stockyard

IN PROGRESS - USE AT YOUR OWN RISK

Distributed time-series data storage written in Go and powered by Postgres using the Citus extension.

⭐️ Why

Storing large volume of accessible time-series data is HARD.

As a developer, you need to manage horizontally scalable database clusters, architecture correct data-structures and successfully integrate into your app. If you don't roll your own solution, you can use third-party software like influxdb or Timescale but self-hosting a cluster is challenging. Don't care about self-hosting and looking for a fully managed service like tidb or dynamodb, then you'll control of managing your servers.

Citus extension for the Postgres database combined with container orchestration software like Docker Swarm makes managing your self-hosted server easy. This project implements a simple time-series data-structure in Citus and provides an API which you can use in your app to store time-series data.

If you know how to use Citus and Docker Swarm then with stockyard you can store large volume of time-series data.

🌐 Use-cases

Data storage dump
Data analyzer

✨ Features:

Runs in background accepting API calls.
Make API calls from your Golang application over network or localhost.
Horizontally scalable database if you know Citus extension.
Easy to setup and manage if you know docker.
If you enable, simple analytics

🐳 Install via Docker

Go to your workdir:

cd /your/project/directory

Get the latest Compose file:

curl https://raw.githubusercontent.com/bartmika/stockyard/master/docker-compose.yml > docker-compose.yml

Get the latest database initialization script:

curl https://raw.githubusercontent.com/bartmika/stockyard/master/docker-compose.sql > docker-compose.sql

Get the latest env_file:

curl https://raw.githubusercontent.com/bartmika/stockyard/master/.env_sample > .env

Afterwords tweak the .env file, here are a few helpful comments:

STOCKYARD_DB_USER - The admin user's username of the your database.
STOCKYARD_DB_PASSWORD - The admin user's password of your database.
STOCKYARD_DB_NAME - The database name. Recommended: stockyard_db.
STOCKYARD_APP_SECRET_KEY - (Optional) The secret key to store and be used internally. Ignore for now.
STOCKYARD_HAS_ANALYZER - The boolean value which controls whether to enable the analyzer to run on ever observation insert or delete. Recommended to set to true.
PGADMIN_DEFAULT_EMAIL - (Optional) The pgadmin administrators account email if you want to use pgadmin.
PGADMIN_DEFAULT_PASSWORD - (Optional) The pgadmin administrators account password if you want to use pgadmin.

Tweak the docker-compose.yml file there according to your needs

$EDITOR ./docker-compose.yml

Start the server.

docker-compose -p stockyard -f docker-compose.yml up

🚀 Usage

1. Create Entity

An entity represents a device, instrument or some grouping of time-series data. The following sample code demonstrates how to create two IoT instruments and add the accompanying readings into it.

There are two types of entities:

EntityObservationDataType - Allows you to store data in the Observation model.
EntityTimeKeyDataType - Allows you to store data in the TimeKey model.

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and create an entity.

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"
	deviceName1 := "temperate-sensor-1" // Give your entity any unique name you like.
	deviceName2 := "backyard-birdfeeder-phototimer"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// Create a `temperature sensor` collection.
	////

	// Execute the remote call.
	entity, err := client.InsertEntity(deviceName1, dtos.EntityObservationDataType, "")
	if err != nil {
		log.Fatal(err)
	}

	if entity == nil {
		log.Fatal("error as nothing was returned")
	}

	// See the results.
	log.Println("id", entity.ID)
	log.Println("uuid", entity.UUID)
	log.Println("name", entity.Name)
	log.Println("data_type", entity.DataType)
	log.Println("meta", entity.Meta)

	////
	//// Create a `phototimer` collection.
	////

	// Execute the remote call.
	entity, err = client.InsertEntity(deviceName2, dtos.EntityTimeKeyDataType, "")
	if err != nil {
		log.Fatal(err)
	}

	if entity == nil {
		log.Fatal("error as nothing was returned")
	}

	// See the results.
	log.Println("id", entity.ID)
	log.Println("uuid", entity.UUID)
	log.Println("name", entity.Name)
	log.Println("data_type", entity.DataType)
	log.Println("meta", entity.Meta)
}

2. List Entities

Here is an example of how to list entities:

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and list all the entities that you previously
// submitted.
//
// PRECONDITION:
// You need to have inserted an entity before running this code.

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// List all our entities.
	////

	// Generate the filter that you want to sort by.
	dto := &dtos.EntityFilterRequestDTO{
		SortOrder: "ASC",
		SortField: "id",
		Offset:    0,
		Limit:     1_000_000,
		IDs:       []uint64{},
	}

	// Execute the remote call.
	entities, err := client.ListEntities(dto)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// View our results.
	////

	// Print the results.
	if entities.Count > 0 {
		for _, entity := range entities.Results {
			log.Println("id", entity.ID)
			log.Println("uuid", entity.UUID)
			log.Println("name", entity.Name)
			log.Println("data_type", entity.DataType)
			log.Println("meta", entity.Meta)
		}
	}
}

3. Create Observation

An observation is essentially our time-series datum that belongs to an entity. Here is a coding example:

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and create an observation for a previously created
// entity.

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"
	deviceID := uint64(1)
	deviceTimestamp := time.Date(2022, 1, 1, 1, 1, 1, 1, time.UTC)
	deviceValue := float64(300)

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// Create data.
	////

	// Execute the remote call.
	observationReq := &dtos.ObservationInsertRequestDTO{
		EntityID:  deviceID,
		Meta:      "",
		Timestamp: deviceTimestamp,
		Value:     deviceValue,
	}

	// Execute the remote call.
	observationRes, err := client.InsertObservation(observationReq)
	if err != nil {
		log.Fatal(err)
	}
	if observationRes == nil {
		log.Fatal("error as nothing was returned")
	}

	////
	//// View results.
	////

	// See the results.
	log.Println("entity_id", observationRes.EntityID)
	log.Println("timestamp", observationRes.Timestamp)
	log.Println("value", observationRes.Value)
	log.Println("meta", observationRes.Meta)
}

4. List Observation

Now that you have data in the database, here is how you get a filtered list:

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and list all the observations that you previously
// submitted.
//
// PRECONDITION:
// You need to have inserted an entity and observation(s) before running this code.

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// List our time-series data within a time range.
	////

	// Generate the filter that you want to sort by.
	req := &dtos.ObservationFilterRequestDTO{
		EntityIDs:                   []uint64{1},
		TimestampGreaterThenOrEqual: time.Date(2022, 01, 01, 0, 0, 0, 0, time.UTC),
		TimestampLessThenOrEqual:    time.Date(2023, 01, 01, 0, 0, 0, 0, time.UTC).AddDate(1, 0, 0),
	}

	// Execute the remote call.
	res, err := client.ListObservations(req)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// View our results.
	////

	// Print the results.
	if res.Count > 0 {
		for _, observation := range res.Results {
			log.Println("entity_id", observation.EntityID)
			log.Println("meta", observation.Meta)
			log.Println("timestamp", observation.Timestamp)
			log.Println("value", observation.Value)
		}
	}
}

5. Insert TimeKey

A TimeKey model is used if you want to store a string data-type value instead of the float64 data-type that you find an observation. Why would you use this? A TimeKey model would be ideal to use if you store files in a remote S3 server and you want to store the s3 keys to access them in a timed manner. Here is an example:

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and create a timekey for a previously created
// entity.
//
// HOW TO RUN:
// cd ./examples/5-insert-timekey
// go run main.go

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"
	deviceID := uint64(2) // Note: Assuming `2` was your phototimer entity.
	value := "s3key/some/remote/location/dir/photo1.png"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// Insert the data.
	////

	// Execute the remote call.
	req := &dtos.TimeKeyInsertRequestDTO{
		EntityID:  deviceID,
		Meta:      "",
		Timestamp: time.Now(),
		Value:     value,
	}

	// Execute the remote call.
	res, err := client.InsertTimeKey(req)
	if err != nil {
		log.Fatal(err)
	}
	if res == nil {
		log.Fatal("error as nothing was returned")
	}

	////
	//// View the result.
	////

	// See the results.
	log.Println("entity_id", res.EntityID)
	log.Println("timestamp", res.Timestamp)
	log.Println("value", res.Value)
	log.Println("meta", res.Meta)
}

6. List TimeKey(s)

Here is how you can list your timekeys:

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and list all the timekey(s) that you previously
// submitted.
//
// HOW TO RUN:
// cd ./examples/6-list-timekey
// go run main.go
//
// PRECONDITION:
// You need to have inserted an entity before running this code.

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// List our data within a time range.
	////

	// Generate the filter that you want to sort by.
	dto := &dtos.TimeKeyFilterRequestDTO{
		EntityIDs:                   []uint64{2},
		TimestampGreaterThenOrEqual: time.Date(2022, 01, 01, 0, 0, 0, 0, time.UTC),
		TimestampLessThenOrEqual:    time.Date(2023, 01, 01, 0, 0, 0, 0, time.UTC).AddDate(1, 0, 0),
	}

	// Execute the remote call.
	res, err := client.ListTimeKeys(dto)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// View our results.
	////

	// Print the results.
	if res.Count > 0 {
		for _, tk := range res.Results {
			log.Println("entity_id", tk.EntityID)
			log.Println("timestamp", tk.Timestamp)
			log.Println("data_type", tk.Value)
			log.Println("meta", tk.Meta)
		}
	} else {
		log.Println("no results returned")
	}
}

7. Delete TimeKey(s)

Here is how you delete timekey(s):

package main

import (
	"fmt"
	"log"
	"time"

	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and delete all the timekey(s) that you previously
// submitted.
//
// HOW TO RUN:
// cd ./examples/7-delete-timekey
// go run main.go
//
// PRECONDITION:
// You need to have inserted an entity before running this code.

func main() {
	////
	//// Connect to the running stockyard server running in the background.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// Delete the timekey(s).
	////

	// Generate the filter that you want to sort by.
	dto := &dtos.TimeKeyFilterRequestDTO{
		EntityIDs:                   []uint64{2},
		TimestampGreaterThenOrEqual: time.Date(2022, 01, 01, 0, 0, 0, 0, time.UTC),
		TimestampLessThenOrEqual:    time.Date(2023, 01, 01, 0, 0, 0, 0, time.UTC).AddDate(1, 0, 0),
	}

	// Execute the remote call.
	err = client.DeleteTimeKeysByFilter(dto)
	if err != nil {
		log.Fatal(err)
	}

	log.Println("successfully deleted")
}

8. List Observation Count

Here is how you list your count computations. If you want to see how average and sum computations are done, please see the /examples folder.

package main

import (
	"fmt"
	"log"
	"time"

	model "github.com/bartmika/stockyard/internal/domain/observation_count"
	"github.com/bartmika/stockyard/pkg/dtos"
	rpc_client "github.com/bartmika/stockyard/pkg/rpc"
)

// DESCRIPTION:
// The purpose of this application is to connect to a running `stockyard` server
// instance on your machine and list all the timekey(s) that you previously
// submitted.
//
// HOW TO RUN:
// cd ./examples/10-list-observation-count
// go run main.go
//
// PRECONDITION:
// You need to have inserted an entity with multiple observation before running this code.
//    cd ../1-insert-entity; go run main.go;
//    cd ../4-insert-observation; go run main.go;

func main() {
	////
	//// Connect to our running stockyard server.
	////

	// Sample data to use in our example code.
	ipAddress := "127.0.0.1"
	port := "8000"

	// Connect to a running server from this appolication.
	applicationAddress := fmt.Sprintf("%s:%s", ipAddress, port)
	client, err := rpc_client.NewClient(applicationAddress, 3, 15*time.Second)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// List our data within a time range.
	////

	// Generate the filter that you want to sort by.
	dto := &dtos.ObservationCountFilterRequestDTO{
		EntityIDs:               []uint64{1},
		Frequency:               model.ObservationCountDayFrequency,
		StartGreaterThenOrEqual: time.Date(2022, 01, 01, 0, 0, 0, 0, time.UTC),
		FinishLessThenOrEqual:   time.Date(2023, 01, 01, 0, 0, 0, 0, time.UTC).AddDate(1, 0, 0),
	}

	// Execute the remote call.
	res, err := client.ListObservationCounts(dto)
	if err != nil {
		log.Fatal(err)
	}

	////
	//// View our results.
	////

	// Print the results.
	if res.Count > 0 {
		log.Println("the `count` results from the server are as follows:")
		for _, tk := range res.Results {
			fmt.Println("entity_id", tk.EntityID)
			fmt.Println("start", tk.Start)
			fmt.Println("finish", tk.Finish)
			// fmt.Println("day", tk.Day)     // Not important but useful if you need it.
			// fmt.Println("week", tk.Week)   // Not important but useful if you need it.
			// fmt.Println("month", tk.Month) // Not important but useful if you need it.
			// fmt.Println("year", tk.Year)   // Not important but useful if you need it.
			fmt.Println("frequency", tk.Frequency)
			fmt.Println("result", tk.Result)
			fmt.Println("")
		}
	} else {
		log.Println("no results returned")
	}
}

🙋‍♂️🤝 Contributing

Found a bug? Want a feature to improve your developer experience when dealing with stockyard? Please create an issue.

📃 License

Made with ❤️ by Bartlomiej Mika.
The project is licensed under the ISC License.

Resource used:

citusdata/citus is the distributed PostgreSQL extension that this server was built upon.
Clean Architecture by Panayiotis Kritiotis was the architecture pattern used on this codebase.

Alternative horizontally scalable databases (for time-series data) that you should check out as well:

InfluxDB
Timescale
Citus
TiDB
DynamoDB

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README.md

README.md

stockyard

⭐️ Why

🌐 Use-cases

✨ Features:

🐳 Install via Docker

🚀 Usage

1. Create Entity

2. List Entities

3. Create Observation

4. List Observation

5. Insert TimeKey

6. List TimeKey(s)

7. Delete TimeKey(s)

8. List Observation Count

🙋‍♂️🤝 Contributing

📃 License

Files

README.md

Latest commit

History

README.md

File metadata and controls

stockyard

⭐️ Why

🌐 Use-cases

✨ Features:

🐳 Install via Docker

🚀 Usage

1. Create Entity

2. List Entities

3. Create Observation

4. List Observation

5. Insert TimeKey

6. List TimeKey(s)

7. Delete TimeKey(s)

8. List Observation Count

🙋‍♂️🤝 Contributing

📃 License