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msk-replication

README.md

CDK Python project for Kinesis Data Analytics application to Replicate Data from One MSK Cluster to Another in a VPC

Prerequisites

Before create Kinesis Data Analytics for Flink Application, you need to finish the following steps in Tutorial: Using a Kinesis Data Analytics application to Replicate Data from One MSK Cluster to Another in a VPC

Using a Kinesis Data Analytics application to Replicate Data from One Topic in an MSK Cluster to Another in a VPC

kda-flink-msk-replication

This is a blank project for Python development with CDK.

The cdk.json file tells the CDK Toolkit how to execute your app.

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, you can install the required dependencies.

$ pip install -r requirements.txt

First, open cdk.context.json and update it properly

{
  "s3_bucket_name": "Your-S3-KDA-App-Code-Location",
  "s3_path_to_flink_app_code": "Your-KDA-App-Code-Path",
  "kda_flink_property_groups": [
    {
      "property_group_id": "KafkaSource",
      "property_map": {
        "topic": "Your-Kafka-Source-Topic",
        "bootstrap.servers": "Your-Kafka-Broker-Servers"
      }
    },
    {
      "property_group_id": "KafkaSink",
      "property_map": {
        "topic": "Your-Kafka-Sink-Topic",
        "bootstrap.servers": "Your-Kafka-Broker-Servers"
        "transaction.timeout.ms": 30000
      }
    }
  ]
}

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

$ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
$ export CDK_DEFAULT_REGION=$(aws configure get region)
$ cdk synth

Use cdk deploy command to create the stack shown above,

$ cdk deploy

After successfully delpoyment, go to Kinesis Data Analytics applications Dashboard, and then select your Kinesis Data Analytics applications and update VPC connectivity, and then Run application.

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Clean Up

Delete the CloudFormation stack by running the below command.

(.venv) $ cdk destroy

Useful commands

  • cdk ls list all stacks in the app
  • cdk synth emits the synthesized CloudFormation template
  • cdk deploy deploy this stack to your default AWS account/region
  • cdk diff compare deployed stack with current state
  • cdk docs open CDK documentation

Enjoy!