Implementing distributed computing with Kafka and Hazelcast in Java

21 Sep 2023

Distributed computing is a powerful paradigm that allows you to divide a large task into smaller sub-tasks and run them simultaneously on multiple machines. This approach can significantly improve the performance and scalability of your application. In this blog post, we will explore how to implement distributed computing using Apache Kafka and Hazelcast, two popular technologies in the Java ecosystem.

Apache Kafka

Apache Kafka is a distributed streaming platform that provides a scalable, fault-tolerant messaging system. It is widely used for building real-time data pipelines and streaming applications. Kafka allows you to publish and subscribe to streams of records, which can be stored and processed in a distributed manner across multiple machines.

To use Kafka in your Java application, you need to include the Kafka client library in your project. You can do this by adding the following dependency to your pom.xml or build.gradle file:

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>{kafka-version}</version>
</dependency>

Hazelcast

Hazelcast is an open-source, in-memory data grid that provides distributed data structures and a highly scalable and fault-tolerant computing platform. It allows you to store and process data in parallel across multiple nodes in a cluster. Hazelcast can be used as a distributed cache, as well as for distributed computing tasks.

To use Hazelcast in your Java application, you need to include the Hazelcast client library in your project. You can do this by adding the following dependency to your pom.xml or build.gradle file:

<dependency>
    <groupId>com.hazelcast</groupId>
    <artifactId>hazelcast-client</artifactId>
    <version>{hazelcast-version}</version>
</dependency>

Integrating Kafka and Hazelcast

To implement distributed computing with Kafka and Hazelcast, you can follow these steps:

Set up a Kafka cluster and create a topic to publish messages.
Write a Kafka producer that publishes messages to the Kafka topic.
Set up a Hazelcast cluster and configure it to connect to the Kafka cluster as a consumer.
Write a Hazelcast consumer that receives messages from Kafka and processes them in parallel across multiple nodes in the Hazelcast cluster.

Here’s an example code snippet of how you can set up a Kafka producer and publish messages to a Kafka topic:

import org.apache.kafka.clients.producer.*;

public class KafkaProducerExample {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:9092");
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);

        for (int i = 0; i < 10; i++) {
            String message = "Message " + i;
            producer.send(new ProducerRecord<>("my-topic", message));
        }

        producer.close();
    }
}

And here’s an example code snippet of how you can set up a Hazelcast consumer to receive messages from Kafka and process them in parallel:

import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.IMap;
import com.hazelcast.core.ITopic;
import com.hazelcast.mapreduce.Context;
import com.hazelcast.mapreduce.Mapper;
import com.hazelcast.mapreduce.Reducer;
import com.hazelcast.mapreduce.ReducerFactory;
import com.hazelcast.mapreduce.Job;
import com.hazelcast.mapreduce.JobTracker;

import java.util.Map;

public class HazelcastConsumerExample {
    public static void main(String[] args) {
        HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();

        IMap<String, String> resultMap = hazelcastInstance.getMap("result-map");

        JobTracker jobTracker = hazelcastInstance.getJobTracker("default");

        Job<String, String> job = jobTracker.newJob(KeyValueSource.fromMap(resultMap));
        
        job
            .mapper(new Mapper<String, String, String, Integer>() {
                @Override
                public void map(String key, String value, Context<String, Integer> context) {
                    String[] words = value.split(" ");
                    for (String word : words) {
                        context.emit(word, 1);
                    }
                }
            })
            .reducer(new ReducerFactory<String, Integer, Integer>() {
                @Override
                public Reducer<Integer, Integer> newReducer(String key) {
                    return new Reducer<Integer, Integer>() {
                        private int sum;

                        @Override
                        public void reduce(Integer value) {
                            sum += value;
                        }
                        
                        @Override
                        public Integer finalizeReduce() {
                            return sum;
                        }
                    };
                }
            })
            .submit();
    }
}

By combining the power of Kafka for distributing messages and Hazelcast for parallel processing, you can build scalable and fault-tolerant distributed computing applications in Java.

#distributedcomputing #kafka #hazelcast