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Low-code real-time analytics with Apache Flink SQL

This sample provides sample code on how to run Apache Flink SQL on Amazon Kinesis Data Analytics Studio. For analysis purposes the TLC Taxi Dataset is used.

Architecture

architecture

Infrastructure Setup

⚠️ This sample only works in the N. Virgina (us-east-1) region

Deploy infrastructure on AWS

Clone the repository and navigate to the cdk directory.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

.venv\Scripts\activate.bat

Once the virtualenv is activated, navigate to the cdk directory and install the required dependencies.

cd cdk

pip install -r requirements.txt

To bootstrap CDK resources run the following command

cdk bootstrap

At this point you can now synthesize the CloudFormation template for this code.

cdk synth --quiet

To deploy run the infrastructure run the following command.

cdk deploy --require-approval never

Configure Kinesis Analytics Studio

  1. Configure VPC

Navigate to the Amazon Kinesis Console, choose Analytics Applications > Studio. Choose the studio that was created, switch to Configuration. Edit the Network Configuration with the following parameters:

  • VPC: FlinkSQLDemoStack
  • Subnets: FlinkSqlDemoStack/streamingVPC/PublicSubnet1 & FlinkSqlDemoStack/streamingVPC/PublicSubnet2
  • Security Group: FlinkSqlDemoStack-kdaSG
  1. Configure Connectors

Edit the AWS supported connectors section, tick the row amazon-kinesis-sql-connector-flink and hit save. After saving click Add custom connector, choose the S3 bucket named flinksqldemostack-kdaconnectorsbucket and define flink-sql-connector-elasticsearch7_2.11-1.11.2.jaras Path.

Edit the AWS Supported connectors section. Tick amazon-kinesis-sql-connector-flink and click Save Changes.

Start the Notebook by clicking Run.

Create Elasticsearch Indices

Set Elasticsearch endpoint as environment variable. Get the ServiceURL from the CDK Output after cdk deploy has been run. Replace the http endpoint with your own. Make sure you use HTTP protocol.

export ES_ENDPOINT = http://Flink-esPro-xxxxxxxxxxxx-xxxxxxxxxx.us-east-1-elb.amazon.com

Trip Duration Time

curl --location --request PUT ${ES_ENDPOINT}/trip_duration \
--header 'Content-Type: application/json' \
--data-raw '{
  "mappings": {
    "properties": {
        "PULocationID": {"type": "integer" },
        "avg_trip_duration": {"type": "integer"},
        "window_start": {"type": "date", "format": "yyyy-MM-dd HH:mm:ss"} }
    }
  }
}'

Analysis

In the Kinesis Data Anyaltics console select the created Notebook and click Open in Apache Zeppelin. Create a new note and run these commands in a the Amazon Kinesis Data Analytics Studio Notebook. Execute each statement. Navigate to Amazon Kinesis Console, select Studio and Run the Notebook. Create a new File and use the following commands.

  1. Create Streaming Data Table

    Replace the STREAM_NAME variable with the Kinesis stream name CDK created for you. You can find it in the Kinesis Console or Cloudformation output.

    %flink.ssql

CREATE TABLE ride_events (
    tpep_pickup_datetime TIMESTAMP(3),
    tpep_dropoff_datetime TIMESTAMP(3),
    PULocationID INTEGER,
    DOLocationID INTEGER,
    WATERMARK FOR tpep_pickup_datetime AS tpep_pickup_datetime - INTERVAL '5' SECOND
)
WITH (
    'connector' = 'kinesis',
    'stream' = 'STREAM_NAME',
    'aws.region' = 'us-east-1',
    'scan.stream.initpos' = 'LATEST',
    'format' = 'json',
    'json.timestamp-format.standard' = 'SQL'
);

    Execute the cell with Shift + Return.

  2. Create Elasticsearch Sink

    Insert a new cell below by pressing ESC followed by Ctrl + Alt + b. Replace the ES_VPC_ENDPOINT variable in the following statement with your own ES endpoint. You can find it ine the Amazon OpenSearch console or the CloudFormation output.

    %flink.ssql

CREATE TABLE trip_duration (
    PULocationID INTEGER,
    avg_trip_duration BIGINT NOT NULL,
    window_start TIMESTAMP(3)
) WITH (
    'connector' = 'elasticsearch-7',
    'hosts' = 'http://ES_VPC_ENDPOINT:80',
    'index' = 'trip_duration'
);

    Execute the cell with Shift + Return.

  3. Analyze Streaming Data

    Insert a new cell below by pressing ESC followed by Ctrl + Alt + b.

    %flink.ssql(type=update)

INSERT INTO trip_duration(
    SELECT PULocationID,
        AVG(UNIX_TIMESTAMP(CAST(tpep_dropoff_datetime AS STRING)) - UNIX_TIMESTAMP(CAST(tpep_pickup_datetime AS STRING))) AS avg_ride_duration,
        TUMBLE_START(tpep_pickup_datetime, INTERVAL '60' minute) AS window_start 
    FROM ride_events
    WHERE TIMESTAMPDIFF(HOUR,tpep_pickup_datetime, tpep_dropoff_datetime) <= 4 
    GROUP BY TUMBLE(tpep_pickup_datetime, INTERVAL '60' minute), PULocationID
)

    Execute the cell (Shift + Return). This will start the analytics application. If you want to deploy to KDA follow step 4.

  4. Deployment to Amazon Kinesis Data Analytics (optional)

On the top right click Actions and then Build. Wait until the a green box appears and then click Deploy.

Kibana Dashboard

Get the Kibana URL from the CDK Output or CFN Output. Create an Index Pattern by navigating to Stack Management / Index Patterns. Define the index pattern name trip_duration. Click next and select window_start as time field.

In the Kibana Dashboard navigate to Stack Management / Save Objects. Import the file dashboard.json from the repo.

If you are not able to access Kibana check the Security Group Settings of your nginx Proxy.

View the visuals in the Dashboard section.

dashboard

Cleanup

To remove any AWS resources deployed in this sample run the following command:

cdk destroy

Remove any remaining resources manually in the AWS console.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.