How to use Python in manged service for apache flink

Amazon Managed Service for Apache Flink supports Python applications using the Apache Flink Python Table API. Here's how you can implement your real-time data pipeline:

Creating a Python Application for Managed Service for Apache Flink

1. Write your Python application: Create a Python script with a main method that defines your data processing logic. A popular approach is to define the application flow using SQL embedded in Python code.

2. Define your data sources and sinks: Use SQL queries on the table environment to read from your Kinesis stream (where IoT Core data is routed) and write to your destination.

3. Package your application:

• You don't need to create a JAR file for Python applications
• Instead, bundle your Python code and dependencies into a ZIP file
• Include the following in your ZIP package:
• Main Python script with your application logic
• Any additional Python code files
• Required Java libraries in JAR files (including any JDBC connectors for PostgreSQL)
• User-defined Java code in JAR files (if needed)

4. Upload and deploy:

• Upload the ZIP file to an Amazon S3 bucket
• When creating your application using the console or CreateApplication action, specify your code files using the kinesis.analytics.flink.run.options property group
• Define file types like python (main Python file), jarfile (Java JAR files), pyFiles (Python resources), and pyArchives (ZIP resources)

Connecting to PostgreSQL RDS

To connect your Flink Python application to PostgreSQL RDS:

1. Include the PostgreSQL JDBC driver JAR in your application package
2. Use the JDBC connector in your Python Flink application to write to PostgreSQL
3. Configure the connection properties with your RDS endpoint, credentials, and database details

Development Environment Setup

Before you start:

1. Install Python 3.11 (the supported version for Flink 1.20)
2. Install JDK 11 and Apache Maven (required for dependency management)
3. Set up a development environment like PyCharm or Visual Studio Code

Note: If you're developing on a Mac with Apple Silicon, you might encounter issues with PyFlink 1.15 dependencies. In that case, it's recommended to run the Python interpreter in Docker.

For local testing, you can run your application locally before deploying it to AWS to verify that your data processing logic and PostgreSQL connection work correctly.
Sources
Get started with Amazon Managed Service for Apache Flink for Python - Managed Service for Apache Flink
Use Python with Managed Service for Apache Flink - Managed Service for Apache Flink
Program your Managed Service for Apache Flink Python application - Managed Service for Apache Flink
Create your Managed Service for Apache Flink Python application - Managed Service for Apache Flink