployment.","s":"Build","u":"/docs/development/local-development","h":"#build","p":691},{"i":716,"t":"We adhere to the Conventional Commits specification to ensure consistent and meaningful commit messages. The structure of our commits follows the pattern: [optional scope]: [optional body][optional footer(s)] For a detailed understanding of Conventional Commits, refer to the Conventional Commits specification.","s":"Conventional Commits","u":"/docs/development/local-development","h":"#conventional-commits","p":691},{"i":718,"t":"APIs & integrations Power BI","s":"Power BI","u":"/docs/integration-examples/power-bi","h":"","p":717},{"i":720,"t":"APIs & integrations Excel","s":"Excel","u":"/docs/integration-examples/excel","h":"","p":719},{"i":722,"t":"License License & Community","s":"License and Community","u":"/docs/license/license-community","h":"","p":721},{"i":724,"t":"License","s":"License","u":"/docs/license","h":"","p":723},{"i":726,"t":"MIT License","s":"📄️ License & Community","u":"/docs/license","h":"","p":723},{"i":728,"t":"Links and Contacts","s":"Links and Contacts","u":"/docs/links-and-contacts","h":"","p":727},{"i":730,"t":"GitHub Repository Slack Channel Docker Hub Meeting Calendar","s":"Useful Links and Contacts","u":"/docs/links-and-contacts","h":"#useful-links-and-contacts","p":727},{"i":732,"t":"Overview","s":"Overview","u":"/docs/overview","h":"","p":731},{"i":734,"t":"In today's data-driven world, the challenges of data management are ever-increasing, especially in fields that rely heavily on data processing, analysis, and transmission. Inefficient data management can result in lost productivity, missed opportunities, and even regulatory compliance issues. The need for a comprehensive solution to address these complexities has never been more pressing. Synmetrix is committed to tackling this challenge head-on and providing a robust solution to streamline data management, analysis, and transmission.","s":"The Data Management Challenge","u":"/docs/overview","h":"#the-data-management-challenge","p":731},{"i":736,"t":"With Synmetrix, you can effortlessly address the complexity and inefficiency in managing, analyzing, and transmitting data across various sectors. Our platform offers powerful features for creating and managing data models, tracking metric changes, and organizing data-related tasks. You can easily share your data with others through interfaces like REST, GraphQL, or SQL, making your data a single source of truth for your entire organization. Whether you're a data engineer, analyst, or manager, Synmetrix has everything you need to excel with big data.","s":"Software Description and Purpose","u":"/docs/overview","h":"#software-description-and-purpose","p":731},{"i":738,"t":"Synmetrix's versatile features find applications across a wide range of sectors, including finance, retail, the power industry, manufacturing, research and development, healthcare, and many more. As the global big data market continues to boom, with projections reaching $103 billion by 2027 and a compound annual growth rate of 10.9%, Synmetrix is poised to seize this massive opportunity. We're here to provide the solution to the complexity and inefficiency in data management, analysis, and transmission.","s":"Tapping into a Vast Market","u":"/docs/overview","h":"#tapping-into-a-vast-market","p":731},{"i":740,"t":"Data Modeling (Semantic layer): Transform raw data into meaningful business indicators with our effective data modeling feature. SQL Interface: Access metrics easily and manipulate data with our intuitive SQL interface. Reporting System: Schedule reports and distribute them through various communication channels with our integrated reporting system. Alerting System: Stay informed about significant data changes with our proactive alerting system. Documentation Generation: Enhance the understanding and usability of your data models with our documentation generation feature.","s":"Key Features","u":"/docs/overview","h":"#key-features","p":731},{"i":742,"t":"JavaScript / Node.js","s":"Programming Language","u":"/docs/overview","h":"#programming-language","p":731},{"i":744,"t":"To make the most of this documentation, you can explore the following sections: Quick Start: Installation and initial setup of Synmetrix. User Guide: In-depth guides on using Synmetrix. Contributing: Information on how to contribute to the development of Synmetrix.","s":"How to Use This Documentation","u":"/docs/overview","h":"#how-to-use-this-documentation","p":731},{"i":746,"t":"We welcome contributions from the community. If you'd like to get involved in the development of Synmetrix or have ideas for improvement, please check out our Contributing Guidelines.","s":"Contributing","u":"/docs/overview","h":"#contributing","p":731},{"i":748,"t":"If you have questions, feedback, or need assistance, don't hesitate to reach out to us. Contact us. or by visiting our GitHub repository. Synmetrix is meticulously designed to streamline your data management, analysis, and transmission workflows, making it the ultimate tool for all your data needs. We look forward to your involvement and contributions!","s":"Contact Us","u":"/docs/overview","h":"#contact-us","p":731},{"i":750,"t":"Production Deployment Requirements","s":"Requirements","u":"/docs/production-deployment/requirements","h":"","p":749},{"i":752,"t":"Docker Node.js (Version 20.8.1 or above) Yarn","s":"Software Requirements","u":"/docs/production-deployment/requirements","h":"#software-requirements","p":749},{"i":754,"t":"Synmetrix is intended for installation and use on servers equipped with processors supporting x86-64 architecture and based on operating systems such as Ubuntu Server 18.04 and higher. Minimum Hardware Requirements: - Processor (CPU): 3.2 GHz or higher, modern processor with multi-threading and virtualization support.- RAM: 8 GB or more to handle computational tasks and data processing.- Disk Space: At least 30 GB of free space for software installation and storing working data.- Network: Internet connectivity is required for cloud services and software updates. note These requirements are minimum and may vary depending on specific use cases and data processing volumes. Scaling the solution may require more powerful servers or distributed systems.","s":"Hardware Requirements","u":"/docs/production-deployment/requirements","h":"#hardware-requirements","p":749},{"i":756,"t":"You can access the source code of Synmetrix on GitHub.","s":"Source Code","u":"/docs/production-deployment/requirements","h":"#source-code","p":749},{"i":758,"t":"Production Deployment","s":"Production Deployment","u":"/docs/production-deployment","h":"","p":757},{"i":760,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.","s":"📄️ Docker Swarm","u":"/docs/production-deployment","h":"","p":757},{"i":762,"t":"Software Requirements","s":"📄️ Requirements","u":"/docs/production-deployment","h":"","p":757},{"i":764,"t":"Quick Start","s":"Quick Start","u":"/docs/quickstart","h":"","p":763},{"i":766,"t":"Ensure the following software is installed before proceeding: Docker Docker Compose","s":"Prerequisite Software","u":"/docs/quickstart","h":"#prerequisite-software","p":763},{"i":768,"t":"The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository: Execute this in a new directory Using wget Using curl wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml curl https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml -o docker-compose.yml","s":"Step 1: Download the docker-compose file","u":"/docs/quickstart","h":"#step-1-download-the-docker-compose-file","p":763},{"i":770,"t":"Execute the following command to start Synmetrix along with a Postgres database for data storage. $ docker-compose up -d Verify if the containers are operational: $ docker psCONTAINER ID IMAGE ... CREATED STATUS PORTS ...c8f342d086f3 synmetrix/stack ... 1m ago Up 1m 80->8888/tcp ...30ea14ddaa5e postgres:12 ... 1m ago Up 1m 5432/tcp The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.","s":"Step 2: Launch Synmetrix","u":"/docs/quickstart","h":"#step-2-launch-synmetrix","p":763},{"i":772,"t":"You can access Synmetrix at http://localhost/ The GraphQL endpoint is located at http://localhost/v1/graphql The Admin Console (Hasura Console) can be found at http://localhost/console The Cube Swagger API can be found at http://localhost:4000/docs note Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated. Default Seed Data: The project comes with default seed data. Use the following credentials to log in: Email: demo@synmetrix.org Password: demodemo If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.","s":"Step 3: Explore Synmetrix","u":"/docs/quickstart","h":"#step-3-explore-synmetrix","p":763},{"i":774,"t":"Demo: app.synmetrix.org Login: demo@synmetrix.org Password: demodemo","s":"Demo online","u":"/docs/quickstart","h":"#demo-online","p":763},{"i":776,"t":"Database type Host Port Database User Password SSL ClickHouse gh-api.clickhouse.tech 443 default play no password true PostgreSQL demo-db.cube.dev 5432 ecom cube 12345 false","s":"Demo credentials","u":"/docs/quickstart","h":"#demo-credentials","p":763},{"i":780,"t":"In the development of Synmetrix, the establishment of a robust SQL interface plays a pivotal role. This interface serves as a unified platform for accessing data sources, ensuring consistent metrics are available to all data consumers. It addresses the challenge of maintaining data integrity and precision across various tools employed by modern organizations.","s":"Introduction","u":"/docs/sql-interface","h":"#introduction","p":777},{"i":782,"t":"Many modern organizations utilize numerous tools to process data from a shared storage but perform individual metric calculations. This often leads to discrepancies in calculations and disparities among different teams. The more tools an organization employs, the greater the potential for inconsistencies, making data-driven decision-making complex. Hence, it is essential to create a centralized hub for defining and maintaining metrics, ensuring that all tools operate with a single source of data.","s":"Problem Statement","u":"/docs/sql-interface","h":"#problem-statement","p":777},{"i":784,"t":"To tackle this challenge, Synmetrix has chosen the Cube.js framework, successfully integrated into the Synmetrix platform. Cube.js offers a significant advantage in its ability to act as a data source proxy, transforming incoming queries—whether in JSON, GraphQL, or SQL format—into native queries to the underlying data store.","s":"Solution: Leveraging Cube.js","u":"/docs/sql-interface","h":"#solution-leveraging-cubejs","p":777},{"i":786,"t":"Creating the SQL interface within Synmetrix presented several challenges effectively addressed using the Cube.js framework and Apache Datafusion. This SQL interface enables Cube.js to provide data through a PostgreSQL-compatible protocol to various data-consuming applications, including but not limited to: BI tools Data exploration tools such as Jupyter or Hex Reverse ETL tools like Census or Hightouch Low-code development platforms such as Retool Automated tasks The SQL interface has been thoroughly tested with a range of tools and platforms, including: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch The SQL API utilizes Apache Datafusion as its SQL execution engine, responsible for query planning and execution.","s":"Implementation of SQL Interface in Synmetrix","u":"/docs/sql-interface","h":"#implementation-of-sql-interface-in-synmetrix","p":777},{"i":788,"t":"The implementation of the SQL interface in Synmetrix comprises several key steps, facilitated by the Cube.js framework and Apache Datafusion: Request Retrieval: Incoming data retrieval requests arrive in SQL format to Cube.js, serving as the initial queries that require processing and execution. Query Transformation: Cube.js transforms the incoming SQL queries into its own query format (JSON). This transformation optimizes and prepares the query for execution against the underlying data store. Query Execution: The SQL query generated in the previous step is sent to the data source. This source can be any supported database to which Cube.js can connect. Data Retrieval: The output is a dataset containing the required data in response to the original query. Load into Apache Datafusion: The dataset is loaded into Apache Datafusion, where it undergoes further processing. Apache Datafusion serves as the SQL execution engine, responsible for query planning and execution. Execution of the Original SQL Query: The original SQL query received by the server is executed within Apache Datafusion. This allows for the full utilization of SQL's capabilities for data analysis and processing while preserving the structure and semantics of the original query. Data Return: Query results are returned through Cube.js, where they can be further processed or directly delivered to end-users. This process ensures high flexibility and power when working with queries while maintaining data consistency and coherence for all consumers. Each stage has been meticulously designed and optimized to ensure efficient query execution and overall system performance.","s":"Implementation Details","u":"/docs/sql-interface","h":"#implementation-details","p":777},{"i":790,"t":"A notable feature of the SQL interface in Synmetrix is how it handles and transforms SQL queries. As previously mentioned, Cube.js converts incoming SQL queries into its own query format (JSON), a process known as \"Cube query rewrite.\" This transformation allows for optimization and query preparation for execution against the underlying data store. Let's illustrate this with an example query to the \"orders\" cube in our data model: Original SQL Query: SELECT city, SUM(amount)FROM ordersWHERE status = 'shipped'GROUP BY 1 This SQL query in the SELECT statement to the \"orders\" cube is transformed into Cube.js' internal query format: Transformed Cube.js Query: { \"measures\": [\"Orders.amount\"], \"dimensions\": [\"Orders.city\"], \"filters\": [ { \"member\": \"Orders.status\", \"operator\": \"equals\", \"values\": [\"shipped\"] } ]} As a result of this transformation, not all functions and expressions are supported within query fragments that execute SELECT statements on cube tables. For example, the following query would not work because the SQL API cannot pass the CASE expression to Cube.js for processing, making it impossible to translate the CASE expression into a metric: Non-supported Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(number)FROM orders CROSS JOIN UsersGROUP BY 1; However, in such cases, nested queries can be employed. You can encapsulate your SELECT query from a cube table within another SELECT query to perform calculations with expressions like CASE. This outer SELECT is not part of the rewritten SQL query, allowing you to use more SQL functions, operators, and expressions. Example Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT Users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN Users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; The above query works because the CASE expression is supported in SELECT queries that do not reference cube tables. These nuances and query handling specifics should be considered when designing and utilizing the SQL interface in Synmetrix.","s":"Transformation of SQL Queries","u":"/docs/sql-interface","h":"#transformation-of-sql-queries","p":777},{"i":792,"t":"For a detailed analysis of the server code and its components, please refer to the Synmetrix SQL Interface Server Code.","s":"Server Code","u":"/docs/sql-interface","h":"#server-code","p":777},{"i":794,"t":"The creation of the SQL interface in Synmetrix has allowed for the development of a unified data access interface, ensuring metric consistency for all data tools. This has reduced the number of discrepancies and conflicts related to metric calculations, providing a more reliable foundation for data-driven decision-making.","s":"Conclusion","u":"/docs/sql-interface","h":"#conclusion","p":777},{"i":798,"t":"For user support and inquiries, you can reach out to us through the following channels: Email Support: hello@synmetrix.org Issue Tracker: GitHub Issues Our support team is dedicated to assisting you with any questions or problems you might encounter while using Synmetrix.","s":"Contact Information","u":"/docs/support-updates","h":"#contact-information","p":795},{"i":800,"t":"We are continuously working to improve Synmetrix and provide you with the best possible experience. Here's a glimpse of our upcoming updates and changes: GitHub","s":"Upcoming Updates and Changes","u":"/docs/support-updates","h":"#upcoming-updates-and-changes","p":795},{"i":802,"t":"Testing and Debugging Testing Guides","s":"Testing Guides","u":"/docs/testing","h":"","p":801},{"i":804,"t":"12 items","s":"🗃️ Test a Data Source","u":"/docs/testing","h":"","p":801},{"i":806,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"📄️ Test SQL Interface for Data Models","u":"/docs/testing","h":"","p":801},{"i":808,"t":"In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI.","s":"📄️ Test Structure","u":"/docs/testing","h":"","p":801},{"i":810,"t":"Testing and Debugging","s":"Testing and Debugging","u":"/docs/testing-and-debugging","h":"","p":809},{"i":812,"t":"3 items","s":"🗃️ Testing Guides","u":"/docs/testing-and-debugging","h":"","p":809},{"i":816,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"SQL Interface Testing Guide for Data Models","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"","p":814},{"i":818,"t":"Before you begin testing, you'll need the following Postgres database credentials: Type: Postgres Host: demo-db.cube.dev Port: 5432 User: cube Password: 12345 Database Name: ecom","s":"Section 1: Test Database Credentials","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-1-test-database-credentials","p":814},{"i":820,"t":"To add an SQL interface, follow these steps: Go to the Settings > SQL API and Click to the \"Attach SQL API\" button. Select the data source for connection; in our case, it's \"Ecom (demo db).\" Enter your login and password or use the default ones (login: \"test\" and password: \"test\"). Click \"Connect new.\"","s":"Section 2: Adding SQL Interface","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-2-adding-sql-interface","p":814},{"i":822,"t":"Refer to the guide in Appendix V.2 for generating data models. For \"Ecom (demo db),\" you'll need to generate data models for the following tables: Line Items Line Items Count by States Orders Product Categories Products Suppliers Users This will result in the following list of data models: LineItems.yml LineItemsCountByStates.yml Orders.yml ProductCategories.yml Products.yml Suppliers.yml Users.yml Each script is labeled with its corresponding name, which is also the table name when accessing via the SQL interface.","s":"Section 3: Generating Data Models","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-3-generating-data-models","p":814},{"i":824,"t":"We recommend using DBeaver of any version (https://dbeaver.io/) for connecting. However, our connection has also been tested with the following tools: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch","s":"Section 4: Connecting via SQL Interface with a Postgres Client","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-4-connecting-via-sql-interface-with-a-postgres-client","p":814},{"i":826,"t":"Create a new PostgresSQL connection. Enter the credentials provided when setting up the SQL interface. In this example: Host: localhost Port: 15432 Database: ecom (you can specify any value) Username: test Password: test Click \"Test Connection\" to verify the connection. Click \"Finish\" and save the connection. Go to the SQL console to test SQL queries against the data models.","s":"4.1: Connecting via DBeaver","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#41-connecting-via-dbeaver","p":814},{"i":828,"t":"Start writing queries for the generated data models following the steps outlined in Appendix D.3. 4.2.1: Sample SQL Queries​ Query: SELECT * FROM orders ORDER BY created_at LIMIT 3; Response: Query: SELECT p.name, SUM(o.count) FROM orders o CROSS JOIN products p GROUP BY 1 LIMIT 5; Response: Query: SELECT MEASURE(count), status, DATE_TRUNC('month', created_at) date FROM orders GROUP BY date, status ORDER BY date ASC; Response: Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; Response:","s":"4.2: Testing SQL Queries","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#42-testing-sql-queries","p":814},{"i":833,"t":"info In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI. StepCI is employed for writing and conducting integration tests, which verify the correctness of the system's operation when its various components interact with each other. This is particularly vital for projects like Synmetrix, which consist of numerous services and components working collaboratively.","s":"Testing Structure for Synmetrix","u":"/docs/testing-and-debugging/test-structure","h":"","p":831},{"i":835,"t":"All test files are located in the test/stepci directory within the Synmetrix project folder. Each file represents an individual test scenario described in YAML format. - name: change_password http: url: ${{env.HASURA_PLUS_ENDPOINT}}/auth/change-password method: POST headers: Content-Type: application/json Authorization: Bearer ${{captures.accessToken}} body: | { \"old_password\": \"pass321\", \"new_password\": \"pass123\" } check: status: 204 This YAML configuration represents a typical StepCI test scenario named \"change_password.\" It performs a POST request with specific headers and a JSON request body to change a password. The response status is checked to ensure it's 204.","s":"Test Structure","u":"/docs/testing-and-debugging/test-structure","h":"#test-structure","p":831},{"i":837,"t":"Testing on the StepCI platform typically involves the following steps: Request Creation: StepCI generates a request based on the input data defined in the test scenario. Request Sending: The request is sent to the tested endpoint in the Synmetrix system. Waiting for Response: StepCI waits for a response from the Synmetrix system. During this time, all necessary actions are performed, such as request processing, interaction with the database, and other services. Response Verification: After receiving the response, StepCI compares it with the expected result defined in the test scenario. If the response matches the expectations, the test is considered passed. Otherwise, StepCI logs an error, and the test is marked as failed. This testing structure ensures the reliability and stability of the Synmetrix system. Automated testing allows developers to quickly identify and rectify errors, as well as ensure that new changes do not disrupt the functionality of existing features. Upon completing a set of tests, StepCI provides a report with a brief description of the tests that were passed. PASS datasource_flow 9.458s 1 1620 bytes 1 16006 bytesTests: 0 failed, 1 passed, 1 totalSteps: 0 failed, 0 skipped, 18 passed, 18 totalTime: 9,533s, estimated 10sCO2:0.04036gWorkflow passed after 9.533s Test was passed. In the event of a test failure, StepCI records details, including the name of the failed testing step, input data and parameters, actual and expected results, and error messages obtained during the execution of the tested code. Subsequent test steps are halted upon encountering a failure. v create_team passed after 0.041sv invite_team_member passed after 0.597s v create_datasource passed after 0.021s v check_connection passed after 0.833s v validate_datasource passed after 0.078s v fetch_tables passed after 0.436s × gen_dataschemas failed after 0.008s A create_exploration skipped after 0s A fetch_dataset skipped after Os A export_data_models skipped after 0s A fetch_meta skipped after 0s A run_query skipped after Os• datasource_flow › gen_dataschemasRequest HTTPPOST http://hasura:8080/v1/graphql HTTP/1.1Content-Type: application/jsonAuthorization: Bearer eyJhbci01JIUzI1N1J9.eyJoYXN1cmEiOnsieC1oYXN1cmEtdXNlci1pZCI6IjU5YjhjOTVjLTdIMmEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMi0\\sidXNlciJdLCJ4LWhhc3VyYS1kzwZhdWx0LXJvbGUi0iJ1c2VyInosInN1Yi16IjU5YjhjOTVjLTdl™mEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsImlzcyI6Im5ob3N0IiwiaWF0Ijo×NjkwoDkoNzcOLCJleHAi0jE20TE1NDI3NzR9.DTtp9T†jNg6ABm1vV7eB0KXZw6k1d2iZx6C-DFicCw™,x-hasura-user-id: 59b8c95c-7e2a-4797-a66c-e51776ccd39e{\"query\": \"mutation (#datasource_id: uuid!, $tables: [SourceTable!]!) f\\n gen_dataschemas (d atasource_id: $datasource_id, tables: $tables) f\\n code\\nmessageln FInrIn\"， \"variables\" :{\"datasource_id\":\"9218058b-160c-4c43-8126-b944612c055f\"\"tables\": K{\"name\": \"default/celltowers\"},{\"name\": \"default/checks\"}1}}ResponseHTTP/1.1 200 OKtransfer-encoding: chunkeddate: Tue 01 Aug 2023 12:59:36 GMTserver: Warp/3.3.23content-type: application/json; charset=utf-8{\"errors\": [\"extensions\":{\"code\": \"validation-failed\", \"path\":\"$.selectionSet. gen_dataschemas args.branch_id\"}, \"message\":\"missing required field 'branch_id'\"}1}ChecksJSONPath× $. data.gen_dataschemas.code: undefined (expected ok) Test was failed.","s":"Testing Algorithm","u":"/docs/testing-and-debugging/test-structure","h":"#testing-algorithm","p":831},{"i":839,"t":"Testing and Debugging Testing Guides Test a Data Source CrateDB","s":"Crate","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"","p":838},{"i":841,"t":"Navigate to the CrateDB example directory located at /docs/examples/crate in the root of the project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/crate","s":"Step 1: Navigate to the CrateDB example directory","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-1-navigate-to-the-cratedb-example-directory","p":838},{"i":843,"t":"In the CrateDB example directory, there is a script called run_crate.sh. Make sure the script is executable by running: chmod +x run_crate.sh Then, start the CrateDB service by running the script: ./run_crate.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the CrateDB service using the script","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-2-run-the-cratedb-service-using-the-script","p":838},{"i":845,"t":"Go to the UI and add a new Crate data source with the following parameters: Name: Crate Host: Host IP (obtained from the script in Step 2) Port: 4200 User: crate","s":"Step 3: Add a data source in the UI","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-3-add-a-data-source-in-the-ui","p":838},{"i":847,"t":"Click the \"Check connection\" button in the UI to verify the connection to the CrateDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-4-test-the-connection","p":838},{"i":850,"t":"Tested on the dremio/dremio-oss:latest image.","s":"Dremio","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"","p":848},{"i":852,"t":"Navigate to the Dremio example directory located at /docs/examples/dremio in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/dremio","s":"Step 1: Navigate to the Dremio example directory","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-1-navigate-to-the-dremio-example-directory","p":848},{"i":854,"t":"In the Dremio example directory, there is a script called run_dremio.sh. Make sure the script is executable by running: chmod +x run_dremio.sh Then, start the Dremio service by running the script: ./run_dremio.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Dremio service using the script","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-2-run-the-dremio-service-using-the-script","p":848},{"i":856,"t":"Open your web browser and go to http://localhost:9047. Follow the prompts to create an administrator account. Log in to the Dremio UI with your new account. Click on \"Add Source\" and select \"PostgreSQL\" from the list of available data sources. Use the credentials provided at https://uibakery.io/sql-playground to configure the PostgreSQL data source.","s":"Step 3: Create an administrator account and add a data source in Dremio","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-3-create-an-administrator-account-and-add-a-data-source-in-dremio","p":848},{"i":858,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: host_ip (obtained from the script in Step 2) Port: 9047 Database name: Name given to the connection in Dremio User: Dremio administrator username Password: Dremio administrator password","s":"Step 4: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-4-add-a-data-source-in-the-synmetrix-ui","p":848},{"i":860,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Dremio data source. If the connection is successful, you will see a confirmation message.","s":"Step 5: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-5-test-the-connection","p":848},{"i":862,"t":"Testing and Debugging Testing Guides Test a Data Source","s":"Test a Data Source","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":864,"t":"Tested on the crate:5.3.0 image.","s":"📄️ CrateDB","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":866,"t":"To connect to a Databricks datasource via JDBC, you will need the following information:","s":"📄️ Databricks","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":868,"t":"Tested on the dremio/dremio-oss:latest image.","s":"📄️ Dremio","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":870,"t":"Step 1: Navigate to the Druid example directory","s":"📄️ Druid","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":872,"t":"Step 1: Navigate to the Elasticsearch Example Directory","s":"📄️ Elasticsearch","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":874,"t":"Connecting to Firebolt datasource","s":"📄️ Firebolt","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":876,"t":"Tested on the confluentinc/cp-ksqldb-server:7.0.9 image.","s":"📄️ KSQL","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":878,"t":"Tested on the materialize/materialized:latest image.","s":"📄️ Materialize","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":880,"t":"Tested on the starburstdata/presto image.","s":"📄️ Presto","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":882,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"📄️ QuestDB","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":884,"t":"This guide provides step-by-step instructions on how to connect Snowflake to Synmetrix.","s":"📄️ Snowflake","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":886,"t":"Tested on the trinodb/trino:latest image.","s":"📄️ Trino","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":861},{"i":890,"t":"Navigate to the Druid example directory located at /docs/examples/druid in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/druid","s":"Step 1: Navigate to the Druid example directory","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-1-navigate-to-the-druid-example-directory","p":887},{"i":892,"t":"In the Druid example directory, there is a script called run_druid.sh. Make sure the script is executable by running: chmod +x run_druid.sh Then, start the Druid services by running the script: ./run_druid.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step. Wait for the cluster to start and go to http://localhost:8889.","s":"Step 2: Run the Druid services using the script","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-2-run-the-druid-services-using-the-script","p":887},{"i":894,"t":"Go to the Services tab and make sure all services are online, as shown in the screenshot below.","s":"Step 3: Check the services","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-3-check-the-services","p":887},{"i":896,"t":"Select the \"Load data\" tab, then choose \"Batch - classic\" from the dropdown menu. Next, select the \"Example data\" tile and click \"Load example\". Click the button with the text \"Next: ...\" in the bottom right corner until the data starts loading (about 9 times). When the data starts loading, you will be redirected to the Ingestion tab and see the message \"Task submitted successfully. Going to task view...\". After the data has finished loading, the task status will change to SUCCESS.","s":"Step 4: Load example data","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-4-load-example-data","p":887},{"i":898,"t":"Fill out the form with the following information: Name: Druid test datasource Host: Host IP (obtained from the script in Step 2) Port: 8082 Currently, the Test Connection feature is not implemented and always returns \"OK\". To ensure functionality, generate a schema and request any data in the Explore tab.","s":"Step 5: Create a new Druid datasource in Synmetrix","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-5-create-a-new-druid-datasource-in-synmetrix","p":887},{"i":900,"t":"Testing and Debugging Testing Guides Test a Data Source Elasticsearch","s":"Elasticsearch","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"","p":899},{"i":902,"t":"Navigate to the Elasticsearch example directory located at /docs/examples/elasticsearch in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/elasticsearch","s":"Step 1: Navigate to the Elasticsearch Example Directory","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-1-navigate-to-the-elasticsearch-example-directory","p":899},{"i":904,"t":"In the Elasticsearch example directory, there is a script called run_elasticsearch.sh. Make sure the script is executable by running: chmod +x run_elasticsearch.sh Then, start the Elasticsearch service by running the script: ./run_elasticsearch.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Elasticsearch Service Using the Script","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-2-run-the-elasticsearch-service-using-the-script","p":899},{"i":906,"t":"Wait for the Elasticsearch service to be ready to accept requests. Then run: curl -X POST -u elastic:password \"http://localhost:9200/_security/user/user\" -H 'Content-Type: application/json' -d'{ \"password\": \"password\", \"roles\": [\"kibana\", \"kibana_system\"], \"full_name\": \"Test user\"}'","s":"Step 3: Wait for the Service to Be Ready and Add a User","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-3-wait-for-the-service-to-be-ready-and-add-a-user","p":899},{"i":908,"t":"Wait for the Kibana service to start, then go to http://localhost:5601 and log in with the credentials: User: elasticPassword: password Click on the Try sample data link: At the bottom of the page, click Other sample data sets: Choose a dataset and click Add data.","s":"Step 4: Access Kibana and Add Sample Data","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-4-access-kibana-and-add-sample-data","p":899},{"i":910,"t":"Now you can create a datasource in Synmetrix using the following information: Name: Elasticsearch test datasource Url: http://:9200 User: user Password: password Save the datasource and click Test connection. You should see a message indicating that the connection is OK.","s":"Step 5: Create a Datasource in Synmetrix","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-5-create-a-datasource-in-synmetrix","p":899},{"i":912,"t":"Testing and Debugging Testing Guides Test a Data Source Firebolt","s":"Firebolt","u":"/docs/testing-and-debugging/tests/data-source/firebolt","h":"","p":911},{"i":914,"t":"To connect to a Firebolt datasource, you will need the following information: Database Name - the name of the database to connect to. Username - the username used to connect to the database. Password - the password used to connect to the database. Engine Name - the name of the engine to connect to. API Endpoint - the Firebolt API endpoint used for authentication, such as api.dev.firebolt.io or api.app.firebolt.io.","s":"Connecting to Firebolt datasource","u":"/docs/testing-and-debugging/tests/data-source/firebolt","h":"#connecting-to-firebolt-datasource","p":911},{"i":916,"t":"Testing and Debugging Testing Guides Test a Data Source Materialize","s":"Materialize","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"","p":915},{"i":918,"t":"Navigate to the Materialize example directory located at /docs/examples/materialize in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/materialize","s":"Step 1: Navigate to the Materialize example directory","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-1-navigate-to-the-materialize-example-directory","p":915},{"i":920,"t":"In the Materialize example directory, there is a script called run_materialize.sh. Make sure the script is executable by running: chmod +x run_materialize.sh Then, start the Materialize service by running the script: ./run_materialize.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Materialize service using the script","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-2-run-the-materialize-service-using-the-script","p":915},{"i":922,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 6875 Database name: Test User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":915},{"i":924,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Materialize data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-4-test-the-connection","p":915},{"i":926,"t":"Testing and Debugging Testing Guides Test a Data Source KSQL","s":"KSQL","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"","p":925},{"i":928,"t":"Navigate to the ksql example directory located at /docs/examples/ksql in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/ksql","s":"Step 1: Navigate to the ksql example directory","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-1-navigate-to-the-ksql-example-directory","p":925},{"i":930,"t":"In the ksql example directory, there is a script called run_ksql.sh. Make sure the script is executable by running: chmod +x run_ksql.sh Then, start the ksql service by running the script: ./run_ksql.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the ksql service using the script","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-2-run-the-ksql-service-using-the-script","p":925},{"i":932,"t":"Go to the Synmetrix UI and add a new datasource with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 8088","s":"Step 3: Add a datasource in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-3-add-a-datasource-in-the-synmetrix-ui","p":925},{"i":934,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to the ksql datasource. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-4-test-the-connection","p":925},{"i":937,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"QuestDB","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"","p":935},{"i":939,"t":"Navigate to the QuestDB example directory located at /docs/examples/questdb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/questdb","s":"Step 1: Navigate to the QuestDB example directory","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-1-navigate-to-the-questdb-example-directory","p":935},{"i":941,"t":"In the QuestDB example directory, there is a script called run_questdb.sh. Make sure the script is executable by running: chmod +x run_questdb.sh Then, start the QuestDB service by running the script: ./run_questdb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the QuestDB service using the script","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-2-run-the-questdb-service-using-the-script","p":935},{"i":943,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: QuestDB Test Host: Host IP (obtained from the script in Step 2) Port: 8812 User: quest Password: quest","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":935},{"i":945,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the QuestDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-4-test-the-connection","p":935},{"i":948,"t":"Tested on the starburstdata/presto image.","s":"PrestoDB","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"","p":946},{"i":950,"t":"Navigate to the PrestoDB example directory located at /docs/examples/prestodb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/prestodb","s":"Step 1: Navigate to the PrestoDB example directory","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-1-navigate-to-the-prestodb-example-directory","p":946},{"i":952,"t":"In the PrestoDB example directory, there is a script called run_prestodb.sh. Make sure the script is executable by running: chmod +x run_prestodb.sh Then, start the PrestoDB service by running the script: ./run_prestodb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the PrestoDB service using the script","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-2-run-the-prestodb-service-using-the-script","p":946},{"i":954,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: PrestoDB Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":946},{"i":956,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the PrestoDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-4-test-the-connection","p":946},{"i":958,"t":"Testing and Debugging Testing Guides Test a Data Source Snowflake","s":"Snowflake","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"","p":957},{"i":960,"t":"Before proceeding, ensure you have the following Snowflake connection details: Database Name Warehouse Organization ID Account ID Username Password Role You can find this information in your Snowflake account.","s":"Step 1: Gather Snowflake connection information","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-1-gather-snowflake-connection-information","p":957},{"i":962,"t":"Go to the Synmetrix UI and navigate to the \"Data Sources\" section. Click on the \"+ Connect\" button. Select \"Snowflake\" from the list of available data sources.","s":"Step 2: Add a new data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-2-add-a-new-data-source-in-the-synmetrix-ui","p":957},{"i":964,"t":"Fill in the Snowflake connection details in the form with the following fields: Name: A custom name for your data source (e.g., \"My Snowflake\") Database Name: The name of your Snowflake database Warehouse: The name of your Snowflake warehouse Organization ID: The ID of your Snowflake organization Account ID: The ID of your Snowflake account Username: Your Snowflake username Password: Your Snowflake password Role: Your Snowflake role","s":"Step 3: Fill in the connection details","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-3-fill-in-the-connection-details","p":957},{"i":966,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to your Snowflake data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-4-test-the-connection","p":957},{"i":968,"t":"Testing and Debugging Testing Guides Test a Data Source Trino","s":"Trino","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"","p":967},{"i":970,"t":"Navigate to the Trino example directory located at /docs/examples/trino in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/trino","s":"Step 1: Navigate to the Trino example directory","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-1-navigate-to-the-trino-example-directory","p":967},{"i":972,"t":"In the Trino example directory, there is a script called run_trino.sh. Make sure the script is executable by running: chmod +x run_trino.sh Then, start the Trino service by running the script: ./run_trino.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Trino service using the script","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-2-run-the-trino-service-using-the-script","p":967},{"i":974,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: Trino Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":967},{"i":976,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Trino data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-4-test-the-connection","p":967},{"i":979,"t":"To connect to a Databricks datasource via JDBC, you will need the following information: Access Token: A personal access token used to authenticate the Databricks connection. Databricks JDBC URL: The URL for a JDBC connection. It should be in the format: jdbc:databricks://{''}:443/default;transportMode=http;ssl=1; Database Name: The name of the database to connect to.","s":"Connecting to Databricks Datasource via JDBC","u":"/docs/usage/user-guide/data-source/databricks","h":"","p":977},{"i":981,"t":"User Guide","s":"User Guide","u":"/docs/user-guide","h":"","p":980},{"i":983,"t":"🗃️ Explore Page 2 items 📄️ Models The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page: 📄️ Logs Overview 🗃️ Settings 6 items Feel free to explore the documentation for each page to gain a comprehensive understanding of the Synmetrix interface and its functionalities. Happy exploring and analyzing data with Synmetrix!","s":"Table of Contents","u":"/docs/user-guide","h":"#table-of-contents","p":980},{"i":985,"t":"2 items","s":"🗃️ Explore Page","u":"/docs/user-guide","h":"","p":980},{"i":987,"t":"The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page:","s":"📄️ Models","u":"/docs/user-guide","h":"","p":980},{"i":989,"t":"Overview","s":"📄️ Logs","u":"/docs/user-guide","h":"","p":980},{"i":991,"t":"6 items","s":"🗃️ Settings","u":"/docs/user-guide","h":"","p":980},{"i":993,"t":"User Guide Explore Page Alerts","s":"Alerts Page","u":"/docs/user-guide/explore/alerts","h":"","p":992},{"i":995,"t":"Click the \"Add Alert\" button on the Explore page. Choose your notification delivery method. Options include Webhook, Slack, or Email, depending on your system integrations and preferences. Complete the alert form with the necessary information:","s":"How to Set Up an Alert","u":"/docs/user-guide/explore/alerts","h":"#how-to-set-up-an-alert","p":992},{"i":997,"t":"Alert Name: Provide a descriptive name that clearly identifies the alert's purpose and content.","s":"Detailed Description of Form Fields on the Alerts Page:","u":"/docs/user-guide/explore/alerts","h":"#detailed-description-of-form-fields-on-the-alerts-page","p":992},{"i":999,"t":"This section offers a comprehensive overview of the Alert or Report setup. Key features include: Query Key: This is the primary query that your alert or report is based on. It's a critical element in the monitoring process. Measures: Select the quantitative data points you wish to track or display. These are crucial for your analysis and will be highlighted in the Preview. Dimensions: These provide context and categorization for your data, allowing a more detailed and segmented analysis. info The Preview amalgamates all these components, offering a clear snapshot of the data and insights being monitored. This ensures that your analysis, whether for an Alert or Report, is accurate and relevant.","s":"Preview Section:","u":"/docs/user-guide/explore/alerts","h":"#preview-section","p":992},{"i":1001,"t":"Customize how and when your alert is triggered with these settings: Schedule: Set how often the alert conditions are checked (e.g., hourly, daily, weekly). Lower Bound: Define the minimum threshold that triggers the alert. Upper Bound: Specify the maximum threshold for triggering the alert. Request Timeout (minutes): Set the maximum time allowed for the data request to process. Timeout On Fire (minutes): Determine how long before an ongoing alert condition is considered \"fired.\"","s":"Trigger Settings:","u":"/docs/user-guide/explore/alerts","h":"#trigger-settings","p":992},{"i":1003,"t":"Options here vary based on your chosen alert delivery method: Webhook: Input the URL for the webhook endpoint. Slack: Enter the Slack channel or user ID. Email: Specify the email addresses of the alert recipients. note After configuring your settings, click \"Save\" to activate the alert. This will commence the monitoring of your specified conditions. Enhancements for Clarity and Efficiency: Clarified instructions for setting up alerts. Detailed descriptions for each section, ensuring comprehensive understanding. Streamlined language for ease of reading and comprehension. Added informative notes and info sections for additional clarity.","s":"Delivery Settings:","u":"/docs/user-guide/explore/alerts","h":"#delivery-settings","p":992},{"i":1007,"t":"The Explore page serves as a dynamic and intuitive platform for users to engage with data, offering tools and options for efficient data exploration and analysis. This includes changing data sources, selecting branches, and manipulating data dimensions, measures, and filters. Additional functionalities allow users to run queries, set up alerts or reports, and export data.","s":"Overview","u":"/docs/user-guide/explore","h":"#overview","p":1004},{"i":1009,"t":"The sidebar in your data tool is where you can do a lot of important stuff. Let's go over how to change your data source, switch branches, and pick the right dimensions and measures.","s":"Sidebar Actions","u":"/docs/user-guide/explore","h":"#sidebar-actions","p":1004},{"i":1011,"t":"To switch to a different data source: Find the Data Source in the sidebar. Pick a New Data Source: Click on the list there and choose the one you want. It's Changed!: Your data source will change right away.","s":"Changing Data Source in Explorer","u":"/docs/user-guide/explore","h":"#changing-data-source-in-explorer","p":1004},{"i":1013,"t":"To change the branch you're working on: Look for Branch Section in the sidebar. Choose Your Branch: Click on the list and pick the branch you need. Branch Changed: Now you're working in the new branch.","s":"Switching Branches in Explorer","u":"/docs/user-guide/explore","h":"#switching-branches-in-explorer","p":1004},{"i":1015,"t":"Picking the right dimensions and measures is super important for looking at your data the right way. Here's what to do: Go to the Sidebar: First, find the sidebar. Click on Desired Model: Click it and you'll see a list. See All the Options: This list shows all the measures, dimensions, time dimensions and segments you can use. Pick What You Need: Choose the ones that you're willing to analyze. Run Your Query: After picking, run your query to see your data. tip Quick Find: Use the search box to find dimensions or measures fast.","s":"Selecting Dimensions and Measures","u":"/docs/user-guide/explore","h":"#selecting-dimensions-and-measures","p":1004},{"i":1017,"t":"Incorporating filters into your analysis is a crucial step for refining and focusing your data exploration. Here’s a professional approach to adding filters through the Explorer Sidebar: Navigate to Explorer Sidebar: Begin by accessing the sidebar options within the Explorer. Hover Over Measures or Dimensions: Move your cursor over either the Measures or Dimensions sections in the sidebar. Initiate Filter Selection: Look for a filter icon on the specific measure or dimension you are interested in. Click on this icon to start setting up a filter. Configure Filter in Result Tab: After clicking the filter button, switch to the \"Result\" tab. Here, you'll find options to customize your filter according to your specific data requirements.","s":"Adding Filters","u":"/docs/user-guide/explore","h":"#adding-filters","p":1004},{"i":1019,"t":"The toolbar in your data analysis tool is equipped with a variety of features to enhance your data exploration and reporting capabilities. Here's a breakdown of the key actions available:","s":"Explore Toolbar Functionality Overview","u":"/docs/user-guide/explore","h":"#explore-toolbar-functionality-overview","p":1004},{"i":1021,"t":"Function: Runs the analysis based on the selected dimensions, measures, and filters. Purpose: To dynamically generate and view data results tailored to your current selections.","s":"Run Query","u":"/docs/user-guide/explore","h":"#run-query","p":1004},{"i":1023,"t":"Function: Allows for the creation of alerts based on specific data conditions. Usage: Configure alert parameters to monitor data changes and receive notifications.","s":"Set Up Alerts","u":"/docs/user-guide/explore","h":"#set-up-alerts","p":1004},{"i":1025,"t":"Function: Creates reports reflecting your current data view. Customization: Offers options to define report parameters and settings for targeted reporting.","s":"Generate Reports","u":"/docs/user-guide/explore","h":"#generate-reports","p":1004},{"i":1027,"t":"Function: Enables the export of data into a CSV file. Application: Useful for offline data analysis or for sharing data externally.","s":"Export to CSV","u":"/docs/user-guide/explore","h":"#export-to-csv","p":1004},{"i":1030,"t":"Display: Shows the outcome of your executed queries. Adjustments: Allows configuration of row limits, offsets, and view of applied filters.","s":"Results Tab","u":"/docs/user-guide/explore","h":"#results-tab","p":1004},{"i":1032,"t":"Function: Displays the automatically generated SQL query post-execution. Utility: Useful for understanding the query structure or for manual SQL query replication.","s":"Generated SQL Tab","u":"/docs/user-guide/explore","h":"#generated-sql-tab","p":1004},{"i":1034,"t":"Access Details: Provides necessary API information for data retrieval. Components: Includes headers and the API endpoint URL. Example API Utilization​ Headers: Authorization: Bearer x-hasura-datasource-id: x-hasura-branch-id: URL: https://cube.synmetrix.org/api/v1/load Body: Follow the API documentation for accurate setup. Execute API Request: Enables sending an API request directly from the interface. 📄️ Alerts The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page: 📄️ Reports The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"REST API Tab","u":"/docs/user-guide/explore","h":"#rest-api-tab","p":1004},{"i":1036,"t":"The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page:","s":"📄️ Alerts","u":"/docs/user-guide/explore","h":"","p":1004},{"i":1038,"t":"The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"📄️ Reports","u":"/docs/user-guide/explore","h":"","p":1004},{"i":1040,"t":"User Guide Logs","s":"Logs Page","u":"/docs/user-guide/logs","h":"","p":1039},{"i":1042,"t":"The Logs page is designed to provide control over the internal workings of the Cube.js service. It allows you to review the requests and the events that occur during the processing of these events. You can access information such as the execution time, start and end time, the actual query, and additional technical details. This enables you to monitor and analyze the performance and behavior of the Cube.js service. Follow the instructions below to utilize the Logs page: Access the Logs page within the project interface. Review the list of logs, which includes details such as the request information, execution time, start and end timestamps, and the actual query being processed. Click on a log entry to expand it and access additional technical information, if available. Use the logs as a resource for troubleshooting, performance optimization, and gaining insights into the inner workings of the Cube.js service.","s":"Overview","u":"/docs/user-guide/logs","h":"#overview","p":1039},{"i":1045,"t":"User Guide Explore Page Reports","s":"Reports Page","u":"/docs/user-guide/explore/reports","h":"","p":1044},{"i":1047,"t":"Click the \"Add Report\" button on the Explore page. Select the destination where the screenshot will be sent. You can choose between Webhook, Slack, or Email based on your preferences and integrations. Fill out the report form with the following details:","s":"How to Create a Report","u":"/docs/user-guide/explore/reports","h":"#how-to-create-a-report","p":1044},{"i":1049,"t":"Report Name: Enter a name that easily identifies the purpose and content of the report.","s":"Form fields description for the Reports page:","u":"/docs/user-guide/explore/reports","h":"#form-fields-description-for-the-reports-page","p":1044},{"i":1051,"t":"In the \"Preview\" section, you will find crucial details for the Alert or Report, including the user-selected metric referred to as the \"Query Key.\" This metric is at the core of your monitoring process and plays a pivotal role in alerting or report generation. Here, you'll also discover various other essential components such as: Measures: These represent the quantitative data that you want to track or display. Users can choose specific measures that matter the most to their analysis, and these will be prominently featured in the Preview. Dimensions: Dimensions help to provide context and categorize your data. They allow you to break down the information based on various attributes, providing a more comprehensive view of your metrics. info In the Preview, all these elements come together to give you a snapshot of the data and insights you're tracking, ensuring that you're monitoring the right information for your analysis, whether it's for an Alert or Report.","s":"Preview:","u":"/docs/user-guide/explore/reports","h":"#preview","p":1044},{"i":1053,"t":"Schedule: Configure the frequency at which the report will be executed by setting a schedule (e.g., daily, weekly, monthly).","s":"Trigger Settings:","u":"/docs/user-guide/explore/reports","h":"#trigger-settings","p":1044},{"i":1055,"t":"The available fields in this section depend on the chosen delivery method for the report. Here are some common options: Webhook: Enter the URL of the webhook endpoint where the report will be sent. Slack: Provide the Slack channel or user ID to which the report will be delivered. Email: Enter the email address(es) of the recipient(s) who will receive the report. note Once you have completed the settings, click the \"Save\" button.","s":"Delivery Settings:","u":"/docs/user-guide/explore/reports","h":"#delivery-settings","p":1044},{"i":1057,"t":"User Guide Models","s":"Models Page","u":"/docs/user-guide/models","h":"","p":1056},{"i":1059,"t":"To get started, you need to generate data models. Select the \"Generate Model\" option from the menu (shown in the screenshot below). In the opened window, navigate to the required sections by opening the corresponding tabs and check the boxes next to the tables or metrics you want to analyze. Then, click the \"Generate\" button (indicated by the arrow in the screenshot below). Now you should see the generated data model files in the right column (as shown in the screenshot below). You can freely edit your models as needed, adding and removing measures and dimensions. All your changes will be tracked by the version control system, the details of which are described below.","s":"How to Get Started","u":"/docs/user-guide/models","h":"#how-to-get-started","p":1056},{"i":1061,"t":"The version control system tracks changes to the data models, allowing you to revert to previous states of the models. To view the saved versions, open the drop-down menu located at the top left of the page and select the \"Show versions\" option (as shown in the screenshot below). In the opened window, you will see all the saved versions of the data models (as shown in the screenshot below). By expanding the rows, you can view the files and their contents. To revert to a specific version, simply click the \"Restore\" button next to it, and it will become the current version. Additionally, the version control system allows you to create separate branches of versions, which can help you switch between versions more efficiently than restoring versions within a single branch. To create a branch, open the branch list located in the top left corner of the page, enter the name of the new branch in the field, and click the plus icon button. This will create a new branch with the current state. To work with the new branch, you need to switch to it and then make it the default branch by clicking the \"Set as default\" button (shown in the screenshot).","s":"Version Control System","u":"/docs/user-guide/models","h":"#version-control-system","p":1056},{"i":1063,"t":"Go to Sidebar: Click on the sidebar in Synmetrix. Click 'Docs': Find and click the 'Docs' button. Get the Docs: The file with the version's documentation will be generated for you.","s":"Viewing Version Docs","u":"/docs/user-guide/models","h":"#viewing-version-docs","p":1056},{"i":1066,"t":"Opening the SQL Runner in Synmetrix for running custom SQL queries is a straightforward process. Here's how to do it: Navigate to Model Page: First, go to the model page in the Synmetrix interface. This is where all your data models are listed. Open SQL Runner Tab: Look for a tab or section labeled \"SQL Runner\" and click on it. This action will open the SQL Runner interface. Compose Your SQL Query: In the provided space, you can write your SQL query. For example, you might write a query to select data from a specific table or to perform a more complex data manipulation. SELECT * FROM your_table_name WHERE condition; Execute the Query: Once you've written your query, click the \"Run\" button. This will execute the SQL query against your data models. View Results: After running the query, the results will be displayed in a table format directly in the SQL Runner. This allows you to review and analyze the data returned by your query. note The Model SQL Runner in Synmetrix is a feature that allows you to run SQL queries directly against your data models. This tool is particularly useful for users who need to perform complex data analysis or want to test and validate their model configurations. Here’s a brief overview: Functionality: The SQL Runner enables you to write and execute SQL queries on the fly. It interacts directly with the data models you've set up in Synmetrix, allowing for real-time data retrieval and analysis. Use Cases: Data Exploration: Quickly run queries to explore data patterns or validate data integrity. Testing Models: Test how new or modified data models perform with actual SQL queries. Advanced Analysis: Perform complex data analysis that might not be possible through standard model exploration. Real-Time Results: Queries executed in the SQL Runner provide immediate feedback, enabling you to rapidly iterate and refine your analysis.","s":"Accessing the SQL Runner in Synmetrix","u":"/docs/user-guide/models","h":"#accessing-the-sql-runner-in-synmetrix","p":1056},{"i":1068,"t":"Open a Model: In sidebar, select and open the model file you wish to edit. Begin Editing: Utilize the Visual Model Editor to make changes to the model. You can adjust various aspects such as dimensions, measures, and relationships. Save Changes or close tab: After completing your edits, ensure to save the model to apply the modifications. note The Visual Model Editor in Synmetrix, enhanced with YAML support, offers an intuitive and user-friendly way to manage data models. YAML's readable format simplifies the process of defining and configuring models, making it accessible even to those with limited coding experience. This combination of a graphical interface and YAML/JS editing provides a flexible and efficient model editing experience, catering to both beginner and advanced users in data model management.","s":"Editing Models in Synmetrix","u":"/docs/user-guide/models","h":"#editing-models-in-synmetrix","p":1056},{"i":1070,"t":"You can upload data model files from your computer to Synmetrix. Easily save and download your data model files from Synmetrix.","s":"Importing or Exporting Data Models","u":"/docs/user-guide/models","h":"#importing-or-exporting-data-models","p":1056},{"i":1072,"t":"Edit and tweak your data models right in Synmetrix with a user-friendly editor.","s":"Using the Visual Model Editor","u":"/docs/user-guide/models","h":"#using-the-visual-model-editor","p":1056},{"i":1074,"t":"Open Sidebar: Start in the sidebar. Find Your Model: Look for the model you want to delete and hover over it. Click the Trash Icon: Hit the trash can icon next to the model. Confirm Delete: Click \"Remove\" to confirm. Done: The model is now deleted.","s":"Deleting a Model","u":"/docs/user-guide/models","h":"#deleting-a-model","p":1056},{"i":1076,"t":"Head to Sidebar: Go to the sidebar in Synmetrix. Choose a Model: Hover over the model you want to change. Edit: Click the pen icon. Make Changes: Change the name and file format (JS or YAML). Finished: Your changes are saved. These steps are a simple way to handle your data models in Synmetrix, whether you're adding, editing, or removing them.","s":"Changing Model Name and File Format","u":"/docs/user-guide/models","h":"#changing-model-name-and-file-format","p":1056},{"i":1079,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"Data Sources Settings","u":"/docs/user-guide/settings/data-sources","h":"","p":1077},{"i":1082,"t":"Click on the \"Connect\" button.","s":"Step 1","u":"/docs/user-guide/settings/data-sources","h":"#step-1","p":1077},{"i":1084,"t":"A list of available datasource types will be displayed. Select the desired type from the options presented.","s":"Step 2","u":"/docs/user-guide/settings/data-sources","h":"#step-2","p":1077},{"i":1086,"t":"In the opened modal window, fill in all the required fields in the form. These fields may include connection details, authentication credentials, and other relevant information. On the screenshot below, you can see a modal window with the datasource creation form. Test Connection To test the connection to the datasource, click the \"Test connection\" button. To save the entered data and create the datasource, click the \"Save\" button. Fast Switching Data Sources in Synmetrix In Synmetrix, switching between data sources is a straightforward process. You can find the data models dropdown in the sidebar menu of both the \"Models\" and \"Explore\" pages. Simply access this menu to view and select from the available data sources. This dropdown is conveniently placed to ensure ease of access, enhancing your experience by allowing quick and efficient transitions between various data repositories within Synmetrix.","s":"Step 3","u":"/docs/user-guide/settings/data-sources","h":"#step-3","p":1077},{"i":1090,"t":"The Members section in your settings allows you to manage the team members who have access to your workspace. This guide provides step-by-step instructions on how to add new members, as well as how to remove existing members.","s":"Overview","u":"/docs/user-guide/settings/members","h":"#overview","p":1087},{"i":1092,"t":"There are three types of members you can add to your account: Owner: The primary account holder with full access and privileges. Admin: Users with administrative rights, including the ability to manage settings and members. Member: Regular users with access to the account but limited control over settings and member management.","s":"Member Types","u":"/docs/user-guide/settings/members","h":"#member-types","p":1087},{"i":1094,"t":"To add a new member to your account, follow these steps: Navigate to Settings > Members in your account. Click the Create Now button. In the popup window, enter the email address of the new member. Select the user type for the new member (either Member or Admin). Click the Invite button. The new member will receive an email with a magic link to sign in to their account.","s":"Adding a New Member","u":"/docs/user-guide/settings/members","h":"#adding-a-new-member","p":1087},{"i":1096,"t":"To remove a member from your account, follow these steps: Go to Settings > Members. Locate the member you wish to remove and find the gear icon on their member card. Click on Delete. Confirm the deletion. After confirming, the member will be removed from your account. The process is complete. This documentation provides a clear and concise guide for managing members within your account settings.","s":"Removing a Member","u":"/docs/user-guide/settings/members","h":"#removing-a-member","p":1087},{"i":1098,"t":"User Guide Settings","s":"Settings","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1100,"t":"📄️ Data Sources The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps: 📄️ Members Managing Members 📄️ Personal Information Overview 📄️ Role Based Access Role Based Access Control 📄️ SQL API SQL API Page 📄️ Teams Introduction","s":"Table of Contents","u":"/docs/user-guide/settings","h":"#table-of-contents","p":1097},{"i":1102,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"📄️ Data Sources","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1104,"t":"Managing Members","s":"📄️ Members","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1106,"t":"Overview","s":"📄️ Personal Information","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1108,"t":"Role Based Access Control","s":"📄️ Role Based Access","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1110,"t":"SQL API Page","s":"📄️ SQL API","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1112,"t":"Introduction","s":"📄️ Teams","u":"/docs/user-guide/settings","h":"","p":1097},{"i":1116,"t":"The \"Personal Info\" section is designed to provide you with full control over your personal settings and security. Here, you can easily update your username, email address, and password, ensuring your account remains secure and personalized. Additionally, you have the option to manage your active sessions, allowing you to logout from all for enhanced security.","s":"Overview","u":"/docs/user-guide/settings/personal-info","h":"#overview","p":1113},{"i":1118,"t":"To change your username: Go to \"Settings\" in the sidebar menu. Navigate to the \"Personal Info\" tab. Locate the \"Full Name\" field under the \"General Information\" section. Click on the \"Full Name\" input field and enter your new username. Click the \"Update\" button. You're all set!","s":"How to Change Your Username?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-change-your-username","p":1113},{"i":1120,"t":"To update your email address: Access \"Settings\" from the sidebar menu. Select the \"Personal Info\" tab. Find the \"Email\" field in the \"General Information\" section. Enter your new email address in the \"Email\" input field. Click the \"Update\" button. Your email is now updated!","s":"How to Change Your Email?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-change-your-email","p":1113},{"i":1122,"t":"To change your password: Visit \"Settings\" from the sidebar menu. Click on the \"Personal Info\" tab. Scroll down to the \"Security\" section. Enter your old password and your desired new password. Click the \"Update Password\" button. Your password has been changed!","s":"How to Change Your Password?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-change-your-password","p":1113},{"i":1124,"t":"To logout from all active sessions: Go to \"Settings\" in the sidebar menu. Choose the \"Personal Info\" tab. Scroll to the \"Active Sessions\" section. Click the \"Logout from all sessions\" button. All sessions have been logged out!","s":"How to Logout from Active Sessions?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-logout-from-active-sessions","p":1113},{"i":1128,"t":"Role Based Access Control (RBAC) is a system for regulating access to resources within your organization based on the roles of individual users. This document outlines how to manage roles and the types of access they grant.","s":"Overview","u":"/docs/user-guide/settings/role-access","h":"#overview","p":1125},{"i":1130,"t":"Partial Access: Grants access to specific Data Sources, Data Models, and various Measures, Dimensions, or Segments. Full Access: Provides unrestricted access to all resources. No Access: Denies access to all resources.","s":"Access Types","u":"/docs/user-guide/settings/role-access","h":"#access-types","p":1125},{"i":1132,"t":"To add a new role, follow these steps: Navigate to Settings > Roles and Access. Click on the Create Now button. In the popup window: Enter the role name. Set access for data source resources by selecting one or multiple data sources. Choose Data Models. Select Measures, Dimensions, and/or Segments. tip Use the \"Select All\" checkbox to select all Measures, Dimensions, and Segments in the current Data Model. Click Create. info Roles display information including the creation date, last update, and data sources along with their respective access types.","s":"Adding a New Role","u":"/docs/user-guide/settings/role-access","h":"#adding-a-new-role","p":1125},{"i":1134,"t":"To delete a role, follow these steps: Go to Settings > Roles and Access. Find the role to be deleted and click the gear icon on their card. Choose Delete from the dropdown list and confirm the deletion. The deletion process is complete.","s":"Deleting a Role","u":"/docs/user-guide/settings/role-access","h":"#deleting-a-role","p":1125},{"i":1136,"t":"To edit an existing role, follow these steps: Navigate to Settings > Roles and Access. Locate the role to edit and click the gear icon on their card. Choose Edit from the dropdown list. In the popup window, make the desired changes: Update the role name. Adjust access to data source resources. Select or change Data Models. Choose Measures, Dimensions, and/or Segments. tip Utilize the \"Select All\" checkbox for selecting all items in the current Data Model. Click Save to apply the changes. This document provides clear guidelines for managing roles within the Role Based Access Control system, ensuring efficient and secure access management.","s":"Editing a Role","u":"/docs/user-guide/settings/role-access","h":"#editing-a-role","p":1125},{"i":1140,"t":"Engage in powerful and flexible data retrieval and manipulation with our SQL API, designed for effortless data management and seamless collaboration.","s":"Effortless Data Management and Uninterrupted Collaboration","u":"/docs/user-guide/settings/sql-api","h":"#effortless-data-management-and-uninterrupted-collaboration","p":1137},{"i":1142,"t":"Synmetrix revolutionizes data sharing with its versatile protocol, ensuring smooth integration with a variety of data tools. Our platform seamlessly connects with business intelligence and data exploration tools (Superset, Tableau), data notebooks (Jupyter, Hex), reverse ETL tools (Census, Hightouch), low-code platforms (Retool), and more, enhancing efficiency and simplicity in data-driven tasks.","s":"Simplified Data Sharing","u":"/docs/user-guide/settings/sql-api","h":"#simplified-data-sharing","p":1137},{"i":1144,"t":"Synmetrix serves as the ultimate repository for all your data needs, centralizing metrics in an organized manner. Regardless of your preferred tools or platforms for data analysis or visualization, Synmetrix provides easy access to essential data.","s":"One-Stop Metrics Solution","u":"/docs/user-guide/settings/sql-api","h":"#one-stop-metrics-solution","p":1137},{"i":1146,"t":"Integrations: Seamlessly integrate into your workflow, including Excel compatibility. Flexibility: Encourage collaborative efforts with shared models in a harmonious team environment. Security: Rely on our robust and reliable security measures. Effortless: Tailor SQL queries to meet your unique requirements.","s":"Key Benefits","u":"/docs/user-guide/settings/sql-api","h":"#key-benefits","p":1137},{"i":1148,"t":"Obtain your SQL API credentials through two distinct methods:","s":"Acquiring SQL Credentials","u":"/docs/user-guide/settings/sql-api","h":"#acquiring-sql-credentials","p":1137},{"i":1150,"t":"Initiate a new account creation. Add your data source. Opt to generate models (optional). Synmetrix generates SQL API access during this step. A pop-up allows you to choose between PSQL or MYSQL for connection, along with credential copying. Connect using PSQL or MYSQL, or opt to \"Download Credentials\" for later use. Finish the onboarding.","s":"First Method: From Onboarding","u":"/docs/user-guide/settings/sql-api","h":"#first-method-from-onboarding","p":1137},{"i":1152,"t":"Navigate to Settings > SQL API. Click the gear icon on the database card and select \"Edit\". The pop-up window provides PSQL or MYSQL connection options and credential copying. Example Credentials Host/URL: cube.synmetrix.org:15432 Database: db Login: 4x4qg8ei76 Password: ********** PSQL Client Connection: psql --host=cube.synmetrix.org --port 15432 --username=4x4qg8ei76 --dbname=db Connect using PSQL or MYSQL, or \"Download Credentials\" for later use.","s":"Second Method: From Settings","u":"/docs/user-guide/settings/sql-api","h":"#second-method-from-settings","p":1137},{"i":1154,"t":"Easily create, edit, or delete SQL API credentials via Settings > SQL API.","s":"Managing SQL API Credentials","u":"/docs/user-guide/settings/sql-api","h":"#managing-sql-api-credentials","p":1137},{"i":1156,"t":"Click the gear icon and select \"Delete\" from the dropdown. Confirm deletion.","s":"Deleting Credentials","u":"/docs/user-guide/settings/sql-api","h":"#deleting-credentials","p":1137},{"i":1158,"t":"Select the \"Create Now\" button on the SQL API page. In the pop-up, specify the Team member and Data source.","s":"Creating Credentials","u":"/docs/user-guide/settings/sql-api","h":"#creating-credentials","p":1137},{"i":1160,"t":"Click the gear icon and select \"Edit\". Modify the credentials as needed. Confirm by clicking \"Finish\".","s":"Editing Credentials","u":"/docs/user-guide/settings/sql-api","h":"#editing-credentials","p":1137},{"i":1164,"t":"The \"Teams\" section of Synmetrix is a dedicated space for creating, managing, and customizing teams. This feature is essential for collaborative work, allowing you to form new teams, set a focus on a current team, remove teams that are no longer needed, and update team names to align with evolving projects or goals.","s":"Introduction","u":"/docs/user-guide/settings/teams","h":"#introduction","p":1161},{"i":1166,"t":"To navigate to the Team page: Hover over your account profile picture at the top-right corner of the page. Select \"Team\" from the dropdown menu.","s":"Accessing the Team Page","u":"/docs/user-guide/settings/teams","h":"#accessing-the-team-page","p":1161},{"i":1168,"t":"On the Team page, you can perform various actions:","s":"Team Page Functions","u":"/docs/user-guide/settings/teams","h":"#team-page-functions","p":1161},{"i":1170,"t":"You can invite new members to join your project by entering their email addresses. This step is crucial for expanding your team and fostering collaboration.","s":"1. Inviting New Users","u":"/docs/user-guide/settings/teams","h":"#1-inviting-new-users","p":1161},{"i":1172,"t":"Based on your access management system, assign appropriate roles and access levels to team members, ensuring they have the necessary permissions to contribute effectively.","s":"2. Assigning Roles and Access Levels","u":"/docs/user-guide/settings/teams","h":"#2-assigning-roles-and-access-levels","p":1161},{"i":1174,"t":"Control access to specific features and resources for each team member, tailoring their permissions to suit their role and needs in the project.","s":"3. Managing Access","u":"/docs/user-guide/settings/teams","h":"#3-managing-access","p":1161},{"i":1176,"t":"The Team page provides a comprehensive view of all team members, including their roles and responsibilities, allowing for efficient team management.","s":"4. Viewing and Managing Team Members","u":"/docs/user-guide/settings/teams","h":"#4-viewing-and-managing-team-members","p":1161},{"i":1178,"t":"To create a new team: Open \"Settings\" from the sidebar. Click on the \"Teams\" tab. In \"Manage Teams\", view your existing teams. Select the gear icon (\"Settings\"). Choose \"Create Team\" from the dropdown and follow the prompts.","s":"How to Create a New Team?","u":"/docs/user-guide/settings/teams","h":"#how-to-create-a-new-team","p":1161},{"i":1180,"t":"To set an active team: Go to \"Settings\" in the sidebar. Choose the \"Teams\" tab. Find your team list in \"Manage Teams\". Click the gear icon next to the team you want to focus on. Select \"Set Current\" from the dropdown.","s":"How to Set a Current Team?","u":"/docs/user-guide/settings/teams","h":"#how-to-set-a-current-team","p":1161},{"i":1182,"t":"To change a team's name: Navigate to \"Settings\" via the sidebar. Select the \"Teams\" tab. Find your team in \"Manage Teams\". Click the gear icon next to the team you want to rename. Choose \"Edit\" from the dropdown. Change the \"Team Name\" in the pop-up. Click \"Save\" to confirm the changes.","s":"How to Edit a Team Name?","u":"/docs/user-guide/settings/teams","h":"#how-to-edit-a-team-name","p":1161},{"i":1184,"t":"To remove a team: Access \"Settings\" from the sidebar. Go to the \"Teams\" tab. Identify the team you want to delete in \"Manage Teams\". Click the gear icon next to the team. Choose \"Delete\" from the dropdown list. I've refined the language for professional tone and clarity, ensuring each step is clear and straightforward.","s":"How to Delete a 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page example You don't need React to write simple standalone pages.","s":"Markdown page example","u":"/markdown-page","h":"","p":1},{"i":4,"t":"Architecture and design","s":"Architecture and Design","u":"/docs/next/architecture-and-design","h":"","p":3},{"i":6,"t":"Database Structure","s":"📄️ Database Design","u":"/docs/next/architecture-and-design","h":"","p":3},{"i":8,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"📄️ System Architecture","u":"/docs/next/architecture-and-design","h":"","p":3},{"i":10,"t":"APIs & integrations","s":"APIs & integrations","u":"/docs/next/apis-integrations","h":"","p":9},{"i":14,"t":"Architecture and design Database Design","s":"Database Structure Design","u":"/docs/next/architecture-and-design/database-design","h":"","p":13},{"i":16,"t":"The \"Synmetrix\" database is organized according to the relational database model and includes the following tables: public.users: Stores information about registered users, including user ID (id), display name (display_name), avatar URL (avatar_url), as well as the creation time and last update time of the record. auth.account_providers: Describes the relationships between user accounts and their authentication providers. auth.accounts: Stores information about user accounts, including unique identifiers, email addresses, passwords, and other data. auth.providers: Provides a list of available authentication providers. auth.refresh_tokens: Contains information about refresh tokens for each user account. auth.roles: Manages user roles. auth.account_roles: Contains information about roles for each account. public.teams: Stores information about user teams. public.datasources: Contains information about data sources used by users. public.dataschemas: Describes data models used to define business metrics for data sources. public.explorations: Describes research tasks performed by users. public.members: Stores information about team members. public.team_roles: Manages user roles within teams. public.member_roles: Contains information about roles for each team member. public.reports: Contains information about the structure and schedule of reports based on metrics needed by users. public.sql_credentials: Manages SQL credentials used to access business metrics through the SQL interface. public.alerts: Stores information about alerts created by users.","s":"Database Structure","u":"/docs/next/architecture-and-design/database-design","h":"#database-structure","p":13},{"i":18,"t":"The architecture of the database, including relationships between tables, primary and foreign keys, and indexes, is represented in the Database Markup Language (DBML). This database structure provides flexibility and scalability to the system, allowing convenient management of users, teams, data sources, reports, and other system elements. Each database table is designed for a specific purpose and can be extended or modified to meet evolving system requirements. note For the complete DBML representation of the database architecture, please refer to Database Markup Language (DBML).","s":"Database Architecture Description","u":"/docs/next/architecture-and-design/database-design","h":"#database-architecture-description","p":13},{"i":21,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"Synmetrix System Architecture","u":"/docs/next/architecture-and-design/system-architecture","h":"","p":19},{"i":23,"t":"Hasura plays a crucial role in the system by connecting to the database and providing a GraphQL API for external services and applications. Hasura simplifies working with the database, offering intuitive and powerful tools for quickly creating queries and mutations in GraphQL, thereby enhancing performance and optimizing data-related workflows.","s":"Hasura","u":"/docs/next/architecture-and-design/system-architecture","h":"#hasura","p":19},{"i":25,"t":"PostgreSQL has been chosen as the primary database management system due to its reliability, high performance, and flexibility. This allows for efficient handling of large volumes of data while ensuring excellent performance and stability.","s":"PostgreSQL","u":"/docs/next/architecture-and-design/system-architecture","h":"#postgresql","p":19},{"i":27,"t":"Node.js is the server-side platform used to create the backend of the application. Node.js is known for its ability to easily and rapidly develop scalable networked applications, thanks to efficient handling of asynchronous operations and event processing.","s":"Node.js","u":"/docs/next/architecture-and-design/system-architecture","h":"#nodejs","p":19},{"i":29,"t":"React.js is a library for developing user interfaces. It provides high performance and simplifies the development process through the use of a component-based architecture.","s":"React.js","u":"/docs/next/architecture-and-design/system-architecture","h":"#reactjs","p":19},{"i":31,"t":"Cube.js is an open-source analytics platform for building business analytics applications using JavaScript. In Synmetrix, Cube.js is used to manage business metrics, providing efficient tools for data processing.","s":"Cube.js","u":"/docs/next/architecture-and-design/system-architecture","h":"#cubejs","p":19},{"i":33,"t":"CubeStore is a distributed database optimized for analytical queries and integrated with Cube.js. It offers capabilities for fast and efficient processing of large volumes of data.","s":"CubeStore","u":"/docs/next/architecture-and-design/system-architecture","h":"#cubestore","p":19},{"i":35,"t":"Redis is a key-value database management system known for its high performance and flexibility. It offers unique features, including support for various data structures such as strings, lists, sets, hashes, and more.","s":"Redis","u":"/docs/next/architecture-and-design/system-architecture","h":"#redis","p":19},{"i":37,"t":"Docker is used for containerization and simplifying the application deployment process, ensuring consistent functionality in any environment.","s":"Docker","u":"/docs/next/architecture-and-design/system-architecture","h":"#docker","p":19},{"i":39,"t":"Docker Swarm is an orchestration and management tool for containers deployed using Docker. It allows for the management and scaling of applications across multiple servers, making infrastructure management easier.","s":"Docker Swarm","u":"/docs/next/architecture-and-design/system-architecture","h":"#docker-swarm","p":19},{"i":41,"t":"Ubuntu has been chosen as the primary operating system for the servers hosting all the services. It is a stable and reliable system well-suited for server environments.","s":"Ubuntu","u":"/docs/next/architecture-and-design/system-architecture","h":"#ubuntu","p":19},{"i":43,"t":"Additionally, Synmetrix has been designed as a microservices-based system. Each microservice performs a specific function and can scale independently of others, providing flexibility and scalability to the system. This architecture allows for the independent updating and modernization of individual system components, simplifying the development and maintenance process of the system as a whole.","s":"Interactions between architecture components","u":"/docs/next/architecture-and-design/system-architecture","h":"#interactions-between-architecture-components","p":19},{"i":46,"t":"Synmetrix offers a robust caching system with two distinct layers to optimize query performance and minimize database load. This system enhances the efficiency of data retrieval and processing. The primary caching layer is an in-memory cache, which is active by default. The second layer is called \"pre-aggregations\" and requires explicit configuration to activate.","s":"Overview","u":"/docs/next/caching","h":"","p":44},{"i":48,"t":"Synmetrix's in-memory cache serves as a temporary buffer for your database. It becomes especially valuable when multiple concurrent users request the same data simultaneously. While pre-aggregations are designed to strike a balance between query response time and performance, the in-memory cache helps smooth out spikes in data demand. In development mode, you can reset the in-memory cache by simply restarting the server. Note: It's generally not recommended to modify the default in-memory caching configuration unless it's absolutely necessary. To improve query performance, consider utilizing pre-aggregations.","s":"In-Memory Cache","u":"/docs/next/caching","h":"#in-memory-cache","p":44},{"i":50,"t":"Pre-aggregations introduce an additional layer of aggregated data, constructed and updated by Synmetrix. They can significantly enhance query performance and concurrency. To create pre-aggregations, Synmetrix might require write access to the pre-aggregations schema within the source database. In this scenario, Synmetrix initially builds pre-aggregations as tables in the source database and then exports them to the pre-aggregations storage. Refer to your specific driver's documentation for details on read-only support and pre-aggregation build strategies. Pre-aggregations are defined within the data model. You can learn more about defining pre-aggregations in the data modeling reference. Using YAML Using JavaScript cubes: - name: orders sql_table: orders measures: - name: total_amount sql: amount type: sum dimensions: - name: created_at sql: created_at type: time pre_aggregations: - name: amount_by_created measures: - total_amount time_dimension: created_at granularity: month cube(`orders`, { sql_table: `orders`, measures: { total_amount: { sql: `amount`, type: `sum`, }, }, dimensions: { created_at: { sql: `created_at`, type: `time`, }, }, pre_aggregations: { amount_by_created: { measures: [total_amount], time_dimension: created_at, granularity: `month`, }, },","s":"Pre-Aggregations","u":"/docs/next/caching","h":"#pre-aggregations","p":44},{"i":52,"t":"Synmetrix utilizes an in-memory cache to store the results of executed queries. The cache key is generated from the SQL statement, including any existing pre-aggregations that the query depends on. Upon receiving an incoming request, Synmetrix first checks the cache using this key. If no match is found in the cache, the query is executed in the database. The result set is returned and simultaneously updates the cache. If a cached value exists, and the refresh_key value for the query remains unchanged, the cached value is returned. Otherwise, an SQL query is executed either against the pre-aggregations storage or the source database to refresh the cache and retrieve the updated results.","s":"In-Memory Cache Operation","u":"/docs/next/caching","h":"#in-memory-cache-operation","p":44},{"i":54,"t":"To avoid unnecessary database queries, Synmetrix defines a refresh_key for each cube. These refresh keys are evaluated to determine if data needs to be refreshed. Here's an example of a refresh_key that instructs Synmetrix to refresh data every 5 minutes: YAML cubes: - name: orders # ... refresh_key: every: 5 minutes JavaScript cube(`orders`, { refresh_key: { every: `5 minute`, },}); Alternatively, the following refresh_key only refreshes data when the value of MAX(created_at) changes. By default, Synmetrix checks this refresh key every 10 seconds: YAML cubes: - name: orders # ... refresh_key: sql: SELECT MAX(created_at) FROM orders JavaScript cube(`orders`, { // ... refresh_key: { sql: `SELECT MAX(created_at) FROM orders`, },});","s":"Refresh Keys","u":"/docs/next/caching","h":"#refresh-keys","p":44},{"i":56,"t":"By default, in development mode, Synmetrix will check and invalidate the cache in the background. For production environments, it's recommended to run a Refresh Worker as a separate instance. We strongly recommend enabling background cache invalidation using a separate Synmetrix worker for production deployments. Please consult the Production Checklist for more information. If background refresh is disabled, Synmetrix will refresh the cache during query execution. To ensure optimal response times for end-users, it's advisable to always enable background refresh.","s":"Default Refresh Keys","u":"/docs/next/caching","h":"#default-refresh-keys","p":44},{"i":58,"t":"To gain insights into whether a query interacts with the in-memory cache, pre-aggregations, or the underlying data source, you can utilize the Synmetrix Playground or Synmetrix Cloud.","s":"Inspecting Queries","u":"/docs/next/caching","h":"#inspecting-queries","p":44},{"i":60,"t":"Caching Getting started with pre-aggregations","s":"Getting started with pre-aggregations","u":"/docs/next/caching/getting-started-with-pre-aggregations","h":"","p":59},{"i":62,"t":"To illustrate pre-aggregations with an example, let's use a sample e-commerce database. We have a data model representing all our orders: YAML cubes: - name: orders sql_table: orders measures: - name: count type: count dimensions: - name: id sql: id type: number primary_key: true - name: status sql: status type: string - name: completed_at sql: completed_at type: time JavaScript cube(`orders`, { sql_table: `orders`, measures: { count: { type: `count`, }, }, dimensions: { id: { sql: `id`, type: `number`, primary_key: true, }, status: { sql: `status`, type: `string`, }, completed_at: { sql: `completed_at`, type: `time`, }, },}); Some sample data from this table might look like: id status completed_at 1 completed 2021-02-15T12:21:11.290 2 completed 2021-02-25T18:15:12.369 3 shipped 2021-03-15T20:40:57.404 4 processing 2021-03-13T10:30:21.360 5 completed 2021-03-10T18:25:32.109 Our first requirement is to populate a dropdown in our front-end application which shows all possible statuses. The Cube query to retrieve this information might look something like: JSON { \"dimensions\": [\"orders.status\"]} In that case, we can add the following pre-aggregation to the orders cube: YAML cubes: - name: orders # ... pre_aggregations: - name: order_statuses dimensions: - status JavaScript cube(`orders`, { // ... pre_aggregations: { order_statuses: { dimensions: [status], }, },});","s":"Pre-Aggregations without Time Dimension","u":"/docs/next/caching/getting-started-with-pre-aggregations","h":"#pre-aggregations-without-time-dimension","p":59},{"i":64,"t":"Development","s":"Development","u":"/docs/next/development","h":"","p":63},{"i":66,"t":"Requirements","s":"📄️ Local Development","u":"/docs/next/development","h":"","p":63},{"i":68,"t":"This guide provides information on how to contribute to Synmetrix, including code changes, helping other users, documentation updates, bug reports, bug fixes, new feature proposals, and coding standards.","s":"📄️ Contributing","u":"/docs/next/development","h":"","p":63},{"i":70,"t":"Development Contributing","s":"Contributing to Synmetrix","u":"/docs/next/development/contributing","h":"","p":69},{"i":72,"t":"One of the most valuable ways to contribute to Synmetrix is by helping answer user questions on the discussion forum or in the Slack channel. There are always new Synmetrix users seeking assistance, and providing answers is a valuable community service that showcases your expertise.","s":"Helping Other Users","u":"/docs/next/development/contributing","h":"#helping-other-users","p":69},{"i":74,"t":"To propose changes to Synmetrix documentation, edit the documentation source files located in the docs/ directory. The README file there explains how to build the documentation locally to test your changes. After making modifications, open a pull request to suggest the changes.","s":"Contributing to Documentation","u":"/docs/next/development/contributing","h":"#contributing-to-documentation","p":69},{"i":76,"t":"Reporting a bug is a simple and effective way to contribute to Synmetrix. It helps identify issues, enabling the team to provide patches and enhance the stability and usefulness of Synmetrix. Use the \"New issue\" button in the issues page to report a bug. Ensure your bug report includes a minimal executable test case to replicate the issue. Follow the instructions in the bug report template provided.","s":"Reporting Bugs","u":"/docs/next/development/contributing","h":"#reporting-bugs","p":69},{"i":78,"t":"Contributing bug fixes is an excellent way to build your experience and credibility within the community. If you are new to the Synmetrix codebase, start by looking at issues labeled with help wanted. If you need guidance on fixing an issue, seek assistance from experienced contributors by commenting on the issue page.","s":"Contributing Bug Fixes","u":"/docs/next/development/contributing","h":"#contributing-bug-fixes","p":69},{"i":80,"t":"Before contributing a new feature, submit a proposal in the issues page and discuss it with the community. This helps identify potential overlaps with planned features and prevents misunderstandings and conflicts.","s":"Proposing New Features","u":"/docs/next/development/contributing","h":"#proposing-new-features","p":69},{"i":82,"t":"When submitting code contributions, you must sign a Developer Certificate of Origin (DCO) to certify your authorship of the source code or your right to submit it to the project. Sign your contributions by adding a Signed-off-by line to your commit message or using the -s option with the git commit command. This is my commit messageSigned-off-by: Random J Developer ","s":"Submitting Code Changes","u":"/docs/next/development/contributing","h":"#submitting-code-changes","p":69},{"i":85,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster. Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.","s":"Docker Swarm deployment guide","u":"/docs/next/docker-swarm","h":"","p":83},{"i":87,"t":"Go to the Synmetrix project directory and run the following commands:","s":"Start Synmetrix on Docker Swarm","u":"/docs/next/docker-swarm","h":"#start-synmetrix-on-docker-swarm","p":83},{"i":89,"t":"./scripts/run-registry.sh","s":"Step 1 - Run Docker Registry","u":"/docs/next/docker-swarm","h":"#step-1---run-docker-registry","p":83},{"i":91,"t":"DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --build --env stage synmetrix Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.","s":"Step 2 - Run Synmetrix services","u":"/docs/next/docker-swarm","h":"#step-2---run-synmetrix-services","p":83},{"i":93,"t":"./migrate.sh","s":"Step 3 - Run migrations","u":"/docs/next/docker-swarm","h":"#step-3---run-migrations","p":83},{"i":95,"t":"./cli.sh swarm destroy synmetrix","s":"Destroy stack","u":"/docs/next/docker-swarm","h":"#destroy-stack","p":83},{"i":97,"t":"./cli.sh swarm logs synmetrix_hasura","s":"Show logs","u":"/docs/next/docker-swarm","h":"#show-logs","p":83},{"i":99,"t":"./cli.sh swarm ps synmetrix For more information about the CLI tool, run the following command: ./cli.sh swarm --help","s":"Show stack tasks status","u":"/docs/next/docker-swarm","h":"#show-stack-tasks-status","p":83},{"i":101,"t":"Development Local Development","s":"Local Development","u":"/docs/next/development/local-development","h":"","p":100},{"i":103,"t":"Before using the backend-side of the project, ensure that you have the following components installed: Docker Node.js (Version 20.8.1 or above) Yarn","s":"Requirements","u":"/docs/next/development/local-development","h":"#requirements","p":100},{"i":106,"t":"Clone the Synmetrix repository: git clone https://github.com/mlcraft-io/mlcraft.gitcd mlcraft Run the initialization script: bash init.sh","s":"Getting Started","u":"/docs/next/development/local-development","h":"#getting-started","p":100},{"i":109,"t":"Before using the client-side of the project, ensure that you have the following components installed: Synmetrix Backend: Ensure the Synmetrix backend is properly set up and running.","s":"Requirements","u":"/docs/next/development/local-development","h":"#requirements-1","p":100},{"i":111,"t":"To install the client-side of the project, follow these steps: # Clone the Synmetrix Client repositorygit clone https://github.com/mlcraft-io/client-v2# Navigate to the project directorycd client-v2# Install required packages using Yarnyarn# Start the client-side applicationyarn start After completing these steps, open your web browser and go to to access the Synmetrix client.","s":"Getting Started","u":"/docs/next/development/local-development","h":"#getting-started-1","p":100},{"i":113,"t":"For detailed guidance on utilizing the Synmetrix client and exploring its features, please consult the Synmetrix Documentation.","s":"Usage","u":"/docs/next/development/local-development","h":"#usage","p":100},{"i":115,"t":"Explore the interactive UI components and functionalities of the Synmetrix client using Storybook. Storybook provides a convenient environment to showcase and test UI components in isolation. To launch Storybook, use the following command: yarn storybook Once the command is executed, navigate to","s":"Storybook Integration","u":"/docs/next/development/local-development","h":"#storybook-integration","p":100},{"i":117,"t":"Maintain code consistency and quality in the Synmetrix client by implementing linting. Follow these steps to configure linting in Visual Studio Code (VS Code): Requirements:​ ESLint Extension Prettier Extension Configuration:​ Install ESLint and Prettier VS Code extensions. Open your VS Code settings (settings.json) and add the following configuration: { \"eslint.enable\": true, \"eslint.format.enable\": true, \"editor.formatOnSave\": true} This configures VS Code to run ESLint and Prettier on file save.","s":"Code Linting","u":"/docs/next/development/local-development","h":"#code-linting","p":100},{"i":119,"t":"In the Synmetrix project, components follow a consistent structure to enhance maintainability and organization. Each component typically consists of the following files: index.tsx: The main file containing the component's implementation. This file includes the React component code. index.module.less: A Less file containing the component's styles. Using module.less ensures that styles are scoped to the component and won't conflict with styles from other components. index.stories.tsx: A file dedicated to Storybook stories for the component. Storybook stories help in visually testing and documenting the component's variations and use cases. index.test.tsx: The file containing unit tests for the component using testing framework vitest. Writing tests ensures the reliability and correctness of the component's functionality. Here's an example directory structure for a component named ExampleComponent: /src /components /ExampleComponent index.tsx index.module.less index.stories.tsx index.test.tsx","s":"Component Structure","u":"/docs/next/development/local-development","h":"#component-structure","p":100},{"i":121,"t":"Ensure the reliability and correctness of the Synmetrix client by utilizing testing functionalities powered by Vitest. Vitest provides a robust testing framework to validate the behavior of your components and functionalities. To run tests, use the following command: yarn test Executing this command will initiate the testing suite, allowing you to assess the client's performance and functionality. Any detected issues or failures will be highlighted, providing valuable insights for debugging and maintaining code quality.","s":"Testing","u":"/docs/next/development/local-development","h":"#testing","p":100},{"i":123,"t":"Efficiently build and package the Synmetrix client with the following commands: Build the Client: yarn build This command utilizes Vite to build the Synmetrix client. Post-Build Tasks: yarn postbuild After the build, this command creates compressed archives (dist.tar.gz and dist.zip) of the generated distribution files. Build Storybook: yarn build-storybook Use this command to build the Storybook for the client. Serve the Built Client Locally: yarn serve This command previews the built client locally using Vite. Incorporate these commands into your workflow to streamline the build process and prepare the Synmetrix client for deployment.","s":"Build","u":"/docs/next/development/local-development","h":"#build","p":100},{"i":125,"t":"We adhere to the Conventional Commits specification to ensure consistent and meaningful commit messages. The structure of our commits follows the pattern: [optional scope]: [optional body][optional footer(s)] For a detailed understanding of Conventional Commits, refer to the Conventional Commits specification.","s":"Conventional Commits","u":"/docs/next/development/local-development","h":"#conventional-commits","p":100},{"i":127,"t":"APIs & integrations Excel","s":"Excel","u":"/docs/next/integration-examples/excel","h":"","p":126},{"i":129,"t":"License License & Community","s":"License and Community","u":"/docs/next/license/license-community","h":"","p":128},{"i":131,"t":"License","s":"License","u":"/docs/next/license","h":"","p":130},{"i":133,"t":"MIT License","s":"📄️ License & Community","u":"/docs/next/license","h":"","p":130},{"i":135,"t":"APIs & integrations Power BI","s":"Power BI","u":"/docs/next/integration-examples/power-bi","h":"","p":134},{"i":137,"t":"Links and Contacts","s":"Links and Contacts","u":"/docs/next/links-and-contacts","h":"","p":136},{"i":139,"t":"GitHub Repository Slack Channel Docker Hub Meeting Calendar","s":"Useful Links and Contacts","u":"/docs/next/links-and-contacts","h":"#useful-links-and-contacts","p":136},{"i":141,"t":"Overview","s":"Overview","u":"/docs/next/overview","h":"","p":140},{"i":143,"t":"In today's data-driven world, the challenges of data management are ever-increasing, especially in fields that rely heavily on data processing, analysis, and transmission. Inefficient data management can result in lost productivity, missed opportunities, and even regulatory compliance issues. The need for a comprehensive solution to address these complexities has never been more pressing. Synmetrix is committed to tackling this challenge head-on and providing a robust solution to streamline data management, analysis, and transmission.","s":"The Data Management Challenge","u":"/docs/next/overview","h":"#the-data-management-challenge","p":140},{"i":145,"t":"With Synmetrix, you can effortlessly address the complexity and inefficiency in managing, analyzing, and transmitting data across various sectors. Our platform offers powerful features for creating and managing data models, tracking metric changes, and organizing data-related tasks. You can easily share your data with others through interfaces like REST, GraphQL, or SQL, making your data a single source of truth for your entire organization. Whether you're a data engineer, analyst, or manager, Synmetrix has everything you need to excel with big data.","s":"Software Description and Purpose","u":"/docs/next/overview","h":"#software-description-and-purpose","p":140},{"i":147,"t":"Synmetrix's versatile features find applications across a wide range of sectors, including finance, retail, the power industry, manufacturing, research and development, healthcare, and many more. As the global big data market continues to boom, with projections reaching $103 billion by 2027 and a compound annual growth rate of 10.9%, Synmetrix is poised to seize this massive opportunity. We're here to provide the solution to the complexity and inefficiency in data management, analysis, and transmission.","s":"Tapping into a Vast Market","u":"/docs/next/overview","h":"#tapping-into-a-vast-market","p":140},{"i":149,"t":"Data Modeling (Semantic layer): Transform raw data into meaningful business indicators with our effective data modeling feature. SQL Interface: Access metrics easily and manipulate data with our intuitive SQL interface. Reporting System: Schedule reports and distribute them through various communication channels with our integrated reporting system. Alerting System: Stay informed about significant data changes with our proactive alerting system. Documentation Generation: Enhance the understanding and usability of your data models with our documentation generation feature.","s":"Key Features","u":"/docs/next/overview","h":"#key-features","p":140},{"i":151,"t":"JavaScript / Node.js","s":"Programming Language","u":"/docs/next/overview","h":"#programming-language","p":140},{"i":153,"t":"To make the most of this documentation, you can explore the following sections: Quick Start: Installation and initial setup of Synmetrix. User Guide: In-depth guides on using Synmetrix. Contributing: Information on how to contribute to the development of Synmetrix.","s":"How to Use This Documentation","u":"/docs/next/overview","h":"#how-to-use-this-documentation","p":140},{"i":155,"t":"We welcome contributions from the community. If you'd like to get involved in the development of Synmetrix or have ideas for improvement, please check out our Contributing Guidelines.","s":"Contributing","u":"/docs/next/overview","h":"#contributing","p":140},{"i":157,"t":"If you have questions, feedback, or need assistance, don't hesitate to reach out to us. Contact us. or by visiting our GitHub repository. Synmetrix is meticulously designed to streamline your data management, analysis, and transmission workflows, making it the ultimate tool for all your data needs. We look forward to your involvement and contributions!","s":"Contact Us","u":"/docs/next/overview","h":"#contact-us","p":140},{"i":159,"t":"Production Deployment","s":"Production Deployment","u":"/docs/next/production-deployment","h":"","p":158},{"i":161,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.","s":"📄️ Docker Swarm","u":"/docs/next/production-deployment","h":"","p":158},{"i":163,"t":"Software Requirements","s":"📄️ Requirements","u":"/docs/next/production-deployment","h":"","p":158},{"i":165,"t":"Production Deployment Requirements","s":"Requirements","u":"/docs/next/production-deployment/requirements","h":"","p":164},{"i":167,"t":"Docker Node.js (Version 20.8.1 or above) Yarn","s":"Software Requirements","u":"/docs/next/production-deployment/requirements","h":"#software-requirements","p":164},{"i":169,"t":"Synmetrix is intended for installation and use on servers equipped with processors supporting x86-64 architecture and based on operating systems such as Ubuntu Server 18.04 and higher. Minimum Hardware Requirements: - Processor (CPU): 3.2 GHz or higher, modern processor with multi-threading and virtualization support.- RAM: 8 GB or more to handle computational tasks and data processing.- Disk Space: At least 30 GB of free space for software installation and storing working data.- Network: Internet connectivity is required for cloud services and software updates. note These requirements are minimum and may vary depending on specific use cases and data processing volumes. Scaling the solution may require more powerful servers or distributed systems.","s":"Hardware Requirements","u":"/docs/next/production-deployment/requirements","h":"#hardware-requirements","p":164},{"i":171,"t":"You can access the source code of Synmetrix on GitHub.","s":"Source Code","u":"/docs/next/production-deployment/requirements","h":"#source-code","p":164},{"i":173,"t":"Quick Start","s":"Quick Start","u":"/docs/next/quickstart","h":"","p":172},{"i":175,"t":"Ensure the following software is installed before proceeding: Docker Docker Compose","s":"Prerequisite Software","u":"/docs/next/quickstart","h":"#prerequisite-software","p":172},{"i":177,"t":"The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository: Execute this in a new directory Using wget Using curl wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml curl https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml -o docker-compose.yml","s":"Step 1: Download the docker-compose file","u":"/docs/next/quickstart","h":"#step-1-download-the-docker-compose-file","p":172},{"i":179,"t":"Execute the following command to start Synmetrix along with a Postgres database for data storage. $ docker-compose pull stack && docker-compose up -d Verify if the containers are operational: $ docker psCONTAINER ID IMAGE ... CREATED STATUS PORTS ...c8f342d086f3 synmetrix/stack ... 1m ago Up 1m 80->8888/tcp ...30ea14ddaa5e postgres:12 ... 1m ago Up 1m 5432/tcp The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.","s":"Step 2: Launch Synmetrix","u":"/docs/next/quickstart","h":"#step-2-launch-synmetrix","p":172},{"i":181,"t":"You can access Synmetrix at http://localhost/ The GraphQL endpoint is located at http://localhost/v1/graphql The Admin Console (Hasura Console) can be found at http://localhost/console The Cube Swagger API can be found at http://localhost:4000/docs note Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated. Preloaded Seed Data: The project is equipped with preloaded seed data. Use the credentials below to sign in: Email: demo@synmetrix.org Password: demodemo This account is pre-configured with two demo datasources and their respective SQL API access. For SQL operations, you can use the following credentials with any SQL client tool such as DBeaver or TablePlus: Host Port Database User Password localhost 15432 db demo_pg_user demo_pg_pass localhost 15432 db demo_clickhouse_user demo_clickhouse_pass If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.","s":"Step 3: Explore Synmetrix","u":"/docs/next/quickstart","h":"#step-3-explore-synmetrix","p":172},{"i":183,"t":"Demo: app.synmetrix.org Login: demo@synmetrix.org Password: demodemo","s":"Demo online","u":"/docs/next/quickstart","h":"#demo-online","p":172},{"i":185,"t":"Database type Host Port Database User Password SSL ClickHouse gh-api.clickhouse.tech 443 default play no password true PostgreSQL demo-db-examples.cube.dev 5432 ecom cube 12345 false","s":"Demo datasource credentials","u":"/docs/next/quickstart","h":"#demo-datasource-credentials","p":172},{"i":187,"t":"Testing and Debugging Testing Guides","s":"Testing Guides","u":"/docs/next/testing","h":"","p":186},{"i":189,"t":"12 items","s":"🗃️ Test a Data Source","u":"/docs/next/testing","h":"","p":186},{"i":191,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"📄️ Test SQL Interface for Data Models","u":"/docs/next/testing","h":"","p":186},{"i":193,"t":"In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI.","s":"📄️ Test Structure","u":"/docs/next/testing","h":"","p":186},{"i":197,"t":"For user support and inquiries, you can reach out to us through the following channels: Email Support: hello@synmetrix.org Issue Tracker: GitHub Issues Our support team is dedicated to assisting you with any questions or problems you might encounter while using Synmetrix.","s":"Contact Information","u":"/docs/next/support-updates","h":"#contact-information","p":194},{"i":199,"t":"We are continuously working to improve Synmetrix and provide you with the best possible experience. Here's a glimpse of our upcoming updates and changes: GitHub","s":"Upcoming Updates and Changes","u":"/docs/next/support-updates","h":"#upcoming-updates-and-changes","p":194},{"i":203,"t":"In the development of Synmetrix, the establishment of a robust SQL interface plays a pivotal role. This interface serves as a unified platform for accessing data sources, ensuring consistent metrics are available to all data consumers. It addresses the challenge of maintaining data integrity and precision across various tools employed by modern organizations.","s":"Introduction","u":"/docs/next/sql-interface","h":"#introduction","p":200},{"i":205,"t":"Many modern organizations utilize numerous tools to process data from a shared storage but perform individual metric calculations. This often leads to discrepancies in calculations and disparities among different teams. The more tools an organization employs, the greater the potential for inconsistencies, making data-driven decision-making complex. Hence, it is essential to create a centralized hub for defining and maintaining metrics, ensuring that all tools operate with a single source of data.","s":"Problem Statement","u":"/docs/next/sql-interface","h":"#problem-statement","p":200},{"i":207,"t":"To tackle this challenge, Synmetrix has chosen the Cube.js framework, successfully integrated into the Synmetrix platform. Cube.js offers a significant advantage in its ability to act as a data source proxy, transforming incoming queries—whether in JSON, GraphQL, or SQL format—into native queries to the underlying data store.","s":"Solution: Leveraging Cube.js","u":"/docs/next/sql-interface","h":"#solution-leveraging-cubejs","p":200},{"i":209,"t":"Creating the SQL interface within Synmetrix presented several challenges effectively addressed using the Cube.js framework and Apache Datafusion. This SQL interface enables Cube.js to provide data through a PostgreSQL-compatible protocol to various data-consuming applications, including but not limited to: BI tools Data exploration tools such as Jupyter or Hex Reverse ETL tools like Census or Hightouch Low-code development platforms such as Retool Automated tasks The SQL interface has been thoroughly tested with a range of tools and platforms, including: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch The SQL API utilizes Apache Datafusion as its SQL execution engine, responsible for query planning and execution.","s":"Implementation of SQL Interface in Synmetrix","u":"/docs/next/sql-interface","h":"#implementation-of-sql-interface-in-synmetrix","p":200},{"i":211,"t":"The implementation of the SQL interface in Synmetrix comprises several key steps, facilitated by the Cube.js framework and Apache Datafusion: Request Retrieval: Incoming data retrieval requests arrive in SQL format to Cube.js, serving as the initial queries that require processing and execution. Query Transformation: Cube.js transforms the incoming SQL queries into its own query format (JSON). This transformation optimizes and prepares the query for execution against the underlying data store. Query Execution: The SQL query generated in the previous step is sent to the data source. This source can be any supported database to which Cube.js can connect. Data Retrieval: The output is a dataset containing the required data in response to the original query. Load into Apache Datafusion: The dataset is loaded into Apache Datafusion, where it undergoes further processing. Apache Datafusion serves as the SQL execution engine, responsible for query planning and execution. Execution of the Original SQL Query: The original SQL query received by the server is executed within Apache Datafusion. This allows for the full utilization of SQL's capabilities for data analysis and processing while preserving the structure and semantics of the original query. Data Return: Query results are returned through Cube.js, where they can be further processed or directly delivered to end-users. This process ensures high flexibility and power when working with queries while maintaining data consistency and coherence for all consumers. Each stage has been meticulously designed and optimized to ensure efficient query execution and overall system performance.","s":"Implementation Details","u":"/docs/next/sql-interface","h":"#implementation-details","p":200},{"i":213,"t":"A notable feature of the SQL interface in Synmetrix is how it handles and transforms SQL queries. As previously mentioned, Cube.js converts incoming SQL queries into its own query format (JSON), a process known as \"Cube query rewrite.\" This transformation allows for optimization and query preparation for execution against the underlying data store. Let's illustrate this with an example query to the \"orders\" cube in our data model: Original SQL Query: SELECT city, SUM(amount)FROM ordersWHERE status = 'shipped'GROUP BY 1 This SQL query in the SELECT statement to the \"orders\" cube is transformed into Cube.js' internal query format: Transformed Cube.js Query: { \"measures\": [\"Orders.amount\"], \"dimensions\": [\"Orders.city\"], \"filters\": [ { \"member\": \"Orders.status\", \"operator\": \"equals\", \"values\": [\"shipped\"] } ]} As a result of this transformation, not all functions and expressions are supported within query fragments that execute SELECT statements on cube tables. For example, the following query would not work because the SQL API cannot pass the CASE expression to Cube.js for processing, making it impossible to translate the CASE expression into a metric: Non-supported Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(number)FROM orders CROSS JOIN UsersGROUP BY 1; However, in such cases, nested queries can be employed. You can encapsulate your SELECT query from a cube table within another SELECT query to perform calculations with expressions like CASE. This outer SELECT is not part of the rewritten SQL query, allowing you to use more SQL functions, operators, and expressions. Example Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT Users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN Users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; The above query works because the CASE expression is supported in SELECT queries that do not reference cube tables. These nuances and query handling specifics should be considered when designing and utilizing the SQL interface in Synmetrix.","s":"Transformation of SQL Queries","u":"/docs/next/sql-interface","h":"#transformation-of-sql-queries","p":200},{"i":215,"t":"For a detailed analysis of the server code and its components, please refer to the Synmetrix SQL Interface Server Code.","s":"Server Code","u":"/docs/next/sql-interface","h":"#server-code","p":200},{"i":217,"t":"The creation of the SQL interface in Synmetrix has allowed for the development of a unified data access interface, ensuring metric consistency for all data tools. This has reduced the number of discrepancies and conflicts related to metric calculations, providing a more reliable foundation for data-driven decision-making.","s":"Conclusion","u":"/docs/next/sql-interface","h":"#conclusion","p":200},{"i":219,"t":"Testing and Debugging","s":"Testing and Debugging","u":"/docs/next/testing-and-debugging","h":"","p":218},{"i":221,"t":"3 items","s":"🗃️ Testing Guides","u":"/docs/next/testing-and-debugging","h":"","p":218},{"i":225,"t":"info In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI. StepCI is employed for writing and conducting integration tests, which verify the correctness of the system's operation when its various components interact with each other. This is particularly vital for projects like Synmetrix, which consist of numerous services and components working collaboratively.","s":"Testing Structure for Synmetrix","u":"/docs/next/testing-and-debugging/test-structure","h":"","p":223},{"i":227,"t":"All test files are located in the test/stepci directory within the Synmetrix project folder. Each file represents an individual test scenario described in YAML format. - name: change_password http: url: ${{env.HASURA_PLUS_ENDPOINT}}/auth/change-password method: POST headers: Content-Type: application/json Authorization: Bearer ${{captures.accessToken}} body: | { \"old_password\": \"pass321\", \"new_password\": \"pass123\" } check: status: 204 This YAML configuration represents a typical StepCI test scenario named \"change_password.\" It performs a POST request with specific headers and a JSON request body to change a password. The response status is checked to ensure it's 204.","s":"Test Structure","u":"/docs/next/testing-and-debugging/test-structure","h":"#test-structure","p":223},{"i":229,"t":"Testing on the StepCI platform typically involves the following steps: Request Creation: StepCI generates a request based on the input data defined in the test scenario. Request Sending: The request is sent to the tested endpoint in the Synmetrix system. Waiting for Response: StepCI waits for a response from the Synmetrix system. During this time, all necessary actions are performed, such as request processing, interaction with the database, and other services. Response Verification: After receiving the response, StepCI compares it with the expected result defined in the test scenario. If the response matches the expectations, the test is considered passed. Otherwise, StepCI logs an error, and the test is marked as failed. This testing structure ensures the reliability and stability of the Synmetrix system. Automated testing allows developers to quickly identify and rectify errors, as well as ensure that new changes do not disrupt the functionality of existing features. Upon completing a set of tests, StepCI provides a report with a brief description of the tests that were passed. PASS datasource_flow 9.458s 1 1620 bytes 1 16006 bytesTests: 0 failed, 1 passed, 1 totalSteps: 0 failed, 0 skipped, 18 passed, 18 totalTime: 9,533s, estimated 10sCO2:0.04036gWorkflow passed after 9.533s Test was passed. In the event of a test failure, StepCI records details, including the name of the failed testing step, input data and parameters, actual and expected results, and error messages obtained during the execution of the tested code. Subsequent test steps are halted upon encountering a failure. v create_team passed after 0.041sv invite_team_member passed after 0.597s v create_datasource passed after 0.021s v check_connection passed after 0.833s v validate_datasource passed after 0.078s v fetch_tables passed after 0.436s × gen_dataschemas failed after 0.008s A create_exploration skipped after 0s A fetch_dataset skipped after Os A export_data_models skipped after 0s A fetch_meta skipped after 0s A run_query skipped after Os• datasource_flow › gen_dataschemasRequest HTTPPOST http://hasura:8080/v1/graphql HTTP/1.1Content-Type: application/jsonAuthorization: Bearer eyJhbci01JIUzI1N1J9.eyJoYXN1cmEiOnsieC1oYXN1cmEtdXNlci1pZCI6IjU5YjhjOTVjLTdIMmEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMi0\\sidXNlciJdLCJ4LWhhc3VyYS1kzwZhdWx0LXJvbGUi0iJ1c2VyInosInN1Yi16IjU5YjhjOTVjLTdl™mEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsImlzcyI6Im5ob3N0IiwiaWF0Ijo×NjkwoDkoNzcOLCJleHAi0jE20TE1NDI3NzR9.DTtp9T†jNg6ABm1vV7eB0KXZw6k1d2iZx6C-DFicCw™,x-hasura-user-id: 59b8c95c-7e2a-4797-a66c-e51776ccd39e{\"query\": \"mutation (#datasource_id: uuid!, $tables: [SourceTable!]!) f\\n gen_dataschemas (d atasource_id: $datasource_id, tables: $tables) f\\n code\\nmessageln FInrIn\"， \"variables\" :{\"datasource_id\":\"9218058b-160c-4c43-8126-b944612c055f\"\"tables\": K{\"name\": \"default/celltowers\"},{\"name\": \"default/checks\"}1}}ResponseHTTP/1.1 200 OKtransfer-encoding: chunkeddate: Tue 01 Aug 2023 12:59:36 GMTserver: Warp/3.3.23content-type: application/json; charset=utf-8{\"errors\": [\"extensions\":{\"code\": \"validation-failed\", \"path\":\"$.selectionSet. gen_dataschemas args.branch_id\"}, \"message\":\"missing required field 'branch_id'\"}1}ChecksJSONPath× $. data.gen_dataschemas.code: undefined (expected ok) Test was failed.","s":"Testing Algorithm","u":"/docs/next/testing-and-debugging/test-structure","h":"#testing-algorithm","p":223},{"i":233,"t":"Testing and Debugging Testing Guides Test a Data Source","s":"Test a Data Source","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":235,"t":"Tested on the crate:5.3.0 image.","s":"📄️ CrateDB","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":237,"t":"To connect to a Databricks datasource via JDBC, you will need the following information:","s":"📄️ Databricks","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":239,"t":"Tested on the dremio/dremio-oss:latest image.","s":"📄️ Dremio","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":241,"t":"Step 1: Navigate to the Druid example directory","s":"📄️ Druid","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":243,"t":"Step 1: Navigate to the Elasticsearch Example Directory","s":"📄️ Elasticsearch","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":245,"t":"Connecting to Firebolt datasource","s":"📄️ Firebolt","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":247,"t":"Tested on the confluentinc/cp-ksqldb-server:7.0.9 image.","s":"📄️ KSQL","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":249,"t":"Tested on the materialize/materialized:latest image.","s":"📄️ Materialize","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":251,"t":"Tested on the starburstdata/presto image.","s":"📄️ Presto","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":253,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"📄️ QuestDB","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":255,"t":"This guide provides step-by-step instructions on how to connect Snowflake to Synmetrix.","s":"📄️ Snowflake","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":257,"t":"Tested on the trinodb/trino:latest image.","s":"📄️ Trino","u":"/docs/next/testing-and-debugging/tests/data-source","h":"","p":232},{"i":260,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"SQL Interface Testing Guide for Data Models","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"","p":258},{"i":262,"t":"Before you begin testing, you'll need the following Postgres database credentials: Type: Postgres Host: demo-db.cube.dev Port: 5432 User: cube Password: 12345 Database Name: ecom","s":"Section 1: Test Database Credentials","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-1-test-database-credentials","p":258},{"i":264,"t":"To add an SQL interface, follow these steps: Go to the Settings > SQL API and Click to the \"Attach SQL API\" button. Select the data source for connection; in our case, it's \"Ecom (demo db).\" Enter your login and password or use the default ones (login: \"test\" and password: \"test\"). Click \"Connect new.\"","s":"Section 2: Adding SQL Interface","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-2-adding-sql-interface","p":258},{"i":266,"t":"Refer to the guide in Appendix V.2 for generating data models. For \"Ecom (demo db),\" you'll need to generate data models for the following tables: Line Items Line Items Count by States Orders Product Categories Products Suppliers Users This will result in the following list of data models: LineItems.yml LineItemsCountByStates.yml Orders.yml ProductCategories.yml Products.yml Suppliers.yml Users.yml Each script is labeled with its corresponding name, which is also the table name when accessing via the SQL interface.","s":"Section 3: Generating Data Models","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-3-generating-data-models","p":258},{"i":268,"t":"We recommend using DBeaver of any version (https://dbeaver.io/) for connecting. However, our connection has also been tested with the following tools: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch","s":"Section 4: Connecting via SQL Interface with a Postgres Client","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#section-4-connecting-via-sql-interface-with-a-postgres-client","p":258},{"i":270,"t":"Create a new PostgresSQL connection. Enter the credentials provided when setting up the SQL interface. In this example: Host: localhost Port: 15432 Database: ecom (you can specify any value) Username: test Password: test Click \"Test Connection\" to verify the connection. Click \"Finish\" and save the connection. Go to the SQL console to test SQL queries against the data models.","s":"4.1: Connecting via DBeaver","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#41-connecting-via-dbeaver","p":258},{"i":272,"t":"Start writing queries for the generated data models following the steps outlined in Appendix D.3. 4.2.1: Sample SQL Queries​ Query: SELECT * FROM orders ORDER BY created_at LIMIT 3; Response: Query: SELECT p.name, SUM(o.count) FROM orders o CROSS JOIN products p GROUP BY 1 LIMIT 5; Response: Query: SELECT MEASURE(count), status, DATE_TRUNC('month', created_at) date FROM orders GROUP BY date, status ORDER BY date ASC; Response: Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; Response:","s":"4.2: Testing SQL Queries","u":"/docs/next/testing-and-debugging/testing/test-sql-data-models","h":"#42-testing-sql-queries","p":258},{"i":274,"t":"Testing and Debugging Testing Guides Test a Data Source CrateDB","s":"Crate","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"","p":273},{"i":276,"t":"Navigate to the CrateDB example directory located at /docs/examples/crate in the root of the project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/crate","s":"Step 1: Navigate to the CrateDB example directory","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-1-navigate-to-the-cratedb-example-directory","p":273},{"i":278,"t":"In the CrateDB example directory, there is a script called run_crate.sh. Make sure the script is executable by running: chmod +x run_crate.sh Then, start the CrateDB service by running the script: ./run_crate.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the CrateDB service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-2-run-the-cratedb-service-using-the-script","p":273},{"i":280,"t":"Go to the UI and add a new Crate data source with the following parameters: Name: Crate Host: Host IP (obtained from the script in Step 2) Port: 4200 User: crate","s":"Step 3: Add a data source in the UI","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-3-add-a-data-source-in-the-ui","p":273},{"i":282,"t":"Click the \"Check connection\" button in the UI to verify the connection to the CrateDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/crate","h":"#step-4-test-the-connection","p":273},{"i":285,"t":"Tested on the dremio/dremio-oss:latest image.","s":"Dremio","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"","p":283},{"i":287,"t":"Navigate to the Dremio example directory located at /docs/examples/dremio in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/dremio","s":"Step 1: Navigate to the Dremio example directory","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-1-navigate-to-the-dremio-example-directory","p":283},{"i":289,"t":"In the Dremio example directory, there is a script called run_dremio.sh. Make sure the script is executable by running: chmod +x run_dremio.sh Then, start the Dremio service by running the script: ./run_dremio.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Dremio service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-2-run-the-dremio-service-using-the-script","p":283},{"i":291,"t":"Open your web browser and go to http://localhost:9047. Follow the prompts to create an administrator account. Log in to the Dremio UI with your new account. Click on \"Add Source\" and select \"PostgreSQL\" from the list of available data sources. Use the credentials provided at https://uibakery.io/sql-playground to configure the PostgreSQL data source.","s":"Step 3: Create an administrator account and add a data source in Dremio","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-3-create-an-administrator-account-and-add-a-data-source-in-dremio","p":283},{"i":293,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: host_ip (obtained from the script in Step 2) Port: 9047 Database name: Name given to the connection in Dremio User: Dremio administrator username Password: Dremio administrator password","s":"Step 4: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-4-add-a-data-source-in-the-synmetrix-ui","p":283},{"i":295,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Dremio data source. If the connection is successful, you will see a confirmation message.","s":"Step 5: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/dremio","h":"#step-5-test-the-connection","p":283},{"i":297,"t":"Testing and Debugging Testing Guides Test a Data Source Elasticsearch","s":"Elasticsearch","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"","p":296},{"i":299,"t":"Navigate to the Elasticsearch example directory located at /docs/examples/elasticsearch in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/elasticsearch","s":"Step 1: Navigate to the Elasticsearch Example Directory","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-1-navigate-to-the-elasticsearch-example-directory","p":296},{"i":301,"t":"In the Elasticsearch example directory, there is a script called run_elasticsearch.sh. Make sure the script is executable by running: chmod +x run_elasticsearch.sh Then, start the Elasticsearch service by running the script: ./run_elasticsearch.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Elasticsearch Service Using the Script","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-2-run-the-elasticsearch-service-using-the-script","p":296},{"i":303,"t":"Wait for the Elasticsearch service to be ready to accept requests. Then run: curl -X POST -u elastic:password \"http://localhost:9200/_security/user/user\" -H 'Content-Type: application/json' -d'{ \"password\": \"password\", \"roles\": [\"kibana\", \"kibana_system\"], \"full_name\": \"Test user\"}'","s":"Step 3: Wait for the Service to Be Ready and Add a User","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-3-wait-for-the-service-to-be-ready-and-add-a-user","p":296},{"i":305,"t":"Wait for the Kibana service to start, then go to http://localhost:5601 and log in with the credentials: User: elasticPassword: password Click on the Try sample data link: At the bottom of the page, click Other sample data sets: Choose a dataset and click Add data.","s":"Step 4: Access Kibana and Add Sample Data","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-4-access-kibana-and-add-sample-data","p":296},{"i":307,"t":"Now you can create a datasource in Synmetrix using the following information: Name: Elasticsearch test datasource Url: http://:9200 User: user Password: password Save the datasource and click Test connection. You should see a message indicating that the connection is OK.","s":"Step 5: Create a Datasource in Synmetrix","u":"/docs/next/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-5-create-a-datasource-in-synmetrix","p":296},{"i":311,"t":"Navigate to the Druid example directory located at /docs/examples/druid in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/druid","s":"Step 1: Navigate to the Druid example directory","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-1-navigate-to-the-druid-example-directory","p":308},{"i":313,"t":"In the Druid example directory, there is a script called run_druid.sh. Make sure the script is executable by running: chmod +x run_druid.sh Then, start the Druid services by running the script: ./run_druid.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step. Wait for the cluster to start and go to http://localhost:8889.","s":"Step 2: Run the Druid services using the script","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-2-run-the-druid-services-using-the-script","p":308},{"i":315,"t":"Go to the Services tab and make sure all services are online, as shown in the screenshot below.","s":"Step 3: Check the services","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-3-check-the-services","p":308},{"i":317,"t":"Select the \"Load data\" tab, then choose \"Batch - classic\" from the dropdown menu. Next, select the \"Example data\" tile and click \"Load example\". Click the button with the text \"Next: ...\" in the bottom right corner until the data starts loading (about 9 times). When the data starts loading, you will be redirected to the Ingestion tab and see the message \"Task submitted successfully. Going to task view...\". After the data has finished loading, the task status will change to SUCCESS.","s":"Step 4: Load example data","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-4-load-example-data","p":308},{"i":319,"t":"Fill out the form with the following information: Name: Druid test datasource Host: Host IP (obtained from the script in Step 2) Port: 8082 Currently, the Test Connection feature is not implemented and always returns \"OK\". To ensure functionality, generate a schema and request any data in the Explore tab.","s":"Step 5: Create a new Druid datasource in Synmetrix","u":"/docs/next/testing-and-debugging/tests/data-source/druid","h":"#step-5-create-a-new-druid-datasource-in-synmetrix","p":308},{"i":321,"t":"Testing and Debugging Testing Guides Test a Data Source KSQL","s":"KSQL","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"","p":320},{"i":323,"t":"Navigate to the ksql example directory located at /docs/examples/ksql in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/ksql","s":"Step 1: Navigate to the ksql example directory","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-1-navigate-to-the-ksql-example-directory","p":320},{"i":325,"t":"In the ksql example directory, there is a script called run_ksql.sh. Make sure the script is executable by running: chmod +x run_ksql.sh Then, start the ksql service by running the script: ./run_ksql.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the ksql service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-2-run-the-ksql-service-using-the-script","p":320},{"i":327,"t":"Go to the Synmetrix UI and add a new datasource with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 8088","s":"Step 3: Add a datasource in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-3-add-a-datasource-in-the-synmetrix-ui","p":320},{"i":329,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to the ksql datasource. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/ksql","h":"#step-4-test-the-connection","p":320},{"i":331,"t":"Testing and Debugging Testing Guides Test a Data Source Materialize","s":"Materialize","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"","p":330},{"i":333,"t":"Navigate to the Materialize example directory located at /docs/examples/materialize in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/materialize","s":"Step 1: Navigate to the Materialize example directory","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-1-navigate-to-the-materialize-example-directory","p":330},{"i":335,"t":"In the Materialize example directory, there is a script called run_materialize.sh. Make sure the script is executable by running: chmod +x run_materialize.sh Then, start the Materialize service by running the script: ./run_materialize.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Materialize service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-2-run-the-materialize-service-using-the-script","p":330},{"i":337,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 6875 Database name: Test User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":330},{"i":339,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Materialize data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/materialize","h":"#step-4-test-the-connection","p":330},{"i":342,"t":"Tested on the starburstdata/presto image.","s":"PrestoDB","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"","p":340},{"i":344,"t":"Navigate to the PrestoDB example directory located at /docs/examples/prestodb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/prestodb","s":"Step 1: Navigate to the PrestoDB example directory","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-1-navigate-to-the-prestodb-example-directory","p":340},{"i":346,"t":"In the PrestoDB example directory, there is a script called run_prestodb.sh. Make sure the script is executable by running: chmod +x run_prestodb.sh Then, start the PrestoDB service by running the script: ./run_prestodb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the PrestoDB service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-2-run-the-prestodb-service-using-the-script","p":340},{"i":348,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: PrestoDB Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":340},{"i":350,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the PrestoDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/presto","h":"#step-4-test-the-connection","p":340},{"i":352,"t":"Testing and Debugging Testing Guides Test a Data Source Firebolt","s":"Firebolt","u":"/docs/next/testing-and-debugging/tests/data-source/firebolt","h":"","p":351},{"i":354,"t":"To connect to a Firebolt datasource, you will need the following information: Database Name - the name of the database to connect to. Username - the username used to connect to the database. Password - the password used to connect to the database. Engine Name - the name of the engine to connect to. API Endpoint - the Firebolt API endpoint used for authentication, such as api.dev.firebolt.io or api.app.firebolt.io.","s":"Connecting to Firebolt datasource","u":"/docs/next/testing-and-debugging/tests/data-source/firebolt","h":"#connecting-to-firebolt-datasource","p":351},{"i":357,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"QuestDB","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"","p":355},{"i":359,"t":"Navigate to the QuestDB example directory located at /docs/examples/questdb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/questdb","s":"Step 1: Navigate to the QuestDB example directory","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-1-navigate-to-the-questdb-example-directory","p":355},{"i":361,"t":"In the QuestDB example directory, there is a script called run_questdb.sh. Make sure the script is executable by running: chmod +x run_questdb.sh Then, start the QuestDB service by running the script: ./run_questdb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the QuestDB service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-2-run-the-questdb-service-using-the-script","p":355},{"i":363,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: QuestDB Test Host: Host IP (obtained from the script in Step 2) Port: 8812 User: quest Password: quest","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":355},{"i":365,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the QuestDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/questdb","h":"#step-4-test-the-connection","p":355},{"i":367,"t":"Testing and Debugging Testing Guides Test a Data Source Snowflake","s":"Snowflake","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"","p":366},{"i":369,"t":"Before proceeding, ensure you have the following Snowflake connection details: Database Name Warehouse Organization ID Account ID Username Password Role You can find this information in your Snowflake account.","s":"Step 1: Gather Snowflake connection information","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-1-gather-snowflake-connection-information","p":366},{"i":371,"t":"Go to the Synmetrix UI and navigate to the \"Data Sources\" section. Click on the \"+ Connect\" button. Select \"Snowflake\" from the list of available data sources.","s":"Step 2: Add a new data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-2-add-a-new-data-source-in-the-synmetrix-ui","p":366},{"i":373,"t":"Fill in the Snowflake connection details in the form with the following fields: Name: A custom name for your data source (e.g., \"My Snowflake\") Database Name: The name of your Snowflake database Warehouse: The name of your Snowflake warehouse Organization ID: The ID of your Snowflake organization Account ID: The ID of your Snowflake account Username: Your Snowflake username Password: Your Snowflake password Role: Your Snowflake role","s":"Step 3: Fill in the connection details","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-3-fill-in-the-connection-details","p":366},{"i":375,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to your Snowflake data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/snowflake","h":"#step-4-test-the-connection","p":366},{"i":377,"t":"Testing and Debugging Testing Guides Test a Data Source Trino","s":"Trino","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"","p":376},{"i":379,"t":"Navigate to the Trino example directory located at /docs/examples/trino in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/trino","s":"Step 1: Navigate to the Trino example directory","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-1-navigate-to-the-trino-example-directory","p":376},{"i":381,"t":"In the Trino example directory, there is a script called run_trino.sh. Make sure the script is executable by running: chmod +x run_trino.sh Then, start the Trino service by running the script: ./run_trino.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Trino service using the script","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-2-run-the-trino-service-using-the-script","p":376},{"i":383,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: Trino Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":376},{"i":385,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Trino data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/next/testing-and-debugging/tests/data-source/trino","h":"#step-4-test-the-connection","p":376},{"i":388,"t":"To connect to a Databricks datasource via JDBC, you will need the following information: Access Token: A personal access token used to authenticate the Databricks connection. Databricks JDBC URL: The URL for a JDBC connection. It should be in the format: jdbc:databricks://{''}:443/default;transportMode=http;ssl=1; Database Name: The name of the database to connect to.","s":"Connecting to Databricks Datasource via JDBC","u":"/docs/next/usage/user-guide/data-source/databricks","h":"","p":386},{"i":390,"t":"User Guide","s":"User Guide","u":"/docs/next/user-guide","h":"","p":389},{"i":392,"t":"🗃️ Explore Page 2 items 📄️ Models The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page: 📄️ Logs Overview 🗃️ Settings 6 items Feel free to explore the documentation for each page to gain a comprehensive understanding of the Synmetrix interface and its functionalities. Happy exploring and analyzing data with Synmetrix!","s":"Table of Contents","u":"/docs/next/user-guide","h":"#table-of-contents","p":389},{"i":394,"t":"2 items","s":"🗃️ Explore Page","u":"/docs/next/user-guide","h":"","p":389},{"i":396,"t":"The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page:","s":"📄️ Models","u":"/docs/next/user-guide","h":"","p":389},{"i":398,"t":"Overview","s":"📄️ Logs","u":"/docs/next/user-guide","h":"","p":389},{"i":400,"t":"6 items","s":"🗃️ Settings","u":"/docs/next/user-guide","h":"","p":389},{"i":404,"t":"The Explore page serves as a dynamic and intuitive platform for users to engage with data, offering tools and options for efficient data exploration and analysis. This includes changing data sources, selecting branches, and manipulating data dimensions, measures, and filters. Additional functionalities allow users to run queries, set up alerts or reports, and export data.","s":"Overview","u":"/docs/next/user-guide/explore","h":"#overview","p":401},{"i":406,"t":"The sidebar in your data tool is where you can do a lot of important stuff. Let's go over how to change your data source, switch branches, and pick the right dimensions and measures.","s":"Sidebar Actions","u":"/docs/next/user-guide/explore","h":"#sidebar-actions","p":401},{"i":408,"t":"To switch to a different data source: Find the Data Source in the sidebar. Pick a New Data Source: Click on the list there and choose the one you want. It's Changed!: Your data source will change right away.","s":"Changing Data Source in Explorer","u":"/docs/next/user-guide/explore","h":"#changing-data-source-in-explorer","p":401},{"i":410,"t":"To change the branch you're working on: Look for Branch Section in the sidebar. Choose Your Branch: Click on the list and pick the branch you need. Branch Changed: Now you're working in the new branch.","s":"Switching Branches in Explorer","u":"/docs/next/user-guide/explore","h":"#switching-branches-in-explorer","p":401},{"i":412,"t":"Picking the right dimensions and measures is super important for looking at your data the right way. Here's what to do: Go to the Sidebar: First, find the sidebar. Click on Desired Model: Click it and you'll see a list. See All the Options: This list shows all the measures, dimensions, time dimensions and segments you can use. Pick What You Need: Choose the ones that you're willing to analyze. Run Your Query: After picking, run your query to see your data. tip Quick Find: Use the search box to find dimensions or measures fast.","s":"Selecting Dimensions and Measures","u":"/docs/next/user-guide/explore","h":"#selecting-dimensions-and-measures","p":401},{"i":414,"t":"Incorporating filters into your analysis is a crucial step for refining and focusing your data exploration. Here’s a professional approach to adding filters through the Explorer Sidebar: Navigate to Explorer Sidebar: Begin by accessing the sidebar options within the Explorer. Hover Over Measures or Dimensions: Move your cursor over either the Measures or Dimensions sections in the sidebar. Initiate Filter Selection: Look for a filter icon on the specific measure or dimension you are interested in. Click on this icon to start setting up a filter. Configure Filter in Result Tab: After clicking the filter button, switch to the \"Result\" tab. Here, you'll find options to customize your filter according to your specific data requirements.","s":"Adding Filters","u":"/docs/next/user-guide/explore","h":"#adding-filters","p":401},{"i":416,"t":"The toolbar in your data analysis tool is equipped with a variety of features to enhance your data exploration and reporting capabilities. Here's a breakdown of the key actions available:","s":"Explore Toolbar Functionality Overview","u":"/docs/next/user-guide/explore","h":"#explore-toolbar-functionality-overview","p":401},{"i":418,"t":"Function: Runs the analysis based on the selected dimensions, measures, and filters. Purpose: To dynamically generate and view data results tailored to your current selections.","s":"Run Query","u":"/docs/next/user-guide/explore","h":"#run-query","p":401},{"i":420,"t":"Function: Allows for the creation of alerts based on specific data conditions. Usage: Configure alert parameters to monitor data changes and receive notifications.","s":"Set Up Alerts","u":"/docs/next/user-guide/explore","h":"#set-up-alerts","p":401},{"i":422,"t":"Function: Creates reports reflecting your current data view. Customization: Offers options to define report parameters and settings for targeted reporting.","s":"Generate Reports","u":"/docs/next/user-guide/explore","h":"#generate-reports","p":401},{"i":424,"t":"Function: Enables the export of data into a CSV file. Application: Useful for offline data analysis or for sharing data externally.","s":"Export to CSV","u":"/docs/next/user-guide/explore","h":"#export-to-csv","p":401},{"i":427,"t":"Display: Shows the outcome of your executed queries. Adjustments: Allows configuration of row limits, offsets, and view of applied filters.","s":"Results Tab","u":"/docs/next/user-guide/explore","h":"#results-tab","p":401},{"i":429,"t":"Function: Displays the automatically generated SQL query post-execution. Utility: Useful for understanding the query structure or for manual SQL query replication.","s":"Generated SQL Tab","u":"/docs/next/user-guide/explore","h":"#generated-sql-tab","p":401},{"i":431,"t":"Access Details: Provides necessary API information for data retrieval. Components: Includes headers and the API endpoint URL. Example API Utilization​ Headers: Authorization: Bearer x-hasura-datasource-id: x-hasura-branch-id: URL: https://cube.synmetrix.org/api/v1/load Body: Follow the API documentation for accurate setup. Execute API Request: Enables sending an API request directly from the interface. 📄️ Alerts The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page: 📄️ Reports The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"REST API Tab","u":"/docs/next/user-guide/explore","h":"#rest-api-tab","p":401},{"i":433,"t":"The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page:","s":"📄️ Alerts","u":"/docs/next/user-guide/explore","h":"","p":401},{"i":435,"t":"The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"📄️ Reports","u":"/docs/next/user-guide/explore","h":"","p":401},{"i":437,"t":"User Guide Explore Page Alerts","s":"Alerts Page","u":"/docs/next/user-guide/explore/alerts","h":"","p":436},{"i":439,"t":"Click the \"Add Alert\" button on the Explore page. Choose your notification delivery method. Options include Webhook, Slack, or Email, depending on your system integrations and preferences. Complete the alert form with the necessary information:","s":"How to Set Up an Alert","u":"/docs/next/user-guide/explore/alerts","h":"#how-to-set-up-an-alert","p":436},{"i":441,"t":"Alert Name: Provide a descriptive name that clearly identifies the alert's purpose and content.","s":"Detailed Description of Form Fields on the Alerts Page:","u":"/docs/next/user-guide/explore/alerts","h":"#detailed-description-of-form-fields-on-the-alerts-page","p":436},{"i":443,"t":"This section offers a comprehensive overview of the Alert or Report setup. Key features include: Query Key: This is the primary query that your alert or report is based on. It's a critical element in the monitoring process. Measures: Select the quantitative data points you wish to track or display. These are crucial for your analysis and will be highlighted in the Preview. Dimensions: These provide context and categorization for your data, allowing a more detailed and segmented analysis. info The Preview amalgamates all these components, offering a clear snapshot of the data and insights being monitored. This ensures that your analysis, whether for an Alert or Report, is accurate and relevant.","s":"Preview Section:","u":"/docs/next/user-guide/explore/alerts","h":"#preview-section","p":436},{"i":445,"t":"Customize how and when your alert is triggered with these settings: Schedule: Set how often the alert conditions are checked (e.g., hourly, daily, weekly). Lower Bound: Define the minimum threshold that triggers the alert. Upper Bound: Specify the maximum threshold for triggering the alert. Request Timeout (minutes): Set the maximum time allowed for the data request to process. Timeout On Fire (minutes): Determine how long before an ongoing alert condition is considered \"fired.\"","s":"Trigger Settings:","u":"/docs/next/user-guide/explore/alerts","h":"#trigger-settings","p":436},{"i":447,"t":"Options here vary based on your chosen alert delivery method: Webhook: Input the URL for the webhook endpoint. Slack: Enter the Slack channel or user ID. Email: Specify the email addresses of the alert recipients. note After configuring your settings, click \"Save\" to activate the alert. This will commence the monitoring of your specified conditions. Enhancements for Clarity and Efficiency: Clarified instructions for setting up alerts. Detailed descriptions for each section, ensuring comprehensive understanding. Streamlined language for ease of reading and comprehension. Added informative notes and info sections for additional clarity.","s":"Delivery Settings:","u":"/docs/next/user-guide/explore/alerts","h":"#delivery-settings","p":436},{"i":449,"t":"User Guide Explore Page Reports","s":"Reports Page","u":"/docs/next/user-guide/explore/reports","h":"","p":448},{"i":451,"t":"Click the \"Add Report\" button on the Explore page. Select the destination where the screenshot will be sent. You can choose between Webhook, Slack, or Email based on your preferences and integrations. Fill out the report form with the following details:","s":"How to Create a Report","u":"/docs/next/user-guide/explore/reports","h":"#how-to-create-a-report","p":448},{"i":453,"t":"Report Name: Enter a name that easily identifies the purpose and content of the report.","s":"Form fields description for the Reports page:","u":"/docs/next/user-guide/explore/reports","h":"#form-fields-description-for-the-reports-page","p":448},{"i":455,"t":"In the \"Preview\" section, you will find crucial details for the Alert or Report, including the user-selected metric referred to as the \"Query Key.\" This metric is at the core of your monitoring process and plays a pivotal role in alerting or report generation. Here, you'll also discover various other essential components such as: Measures: These represent the quantitative data that you want to track or display. Users can choose specific measures that matter the most to their analysis, and these will be prominently featured in the Preview. Dimensions: Dimensions help to provide context and categorize your data. They allow you to break down the information based on various attributes, providing a more comprehensive view of your metrics. info In the Preview, all these elements come together to give you a snapshot of the data and insights you're tracking, ensuring that you're monitoring the right information for your analysis, whether it's for an Alert or Report.","s":"Preview:","u":"/docs/next/user-guide/explore/reports","h":"#preview","p":448},{"i":457,"t":"Schedule: Configure the frequency at which the report will be executed by setting a schedule (e.g., daily, weekly, monthly).","s":"Trigger Settings:","u":"/docs/next/user-guide/explore/reports","h":"#trigger-settings","p":448},{"i":459,"t":"The available fields in this section depend on the chosen delivery method for the report. Here are some common options: Webhook: Enter the URL of the webhook endpoint where the report will be sent. Slack: Provide the Slack channel or user ID to which the report will be delivered. Email: Enter the email address(es) of the recipient(s) who will receive the report. note Once you have completed the settings, click the \"Save\" button.","s":"Delivery Settings:","u":"/docs/next/user-guide/explore/reports","h":"#delivery-settings","p":448},{"i":461,"t":"User Guide Logs","s":"Logs Page","u":"/docs/next/user-guide/logs","h":"","p":460},{"i":463,"t":"The Logs page is designed to provide control over the internal workings of the Cube.js service. It allows you to review the requests and the events that occur during the processing of these events. You can access information such as the execution time, start and end time, the actual query, and additional technical details. This enables you to monitor and analyze the performance and behavior of the Cube.js service. Follow the instructions below to utilize the Logs page: Access the Logs page within the project interface. Review the list of logs, which includes details such as the request information, execution time, start and end timestamps, and the actual query being processed. Click on a log entry to expand it and access additional technical information, if available. Use the logs as a resource for troubleshooting, performance optimization, and gaining insights into the inner workings of the Cube.js service.","s":"Overview","u":"/docs/next/user-guide/logs","h":"#overview","p":460},{"i":466,"t":"User Guide Models","s":"Models Page","u":"/docs/next/user-guide/models","h":"","p":465},{"i":468,"t":"To get started, you need to generate data models. Select the \"Generate Model\" option from the menu (shown in the screenshot below). In the opened window, navigate to the required sections by opening the corresponding tabs and check the boxes next to the tables or metrics you want to analyze. Then, click the \"Generate\" button (indicated by the arrow in the screenshot below). Now you should see the generated data model files in the right column (as shown in the screenshot below). You can freely edit your models as needed, adding and removing measures and dimensions. All your changes will be tracked by the version control system, the details of which are described below.","s":"How to Get Started","u":"/docs/next/user-guide/models","h":"#how-to-get-started","p":465},{"i":470,"t":"The version control system tracks changes to the data models, allowing you to revert to previous states of the models. To view the saved versions, open the drop-down menu located at the top left of the page and select the \"Show versions\" option (as shown in the screenshot below). In the opened window, you will see all the saved versions of the data models (as shown in the screenshot below). By expanding the rows, you can view the files and their contents. To revert to a specific version, simply click the \"Restore\" button next to it, and it will become the current version. Additionally, the version control system allows you to create separate branches of versions, which can help you switch between versions more efficiently than restoring versions within a single branch. To create a branch, open the branch list located in the top left corner of the page, enter the name of the new branch in the field, and click the plus icon button. This will create a new branch with the current state. To work with the new branch, you need to switch to it and then make it the default branch by clicking the \"Set as default\" button (shown in the screenshot).","s":"Version Control System","u":"/docs/next/user-guide/models","h":"#version-control-system","p":465},{"i":472,"t":"Go to Sidebar: Click on the sidebar in Synmetrix. Click 'Docs': Find and click the 'Docs' button. Get the Docs: The file with the version's documentation will be generated for you.","s":"Viewing Version Docs","u":"/docs/next/user-guide/models","h":"#viewing-version-docs","p":465},{"i":475,"t":"Opening the SQL Runner in Synmetrix for running custom SQL queries is a straightforward process. Here's how to do it: Navigate to Model Page: First, go to the model page in the Synmetrix interface. This is where all your data models are listed. Open SQL Runner Tab: Look for a tab or section labeled \"SQL Runner\" and click on it. This action will open the SQL Runner interface. Compose Your SQL Query: In the provided space, you can write your SQL query. For example, you might write a query to select data from a specific table or to perform a more complex data manipulation. SELECT * FROM your_table_name WHERE condition; Execute the Query: Once you've written your query, click the \"Run\" button. This will execute the SQL query against your data models. View Results: After running the query, the results will be displayed in a table format directly in the SQL Runner. This allows you to review and analyze the data returned by your query. note The Model SQL Runner in Synmetrix is a feature that allows you to run SQL queries directly against your data models. This tool is particularly useful for users who need to perform complex data analysis or want to test and validate their model configurations. Here’s a brief overview: Functionality: The SQL Runner enables you to write and execute SQL queries on the fly. It interacts directly with the data models you've set up in Synmetrix, allowing for real-time data retrieval and analysis. Use Cases: Data Exploration: Quickly run queries to explore data patterns or validate data integrity. Testing Models: Test how new or modified data models perform with actual SQL queries. Advanced Analysis: Perform complex data analysis that might not be possible through standard model exploration. Real-Time Results: Queries executed in the SQL Runner provide immediate feedback, enabling you to rapidly iterate and refine your analysis.","s":"Accessing the SQL Runner in Synmetrix","u":"/docs/next/user-guide/models","h":"#accessing-the-sql-runner-in-synmetrix","p":465},{"i":477,"t":"Open a Model: In sidebar, select and open the model file you wish to edit. Begin Editing: Utilize the Visual Model Editor to make changes to the model. You can adjust various aspects such as dimensions, measures, and relationships. Save Changes or close tab: After completing your edits, ensure to save the model to apply the modifications. note The Visual Model Editor in Synmetrix, enhanced with YAML support, offers an intuitive and user-friendly way to manage data models. YAML's readable format simplifies the process of defining and configuring models, making it accessible even to those with limited coding experience. This combination of a graphical interface and YAML/JS editing provides a flexible and efficient model editing experience, catering to both beginner and advanced users in data model management.","s":"Editing Models in Synmetrix","u":"/docs/next/user-guide/models","h":"#editing-models-in-synmetrix","p":465},{"i":479,"t":"You can upload data model files from your computer to Synmetrix. Easily save and download your data model files from Synmetrix.","s":"Importing or Exporting Data Models","u":"/docs/next/user-guide/models","h":"#importing-or-exporting-data-models","p":465},{"i":481,"t":"Edit and tweak your data models right in Synmetrix with a user-friendly editor.","s":"Using the Visual Model Editor","u":"/docs/next/user-guide/models","h":"#using-the-visual-model-editor","p":465},{"i":483,"t":"Open Sidebar: Start in the sidebar. Find Your Model: Look for the model you want to delete and hover over it. Click the Trash Icon: Hit the trash can icon next to the model. Confirm Delete: Click \"Remove\" to confirm. Done: The model is now deleted.","s":"Deleting a Model","u":"/docs/next/user-guide/models","h":"#deleting-a-model","p":465},{"i":485,"t":"Head to Sidebar: Go to the sidebar in Synmetrix. Choose a Model: Hover over the model you want to change. Edit: Click the pen icon. Make Changes: Change the name and file format (JS or YAML). Finished: Your changes are saved. These steps are a simple way to handle your data models in Synmetrix, whether you're adding, editing, or removing them.","s":"Changing Model Name and File Format","u":"/docs/next/user-guide/models","h":"#changing-model-name-and-file-format","p":465},{"i":488,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"Data Sources Settings","u":"/docs/next/user-guide/settings/data-sources","h":"","p":486},{"i":491,"t":"Click on the \"Connect\" button.","s":"Step 1","u":"/docs/next/user-guide/settings/data-sources","h":"#step-1","p":486},{"i":493,"t":"A list of available datasource types will be displayed. Select the desired type from the options presented.","s":"Step 2","u":"/docs/next/user-guide/settings/data-sources","h":"#step-2","p":486},{"i":495,"t":"In the opened modal window, fill in all the required fields in the form. These fields may include connection details, authentication credentials, and other relevant information. On the screenshot below, you can see a modal window with the datasource creation form. Test Connection To test the connection to the datasource, click the \"Test connection\" button. To save the entered data and create the datasource, click the \"Save\" button. Fast Switching Data Sources in Synmetrix In Synmetrix, switching between data sources is a straightforward process. You can find the data models dropdown in the sidebar menu of both the \"Models\" and \"Explore\" pages. Simply access this menu to view and select from the available data sources. This dropdown is conveniently placed to ensure ease of access, enhancing your experience by allowing quick and efficient transitions between various data repositories within Synmetrix.","s":"Step 3","u":"/docs/next/user-guide/settings/data-sources","h":"#step-3","p":486},{"i":497,"t":"User Guide Settings","s":"Settings","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":499,"t":"📄️ Data Sources The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps: 📄️ Members Managing Members 📄️ Personal Information Overview 📄️ Role Based Access Role Based Access Control 📄️ SQL API SQL API Page 📄️ Teams Introduction","s":"Table of Contents","u":"/docs/next/user-guide/settings","h":"#table-of-contents","p":496},{"i":501,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"📄️ Data Sources","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":503,"t":"Managing Members","s":"📄️ Members","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":505,"t":"Overview","s":"📄️ Personal Information","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":507,"t":"Role Based Access Control","s":"📄️ Role Based Access","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":509,"t":"SQL API Page","s":"📄️ SQL API","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":511,"t":"Introduction","s":"📄️ Teams","u":"/docs/next/user-guide/settings","h":"","p":496},{"i":515,"t":"The \"Personal Info\" section is designed to provide you with full control over your personal settings and security. Here, you can easily update your username, email address, and password, ensuring your account remains secure and personalized. Additionally, you have the option to manage your active sessions, allowing you to logout from all for enhanced security.","s":"Overview","u":"/docs/next/user-guide/settings/personal-info","h":"#overview","p":512},{"i":517,"t":"To change your username: Go to \"Settings\" in the sidebar menu. Navigate to the \"Personal Info\" tab. Locate the \"Full Name\" field under the \"General Information\" section. Click on the \"Full Name\" input field and enter your new username. Click the \"Update\" button. You're all set!","s":"How to Change Your Username?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-change-your-username","p":512},{"i":519,"t":"To update your email address: Access \"Settings\" from the sidebar menu. Select the \"Personal Info\" tab. Find the \"Email\" field in the \"General Information\" section. Enter your new email address in the \"Email\" input field. Click the \"Update\" button. Your email is now updated!","s":"How to Change Your Email?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-change-your-email","p":512},{"i":521,"t":"To change your password: Visit \"Settings\" from the sidebar menu. Click on the \"Personal Info\" tab. Scroll down to the \"Security\" section. Enter your old password and your desired new password. Click the \"Update Password\" button. Your password has been changed!","s":"How to Change Your Password?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-change-your-password","p":512},{"i":523,"t":"To logout from all active sessions: Go to \"Settings\" in the sidebar menu. Choose the \"Personal Info\" tab. Scroll to the \"Active Sessions\" section. Click the \"Logout from all sessions\" button. All sessions have been logged out!","s":"How to Logout from Active Sessions?","u":"/docs/next/user-guide/settings/personal-info","h":"#how-to-logout-from-active-sessions","p":512},{"i":527,"t":"Role Based Access Control (RBAC) is a system for regulating access to resources within your organization based on the roles of individual users. This document outlines how to manage roles and the types of access they grant.","s":"Overview","u":"/docs/next/user-guide/settings/role-access","h":"#overview","p":524},{"i":529,"t":"Partial Access: Grants access to specific Data Sources, Data Models, and various Measures, Dimensions, or Segments. Full Access: Provides unrestricted access to all resources. No Access: Denies access to all resources.","s":"Access Types","u":"/docs/next/user-guide/settings/role-access","h":"#access-types","p":524},{"i":531,"t":"To add a new role, follow these steps: Navigate to Settings > Roles and Access. Click on the Create Now button. In the popup window: Enter the role name. Set access for data source resources by selecting one or multiple data sources. Choose Data Models. Select Measures, Dimensions, and/or Segments. tip Use the \"Select All\" checkbox to select all Measures, Dimensions, and Segments in the current Data Model. Click Create. info Roles display information including the creation date, last update, and data sources along with their respective access types.","s":"Adding a New Role","u":"/docs/next/user-guide/settings/role-access","h":"#adding-a-new-role","p":524},{"i":533,"t":"To delete a role, follow these steps: Go to Settings > Roles and Access. Find the role to be deleted and click the gear icon on their card. Choose Delete from the dropdown list and confirm the deletion. The deletion process is complete.","s":"Deleting a Role","u":"/docs/next/user-guide/settings/role-access","h":"#deleting-a-role","p":524},{"i":535,"t":"To edit an existing role, follow these steps: Navigate to Settings > Roles and Access. Locate the role to edit and click the gear icon on their card. Choose Edit from the dropdown list. In the popup window, make the desired changes: Update the role name. Adjust access to data source resources. Select or change Data Models. Choose Measures, Dimensions, and/or Segments. tip Utilize the \"Select All\" checkbox for selecting all items in the current Data Model. Click Save to apply the changes. This document provides clear guidelines for managing roles within the Role Based Access Control system, ensuring efficient and secure access management.","s":"Editing a Role","u":"/docs/next/user-guide/settings/role-access","h":"#editing-a-role","p":524},{"i":539,"t":"The Members section in your settings allows you to manage the team members who have access to your workspace. This guide provides step-by-step instructions on how to add new members, as well as how to remove existing members.","s":"Overview","u":"/docs/next/user-guide/settings/members","h":"#overview","p":536},{"i":541,"t":"There are three types of members you can add to your account: Owner: The primary account holder with full access and privileges. Admin: Users with administrative rights, including the ability to manage settings and members. Member: Regular users with access to the account but limited control over settings and member management.","s":"Member Types","u":"/docs/next/user-guide/settings/members","h":"#member-types","p":536},{"i":543,"t":"To add a new member to your account, follow these steps: Navigate to Settings > Members in your account. Click the Create Now button. In the popup window, enter the email address of the new member. Select the user type for the new member (either Member or Admin). Click the Invite button. The new member will receive an email with a magic link to sign in to their account.","s":"Adding a New Member","u":"/docs/next/user-guide/settings/members","h":"#adding-a-new-member","p":536},{"i":545,"t":"To remove a member from your account, follow these steps: Go to Settings > Members. Locate the member you wish to remove and find the gear icon on their member card. Click on Delete. Confirm the deletion. After confirming, the member will be removed from your account. The process is complete. This documentation provides a clear and concise guide for managing members within your account settings.","s":"Removing a Member","u":"/docs/next/user-guide/settings/members","h":"#removing-a-member","p":536},{"i":549,"t":"Engage in powerful and flexible data retrieval and manipulation with our SQL API, designed for effortless data management and seamless collaboration.","s":"Effortless Data Management and Uninterrupted Collaboration","u":"/docs/next/user-guide/settings/sql-api","h":"#effortless-data-management-and-uninterrupted-collaboration","p":546},{"i":551,"t":"Synmetrix revolutionizes data sharing with its versatile protocol, ensuring smooth integration with a variety of data tools. Our platform seamlessly connects with business intelligence and data exploration tools (Superset, Tableau), data notebooks (Jupyter, Hex), reverse ETL tools (Census, Hightouch), low-code platforms (Retool), and more, enhancing efficiency and simplicity in data-driven tasks.","s":"Simplified Data Sharing","u":"/docs/next/user-guide/settings/sql-api","h":"#simplified-data-sharing","p":546},{"i":553,"t":"Synmetrix serves as the ultimate repository for all your data needs, centralizing metrics in an organized manner. Regardless of your preferred tools or platforms for data analysis or visualization, Synmetrix provides easy access to essential data.","s":"One-Stop Metrics Solution","u":"/docs/next/user-guide/settings/sql-api","h":"#one-stop-metrics-solution","p":546},{"i":555,"t":"Integrations: Seamlessly integrate into your workflow, including Excel compatibility. Flexibility: Encourage collaborative efforts with shared models in a harmonious team environment. Security: Rely on our robust and reliable security measures. Effortless: Tailor SQL queries to meet your unique requirements.","s":"Key Benefits","u":"/docs/next/user-guide/settings/sql-api","h":"#key-benefits","p":546},{"i":557,"t":"Obtain your SQL API credentials through two distinct methods:","s":"Acquiring SQL Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#acquiring-sql-credentials","p":546},{"i":559,"t":"Initiate a new account creation. Add your data source. Opt to generate models (optional). Synmetrix generates SQL API access during this step. A pop-up allows you to choose between PSQL or MYSQL for connection, along with credential copying. Connect using PSQL or MYSQL, or opt to \"Download Credentials\" for later use. Finish the onboarding.","s":"First Method: From Onboarding","u":"/docs/next/user-guide/settings/sql-api","h":"#first-method-from-onboarding","p":546},{"i":561,"t":"Navigate to Settings > SQL API. Click the gear icon on the database card and select \"Edit\". The pop-up window provides PSQL or MYSQL connection options and credential copying. Example Credentials Host/URL: cube.synmetrix.org:15432 Database: db Login: 4x4qg8ei76 Password: ********** PSQL Client Connection: psql --host=cube.synmetrix.org --port 15432 --username=4x4qg8ei76 --dbname=db Connect using PSQL or MYSQL, or \"Download Credentials\" for later use.","s":"Second Method: From Settings","u":"/docs/next/user-guide/settings/sql-api","h":"#second-method-from-settings","p":546},{"i":563,"t":"Easily create, edit, or delete SQL API credentials via Settings > SQL API.","s":"Managing SQL API Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#managing-sql-api-credentials","p":546},{"i":565,"t":"Click the gear icon and select \"Delete\" from the dropdown. Confirm deletion.","s":"Deleting Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#deleting-credentials","p":546},{"i":567,"t":"Select the \"Create Now\" button on the SQL API page. In the pop-up, specify the Team member and Data source.","s":"Creating Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#creating-credentials","p":546},{"i":569,"t":"Click the gear icon and select \"Edit\". Modify the credentials as needed. Confirm by clicking \"Finish\".","s":"Editing Credentials","u":"/docs/next/user-guide/settings/sql-api","h":"#editing-credentials","p":546},{"i":573,"t":"The \"Teams\" section of Synmetrix is a dedicated space for creating, managing, and customizing teams. This feature is essential for collaborative work, allowing you to form new teams, set a focus on a current team, remove teams that are no longer needed, and update team names to align with evolving projects or goals.","s":"Introduction","u":"/docs/next/user-guide/settings/teams","h":"#introduction","p":570},{"i":575,"t":"To navigate to the Team page: Hover over your account profile picture at the top-right corner of the page. Select \"Team\" from the dropdown menu.","s":"Accessing the Team Page","u":"/docs/next/user-guide/settings/teams","h":"#accessing-the-team-page","p":570},{"i":577,"t":"On the Team page, you can perform various actions:","s":"Team Page Functions","u":"/docs/next/user-guide/settings/teams","h":"#team-page-functions","p":570},{"i":579,"t":"You can invite new members to join your project by entering their email addresses. This step is crucial for expanding your team and fostering collaboration.","s":"1. Inviting New Users","u":"/docs/next/user-guide/settings/teams","h":"#1-inviting-new-users","p":570},{"i":581,"t":"Based on your access management system, assign appropriate roles and access levels to team members, ensuring they have the necessary permissions to contribute effectively.","s":"2. Assigning Roles and Access Levels","u":"/docs/next/user-guide/settings/teams","h":"#2-assigning-roles-and-access-levels","p":570},{"i":583,"t":"Control access to specific features and resources for each team member, tailoring their permissions to suit their role and needs in the project.","s":"3. Managing Access","u":"/docs/next/user-guide/settings/teams","h":"#3-managing-access","p":570},{"i":585,"t":"The Team page provides a comprehensive view of all team members, including their roles and responsibilities, allowing for efficient team management.","s":"4. Viewing and Managing Team Members","u":"/docs/next/user-guide/settings/teams","h":"#4-viewing-and-managing-team-members","p":570},{"i":587,"t":"To create a new team: Open \"Settings\" from the sidebar. Click on the \"Teams\" tab. In \"Manage Teams\", view your existing teams. Select the gear icon (\"Settings\"). Choose \"Create Team\" from the dropdown and follow the prompts.","s":"How to Create a New Team?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-create-a-new-team","p":570},{"i":589,"t":"To set an active team: Go to \"Settings\" in the sidebar. Choose the \"Teams\" tab. Find your team list in \"Manage Teams\". Click the gear icon next to the team you want to focus on. Select \"Set Current\" from the dropdown.","s":"How to Set a Current Team?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-set-a-current-team","p":570},{"i":591,"t":"To change a team's name: Navigate to \"Settings\" via the sidebar. Select the \"Teams\" tab. Find your team in \"Manage Teams\". Click the gear icon next to the team you want to rename. Choose \"Edit\" from the dropdown. Change the \"Team Name\" in the pop-up. Click \"Save\" to confirm the changes.","s":"How to Edit a Team Name?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-edit-a-team-name","p":570},{"i":593,"t":"To remove a team: Access \"Settings\" from the sidebar. Go to the \"Teams\" tab. Identify the team you want to delete in \"Manage Teams\". Click the gear icon next to the team. Choose \"Delete\" from the dropdown list. I've refined the language for professional tone and clarity, ensuring each step is clear and straightforward.","s":"How to Delete a Team?","u":"/docs/next/user-guide/settings/teams","h":"#how-to-delete-a-team","p":570},{"i":595,"t":"APIs & integrations","s":"APIs & integrations","u":"/docs/apis-integrations","h":"","p":594},{"i":599,"t":"Architecture and design","s":"Architecture and Design","u":"/docs/architecture-and-design","h":"","p":598},{"i":601,"t":"Database Structure","s":"📄️ Database Design","u":"/docs/architecture-and-design","h":"","p":598},{"i":603,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"📄️ System Architecture","u":"/docs/architecture-and-design","h":"","p":598},{"i":605,"t":"Architecture and design Database Design","s":"Database Structure Design","u":"/docs/architecture-and-design/database-design","h":"","p":604},{"i":607,"t":"The \"Synmetrix\" database is organized according to the relational database model and includes the following tables: public.users: Stores information about registered users, including user ID (id), display name (display_name), avatar URL (avatar_url), as well as the creation time and last update time of the record. auth.account_providers: Describes the relationships between user accounts and their authentication providers. auth.accounts: Stores information about user accounts, including unique identifiers, email addresses, passwords, and other data. auth.providers: Provides a list of available authentication providers. auth.refresh_tokens: Contains information about refresh tokens for each user account. auth.roles: Manages user roles. auth.account_roles: Contains information about roles for each account. public.teams: Stores information about user teams. public.datasources: Contains information about data sources used by users. public.dataschemas: Describes data models used to define business metrics for data sources. public.explorations: Describes research tasks performed by users. public.members: Stores information about team members. public.team_roles: Manages user roles within teams. public.member_roles: Contains information about roles for each team member. public.reports: Contains information about the structure and schedule of reports based on metrics needed by users. public.sql_credentials: Manages SQL credentials used to access business metrics through the SQL interface. public.alerts: Stores information about alerts created by users.","s":"Database Structure","u":"/docs/architecture-and-design/database-design","h":"#database-structure","p":604},{"i":609,"t":"The architecture of the database, including relationships between tables, primary and foreign keys, and indexes, is represented in the Database Markup Language (DBML). This database structure provides flexibility and scalability to the system, allowing convenient management of users, teams, data sources, reports, and other system elements. Each database table is designed for a specific purpose and can be extended or modified to meet evolving system requirements. note For the complete DBML representation of the database architecture, please refer to Database Markup Language (DBML).","s":"Database Architecture Description","u":"/docs/architecture-and-design/database-design","h":"#database-architecture-description","p":604},{"i":612,"t":"The architecture of the Synmetrix system has been meticulously designed with a focus on details, taking into account the core requirements of scalability, reliability, and flexibility. When creating the architecture, modern technologies were utilized, including the following key components:","s":"Synmetrix System Architecture","u":"/docs/architecture-and-design/system-architecture","h":"","p":610},{"i":614,"t":"Hasura plays a crucial role in the system by connecting to the database and providing a GraphQL API for external services and applications. Hasura simplifies working with the database, offering intuitive and powerful tools for quickly creating queries and mutations in GraphQL, thereby enhancing performance and optimizing data-related workflows.","s":"Hasura","u":"/docs/architecture-and-design/system-architecture","h":"#hasura","p":610},{"i":616,"t":"PostgreSQL has been chosen as the primary database management system due to its reliability, high performance, and flexibility. This allows for efficient handling of large volumes of data while ensuring excellent performance and stability.","s":"PostgreSQL","u":"/docs/architecture-and-design/system-architecture","h":"#postgresql","p":610},{"i":618,"t":"Node.js is the server-side platform used to create the backend of the application. Node.js is known for its ability to easily and rapidly develop scalable networked applications, thanks to efficient handling of asynchronous operations and event processing.","s":"Node.js","u":"/docs/architecture-and-design/system-architecture","h":"#nodejs","p":610},{"i":620,"t":"React.js is a library for developing user interfaces. It provides high performance and simplifies the development process through the use of a component-based architecture.","s":"React.js","u":"/docs/architecture-and-design/system-architecture","h":"#reactjs","p":610},{"i":622,"t":"Cube.js is an open-source analytics platform for building business analytics applications using JavaScript. In Synmetrix, Cube.js is used to manage business metrics, providing efficient tools for data processing.","s":"Cube.js","u":"/docs/architecture-and-design/system-architecture","h":"#cubejs","p":610},{"i":624,"t":"CubeStore is a distributed database optimized for analytical queries and integrated with Cube.js. It offers capabilities for fast and efficient processing of large volumes of data.","s":"CubeStore","u":"/docs/architecture-and-design/system-architecture","h":"#cubestore","p":610},{"i":626,"t":"Redis is a key-value database management system known for its high performance and flexibility. It offers unique features, including support for various data structures such as strings, lists, sets, hashes, and more.","s":"Redis","u":"/docs/architecture-and-design/system-architecture","h":"#redis","p":610},{"i":628,"t":"Docker is used for containerization and simplifying the application deployment process, ensuring consistent functionality in any environment.","s":"Docker","u":"/docs/architecture-and-design/system-architecture","h":"#docker","p":610},{"i":630,"t":"Docker Swarm is an orchestration and management tool for containers deployed using Docker. It allows for the management and scaling of applications across multiple servers, making infrastructure management easier.","s":"Docker Swarm","u":"/docs/architecture-and-design/system-architecture","h":"#docker-swarm","p":610},{"i":632,"t":"Ubuntu has been chosen as the primary operating system for the servers hosting all the services. It is a stable and reliable system well-suited for server environments.","s":"Ubuntu","u":"/docs/architecture-and-design/system-architecture","h":"#ubuntu","p":610},{"i":634,"t":"Additionally, Synmetrix has been designed as a microservices-based system. Each microservice performs a specific function and can scale independently of others, providing flexibility and scalability to the system. This architecture allows for the independent updating and modernization of individual system components, simplifying the development and maintenance process of the system as a whole.","s":"Interactions between architecture components","u":"/docs/architecture-and-design/system-architecture","h":"#interactions-between-architecture-components","p":610},{"i":636,"t":"Caching Getting started with pre-aggregations","s":"Getting started with pre-aggregations","u":"/docs/caching/getting-started-with-pre-aggregations","h":"","p":635},{"i":638,"t":"To illustrate pre-aggregations with an example, let's use a sample e-commerce database. We have a data model representing all our orders: YAML cubes: - name: orders sql_table: orders measures: - name: count type: count dimensions: - name: id sql: id type: number primary_key: true - name: status sql: status type: string - name: completed_at sql: completed_at type: time JavaScript cube(`orders`, { sql_table: `orders`, measures: { count: { type: `count`, }, }, dimensions: { id: { sql: `id`, type: `number`, primary_key: true, }, status: { sql: `status`, type: `string`, }, completed_at: { sql: `completed_at`, type: `time`, }, },}); Some sample data from this table might look like: id status completed_at 1 completed 2021-02-15T12:21:11.290 2 completed 2021-02-25T18:15:12.369 3 shipped 2021-03-15T20:40:57.404 4 processing 2021-03-13T10:30:21.360 5 completed 2021-03-10T18:25:32.109 Our first requirement is to populate a dropdown in our front-end application which shows all possible statuses. The Cube query to retrieve this information might look something like: JSON { \"dimensions\": [\"orders.status\"]} In that case, we can add the following pre-aggregation to the orders cube: YAML cubes: - name: orders # ... pre_aggregations: - name: order_statuses dimensions: - status JavaScript cube(`orders`, { // ... pre_aggregations: { order_statuses: { dimensions: [status], }, },});","s":"Pre-Aggregations without Time Dimension","u":"/docs/caching/getting-started-with-pre-aggregations","h":"#pre-aggregations-without-time-dimension","p":635},{"i":640,"t":"Development Contributing","s":"Contributing to Synmetrix","u":"/docs/development/contributing","h":"","p":639},{"i":642,"t":"One of the most valuable ways to contribute to Synmetrix is by helping answer user questions on the discussion forum or in the Slack channel. There are always new Synmetrix users seeking assistance, and providing answers is a valuable community service that showcases your expertise.","s":"Helping Other Users","u":"/docs/development/contributing","h":"#helping-other-users","p":639},{"i":644,"t":"To propose changes to Synmetrix documentation, edit the documentation source files located in the docs/ directory. The README file there explains how to build the documentation locally to test your changes. After making modifications, open a pull request to suggest the changes.","s":"Contributing to Documentation","u":"/docs/development/contributing","h":"#contributing-to-documentation","p":639},{"i":646,"t":"Reporting a bug is a simple and effective way to contribute to Synmetrix. It helps identify issues, enabling the team to provide patches and enhance the stability and usefulness of Synmetrix. Use the \"New issue\" button in the issues page to report a bug. Ensure your bug report includes a minimal executable test case to replicate the issue. Follow the instructions in the bug report template provided.","s":"Reporting Bugs","u":"/docs/development/contributing","h":"#reporting-bugs","p":639},{"i":648,"t":"Contributing bug fixes is an excellent way to build your experience and credibility within the community. If you are new to the Synmetrix codebase, start by looking at issues labeled with help wanted. If you need guidance on fixing an issue, seek assistance from experienced contributors by commenting on the issue page.","s":"Contributing Bug Fixes","u":"/docs/development/contributing","h":"#contributing-bug-fixes","p":639},{"i":650,"t":"Before contributing a new feature, submit a proposal in the issues page and discuss it with the community. This helps identify potential overlaps with planned features and prevents misunderstandings and conflicts.","s":"Proposing New Features","u":"/docs/development/contributing","h":"#proposing-new-features","p":639},{"i":652,"t":"When submitting code contributions, you must sign a Developer Certificate of Origin (DCO) to certify your authorship of the source code or your right to submit it to the project. Sign your contributions by adding a Signed-off-by line to your commit message or using the -s option with the git commit command. This is my commit messageSigned-off-by: Random J Developer ","s":"Submitting Code Changes","u":"/docs/development/contributing","h":"#submitting-code-changes","p":639},{"i":654,"t":"Development","s":"Development","u":"/docs/development","h":"","p":653},{"i":656,"t":"Requirements","s":"📄️ Local Development","u":"/docs/development","h":"","p":653},{"i":658,"t":"This guide provides information on how to contribute to Synmetrix, including code changes, helping other users, documentation updates, bug reports, bug fixes, new feature proposals, and coding standards.","s":"📄️ Contributing","u":"/docs/development","h":"","p":653},{"i":661,"t":"Synmetrix offers a robust caching system with two distinct layers to optimize query performance and minimize database load. This system enhances the efficiency of data retrieval and processing. The primary caching layer is an in-memory cache, which is active by default. The second layer is called \"pre-aggregations\" and requires explicit configuration to activate.","s":"Overview","u":"/docs/caching","h":"","p":659},{"i":663,"t":"Synmetrix's in-memory cache serves as a temporary buffer for your database. It becomes especially valuable when multiple concurrent users request the same data simultaneously. While pre-aggregations are designed to strike a balance between query response time and performance, the in-memory cache helps smooth out spikes in data demand. In development mode, you can reset the in-memory cache by simply restarting the server. Note: It's generally not recommended to modify the default in-memory caching configuration unless it's absolutely necessary. To improve query performance, consider utilizing pre-aggregations.","s":"In-Memory Cache","u":"/docs/caching","h":"#in-memory-cache","p":659},{"i":665,"t":"Pre-aggregations introduce an additional layer of aggregated data, constructed and updated by Synmetrix. They can significantly enhance query performance and concurrency. To create pre-aggregations, Synmetrix might require write access to the pre-aggregations schema within the source database. In this scenario, Synmetrix initially builds pre-aggregations as tables in the source database and then exports them to the pre-aggregations storage. Refer to your specific driver's documentation for details on read-only support and pre-aggregation build strategies. Pre-aggregations are defined within the data model. You can learn more about defining pre-aggregations in the data modeling reference. Using YAML Using JavaScript cubes: - name: orders sql_table: orders measures: - name: total_amount sql: amount type: sum dimensions: - name: created_at sql: created_at type: time pre_aggregations: - name: amount_by_created measures: - total_amount time_dimension: created_at granularity: month cube(`orders`, { sql_table: `orders`, measures: { total_amount: { sql: `amount`, type: `sum`, }, }, dimensions: { created_at: { sql: `created_at`, type: `time`, }, }, pre_aggregations: { amount_by_created: { measures: [total_amount], time_dimension: created_at, granularity: `month`, }, },","s":"Pre-Aggregations","u":"/docs/caching","h":"#pre-aggregations","p":659},{"i":667,"t":"Synmetrix utilizes an in-memory cache to store the results of executed queries. The cache key is generated from the SQL statement, including any existing pre-aggregations that the query depends on. Upon receiving an incoming request, Synmetrix first checks the cache using this key. If no match is found in the cache, the query is executed in the database. The result set is returned and simultaneously updates the cache. If a cached value exists, and the refresh_key value for the query remains unchanged, the cached value is returned. Otherwise, an SQL query is executed either against the pre-aggregations storage or the source database to refresh the cache and retrieve the updated results.","s":"In-Memory Cache Operation","u":"/docs/caching","h":"#in-memory-cache-operation","p":659},{"i":669,"t":"To avoid unnecessary database queries, Synmetrix defines a refresh_key for each cube. These refresh keys are evaluated to determine if data needs to be refreshed. Here's an example of a refresh_key that instructs Synmetrix to refresh data every 5 minutes: YAML cubes: - name: orders # ... refresh_key: every: 5 minutes JavaScript cube(`orders`, { refresh_key: { every: `5 minute`, },}); Alternatively, the following refresh_key only refreshes data when the value of MAX(created_at) changes. By default, Synmetrix checks this refresh key every 10 seconds: YAML cubes: - name: orders # ... refresh_key: sql: SELECT MAX(created_at) FROM orders JavaScript cube(`orders`, { // ... refresh_key: { sql: `SELECT MAX(created_at) FROM orders`, },});","s":"Refresh Keys","u":"/docs/caching","h":"#refresh-keys","p":659},{"i":671,"t":"By default, in development mode, Synmetrix will check and invalidate the cache in the background. For production environments, it's recommended to run a Refresh Worker as a separate instance. We strongly recommend enabling background cache invalidation using a separate Synmetrix worker for production deployments. Please consult the Production Checklist for more information. If background refresh is disabled, Synmetrix will refresh the cache during query execution. To ensure optimal response times for end-users, it's advisable to always enable background refresh.","s":"Default Refresh Keys","u":"/docs/caching","h":"#default-refresh-keys","p":659},{"i":673,"t":"To gain insights into whether a query interacts with the in-memory cache, pre-aggregations, or the underlying data source, you can utilize the Synmetrix Playground or Synmetrix Cloud.","s":"Inspecting Queries","u":"/docs/caching","h":"#inspecting-queries","p":659},{"i":675,"t":"Development Local Development","s":"Local Development","u":"/docs/development/local-development","h":"","p":674},{"i":677,"t":"Before using the backend-side of the project, ensure that you have the following components installed: Docker Node.js (Version 20.8.1 or above) Yarn","s":"Requirements","u":"/docs/development/local-development","h":"#requirements","p":674},{"i":680,"t":"Clone the Synmetrix repository: git clone https://github.com/mlcraft-io/mlcraft.gitcd mlcraft Run the initialization script: bash init.sh","s":"Getting Started","u":"/docs/development/local-development","h":"#getting-started","p":674},{"i":683,"t":"Before using the client-side of the project, ensure that you have the following components installed: Synmetrix Backend: Ensure the Synmetrix backend is properly set up and running.","s":"Requirements","u":"/docs/development/local-development","h":"#requirements-1","p":674},{"i":685,"t":"To install the client-side of the project, follow these steps: # Clone the Synmetrix Client repositorygit clone https://github.com/mlcraft-io/client-v2# Navigate to the project directorycd client-v2# Install required packages using Yarnyarn# Start the client-side applicationyarn start After completing these steps, open your web browser and go to to access the Synmetrix client.","s":"Getting Started","u":"/docs/development/local-development","h":"#getting-started-1","p":674},{"i":687,"t":"For detailed guidance on utilizing the Synmetrix client and exploring its features, please consult the Synmetrix Documentation.","s":"Usage","u":"/docs/development/local-development","h":"#usage","p":674},{"i":689,"t":"Explore the interactive UI components and functionalities of the Synmetrix client using Storybook. Storybook provides a convenient environment to showcase and test UI components in isolation. To launch Storybook, use the following command: yarn storybook Once the command is executed, navigate to","s":"Storybook Integration","u":"/docs/development/local-development","h":"#storybook-integration","p":674},{"i":691,"t":"Maintain code consistency and quality in the Synmetrix client by implementing linting. Follow these steps to configure linting in Visual Studio Code (VS Code): Requirements:​ ESLint Extension Prettier Extension Configuration:​ Install ESLint and Prettier VS Code extensions. Open your VS Code settings (settings.json) and add the following configuration: { \"eslint.enable\": true, \"eslint.format.enable\": true, \"editor.formatOnSave\": true} This configures VS Code to run ESLint and Prettier on file save.","s":"Code Linting","u":"/docs/development/local-development","h":"#code-linting","p":674},{"i":693,"t":"In the Synmetrix project, components follow a consistent structure to enhance maintainability and organization. Each component typically consists of the following files: index.tsx: The main file containing the component's implementation. This file includes the React component code. index.module.less: A Less file containing the component's styles. Using module.less ensures that styles are scoped to the component and won't conflict with styles from other components. index.stories.tsx: A file dedicated to Storybook stories for the component. Storybook stories help in visually testing and documenting the component's variations and use cases. index.test.tsx: The file containing unit tests for the component using testing framework vitest. Writing tests ensures the reliability and correctness of the component's functionality. Here's an example directory structure for a component named ExampleComponent: /src /components /ExampleComponent index.tsx index.module.less index.stories.tsx index.test.tsx","s":"Component Structure","u":"/docs/development/local-development","h":"#component-structure","p":674},{"i":695,"t":"Ensure the reliability and correctness of the Synmetrix client by utilizing testing functionalities powered by Vitest. Vitest provides a robust testing framework to validate the behavior of your components and functionalities. To run tests, use the following command: yarn test Executing this command will initiate the testing suite, allowing you to assess the client's performance and functionality. Any detected issues or failures will be highlighted, providing valuable insights for debugging and maintaining code quality.","s":"Testing","u":"/docs/development/local-development","h":"#testing","p":674},{"i":697,"t":"Efficiently build and package the Synmetrix client with the following commands: Build the Client: yarn build This command utilizes Vite to build the Synmetrix client. Post-Build Tasks: yarn postbuild After the build, this command creates compressed archives (dist.tar.gz and dist.zip) of the generated distribution files. Build Storybook: yarn build-storybook Use this command to build the Storybook for the client. Serve the Built Client Locally: yarn serve This command previews the built client locally using Vite. Incorporate these commands into your workflow to streamline the build process and prepare the Synmetrix client for deployment.","s":"Build","u":"/docs/development/local-development","h":"#build","p":674},{"i":699,"t":"We adhere to the Conventional Commits specification to ensure consistent and meaningful commit messages. The structure of our commits follows the pattern: [optional scope]: [optional body][optional footer(s)] For a detailed understanding of Conventional Commits, refer to the Conventional Commits specification.","s":"Conventional Commits","u":"/docs/development/local-development","h":"#conventional-commits","p":674},{"i":702,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster. Synmetrix provides a CLI tool for managing services and stacks, which can be utilized for deploying Synmetrix on a Docker Swarm cluster.","s":"Docker Swarm deployment guide","u":"/docs/docker-swarm","h":"","p":700},{"i":704,"t":"Go to the Synmetrix project directory and run the following commands:","s":"Start Synmetrix on Docker Swarm","u":"/docs/docker-swarm","h":"#start-synmetrix-on-docker-swarm","p":700},{"i":706,"t":"./scripts/run-registry.sh","s":"Step 1 - Run Docker Registry","u":"/docs/docker-swarm","h":"#step-1---run-docker-registry","p":700},{"i":708,"t":"DOMAIN=localhost REGISTRY_HOST=127.0.0.1:50001 ./cli.sh swarm up --init --build --env stage synmetrix Where DOMAIN is the domain name of your Synmetrix instance and REGISTRY_HOST is the host of the Docker registry.","s":"Step 2 - Run Synmetrix services","u":"/docs/docker-swarm","h":"#step-2---run-synmetrix-services","p":700},{"i":710,"t":"./migrate.sh","s":"Step 3 - Run migrations","u":"/docs/docker-swarm","h":"#step-3---run-migrations","p":700},{"i":712,"t":"./cli.sh swarm destroy synmetrix","s":"Destroy stack","u":"/docs/docker-swarm","h":"#destroy-stack","p":700},{"i":714,"t":"./cli.sh swarm logs synmetrix_hasura","s":"Show logs","u":"/docs/docker-swarm","h":"#show-logs","p":700},{"i":716,"t":"./cli.sh swarm ps synmetrix For more information about the CLI tool, run the following command: ./cli.sh swarm --help","s":"Show stack tasks status","u":"/docs/docker-swarm","h":"#show-stack-tasks-status","p":700},{"i":718,"t":"APIs & integrations Excel","s":"Excel","u":"/docs/integration-examples/excel","h":"","p":717},{"i":720,"t":"APIs & integrations Power BI","s":"Power BI","u":"/docs/integration-examples/power-bi","h":"","p":719},{"i":722,"t":"License","s":"License","u":"/docs/license","h":"","p":721},{"i":724,"t":"MIT License","s":"📄️ License & Community","u":"/docs/license","h":"","p":721},{"i":726,"t":"License License & Community","s":"License and Community","u":"/docs/license/license-community","h":"","p":725},{"i":728,"t":"Links and Contacts","s":"Links and Contacts","u":"/docs/links-and-contacts","h":"","p":727},{"i":730,"t":"GitHub Repository Slack Channel Docker Hub Meeting Calendar","s":"Useful Links and Contacts","u":"/docs/links-and-contacts","h":"#useful-links-and-contacts","p":727},{"i":732,"t":"Overview","s":"Overview","u":"/docs/overview","h":"","p":731},{"i":734,"t":"In today's data-driven world, the challenges of data management are ever-increasing, especially in fields that rely heavily on data processing, analysis, and transmission. Inefficient data management can result in lost productivity, missed opportunities, and even regulatory compliance issues. The need for a comprehensive solution to address these complexities has never been more pressing. Synmetrix is committed to tackling this challenge head-on and providing a robust solution to streamline data management, analysis, and transmission.","s":"The Data Management Challenge","u":"/docs/overview","h":"#the-data-management-challenge","p":731},{"i":736,"t":"With Synmetrix, you can effortlessly address the complexity and inefficiency in managing, analyzing, and transmitting data across various sectors. Our platform offers powerful features for creating and managing data models, tracking metric changes, and organizing data-related tasks. You can easily share your data with others through interfaces like REST, GraphQL, or SQL, making your data a single source of truth for your entire organization. Whether you're a data engineer, analyst, or manager, Synmetrix has everything you need to excel with big data.","s":"Software Description and Purpose","u":"/docs/overview","h":"#software-description-and-purpose","p":731},{"i":738,"t":"Synmetrix's versatile features find applications across a wide range of sectors, including finance, retail, the power industry, manufacturing, research and development, healthcare, and many more. As the global big data market continues to boom, with projections reaching $103 billion by 2027 and a compound annual growth rate of 10.9%, Synmetrix is poised to seize this massive opportunity. We're here to provide the solution to the complexity and inefficiency in data management, analysis, and transmission.","s":"Tapping into a Vast Market","u":"/docs/overview","h":"#tapping-into-a-vast-market","p":731},{"i":740,"t":"Data Modeling (Semantic layer): Transform raw data into meaningful business indicators with our effective data modeling feature. SQL Interface: Access metrics easily and manipulate data with our intuitive SQL interface. Reporting System: Schedule reports and distribute them through various communication channels with our integrated reporting system. Alerting System: Stay informed about significant data changes with our proactive alerting system. Documentation Generation: Enhance the understanding and usability of your data models with our documentation generation feature.","s":"Key Features","u":"/docs/overview","h":"#key-features","p":731},{"i":742,"t":"JavaScript / Node.js","s":"Programming Language","u":"/docs/overview","h":"#programming-language","p":731},{"i":744,"t":"To make the most of this documentation, you can explore the following sections: Quick Start: Installation and initial setup of Synmetrix. User Guide: In-depth guides on using Synmetrix. Contributing: Information on how to contribute to the development of Synmetrix.","s":"How to Use This Documentation","u":"/docs/overview","h":"#how-to-use-this-documentation","p":731},{"i":746,"t":"We welcome contributions from the community. If you'd like to get involved in the development of Synmetrix or have ideas for improvement, please check out our Contributing Guidelines.","s":"Contributing","u":"/docs/overview","h":"#contributing","p":731},{"i":748,"t":"If you have questions, feedback, or need assistance, don't hesitate to reach out to us. Contact us. or by visiting our GitHub repository. Synmetrix is meticulously designed to streamline your data management, analysis, and transmission workflows, making it the ultimate tool for all your data needs. We look forward to your involvement and contributions!","s":"Contact Us","u":"/docs/overview","h":"#contact-us","p":731},{"i":750,"t":"Production Deployment","s":"Production Deployment","u":"/docs/production-deployment","h":"","p":749},{"i":752,"t":"For deploying Synmetrix in a production environment, Docker Swarm is recommended. This guide will assist you in setting up Synmetrix on a Docker Swarm cluster.","s":"📄️ Docker Swarm","u":"/docs/production-deployment","h":"","p":749},{"i":754,"t":"Software Requirements","s":"📄️ Requirements","u":"/docs/production-deployment","h":"","p":749},{"i":756,"t":"Production Deployment Requirements","s":"Requirements","u":"/docs/production-deployment/requirements","h":"","p":755},{"i":758,"t":"Docker Node.js (Version 20.8.1 or above) Yarn","s":"Software Requirements","u":"/docs/production-deployment/requirements","h":"#software-requirements","p":755},{"i":760,"t":"Synmetrix is intended for installation and use on servers equipped with processors supporting x86-64 architecture and based on operating systems such as Ubuntu Server 18.04 and higher. Minimum Hardware Requirements: - Processor (CPU): 3.2 GHz or higher, modern processor with multi-threading and virtualization support.- RAM: 8 GB or more to handle computational tasks and data processing.- Disk Space: At least 30 GB of free space for software installation and storing working data.- Network: Internet connectivity is required for cloud services and software updates. note These requirements are minimum and may vary depending on specific use cases and data processing volumes. Scaling the solution may require more powerful servers or distributed systems.","s":"Hardware Requirements","u":"/docs/production-deployment/requirements","h":"#hardware-requirements","p":755},{"i":762,"t":"You can access the source code of Synmetrix on GitHub.","s":"Source Code","u":"/docs/production-deployment/requirements","h":"#source-code","p":755},{"i":764,"t":"Quick Start","s":"Quick Start","u":"/docs/quickstart","h":"","p":763},{"i":766,"t":"Ensure the following software is installed before proceeding: Docker Docker Compose","s":"Prerequisite Software","u":"/docs/quickstart","h":"#prerequisite-software","p":763},{"i":768,"t":"The repository mlcraft-io/mlcraft/install-manifests houses all the necessary installation manifests for deploying Synmetrix anywhere. You can download the docker compose file from this repository: Execute this in a new directory Using wget Using curl wget https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml curl https://raw.githubusercontent.com/mlcraft-io/mlcraft/main/install-manifests/docker-compose/docker-compose.yml -o docker-compose.yml","s":"Step 1: Download the docker-compose file","u":"/docs/quickstart","h":"#step-1-download-the-docker-compose-file","p":763},{"i":770,"t":"Execute the following command to start Synmetrix along with a Postgres database for data storage. $ docker-compose pull stack && docker-compose up -d Verify if the containers are operational: $ docker psCONTAINER ID IMAGE ... CREATED STATUS PORTS ...c8f342d086f3 synmetrix/stack ... 1m ago Up 1m 80->8888/tcp ...30ea14ddaa5e postgres:12 ... 1m ago Up 1m 5432/tcp The installation of all dependencies will take approximately 5-7 minutes. Wait until you see the Synmetrix Stack is ready message. You can view the logs using docker-compose logs -f to confirm if the process has completed.","s":"Step 2: Launch Synmetrix","u":"/docs/quickstart","h":"#step-2-launch-synmetrix","p":763},{"i":772,"t":"You can access Synmetrix at http://localhost/ The GraphQL endpoint is located at http://localhost/v1/graphql The Admin Console (Hasura Console) can be found at http://localhost/console The Cube Swagger API can be found at http://localhost:4000/docs note Admin Console Access: Ensure to check HASURA_GRAPHQL_ADMIN_SECRET in the docker-compose file. This is mandatory for accessing the Admin Console. The default value is adminsecret. Remember to modify this in a production environment. Environment Variables: Set up all necessary environment variables. Synmetrix will function with the default values, but certain features might not perform as anticipated. Preloaded Seed Data: The project is equipped with preloaded seed data. Use the credentials below to sign in: Email: demo@synmetrix.org Password: demodemo This account is pre-configured with two demo datasources and their respective SQL API access. For SQL operations, you can use the following credentials with any SQL client tool such as DBeaver or TablePlus: Host Port Database User Password localhost 15432 db demo_pg_user demo_pg_pass localhost 15432 db demo_clickhouse_user demo_clickhouse_pass If you need assistance or want to schedule a 30-minute call with our team to help you get set up, please select a time directly here.","s":"Step 3: Explore Synmetrix","u":"/docs/quickstart","h":"#step-3-explore-synmetrix","p":763},{"i":774,"t":"Demo: app.synmetrix.org Login: demo@synmetrix.org Password: demodemo","s":"Demo online","u":"/docs/quickstart","h":"#demo-online","p":763},{"i":776,"t":"Database type Host Port Database User Password SSL ClickHouse gh-api.clickhouse.tech 443 default play no password true PostgreSQL demo-db-examples.cube.dev 5432 ecom cube 12345 false","s":"Demo datasource credentials","u":"/docs/quickstart","h":"#demo-datasource-credentials","p":763},{"i":780,"t":"In the development of Synmetrix, the establishment of a robust SQL interface plays a pivotal role. This interface serves as a unified platform for accessing data sources, ensuring consistent metrics are available to all data consumers. It addresses the challenge of maintaining data integrity and precision across various tools employed by modern organizations.","s":"Introduction","u":"/docs/sql-interface","h":"#introduction","p":777},{"i":782,"t":"Many modern organizations utilize numerous tools to process data from a shared storage but perform individual metric calculations. This often leads to discrepancies in calculations and disparities among different teams. The more tools an organization employs, the greater the potential for inconsistencies, making data-driven decision-making complex. Hence, it is essential to create a centralized hub for defining and maintaining metrics, ensuring that all tools operate with a single source of data.","s":"Problem Statement","u":"/docs/sql-interface","h":"#problem-statement","p":777},{"i":784,"t":"To tackle this challenge, Synmetrix has chosen the Cube.js framework, successfully integrated into the Synmetrix platform. Cube.js offers a significant advantage in its ability to act as a data source proxy, transforming incoming queries—whether in JSON, GraphQL, or SQL format—into native queries to the underlying data store.","s":"Solution: Leveraging Cube.js","u":"/docs/sql-interface","h":"#solution-leveraging-cubejs","p":777},{"i":786,"t":"Creating the SQL interface within Synmetrix presented several challenges effectively addressed using the Cube.js framework and Apache Datafusion. This SQL interface enables Cube.js to provide data through a PostgreSQL-compatible protocol to various data-consuming applications, including but not limited to: BI tools Data exploration tools such as Jupyter or Hex Reverse ETL tools like Census or Hightouch Low-code development platforms such as Retool Automated tasks The SQL interface has been thoroughly tested with a range of tools and platforms, including: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch The SQL API utilizes Apache Datafusion as its SQL execution engine, responsible for query planning and execution.","s":"Implementation of SQL Interface in Synmetrix","u":"/docs/sql-interface","h":"#implementation-of-sql-interface-in-synmetrix","p":777},{"i":788,"t":"The implementation of the SQL interface in Synmetrix comprises several key steps, facilitated by the Cube.js framework and Apache Datafusion: Request Retrieval: Incoming data retrieval requests arrive in SQL format to Cube.js, serving as the initial queries that require processing and execution. Query Transformation: Cube.js transforms the incoming SQL queries into its own query format (JSON). This transformation optimizes and prepares the query for execution against the underlying data store. Query Execution: The SQL query generated in the previous step is sent to the data source. This source can be any supported database to which Cube.js can connect. Data Retrieval: The output is a dataset containing the required data in response to the original query. Load into Apache Datafusion: The dataset is loaded into Apache Datafusion, where it undergoes further processing. Apache Datafusion serves as the SQL execution engine, responsible for query planning and execution. Execution of the Original SQL Query: The original SQL query received by the server is executed within Apache Datafusion. This allows for the full utilization of SQL's capabilities for data analysis and processing while preserving the structure and semantics of the original query. Data Return: Query results are returned through Cube.js, where they can be further processed or directly delivered to end-users. This process ensures high flexibility and power when working with queries while maintaining data consistency and coherence for all consumers. Each stage has been meticulously designed and optimized to ensure efficient query execution and overall system performance.","s":"Implementation Details","u":"/docs/sql-interface","h":"#implementation-details","p":777},{"i":790,"t":"A notable feature of the SQL interface in Synmetrix is how it handles and transforms SQL queries. As previously mentioned, Cube.js converts incoming SQL queries into its own query format (JSON), a process known as \"Cube query rewrite.\" This transformation allows for optimization and query preparation for execution against the underlying data store. Let's illustrate this with an example query to the \"orders\" cube in our data model: Original SQL Query: SELECT city, SUM(amount)FROM ordersWHERE status = 'shipped'GROUP BY 1 This SQL query in the SELECT statement to the \"orders\" cube is transformed into Cube.js' internal query format: Transformed Cube.js Query: { \"measures\": [\"Orders.amount\"], \"dimensions\": [\"Orders.city\"], \"filters\": [ { \"member\": \"Orders.status\", \"operator\": \"equals\", \"values\": [\"shipped\"] } ]} As a result of this transformation, not all functions and expressions are supported within query fragments that execute SELECT statements on cube tables. For example, the following query would not work because the SQL API cannot pass the CASE expression to Cube.js for processing, making it impossible to translate the CASE expression into a metric: Non-supported Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(number)FROM orders CROSS JOIN UsersGROUP BY 1; However, in such cases, nested queries can be employed. You can encapsulate your SELECT query from a cube table within another SELECT query to perform calculations with expressions like CASE. This outer SELECT is not part of the rewritten SQL query, allowing you to use more SQL functions, operators, and expressions. Example Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT Users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN Users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; The above query works because the CASE expression is supported in SELECT queries that do not reference cube tables. These nuances and query handling specifics should be considered when designing and utilizing the SQL interface in Synmetrix.","s":"Transformation of SQL Queries","u":"/docs/sql-interface","h":"#transformation-of-sql-queries","p":777},{"i":792,"t":"For a detailed analysis of the server code and its components, please refer to the Synmetrix SQL Interface Server Code.","s":"Server Code","u":"/docs/sql-interface","h":"#server-code","p":777},{"i":794,"t":"The creation of the SQL interface in Synmetrix has allowed for the development of a unified data access interface, ensuring metric consistency for all data tools. This has reduced the number of discrepancies and conflicts related to metric calculations, providing a more reliable foundation for data-driven decision-making.","s":"Conclusion","u":"/docs/sql-interface","h":"#conclusion","p":777},{"i":798,"t":"For user support and inquiries, you can reach out to us through the following channels: Email Support: hello@synmetrix.org Issue Tracker: GitHub Issues Our support team is dedicated to assisting you with any questions or problems you might encounter while using Synmetrix.","s":"Contact Information","u":"/docs/support-updates","h":"#contact-information","p":795},{"i":800,"t":"We are continuously working to improve Synmetrix and provide you with the best possible experience. Here's a glimpse of our upcoming updates and changes: GitHub","s":"Upcoming Updates and Changes","u":"/docs/support-updates","h":"#upcoming-updates-and-changes","p":795},{"i":802,"t":"Testing and Debugging","s":"Testing and Debugging","u":"/docs/testing-and-debugging","h":"","p":801},{"i":804,"t":"3 items","s":"🗃️ Testing Guides","u":"/docs/testing-and-debugging","h":"","p":801},{"i":807,"t":"Testing and Debugging Testing Guides","s":"Testing Guides","u":"/docs/testing","h":"","p":806},{"i":809,"t":"12 items","s":"🗃️ Test a Data Source","u":"/docs/testing","h":"","p":806},{"i":811,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"📄️ Test SQL Interface for Data Models","u":"/docs/testing","h":"","p":806},{"i":813,"t":"In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI.","s":"📄️ Test Structure","u":"/docs/testing","h":"","p":806},{"i":818,"t":"Welcome to the SQL Interface Testing Guide for Data Models. This comprehensive guide will walk you through the process of testing SQL interfaces for data models within our system.","s":"SQL Interface Testing Guide for Data Models","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"","p":816},{"i":820,"t":"Before you begin testing, you'll need the following Postgres database credentials: Type: Postgres Host: demo-db.cube.dev Port: 5432 User: cube Password: 12345 Database Name: ecom","s":"Section 1: Test Database Credentials","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-1-test-database-credentials","p":816},{"i":822,"t":"To add an SQL interface, follow these steps: Go to the Settings > SQL API and Click to the \"Attach SQL API\" button. Select the data source for connection; in our case, it's \"Ecom (demo db).\" Enter your login and password or use the default ones (login: \"test\" and password: \"test\"). Click \"Connect new.\"","s":"Section 2: Adding SQL Interface","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-2-adding-sql-interface","p":816},{"i":824,"t":"Refer to the guide in Appendix V.2 for generating data models. For \"Ecom (demo db),\" you'll need to generate data models for the following tables: Line Items Line Items Count by States Orders Product Categories Products Suppliers Users This will result in the following list of data models: LineItems.yml LineItemsCountByStates.yml Orders.yml ProductCategories.yml Products.yml Suppliers.yml Users.yml Each script is labeled with its corresponding name, which is also the table name when accessing via the SQL interface.","s":"Section 3: Generating Data Models","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-3-generating-data-models","p":816},{"i":826,"t":"We recommend using DBeaver of any version (https://dbeaver.io/) for connecting. However, our connection has also been tested with the following tools: psql CLI Apache Superset Tableau Cloud Tableau Desktop with JDBC driver Power BI Metabase Google Data Studio Excel through Devart plugin Deepnote Hex Observable Streamlit Jupyter notebook Hightouch","s":"Section 4: Connecting via SQL Interface with a Postgres Client","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#section-4-connecting-via-sql-interface-with-a-postgres-client","p":816},{"i":828,"t":"Create a new PostgresSQL connection. Enter the credentials provided when setting up the SQL interface. In this example: Host: localhost Port: 15432 Database: ecom (you can specify any value) Username: test Password: test Click \"Test Connection\" to verify the connection. Click \"Finish\" and save the connection. Go to the SQL console to test SQL queries against the data models.","s":"4.1: Connecting via DBeaver","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#41-connecting-via-dbeaver","p":816},{"i":830,"t":"Start writing queries for the generated data models following the steps outlined in Appendix D.3. 4.2.1: Sample SQL Queries​ Query: SELECT * FROM orders ORDER BY created_at LIMIT 3; Response: Query: SELECT p.name, SUM(o.count) FROM orders o CROSS JOIN products p GROUP BY 1 LIMIT 5; Response: Query: SELECT MEASURE(count), status, DATE_TRUNC('month', created_at) date FROM orders GROUP BY date, status ORDER BY date ASC; Response: Query: SELECT city, CASE WHEN status = 'shipped' THEN 'done' ELSE 'in-progress' END real_status, SUM(amount) AS totalFROM ( SELECT users.city AS city, SUM(number) AS amount, orders.status FROM orders CROSS JOIN users GROUP BY 1, 3) AS innerGROUP BY 1, 2ORDER BY 1; Response:","s":"4.2: Testing SQL Queries","u":"/docs/testing-and-debugging/testing/test-sql-data-models","h":"#42-testing-sql-queries","p":816},{"i":832,"t":"Testing and Debugging Testing Guides Test a Data Source","s":"Test a Data Source","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":834,"t":"Tested on the crate:5.3.0 image.","s":"📄️ CrateDB","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":836,"t":"To connect to a Databricks datasource via JDBC, you will need the following information:","s":"📄️ Databricks","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":838,"t":"Tested on the dremio/dremio-oss:latest image.","s":"📄️ Dremio","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":840,"t":"Step 1: Navigate to the Druid example directory","s":"📄️ Druid","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":842,"t":"Step 1: Navigate to the Elasticsearch Example Directory","s":"📄️ Elasticsearch","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":844,"t":"Connecting to Firebolt datasource","s":"📄️ Firebolt","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":846,"t":"Tested on the confluentinc/cp-ksqldb-server:7.0.9 image.","s":"📄️ KSQL","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":848,"t":"Tested on the materialize/materialized:latest image.","s":"📄️ Materialize","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":850,"t":"Tested on the starburstdata/presto image.","s":"📄️ Presto","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":852,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"📄️ QuestDB","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":854,"t":"This guide provides step-by-step instructions on how to connect Snowflake to Synmetrix.","s":"📄️ Snowflake","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":856,"t":"Tested on the trinodb/trino:latest image.","s":"📄️ Trino","u":"/docs/testing-and-debugging/tests/data-source","h":"","p":831},{"i":859,"t":"info In the development process of Synmetrix, continuous testing plays a crucial role in ensuring system stability and reliability. To automate this process, we utilize the Continuous Integration (CI) tool, specifically the open-source platform StepCI. StepCI is employed for writing and conducting integration tests, which verify the correctness of the system's operation when its various components interact with each other. This is particularly vital for projects like Synmetrix, which consist of numerous services and components working collaboratively.","s":"Testing Structure for Synmetrix","u":"/docs/testing-and-debugging/test-structure","h":"","p":857},{"i":861,"t":"All test files are located in the test/stepci directory within the Synmetrix project folder. Each file represents an individual test scenario described in YAML format. - name: change_password http: url: ${{env.HASURA_PLUS_ENDPOINT}}/auth/change-password method: POST headers: Content-Type: application/json Authorization: Bearer ${{captures.accessToken}} body: | { \"old_password\": \"pass321\", \"new_password\": \"pass123\" } check: status: 204 This YAML configuration represents a typical StepCI test scenario named \"change_password.\" It performs a POST request with specific headers and a JSON request body to change a password. The response status is checked to ensure it's 204.","s":"Test Structure","u":"/docs/testing-and-debugging/test-structure","h":"#test-structure","p":857},{"i":863,"t":"Testing on the StepCI platform typically involves the following steps: Request Creation: StepCI generates a request based on the input data defined in the test scenario. Request Sending: The request is sent to the tested endpoint in the Synmetrix system. Waiting for Response: StepCI waits for a response from the Synmetrix system. During this time, all necessary actions are performed, such as request processing, interaction with the database, and other services. Response Verification: After receiving the response, StepCI compares it with the expected result defined in the test scenario. If the response matches the expectations, the test is considered passed. Otherwise, StepCI logs an error, and the test is marked as failed. This testing structure ensures the reliability and stability of the Synmetrix system. Automated testing allows developers to quickly identify and rectify errors, as well as ensure that new changes do not disrupt the functionality of existing features. Upon completing a set of tests, StepCI provides a report with a brief description of the tests that were passed. PASS datasource_flow 9.458s 1 1620 bytes 1 16006 bytesTests: 0 failed, 1 passed, 1 totalSteps: 0 failed, 0 skipped, 18 passed, 18 totalTime: 9,533s, estimated 10sCO2:0.04036gWorkflow passed after 9.533s Test was passed. In the event of a test failure, StepCI records details, including the name of the failed testing step, input data and parameters, actual and expected results, and error messages obtained during the execution of the tested code. Subsequent test steps are halted upon encountering a failure. v create_team passed after 0.041sv invite_team_member passed after 0.597s v create_datasource passed after 0.021s v check_connection passed after 0.833s v validate_datasource passed after 0.078s v fetch_tables passed after 0.436s × gen_dataschemas failed after 0.008s A create_exploration skipped after 0s A fetch_dataset skipped after Os A export_data_models skipped after 0s A fetch_meta skipped after 0s A run_query skipped after Os• datasource_flow › gen_dataschemasRequest HTTPPOST http://hasura:8080/v1/graphql HTTP/1.1Content-Type: application/jsonAuthorization: Bearer eyJhbci01JIUzI1N1J9.eyJoYXN1cmEiOnsieC1oYXN1cmEtdXNlci1pZCI6IjU5YjhjOTVjLTdIMmEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMi0\\sidXNlciJdLCJ4LWhhc3VyYS1kzwZhdWx0LXJvbGUi0iJ1c2VyInosInN1Yi16IjU5YjhjOTVjLTdl™mEtNDc5Ny1hNjZjLWU1MTc3NmNjZDM5ZSIsImlzcyI6Im5ob3N0IiwiaWF0Ijo×NjkwoDkoNzcOLCJleHAi0jE20TE1NDI3NzR9.DTtp9T†jNg6ABm1vV7eB0KXZw6k1d2iZx6C-DFicCw™,x-hasura-user-id: 59b8c95c-7e2a-4797-a66c-e51776ccd39e{\"query\": \"mutation (#datasource_id: uuid!, $tables: [SourceTable!]!) f\\n gen_dataschemas (d atasource_id: $datasource_id, tables: $tables) f\\n code\\nmessageln FInrIn\"， \"variables\" :{\"datasource_id\":\"9218058b-160c-4c43-8126-b944612c055f\"\"tables\": K{\"name\": \"default/celltowers\"},{\"name\": \"default/checks\"}1}}ResponseHTTP/1.1 200 OKtransfer-encoding: chunkeddate: Tue 01 Aug 2023 12:59:36 GMTserver: Warp/3.3.23content-type: application/json; charset=utf-8{\"errors\": [\"extensions\":{\"code\": \"validation-failed\", \"path\":\"$.selectionSet. gen_dataschemas args.branch_id\"}, \"message\":\"missing required field 'branch_id'\"}1}ChecksJSONPath× $. data.gen_dataschemas.code: undefined (expected ok) Test was failed.","s":"Testing Algorithm","u":"/docs/testing-and-debugging/test-structure","h":"#testing-algorithm","p":857},{"i":865,"t":"Testing and Debugging Testing Guides Test a Data Source CrateDB","s":"Crate","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"","p":864},{"i":867,"t":"Navigate to the CrateDB example directory located at /docs/examples/crate in the root of the project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/crate","s":"Step 1: Navigate to the CrateDB example directory","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-1-navigate-to-the-cratedb-example-directory","p":864},{"i":869,"t":"In the CrateDB example directory, there is a script called run_crate.sh. Make sure the script is executable by running: chmod +x run_crate.sh Then, start the CrateDB service by running the script: ./run_crate.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the CrateDB service using the script","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-2-run-the-cratedb-service-using-the-script","p":864},{"i":871,"t":"Go to the UI and add a new Crate data source with the following parameters: Name: Crate Host: Host IP (obtained from the script in Step 2) Port: 4200 User: crate","s":"Step 3: Add a data source in the UI","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-3-add-a-data-source-in-the-ui","p":864},{"i":873,"t":"Click the \"Check connection\" button in the UI to verify the connection to the CrateDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/crate","h":"#step-4-test-the-connection","p":864},{"i":876,"t":"Tested on the dremio/dremio-oss:latest image.","s":"Dremio","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"","p":874},{"i":878,"t":"Navigate to the Dremio example directory located at /docs/examples/dremio in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/dremio","s":"Step 1: Navigate to the Dremio example directory","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-1-navigate-to-the-dremio-example-directory","p":874},{"i":880,"t":"In the Dremio example directory, there is a script called run_dremio.sh. Make sure the script is executable by running: chmod +x run_dremio.sh Then, start the Dremio service by running the script: ./run_dremio.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Dremio service using the script","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-2-run-the-dremio-service-using-the-script","p":874},{"i":882,"t":"Open your web browser and go to http://localhost:9047. Follow the prompts to create an administrator account. Log in to the Dremio UI with your new account. Click on \"Add Source\" and select \"PostgreSQL\" from the list of available data sources. Use the credentials provided at https://uibakery.io/sql-playground to configure the PostgreSQL data source.","s":"Step 3: Create an administrator account and add a data source in Dremio","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-3-create-an-administrator-account-and-add-a-data-source-in-dremio","p":874},{"i":884,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: host_ip (obtained from the script in Step 2) Port: 9047 Database name: Name given to the connection in Dremio User: Dremio administrator username Password: Dremio administrator password","s":"Step 4: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-4-add-a-data-source-in-the-synmetrix-ui","p":874},{"i":886,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Dremio data source. If the connection is successful, you will see a confirmation message.","s":"Step 5: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/dremio","h":"#step-5-test-the-connection","p":874},{"i":888,"t":"Testing and Debugging Testing Guides Test a Data Source Elasticsearch","s":"Elasticsearch","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"","p":887},{"i":890,"t":"Navigate to the Elasticsearch example directory located at /docs/examples/elasticsearch in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/elasticsearch","s":"Step 1: Navigate to the Elasticsearch Example Directory","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-1-navigate-to-the-elasticsearch-example-directory","p":887},{"i":892,"t":"In the Elasticsearch example directory, there is a script called run_elasticsearch.sh. Make sure the script is executable by running: chmod +x run_elasticsearch.sh Then, start the Elasticsearch service by running the script: ./run_elasticsearch.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Elasticsearch Service Using the Script","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-2-run-the-elasticsearch-service-using-the-script","p":887},{"i":894,"t":"Wait for the Elasticsearch service to be ready to accept requests. Then run: curl -X POST -u elastic:password \"http://localhost:9200/_security/user/user\" -H 'Content-Type: application/json' -d'{ \"password\": \"password\", \"roles\": [\"kibana\", \"kibana_system\"], \"full_name\": \"Test user\"}'","s":"Step 3: Wait for the Service to Be Ready and Add a User","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-3-wait-for-the-service-to-be-ready-and-add-a-user","p":887},{"i":896,"t":"Wait for the Kibana service to start, then go to http://localhost:5601 and log in with the credentials: User: elasticPassword: password Click on the Try sample data link: At the bottom of the page, click Other sample data sets: Choose a dataset and click Add data.","s":"Step 4: Access Kibana and Add Sample Data","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-4-access-kibana-and-add-sample-data","p":887},{"i":898,"t":"Now you can create a datasource in Synmetrix using the following information: Name: Elasticsearch test datasource Url: http://:9200 User: user Password: password Save the datasource and click Test connection. You should see a message indicating that the connection is OK.","s":"Step 5: Create a Datasource in Synmetrix","u":"/docs/testing-and-debugging/tests/data-source/elasticsearch","h":"#step-5-create-a-datasource-in-synmetrix","p":887},{"i":900,"t":"Testing and Debugging Testing Guides Test a Data Source Firebolt","s":"Firebolt","u":"/docs/testing-and-debugging/tests/data-source/firebolt","h":"","p":899},{"i":902,"t":"To connect to a Firebolt datasource, you will need the following information: Database Name - the name of the database to connect to. Username - the username used to connect to the database. Password - the password used to connect to the database. Engine Name - the name of the engine to connect to. API Endpoint - the Firebolt API endpoint used for authentication, such as api.dev.firebolt.io or api.app.firebolt.io.","s":"Connecting to Firebolt datasource","u":"/docs/testing-and-debugging/tests/data-source/firebolt","h":"#connecting-to-firebolt-datasource","p":899},{"i":904,"t":"Testing and Debugging Testing Guides Test a Data Source Materialize","s":"Materialize","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"","p":903},{"i":906,"t":"Navigate to the Materialize example directory located at /docs/examples/materialize in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/materialize","s":"Step 1: Navigate to the Materialize example directory","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-1-navigate-to-the-materialize-example-directory","p":903},{"i":908,"t":"In the Materialize example directory, there is a script called run_materialize.sh. Make sure the script is executable by running: chmod +x run_materialize.sh Then, start the Materialize service by running the script: ./run_materialize.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Materialize service using the script","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-2-run-the-materialize-service-using-the-script","p":903},{"i":910,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 6875 Database name: Test User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":903},{"i":912,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Materialize data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/materialize","h":"#step-4-test-the-connection","p":903},{"i":916,"t":"Navigate to the Druid example directory located at /docs/examples/druid in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/druid","s":"Step 1: Navigate to the Druid example directory","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-1-navigate-to-the-druid-example-directory","p":913},{"i":918,"t":"In the Druid example directory, there is a script called run_druid.sh. Make sure the script is executable by running: chmod +x run_druid.sh Then, start the Druid services by running the script: ./run_druid.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step. Wait for the cluster to start and go to http://localhost:8889.","s":"Step 2: Run the Druid services using the script","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-2-run-the-druid-services-using-the-script","p":913},{"i":920,"t":"Go to the Services tab and make sure all services are online, as shown in the screenshot below.","s":"Step 3: Check the services","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-3-check-the-services","p":913},{"i":922,"t":"Select the \"Load data\" tab, then choose \"Batch - classic\" from the dropdown menu. Next, select the \"Example data\" tile and click \"Load example\". Click the button with the text \"Next: ...\" in the bottom right corner until the data starts loading (about 9 times). When the data starts loading, you will be redirected to the Ingestion tab and see the message \"Task submitted successfully. Going to task view...\". After the data has finished loading, the task status will change to SUCCESS.","s":"Step 4: Load example data","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-4-load-example-data","p":913},{"i":924,"t":"Fill out the form with the following information: Name: Druid test datasource Host: Host IP (obtained from the script in Step 2) Port: 8082 Currently, the Test Connection feature is not implemented and always returns \"OK\". To ensure functionality, generate a schema and request any data in the Explore tab.","s":"Step 5: Create a new Druid datasource in Synmetrix","u":"/docs/testing-and-debugging/tests/data-source/druid","h":"#step-5-create-a-new-druid-datasource-in-synmetrix","p":913},{"i":926,"t":"Testing and Debugging Testing Guides Test a Data Source KSQL","s":"KSQL","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"","p":925},{"i":928,"t":"Navigate to the ksql example directory located at /docs/examples/ksql in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/ksql","s":"Step 1: Navigate to the ksql example directory","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-1-navigate-to-the-ksql-example-directory","p":925},{"i":930,"t":"In the ksql example directory, there is a script called run_ksql.sh. Make sure the script is executable by running: chmod +x run_ksql.sh Then, start the ksql service by running the script: ./run_ksql.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the ksql service using the script","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-2-run-the-ksql-service-using-the-script","p":925},{"i":932,"t":"Go to the Synmetrix UI and add a new datasource with the following parameters: Host: Host IP (obtained from the script in Step 2) Port: 8088","s":"Step 3: Add a datasource in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-3-add-a-datasource-in-the-synmetrix-ui","p":925},{"i":934,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to the ksql datasource. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/ksql","h":"#step-4-test-the-connection","p":925},{"i":937,"t":"Tested on the starburstdata/presto image.","s":"PrestoDB","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"","p":935},{"i":939,"t":"Navigate to the PrestoDB example directory located at /docs/examples/prestodb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/prestodb","s":"Step 1: Navigate to the PrestoDB example directory","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-1-navigate-to-the-prestodb-example-directory","p":935},{"i":941,"t":"In the PrestoDB example directory, there is a script called run_prestodb.sh. Make sure the script is executable by running: chmod +x run_prestodb.sh Then, start the PrestoDB service by running the script: ./run_prestodb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the PrestoDB service using the script","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-2-run-the-prestodb-service-using-the-script","p":935},{"i":943,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: PrestoDB Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":935},{"i":945,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the PrestoDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/presto","h":"#step-4-test-the-connection","p":935},{"i":948,"t":"Tested on the questdb/questdb:7.1.1 image.","s":"QuestDB","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"","p":946},{"i":950,"t":"Navigate to the QuestDB example directory located at /docs/examples/questdb in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/questdb","s":"Step 1: Navigate to the QuestDB example directory","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-1-navigate-to-the-questdb-example-directory","p":946},{"i":952,"t":"In the QuestDB example directory, there is a script called run_questdb.sh. Make sure the script is executable by running: chmod +x run_questdb.sh Then, start the QuestDB service by running the script: ./run_questdb.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the QuestDB service using the script","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-2-run-the-questdb-service-using-the-script","p":946},{"i":954,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: QuestDB Test Host: Host IP (obtained from the script in Step 2) Port: 8812 User: quest Password: quest","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":946},{"i":956,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the QuestDB data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/questdb","h":"#step-4-test-the-connection","p":946},{"i":958,"t":"Testing and Debugging Testing Guides Test a Data Source Snowflake","s":"Snowflake","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"","p":957},{"i":960,"t":"Before proceeding, ensure you have the following Snowflake connection details: Database Name Warehouse Organization ID Account ID Username Password Role You can find this information in your Snowflake account.","s":"Step 1: Gather Snowflake connection information","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-1-gather-snowflake-connection-information","p":957},{"i":962,"t":"Go to the Synmetrix UI and navigate to the \"Data Sources\" section. Click on the \"+ Connect\" button. Select \"Snowflake\" from the list of available data sources.","s":"Step 2: Add a new data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-2-add-a-new-data-source-in-the-synmetrix-ui","p":957},{"i":964,"t":"Fill in the Snowflake connection details in the form with the following fields: Name: A custom name for your data source (e.g., \"My Snowflake\") Database Name: The name of your Snowflake database Warehouse: The name of your Snowflake warehouse Organization ID: The ID of your Snowflake organization Account ID: The ID of your Snowflake account Username: Your Snowflake username Password: Your Snowflake password Role: Your Snowflake role","s":"Step 3: Fill in the connection details","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-3-fill-in-the-connection-details","p":957},{"i":966,"t":"Click the \"Test connection\" button in the Synmetrix UI to verify the connection to your Snowflake data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/snowflake","h":"#step-4-test-the-connection","p":957},{"i":968,"t":"Testing and Debugging Testing Guides Test a Data Source Trino","s":"Trino","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"","p":967},{"i":970,"t":"Navigate to the Trino example directory located at /docs/examples/trino in the root of the Synmetrix project. You can also download this directory from the GitHub repository. To navigate to the directory, run the following command: cd ./docs/examples/trino","s":"Step 1: Navigate to the Trino example directory","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-1-navigate-to-the-trino-example-directory","p":967},{"i":972,"t":"In the Trino example directory, there is a script called run_trino.sh. Make sure the script is executable by running: chmod +x run_trino.sh Then, start the Trino service by running the script: ./run_trino.sh After the script has finished running, it will return the host IP address of your machine. Take note of this IP address for the next step.","s":"Step 2: Run the Trino service using the script","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-2-run-the-trino-service-using-the-script","p":967},{"i":974,"t":"Go to the Synmetrix UI and add a new data source with the following parameters: Name: Trino Test Host: Host IP (obtained from the script in Step 2) Port: 8050 Catalog: tpch User: user","s":"Step 3: Add a data source in the Synmetrix UI","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-3-add-a-data-source-in-the-synmetrix-ui","p":967},{"i":976,"t":"Click the \"Check connection\" button in the Synmetrix UI to verify the connection to the Trino data source. If the connection is successful, you will see a confirmation message.","s":"Step 4: Test the connection","u":"/docs/testing-and-debugging/tests/data-source/trino","h":"#step-4-test-the-connection","p":967},{"i":979,"t":"To connect to a Databricks datasource via JDBC, you will need the following information: Access Token: A personal access token used to authenticate the Databricks connection. Databricks JDBC URL: The URL for a JDBC connection. It should be in the format: jdbc:databricks://{''}:443/default;transportMode=http;ssl=1; Database Name: The name of the database to connect to.","s":"Connecting to Databricks Datasource via JDBC","u":"/docs/usage/user-guide/data-source/databricks","h":"","p":977},{"i":981,"t":"User Guide","s":"User Guide","u":"/docs/user-guide","h":"","p":980},{"i":983,"t":"🗃️ Explore Page 2 items 📄️ Models The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page: 📄️ Logs Overview 🗃️ Settings 6 items Feel free to explore the documentation for each page to gain a comprehensive understanding of the Synmetrix interface and its functionalities. Happy exploring and analyzing data with Synmetrix!","s":"Table of Contents","u":"/docs/user-guide","h":"#table-of-contents","p":980},{"i":985,"t":"2 items","s":"🗃️ Explore Page","u":"/docs/user-guide","h":"","p":980},{"i":987,"t":"The Models page is a powerful tool for generating and managing models in your project. It allows you to define the structure and relationships of your data, ensuring consistency and integrity. Models are generated using Cube.js, a powerful open-source analytical API platform. You can refer to the Cube.js documentation for more details. Additionally, the page provides features for version controlling your schemas and executing SQL queries on the associated data sources. Here's how you can make the most of this page:","s":"📄️ Models","u":"/docs/user-guide","h":"","p":980},{"i":989,"t":"Overview","s":"📄️ Logs","u":"/docs/user-guide","h":"","p":980},{"i":991,"t":"6 items","s":"🗃️ Settings","u":"/docs/user-guide","h":"","p":980},{"i":993,"t":"User Guide Explore Page Reports","s":"Reports Page","u":"/docs/user-guide/explore/reports","h":"","p":992},{"i":995,"t":"Click the \"Add Report\" button on the Explore page. Select the destination where the screenshot will be sent. You can choose between Webhook, Slack, or Email based on your preferences and integrations. Fill out the report form with the following details:","s":"How to Create a Report","u":"/docs/user-guide/explore/reports","h":"#how-to-create-a-report","p":992},{"i":997,"t":"Report Name: Enter a name that easily identifies the purpose and content of the report.","s":"Form fields description for the Reports page:","u":"/docs/user-guide/explore/reports","h":"#form-fields-description-for-the-reports-page","p":992},{"i":999,"t":"In the \"Preview\" section, you will find crucial details for the Alert or Report, including the user-selected metric referred to as the \"Query Key.\" This metric is at the core of your monitoring process and plays a pivotal role in alerting or report generation. Here, you'll also discover various other essential components such as: Measures: These represent the quantitative data that you want to track or display. Users can choose specific measures that matter the most to their analysis, and these will be prominently featured in the Preview. Dimensions: Dimensions help to provide context and categorize your data. They allow you to break down the information based on various attributes, providing a more comprehensive view of your metrics. info In the Preview, all these elements come together to give you a snapshot of the data and insights you're tracking, ensuring that you're monitoring the right information for your analysis, whether it's for an Alert or Report.","s":"Preview:","u":"/docs/user-guide/explore/reports","h":"#preview","p":992},{"i":1001,"t":"Schedule: Configure the frequency at which the report will be executed by setting a schedule (e.g., daily, weekly, monthly).","s":"Trigger Settings:","u":"/docs/user-guide/explore/reports","h":"#trigger-settings","p":992},{"i":1003,"t":"The available fields in this section depend on the chosen delivery method for the report. Here are some common options: Webhook: Enter the URL of the webhook endpoint where the report will be sent. Slack: Provide the Slack channel or user ID to which the report will be delivered. Email: Enter the email address(es) of the recipient(s) who will receive the report. note Once you have completed the settings, click the \"Save\" button.","s":"Delivery Settings:","u":"/docs/user-guide/explore/reports","h":"#delivery-settings","p":992},{"i":1007,"t":"The Explore page serves as a dynamic and intuitive platform for users to engage with data, offering tools and options for efficient data exploration and analysis. This includes changing data sources, selecting branches, and manipulating data dimensions, measures, and filters. Additional functionalities allow users to run queries, set up alerts or reports, and export data.","s":"Overview","u":"/docs/user-guide/explore","h":"#overview","p":1004},{"i":1009,"t":"The sidebar in your data tool is where you can do a lot of important stuff. Let's go over how to change your data source, switch branches, and pick the right dimensions and measures.","s":"Sidebar Actions","u":"/docs/user-guide/explore","h":"#sidebar-actions","p":1004},{"i":1011,"t":"To switch to a different data source: Find the Data Source in the sidebar. Pick a New Data Source: Click on the list there and choose the one you want. It's Changed!: Your data source will change right away.","s":"Changing Data Source in Explorer","u":"/docs/user-guide/explore","h":"#changing-data-source-in-explorer","p":1004},{"i":1013,"t":"To change the branch you're working on: Look for Branch Section in the sidebar. Choose Your Branch: Click on the list and pick the branch you need. Branch Changed: Now you're working in the new branch.","s":"Switching Branches in Explorer","u":"/docs/user-guide/explore","h":"#switching-branches-in-explorer","p":1004},{"i":1015,"t":"Picking the right dimensions and measures is super important for looking at your data the right way. Here's what to do: Go to the Sidebar: First, find the sidebar. Click on Desired Model: Click it and you'll see a list. See All the Options: This list shows all the measures, dimensions, time dimensions and segments you can use. Pick What You Need: Choose the ones that you're willing to analyze. Run Your Query: After picking, run your query to see your data. tip Quick Find: Use the search box to find dimensions or measures fast.","s":"Selecting Dimensions and Measures","u":"/docs/user-guide/explore","h":"#selecting-dimensions-and-measures","p":1004},{"i":1017,"t":"Incorporating filters into your analysis is a crucial step for refining and focusing your data exploration. Here’s a professional approach to adding filters through the Explorer Sidebar: Navigate to Explorer Sidebar: Begin by accessing the sidebar options within the Explorer. Hover Over Measures or Dimensions: Move your cursor over either the Measures or Dimensions sections in the sidebar. Initiate Filter Selection: Look for a filter icon on the specific measure or dimension you are interested in. Click on this icon to start setting up a filter. Configure Filter in Result Tab: After clicking the filter button, switch to the \"Result\" tab. Here, you'll find options to customize your filter according to your specific data requirements.","s":"Adding Filters","u":"/docs/user-guide/explore","h":"#adding-filters","p":1004},{"i":1019,"t":"The toolbar in your data analysis tool is equipped with a variety of features to enhance your data exploration and reporting capabilities. Here's a breakdown of the key actions available:","s":"Explore Toolbar Functionality Overview","u":"/docs/user-guide/explore","h":"#explore-toolbar-functionality-overview","p":1004},{"i":1021,"t":"Function: Runs the analysis based on the selected dimensions, measures, and filters. Purpose: To dynamically generate and view data results tailored to your current selections.","s":"Run Query","u":"/docs/user-guide/explore","h":"#run-query","p":1004},{"i":1023,"t":"Function: Allows for the creation of alerts based on specific data conditions. Usage: Configure alert parameters to monitor data changes and receive notifications.","s":"Set Up Alerts","u":"/docs/user-guide/explore","h":"#set-up-alerts","p":1004},{"i":1025,"t":"Function: Creates reports reflecting your current data view. Customization: Offers options to define report parameters and settings for targeted reporting.","s":"Generate Reports","u":"/docs/user-guide/explore","h":"#generate-reports","p":1004},{"i":1027,"t":"Function: Enables the export of data into a CSV file. Application: Useful for offline data analysis or for sharing data externally.","s":"Export to CSV","u":"/docs/user-guide/explore","h":"#export-to-csv","p":1004},{"i":1030,"t":"Display: Shows the outcome of your executed queries. Adjustments: Allows configuration of row limits, offsets, and view of applied filters.","s":"Results Tab","u":"/docs/user-guide/explore","h":"#results-tab","p":1004},{"i":1032,"t":"Function: Displays the automatically generated SQL query post-execution. Utility: Useful for understanding the query structure or for manual SQL query replication.","s":"Generated SQL Tab","u":"/docs/user-guide/explore","h":"#generated-sql-tab","p":1004},{"i":1034,"t":"Access Details: Provides necessary API information for data retrieval. Components: Includes headers and the API endpoint URL. Example API Utilization​ Headers: Authorization: Bearer x-hasura-datasource-id: x-hasura-branch-id: URL: https://cube.synmetrix.org/api/v1/load Body: Follow the API documentation for accurate setup. Execute API Request: Enables sending an API request directly from the interface. 📄️ Alerts The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page: 📄️ Reports The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"REST API Tab","u":"/docs/user-guide/explore","h":"#rest-api-tab","p":1004},{"i":1036,"t":"The Alerts page is a powerful feature that enables you to configure notifications based on specific conditions within your data. When these conditions are met, an alert is triggered, keeping you informed in real-time through your chosen notification method. Here's how to set up an alert from the Explore page:","s":"📄️ Alerts","u":"/docs/user-guide/explore","h":"","p":1004},{"i":1038,"t":"The Reports page allows you to generate and send a screenshot of the Explore page with predefined parameters, enabling you to monitor metrics at your desired frequency. To create a report, follow these steps from the Explore page:","s":"📄️ Reports","u":"/docs/user-guide/explore","h":"","p":1004},{"i":1040,"t":"User Guide Logs","s":"Logs Page","u":"/docs/user-guide/logs","h":"","p":1039},{"i":1042,"t":"The Logs page is designed to provide control over the internal workings of the Cube.js service. It allows you to review the requests and the events that occur during the processing of these events. You can access information such as the execution time, start and end time, the actual query, and additional technical details. This enables you to monitor and analyze the performance and behavior of the Cube.js service. Follow the instructions below to utilize the Logs page: Access the Logs page within the project interface. Review the list of logs, which includes details such as the request information, execution time, start and end timestamps, and the actual query being processed. Click on a log entry to expand it and access additional technical information, if available. Use the logs as a resource for troubleshooting, performance optimization, and gaining insights into the inner workings of the Cube.js service.","s":"Overview","u":"/docs/user-guide/logs","h":"#overview","p":1039},{"i":1045,"t":"User Guide Explore Page Alerts","s":"Alerts Page","u":"/docs/user-guide/explore/alerts","h":"","p":1044},{"i":1047,"t":"Click the \"Add Alert\" button on the Explore page. Choose your notification delivery method. Options include Webhook, Slack, or Email, depending on your system integrations and preferences. Complete the alert form with the necessary information:","s":"How to Set Up an Alert","u":"/docs/user-guide/explore/alerts","h":"#how-to-set-up-an-alert","p":1044},{"i":1049,"t":"Alert Name: Provide a descriptive name that clearly identifies the alert's purpose and content.","s":"Detailed Description of Form Fields on the Alerts Page:","u":"/docs/user-guide/explore/alerts","h":"#detailed-description-of-form-fields-on-the-alerts-page","p":1044},{"i":1051,"t":"This section offers a comprehensive overview of the Alert or Report setup. Key features include: Query Key: This is the primary query that your alert or report is based on. It's a critical element in the monitoring process. Measures: Select the quantitative data points you wish to track or display. These are crucial for your analysis and will be highlighted in the Preview. Dimensions: These provide context and categorization for your data, allowing a more detailed and segmented analysis. info The Preview amalgamates all these components, offering a clear snapshot of the data and insights being monitored. This ensures that your analysis, whether for an Alert or Report, is accurate and relevant.","s":"Preview Section:","u":"/docs/user-guide/explore/alerts","h":"#preview-section","p":1044},{"i":1053,"t":"Customize how and when your alert is triggered with these settings: Schedule: Set how often the alert conditions are checked (e.g., hourly, daily, weekly). Lower Bound: Define the minimum threshold that triggers the alert. Upper Bound: Specify the maximum threshold for triggering the alert. Request Timeout (minutes): Set the maximum time allowed for the data request to process. Timeout On Fire (minutes): Determine how long before an ongoing alert condition is considered \"fired.\"","s":"Trigger Settings:","u":"/docs/user-guide/explore/alerts","h":"#trigger-settings","p":1044},{"i":1055,"t":"Options here vary based on your chosen alert delivery method: Webhook: Input the URL for the webhook endpoint. Slack: Enter the Slack channel or user ID. Email: Specify the email addresses of the alert recipients. note After configuring your settings, click \"Save\" to activate the alert. This will commence the monitoring of your specified conditions. Enhancements for Clarity and Efficiency: Clarified instructions for setting up alerts. Detailed descriptions for each section, ensuring comprehensive understanding. Streamlined language for ease of reading and comprehension. Added informative notes and info sections for additional clarity.","s":"Delivery Settings:","u":"/docs/user-guide/explore/alerts","h":"#delivery-settings","p":1044},{"i":1057,"t":"User Guide Models","s":"Models Page","u":"/docs/user-guide/models","h":"","p":1056},{"i":1059,"t":"To get started, you need to generate data models. Select the \"Generate Model\" option from the menu (shown in the screenshot below). In the opened window, navigate to the required sections by opening the corresponding tabs and check the boxes next to the tables or metrics you want to analyze. Then, click the \"Generate\" button (indicated by the arrow in the screenshot below). Now you should see the generated data model files in the right column (as shown in the screenshot below). You can freely edit your models as needed, adding and removing measures and dimensions. All your changes will be tracked by the version control system, the details of which are described below.","s":"How to Get Started","u":"/docs/user-guide/models","h":"#how-to-get-started","p":1056},{"i":1061,"t":"The version control system tracks changes to the data models, allowing you to revert to previous states of the models. To view the saved versions, open the drop-down menu located at the top left of the page and select the \"Show versions\" option (as shown in the screenshot below). In the opened window, you will see all the saved versions of the data models (as shown in the screenshot below). By expanding the rows, you can view the files and their contents. To revert to a specific version, simply click the \"Restore\" button next to it, and it will become the current version. Additionally, the version control system allows you to create separate branches of versions, which can help you switch between versions more efficiently than restoring versions within a single branch. To create a branch, open the branch list located in the top left corner of the page, enter the name of the new branch in the field, and click the plus icon button. This will create a new branch with the current state. To work with the new branch, you need to switch to it and then make it the default branch by clicking the \"Set as default\" button (shown in the screenshot).","s":"Version Control System","u":"/docs/user-guide/models","h":"#version-control-system","p":1056},{"i":1063,"t":"Go to Sidebar: Click on the sidebar in Synmetrix. Click 'Docs': Find and click the 'Docs' button. Get the Docs: The file with the version's documentation will be generated for you.","s":"Viewing Version Docs","u":"/docs/user-guide/models","h":"#viewing-version-docs","p":1056},{"i":1066,"t":"Opening the SQL Runner in Synmetrix for running custom SQL queries is a straightforward process. Here's how to do it: Navigate to Model Page: First, go to the model page in the Synmetrix interface. This is where all your data models are listed. Open SQL Runner Tab: Look for a tab or section labeled \"SQL Runner\" and click on it. This action will open the SQL Runner interface. Compose Your SQL Query: In the provided space, you can write your SQL query. For example, you might write a query to select data from a specific table or to perform a more complex data manipulation. SELECT * FROM your_table_name WHERE condition; Execute the Query: Once you've written your query, click the \"Run\" button. This will execute the SQL query against your data models. View Results: After running the query, the results will be displayed in a table format directly in the SQL Runner. This allows you to review and analyze the data returned by your query. note The Model SQL Runner in Synmetrix is a feature that allows you to run SQL queries directly against your data models. This tool is particularly useful for users who need to perform complex data analysis or want to test and validate their model configurations. Here’s a brief overview: Functionality: The SQL Runner enables you to write and execute SQL queries on the fly. It interacts directly with the data models you've set up in Synmetrix, allowing for real-time data retrieval and analysis. Use Cases: Data Exploration: Quickly run queries to explore data patterns or validate data integrity. Testing Models: Test how new or modified data models perform with actual SQL queries. Advanced Analysis: Perform complex data analysis that might not be possible through standard model exploration. Real-Time Results: Queries executed in the SQL Runner provide immediate feedback, enabling you to rapidly iterate and refine your analysis.","s":"Accessing the SQL Runner in Synmetrix","u":"/docs/user-guide/models","h":"#accessing-the-sql-runner-in-synmetrix","p":1056},{"i":1068,"t":"Open a Model: In sidebar, select and open the model file you wish to edit. Begin Editing: Utilize the Visual Model Editor to make changes to the model. You can adjust various aspects such as dimensions, measures, and relationships. Save Changes or close tab: After completing your edits, ensure to save the model to apply the modifications. note The Visual Model Editor in Synmetrix, enhanced with YAML support, offers an intuitive and user-friendly way to manage data models. YAML's readable format simplifies the process of defining and configuring models, making it accessible even to those with limited coding experience. This combination of a graphical interface and YAML/JS editing provides a flexible and efficient model editing experience, catering to both beginner and advanced users in data model management.","s":"Editing Models in Synmetrix","u":"/docs/user-guide/models","h":"#editing-models-in-synmetrix","p":1056},{"i":1070,"t":"You can upload data model files from your computer to Synmetrix. Easily save and download your data model files from Synmetrix.","s":"Importing or Exporting Data Models","u":"/docs/user-guide/models","h":"#importing-or-exporting-data-models","p":1056},{"i":1072,"t":"Edit and tweak your data models right in Synmetrix with a user-friendly editor.","s":"Using the Visual Model Editor","u":"/docs/user-guide/models","h":"#using-the-visual-model-editor","p":1056},{"i":1074,"t":"Open Sidebar: Start in the sidebar. Find Your Model: Look for the model you want to delete and hover over it. Click the Trash Icon: Hit the trash can icon next to the model. Confirm Delete: Click \"Remove\" to confirm. Done: The model is now deleted.","s":"Deleting a Model","u":"/docs/user-guide/models","h":"#deleting-a-model","p":1056},{"i":1076,"t":"Head to Sidebar: Go to the sidebar in Synmetrix. Choose a Model: Hover over the model you want to change. Edit: Click the pen icon. Make Changes: Change the name and file format (JS or YAML). Finished: Your changes are saved. These steps are a simple way to handle your data models in Synmetrix, whether you're adding, editing, or removing them.","s":"Changing Model Name and File Format","u":"/docs/user-guide/models","h":"#changing-model-name-and-file-format","p":1056},{"i":1078,"t":"User Guide Settings","s":"Settings","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1080,"t":"📄️ Data Sources The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps: 📄️ Members Managing Members 📄️ Personal Information Overview 📄️ Role Based Access Role Based Access Control 📄️ SQL API SQL API Page 📄️ Teams Introduction","s":"Table of Contents","u":"/docs/user-guide/settings","h":"#table-of-contents","p":1077},{"i":1082,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"📄️ Data Sources","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1084,"t":"Managing Members","s":"📄️ Members","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1086,"t":"Overview","s":"📄️ Personal Information","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1088,"t":"Role Based Access Control","s":"📄️ Role Based Access","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1090,"t":"SQL API Page","s":"📄️ SQL API","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1092,"t":"Introduction","s":"📄️ Teams","u":"/docs/user-guide/settings","h":"","p":1077},{"i":1096,"t":"The Members section in your settings allows you to manage the team members who have access to your workspace. This guide provides step-by-step instructions on how to add new members, as well as how to remove existing members.","s":"Overview","u":"/docs/user-guide/settings/members","h":"#overview","p":1093},{"i":1098,"t":"There are three types of members you can add to your account: Owner: The primary account holder with full access and privileges. Admin: Users with administrative rights, including the ability to manage settings and members. Member: Regular users with access to the account but limited control over settings and member management.","s":"Member Types","u":"/docs/user-guide/settings/members","h":"#member-types","p":1093},{"i":1100,"t":"To add a new member to your account, follow these steps: Navigate to Settings > Members in your account. Click the Create Now button. In the popup window, enter the email address of the new member. Select the user type for the new member (either Member or Admin). Click the Invite button. The new member will receive an email with a magic link to sign in to their account.","s":"Adding a New Member","u":"/docs/user-guide/settings/members","h":"#adding-a-new-member","p":1093},{"i":1102,"t":"To remove a member from your account, follow these steps: Go to Settings > Members. Locate the member you wish to remove and find the gear icon on their member card. Click on Delete. Confirm the deletion. After confirming, the member will be removed from your account. The process is complete. This documentation provides a clear and concise guide for managing members within your account settings.","s":"Removing a Member","u":"/docs/user-guide/settings/members","h":"#removing-a-member","p":1093},{"i":1105,"t":"The Datasources page provides a user-friendly interface for adding various datasources to your project. It offers a wide selection of possible databases, drivers, and other data storage solutions, giving you flexibility in choosing the right option for your needs. To add a data source, follow these steps:","s":"Data Sources Settings","u":"/docs/user-guide/settings/data-sources","h":"","p":1103},{"i":1108,"t":"Click on the \"Connect\" button.","s":"Step 1","u":"/docs/user-guide/settings/data-sources","h":"#step-1","p":1103},{"i":1110,"t":"A list of available datasource types will be displayed. Select the desired type from the options presented.","s":"Step 2","u":"/docs/user-guide/settings/data-sources","h":"#step-2","p":1103},{"i":1112,"t":"In the opened modal window, fill in all the required fields in the form. These fields may include connection details, authentication credentials, and other relevant information. On the screenshot below, you can see a modal window with the datasource creation form. Test Connection To test the connection to the datasource, click the \"Test connection\" button. To save the entered data and create the datasource, click the \"Save\" button. Fast Switching Data Sources in Synmetrix In Synmetrix, switching between data sources is a straightforward process. You can find the data models dropdown in the sidebar menu of both the \"Models\" and \"Explore\" pages. Simply access this menu to view and select from the available data sources. This dropdown is conveniently placed to ensure ease of access, enhancing your experience by allowing quick and efficient transitions between various data repositories within Synmetrix.","s":"Step 3","u":"/docs/user-guide/settings/data-sources","h":"#step-3","p":1103},{"i":1116,"t":"The \"Personal Info\" section is designed to provide you with full control over your personal settings and security. Here, you can easily update your username, email address, and password, ensuring your account remains secure and personalized. Additionally, you have the option to manage your active sessions, allowing you to logout from all for enhanced security.","s":"Overview","u":"/docs/user-guide/settings/personal-info","h":"#overview","p":1113},{"i":1118,"t":"To change your username: Go to \"Settings\" in the sidebar menu. Navigate to the \"Personal Info\" tab. Locate the \"Full Name\" field under the \"General Information\" section. Click on the \"Full Name\" input field and enter your new username. Click the \"Update\" button. You're all set!","s":"How to Change Your Username?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-change-your-username","p":1113},{"i":1120,"t":"To update your email address: Access \"Settings\" from the sidebar menu. Select the \"Personal Info\" tab. Find the \"Email\" field in the \"General Information\" section. Enter your new email address in the \"Email\" input field. Click the \"Update\" button. Your email is now updated!","s":"How to Change Your Email?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-change-your-email","p":1113},{"i":1122,"t":"To change your password: Visit \"Settings\" from the sidebar menu. Click on the \"Personal Info\" tab. Scroll down to the \"Security\" section. Enter your old password and your desired new password. Click the \"Update Password\" button. Your password has been changed!","s":"How to Change Your Password?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-change-your-password","p":1113},{"i":1124,"t":"To logout from all active sessions: Go to \"Settings\" in the sidebar menu. Choose the \"Personal Info\" tab. Scroll to the \"Active Sessions\" section. Click the \"Logout from all sessions\" button. All sessions have been logged out!","s":"How to Logout from Active Sessions?","u":"/docs/user-guide/settings/personal-info","h":"#how-to-logout-from-active-sessions","p":1113},{"i":1128,"t":"Role Based Access Control (RBAC) is a system for regulating access to resources within your organization based on the roles of individual users. This document outlines how to manage roles and the types of access they grant.","s":"Overview","u":"/docs/user-guide/settings/role-access","h":"#overview","p":1125},{"i":1130,"t":"Partial Access: Grants access to specific Data Sources, Data Models, and various Measures, Dimensions, or Segments. Full Access: Provides unrestricted access to all resources. No Access: Denies access to all resources.","s":"Access Types","u":"/docs/user-guide/settings/role-access","h":"#access-types","p":1125},{"i":1132,"t":"To add a new role, follow these steps: Navigate to Settings > Roles and Access. Click on the Create Now button. In the popup window: Enter the role name. Set access for data source resources by selecting one or multiple data sources. Choose Data Models. Select Measures, Dimensions, and/or Segments. tip Use the \"Select All\" checkbox to select all Measures, Dimensions, and Segments in the current Data Model. Click Create. info Roles display information including the creation date, last update, and data sources along with their respective access types.","s":"Adding a New Role","u":"/docs/user-guide/settings/role-access","h":"#adding-a-new-role","p":1125},{"i":1134,"t":"To delete a role, follow these steps: Go to Settings > Roles and Access. Find the role to be deleted and click the gear icon on their card. Choose Delete from the dropdown list and confirm the deletion. The deletion process is complete.","s":"Deleting a Role","u":"/docs/user-guide/settings/role-access","h":"#deleting-a-role","p":1125},{"i":1136,"t":"To edit an existing role, follow these steps: Navigate to Settings > Roles and Access. Locate the role to edit and click the gear icon on their card. Choose Edit from the dropdown list. In the popup window, make the desired changes: Update the role name. Adjust access to data source resources. Select or change Data Models. Choose Measures, Dimensions, and/or Segments. tip Utilize the \"Select All\" checkbox for selecting all items in the current Data Model. Click Save to apply the changes. This document provides clear guidelines for managing roles within the Role Based Access Control system, ensuring efficient and secure access management.","s":"Editing a Role","u":"/docs/user-guide/settings/role-access","h":"#editing-a-role","p":1125},{"i":1140,"t":"The \"Teams\" section of Synmetrix is a dedicated space for creating, managing, and customizing teams. This feature is essential for collaborative work, allowing you to form new teams, set a focus on a current team, remove teams that are no longer needed, and update team names to align with evolving projects or goals.","s":"Introduction","u":"/docs/user-guide/settings/teams","h":"#introduction","p":1137},{"i":1142,"t":"To navigate to the Team page: Hover over your account profile picture at the top-right corner of the page. Select \"Team\" from the dropdown menu.","s":"Accessing the Team Page","u":"/docs/user-guide/settings/teams","h":"#accessing-the-team-page","p":1137},{"i":1144,"t":"On the Team page, you can perform various actions:","s":"Team Page Functions","u":"/docs/user-guide/settings/teams","h":"#team-page-functions","p":1137},{"i":1146,"t":"You can invite new members to join your project by entering their email addresses. This step is crucial for expanding your team and fostering collaboration.","s":"1. Inviting New Users","u":"/docs/user-guide/settings/teams","h":"#1-inviting-new-users","p":1137},{"i":1148,"t":"Based on your access management system, assign appropriate roles and access levels to team members, ensuring they have the necessary permissions to contribute effectively.","s":"2. Assigning Roles and Access Levels","u":"/docs/user-guide/settings/teams","h":"#2-assigning-roles-and-access-levels","p":1137},{"i":1150,"t":"Control access to specific features and resources for each team member, tailoring their permissions to suit their role and needs in the project.","s":"3. Managing Access","u":"/docs/user-guide/settings/teams","h":"#3-managing-access","p":1137},{"i":1152,"t":"The Team page provides a comprehensive view of all team members, including their roles and responsibilities, allowing for efficient team management.","s":"4. Viewing and Managing Team Members","u":"/docs/user-guide/settings/teams","h":"#4-viewing-and-managing-team-members","p":1137},{"i":1154,"t":"To create a new team: Open \"Settings\" from the sidebar. Click on the \"Teams\" tab. In \"Manage Teams\", view your existing teams. Select the gear icon (\"Settings\"). Choose \"Create Team\" from the dropdown and follow the prompts.","s":"How to Create a New Team?","u":"/docs/user-guide/settings/teams","h":"#how-to-create-a-new-team","p":1137},{"i":1156,"t":"To set an active team: Go to \"Settings\" in the sidebar. Choose the \"Teams\" tab. Find your team list in \"Manage Teams\". Click the gear icon next to the team you want to focus on. Select \"Set Current\" from the dropdown.","s":"How to Set a Current Team?","u":"/docs/user-guide/settings/teams","h":"#how-to-set-a-current-team","p":1137},{"i":1158,"t":"To change a team's name: Navigate to \"Settings\" via the sidebar. Select the \"Teams\" tab. Find your team in \"Manage Teams\". Click the gear icon next to the team you want to rename. Choose \"Edit\" from the dropdown. Change the \"Team Name\" in the pop-up. Click \"Save\" to confirm the changes.","s":"How to Edit a Team Name?","u":"/docs/user-guide/settings/teams","h":"#how-to-edit-a-team-name","p":1137},{"i":1160,"t":"To remove a team: Access \"Settings\" from the sidebar. Go to the \"Teams\" tab. Identify the team you want to delete in \"Manage Teams\". Click the gear icon next to the team. Choose \"Delete\" from the dropdown list. I've refined the language for professional tone and clarity, ensuring each step is clear and straightforward.","s":"How to Delete a Team?","u":"/docs/user-guide/settings/teams","h":"#how-to-delete-a-team","p":1137},{"i":1164,"t":"Engage in powerful and flexible data retrieval and manipulation with our SQL API, designed for effortless data management and seamless collaboration.","s":"Effortless Data Management and Uninterrupted Collaboration","u":"/docs/user-guide/settings/sql-api","h":"#effortless-data-management-and-uninterrupted-collaboration","p":1161},{"i":1166,"t":"Synmetrix revolutionizes data sharing with its versatile protocol, ensuring smooth integration with a variety of data tools. Our platform seamlessly connects with business intelligence and data exploration tools (Superset, Tableau), data notebooks (Jupyter, Hex), reverse ETL tools (Census, Hightouch), low-code platforms (Retool), and more, enhancing efficiency and simplicity in data-driven tasks.","s":"Simplified Data Sharing","u":"/docs/user-guide/settings/sql-api","h":"#simplified-data-sharing","p":1161},{"i":1168,"t":"Synmetrix serves as the ultimate repository for all your data needs, centralizing metrics in an organized manner. Regardless of your preferred tools or platforms for data analysis or visualization, Synmetrix provides easy access to essential data.","s":"One-Stop Metrics Solution","u":"/docs/user-guide/settings/sql-api","h":"#one-stop-metrics-solution","p":1161},{"i":1170,"t":"Integrations: Seamlessly integrate into your workflow, including Excel compatibility. Flexibility: Encourage collaborative efforts with shared models in a harmonious team environment. Security: Rely on our robust and reliable security measures. Effortless: Tailor SQL queries to meet your unique requirements.","s":"Key Benefits","u":"/docs/user-guide/settings/sql-api","h":"#key-benefits","p":1161},{"i":1172,"t":"Obtain your SQL API credentials through two distinct methods:","s":"Acquiring SQL Credentials","u":"/docs/user-guide/settings/sql-api","h":"#acquiring-sql-credentials","p":1161},{"i":1174,"t":"Initiate a new account creation. Add your data source. Opt to generate models (optional). Synmetrix generates SQL API access during this step. A pop-up allows you to choose between PSQL or MYSQL for connection, along with credential copying. Connect using PSQL or MYSQL, or opt to \"Download Credentials\" for later use. Finish the onboarding.","s":"First Method: From Onboarding","u":"/docs/user-guide/settings/sql-api","h":"#first-method-from-onboarding","p":1161},{"i":1176,"t":"Navigate to Settings > SQL API. Click the gear icon on the database card and select \"Edit\". The pop-up window provides PSQL or MYSQL connection options and credential copying. Example Credentials Host/URL: cube.synmetrix.org:15432 Database: db Login: 4x4qg8ei76 Password: ********** PSQL Client Connection: psql --host=cube.synmetrix.org --port 15432 --username=4x4qg8ei76 --dbname=db Connect using PSQL or MYSQL, or \"Download Credentials\" for later use.","s":"Second Method: From Settings","u":"/docs/user-guide/settings/sql-api","h":"#second-method-from-settings","p":1161},{"i":1178,"t":"Easily create, edit, or delete SQL API credentials via Settings > SQL API.","s":"Managing SQL API Credentials","u":"/docs/user-guide/settings/sql-api","h":"#managing-sql-api-credentials","p":1161},{"i":1180,"t":"Click the gear icon and select \"Delete\" from the dropdown. Confirm deletion.","s":"Deleting Credentials","u":"/docs/user-guide/settings/sql-api","h":"#deleting-credentials","p":1161},{"i":1182,"t":"Select the \"Create Now\" button on the SQL API page. In the pop-up, specify the Team member and Data source.","s":"Creating Credentials","u":"/docs/user-guide/settings/sql-api","h":"#creating-credentials","p":1161},{"i":1184,"t":"Click the gear icon and select \"Edit\". Modify the credentials as needed. 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