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Use Apache Flink® with Aiven for Apache Kafka®

Apache Flink® is an open-source platform for handling distributed streaming and batch data. It enhances Apache Kafka's® event streaming abilities by offering advanced features for consuming, transforming, aggregating, and enriching data.


To experience the power of streaming SQL transformations with Flink, Aiven provides a managed Aiven for Apache Flink® with built-in data flow integration with Aiven for Apache Kafka®.

The following example demonstrates how to create a simple Java Flink job. This job reads data from a Apache Kafka topic, processes it,and sends it to another Apache Kafka topic. It uses the Java API on a local installation of Apache Flink 1.16. However, the same approach can be applied to use Aiven for Apache Kafka with any self-hosted cluster.

Before you start, make sure you have the following:

  • An active Aiven for Apache Kafka service with two topics: test-flink-input and test-flink-output. To create topics, see Create an Apache Kafka topic.
  • Gather the following details about your Aiven for Apache Kafka service:
    • APACHE_KAFKA_HOST: The hostname of your Apache Kafka service.
    • APACHE_KAFKA_PORT: The port number of your Apache Kafka service.
  • Apache Maven™ installed on your machine build the example.

Setup the truststore and keystore

Create a Java keystore and truststore for the Aiven for Apache Kafka service. For this example, the configuration is as follows:

  • The keystore is available at KEYSTORE_PATH/client.keystore.p12
  • The truststore is available at TRUSTSTORE_PATH/client.truststore.jks
  • For simplicity, use the same secret (password) for both the keystore and the truststore, referred to as KEY_TRUST_SECRET

The following example shows how to customise the DataStreamJob generated from the Quickstart to work with Aiven for Apache Kafka.


The full code to run this example can be found in the Aiven examples GitHub repository.

  1. Generate a Flink job skeleton named flink-capitalizer using the Maven archetype:

    mvn archetype:generate -DinteractiveMode=false  \
    -DarchetypeGroupId=org.apache.flink \
    -DarchetypeArtifactId=flink-quickstart-java \
    -DarchetypeVersion=1.16.0 \
    -DgroupId=io.aiven.example \
    -DartifactId=flink-capitalizer \
    -Dpackage=io.aiven.example.flinkcapitalizer \
  2. Uncomment the Kafka connector in `pom.xml`:


Customize the DataStreamJob application

In the generated code, DataStreamJob is the main entry point, and has already been configured with all of the context necessary to interact with the cluster for your processing.

  1. Create a class called io.aiven.example.flinkcapitalizer.StringCapitalizer which performs a MapFunction transformation on incoming records, emitting every incoming string in uppercase.

    package io.aiven.example.flinkcapitalizer;

    import org.apache.flink.api.common.functions.MapFunction;

    public class StringCapitalizer implements MapFunction<String, String> {
    public String map(String s) {
    return s.toUpperCase();
  2. Import the following classes in the DataStreamJob

    import java.util.Properties;
    import org.apache.flink.api.common.eventtime.WatermarkStrategy;
    import org.apache.flink.api.common.serialization.SimpleStringSchema;
    import org.apache.flink.connector.base.DeliveryGuarantee;
    import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
    import org.apache.flink.connector.kafka.sink.KafkaSink;
    import org.apache.flink.connector.kafka.source.KafkaSource;
    import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
  3. Modify the main method in DataStreamJob to read and write from the Apache Kafka topics, replacing the APACHE_KAFKA_HOST, APACHE_KAFKA_PORT, KEYSTORE_PATH, TRUSTSTORE_PATH and KEY_TRUST_SECRET placeholders with the values from the prerequisites.

    public static void main(String[] args) throws Exception {
    final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    Properties props = new Properties();
    props.put("security.protocol", "SSL");
    props.put("ssl.keystore.type", "PKCS12");
    props.put("ssl.keystore.location", "KEYSTORE_PATH/client.keystore.p12");
    props.put("ssl.keystore.password", "KEY_TRUST_SECRET");
    props.put("ssl.key.password", "KEY_TRUST_SECRET");
    props.put("ssl.truststore.type", "JKS");
    props.put("ssl.truststore.location", "TRUSTSTORE_PATH/client.truststore.jks");
    props.put("ssl.truststore.password", "KEY_TRUST_SECRET");

    KafkaSource<String> source = KafkaSource.<String>builder()
    .setValueOnlyDeserializer(new SimpleStringSchema())

    KafkaSink<String> sink = KafkaSink.<String>builder()
    .setValueSerializationSchema(new SimpleStringSchema())

    // ... processing continues here
  4. Tie the Apache Kafka sources and sinks together with the StringCapitalizer in a single processing pipeline.

    // ... processing continues here
    .fromSource(source, WatermarkStrategy.noWatermarks(), "Kafka Source")
    .map(new StringCapitalizer())
    env.execute("Flink Java capitalizer");

Build the application

From the main flink-capitalizer folder, execute the following Maven command to build the application:

mvn -DskipTests=true clean package

The above command should create a jar file named target/flink-capitalizer-0.0.1-SNAPSHOT.jar.

Run the applications

If you have installed a local cluster installation of Apache Flink 1.16, you can launch the job on your local machine. $FLINK_HOME is the Flink installation directory.

$FLINK_HOME/bin/flink run target/flink-capitalizer-0.0.1-SNAPSHOT.jar

You can see that the job is running in the Flink web UI at http://localhost:8081.

By integrating Aiven for Apache Flink® with Aiven for Apache Kafka®, you can process string events and transform them to uppercase before forwarding them to the output topic.