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Flink Kafka Source & Sink Examples

  • Flink version: 1.20
  • Flink API: DataStream API
  • Language: Java (11)

This example demonstrate how to use Flink Kafka Connector, source and sink.

This example uses KafkaSource and KafkaSink.

This example expects a JSON payload as input and outputs a corresponding JSON output. The JSON input follows the structure set in Stock.java and can be automatically generated with stock_kafka.py under the python/data_generator directory.

Flink Example,

Note that the old FlinkKafkaConsumer and FlinkKafkaProducer were removed in Flink 1.17 and 1.15, respectively.

Runtime configuration

When running on Amazon Managed Service for Apache Flink the runtime configuration is read from Runtime Properties.

When running locally, the configuration is read from the resources/flink-application-properties-dev.json file located in the resources folder.

Runtime parameters:

Group ID Key Description
Input0 bootstrap.servers Source cluster boostrap servers.
Input0 topic Source topic (default: source).
Input0 group.id Source group id (default: my-group)
Output0 bootstrap.servers Destination cluster bootstrap servers.
Output0 topic Destination topic (default: destination).

If you are connecting with no-auth and no SSL, above will work. Else you need additional configuration for both source and sink.

For IAM Auth

When using IAM Auth, the following Runtime Properties are expected at the Group ID AuthProperties:

  • sasl.mechanism = AWS_MSK_IAM
  • sasl.client.callback.handler.class = software.amazon.msk.auth.iam.IAMClientCallbackHandler
  • sasl.jaas.config = software.amazon.msk.auth.iam.IAMLoginModule required;
  • security.protocol = SASL_SSL

The properties in the AuthProperties group are passed to both Kafka Source and Kafka Sink configurations.

Data Generator

To generate the JSON data expected by this example, you can use the Python data-generator script you can find in the Python examples.

Alternatively, you can use the Flink DataGenerator application, which generates the same schema.

Running locally in IntelliJ

To run the example locally we provide a docker-compose file which starts a local Kafka cluster with 3 brokers on locakhost:9092, locakhost:9093, and locakhost:9094. It also runs a Kafka UI responding on http://localhost:8080.

You can run this example directly in IntelliJ, without any local Flink cluster or local Flink installation.

Note that you can run both the Flink DataGenerator and this example locally, in the IDE, to observe data being consumed and re-published to Kafka.

See Running examples locally for details.