Kafka simple Consumer and Consumer Group

Kafka topics can be consumed using a single-threaded Consumer or using a multi-threaded Consumer. In this tutorial we will learn the differences between them.

Let's start from the simplest use case of Consumer, which consumes messages serially in a single thread:

import java.util.Properties;
import java.util.Arrays;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.ConsumerRecord;

public class SimpleConsumer {
   public static void main(String[] args) throws Exception {
      if(args.length == 0){
         System.out.println("Enter topic name");
      //Kafka consumer configuration settings
      String topicName = args[0].toString();
      Properties props = new Properties();
      props.put("bootstrap.servers", "localhost:9092");
      props.put("group.id", "test");
      props.put("enable.auto.commit", "true");
      props.put("auto.commit.interval.ms", "1000");
      props.put("session.timeout.ms", "30000");
      KafkaConsumer<String, String> consumer = new KafkaConsumer
         <String, String>(props);
      //Kafka Consumer subscribes list of topics here.
      //print the topic name
      System.out.println("Subscribed to topic " + topicName);
      int i = 0;
      while (true) {
         ConsumerRecords<String, String> records = con-sumer.poll(100);
         for (ConsumerRecord<String, String> record : records)
         // print the offset,key and value for the consumer records.
         System.out.printf("offset = %d, key = %s, value = %s\n", 
            record.offset(), record.key(), record.value());

A Consumer group, on the other hand, is a multi-threaded or multi-machine consumption from Kafka topics.

  • Consumers can join a group by using the same"group.id."
  • The maximum parallelism of a group can be achieved when the number of consumers in the group equals to the number of partitions.
  • Kafka assigns the partitions of a topic to the consumer in a group, so that each partition is consumed by exactly one consumer in the group.
  • Kafka also guarantees that a message is consumed by a single consumer in the group.
  • Kafka Consumers can see the message in the order they were stored in the log.

Here is an example of Consumer Group:

package com.masteringintegration.kafka;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.errors.WakeupException;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.time.Duration;
import java.util.Collections;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;

public class SampleKafkaConsumer {

    public static void main(String[] args) {
        String server = "";
        String groupId = "SampleKafkaConsumer";
        String topic = "testTopic";

        new SampleKafkaConsumer(server, groupId, topic).run();

    // Variables

    private final Logger mLogger = LoggerFactory.getLogger(SampleKafkaConsumer.class.getName());
    private final String mBootstrapServer;
    private final String mGroupId;
    private final String mTopic;

    // Constructor

    SampleKafkaConsumer(String bootstrapServer, String groupId, String topic) {
        mBootstrapServer = bootstrapServer;
        mGroupId = groupId;
        mTopic = topic;

    // Public

    void run() {
        mLogger.info("Creating consumer thread");

        CountDownLatch latch = new CountDownLatch(1);

        ConsumerRunnable consumerRunnable = new ConsumerRunnable(mBootstrapServer, mGroupId, mTopic, latch);
        Thread thread = new Thread(consumerRunnable);

        Runtime.getRuntime().addShutdownHook(new Thread(() -> {
            mLogger.info("Caught shutdown hook");

            mLogger.info("Application has exited");


    // Private

    void await(CountDownLatch latch) {
        try {
        } catch (InterruptedException e) {
            mLogger.error("Application got interrupted", e);
        } finally {
            mLogger.info("Application is closing");

    // Inner classes

    private class ConsumerRunnable implements Runnable {

        private CountDownLatch mLatch;
        private KafkaConsumer<String, String> mConsumer;

        ConsumerRunnable(String bootstrapServer, String groupId, String topic, CountDownLatch latch) {
            mLatch = latch;

            Properties props = consumerProps(bootstrapServer, groupId);
            mConsumer = new KafkaConsumer<>(props);

        public void run() {
            try {
                while (true) {
                    ConsumerRecords<String, String> records = mConsumer.poll(Duration.ofMillis(100));

                    for (ConsumerRecord<String, String> record : records) {
                        mLogger.info("Key: " + record.key() + ", Value: " + record.value());
                        mLogger.info("Partition: " + record.partition() + ", Offset: " + record.offset());
            } catch (WakeupException e) {
                mLogger.info("Received shutdown signal!");
            } finally {

        void shutdown() {

        private Properties consumerProps(String bootstrapServer, String groupId) {
            String deserializer = StringDeserializer.class.getName();
            Properties properties = new Properties();
            properties.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServer);
            properties.setProperty(ConsumerConfig.GROUP_ID_CONFIG, groupId);
            properties.setProperty(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, deserializer);
            properties.setProperty(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, deserializer);
            properties.setProperty(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");

            return properties;

By adding more processes/threads will let Kafka to re-balance. That is, uf any consumer or broker fails to send heartbeat to ZooKeeper, then it can be re-configured via the Kafka cluster. During this re-balance, Kafka will assign available partitions to the available threads, possibly moving a partition to another process.

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