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How to Effectively Use ExecutorService in Kafka Consumers

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How to Effectively Use ExecutorService in Kafka Consumers

Apache Kakfa is one of the most popular open source tools for working with big data and streaming data. Learn a little bit more about Kakfa with this tutorial.

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Apache Kafka is one of today's most commonly used event streaming platforms. While using the Kafka platform, quite often, we run into a scenario where we have to process a large number of events/messages that are placed on a broker. Traditional approaches, where a consumer is listening to a topic and then processes these message within the consumer itself, can become a performance bottleneck if the number of messages being placed on the topic is high. In such cases, the rate at which a consumer can process messages will be very low, as there are a large number of messages getting placed on the topic. A potential solution that can be applied in such a scenario is to offload message processing to the worker threads in a thread pool.

In this section, we will take a look into how a Kafka consumer can offload its work to a thread pool. We will leverage Java’s ExecutorService framework to create a thread pool.

This approach primarily involves two steps. The first step is to create a KafkaConsumer that can read messages from a topic. Once the messages are read, they are delivered to a threadpool for further processing. The second step is to create worker threads that perform further processing of each message.

Step 1, Kafka Consumer Implementation: Here, we read the messages from a topic and dispatch the messages to a thread pool created using ThreadPoolExecutorService.

public class KafkaProcessor {

    private final KafkaConsumer<String, String> myConsumer;
    private ExecutorService executor;
    private static final Properties KAFKA_PROPERTIES = new Properties();
    static {
        KAFKA_PROPERTIES.put("bootstrap.servers", "localhost:9092");
        KAFKA_PROPERTIES.put("group.id", "test-consumer-group");
        KAFKA_PROPERTIES.put("enable.auto.commit", "true");
        KAFKA_PROPERTIES.put("auto.commit.interval.ms", "1000");
        KAFKA_PROPERTIES.put("session.timeout.ms", "30000");
        KAFKA_PROPERTIES.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        KAFKA_PROPERTIES.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

    public KafkaProcessor() {
        this.myConsumer = new KafkaConsumer<>(KAFKA_PROPERTIES);

public void init(int numberOfThreads) {
      //Create a threadpool
      executor = new ThreadPoolExecutor(numberOfThreads, numberOfThreads, 0L, TimeUnit.MILLISECONDS,
      new ArrayBlockingQueue<Runnable>(1000), new ThreadPoolExecutor.CallerRunsPolicy());

      while (true) {
        ConsumerRecords<String, String> records = myConsumer.poll(100);
        for (final ConsumerRecord<String, String> record : records) {
        executor.submit(new KafkaRecordHandler(record)); 


public void shutdown() {
        if (myConsumer != null) {
        if (executor != null) {
        try {
          if (executor != null && !executor.awaitTermination(60, TimeUnit.MILLISECONDS)) {
        }catch (InterruptedException e) {

Step 2, Worker Thread(Message/Record Handler) Implementation: Here, we perform further processing of the messages.

public class KafkaRecordHandler implements Runnable {
private ConsumerRecord<String, String> record;

    public KafkaRecordHandler(ConsumerRecord<String, String> record) {
    this.record = record;

    public void run() { // this is where further processing happens
        System.out.println("value = "+record.value());
        System.out.println("Thread id = "+ Thread.currentThread().getId());

The final step is to create a KafkaConsumer (KafkaProcessor) and specify the number of worker threads through the init() method.

public class ConsumerTest {

    public static void main(String[] args) {
      KafkaProcessor processor = new KafkaProcessor();
      try {
      }catch (Exception exp) {

This approach might not be needed/suitable for all scenarios. You have to carefully evaluate the best approach to be used with your Kafka consumer implementation.

big data ,kafka tutorial ,streaming data ,big data tutorial ,apache kafka

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