In modern distributed systems, achieving low latency, high throughput, and scalable event processing is paramount. Hazelcast, a powerful in-memory data grid, combined with Apache Kafka’s distributed streaming platform, offers an excellent synergy for building sophisticated event driven architectures. This blog post dives deep into the technical integration of Hazelcast with Kafka, focusing on how they complement each other to enable scalable messaging and stream processing solutions tailored for intermediate and advanced users.

Why Integrate Hazelcast with Apache Kafka

Both Hazelcast and Kafka excel in distributed environments but serve different purposes:

  • Hazelcast provides an in-memory data grid with distributed caching, fast data access, and computation capabilities.
  • Apache Kafka functions as a durable, partitioned, and distributed event streaming platform ideal for handling real-time data feeds.

Integrating these two allows developers to leverage Kafka’s reliable event streaming with Hazelcast’s fast in-memory processing and state management. This combination results in high-performance event-driven applications with real-time analytics, stateful stream processing, and dynamic scaling.

Core Components of Integration

The main integration points include:

  • Kafka Consumers/Producers in Hazelcast Jet: Hazelcast Jet, Hazelcast’s stream processing engine, can act as Kafka consumers and producers, ingesting Kafka topics and outputting processed events back into Kafka or Hazelcast data structures.
  • Hazelcast Kafka Connector: This connector enables seamless data flow between Kafka topics and Hazelcast distributed maps, queues, or caches.
  • Stateful Stream Processing: Hazelcast Jet allows maintaining local state that can be backed by Kafka changelog topics for fault tolerance and recovery.

Setting Up Hazelcast and Kafka Environment

To get started:

  1. Deploy Kafka Cluster: Utilize Apache Kafka’s standard setup with Zookeeper or Kafka’s newer KRaft mode for managing metadata.
  2. Set up Hazelcast Cluster: Run Hazelcast IMDG nodes or use Hazelcast Cloud. Ensure Hazelcast Jet is enabled if stream processing is required.
  3. Include Dependencies: Add Hazelcast and Kafka client libraries to your project, such as hazelcast, hazelcast-jet, and kafka-clients.

Example Maven dependencies snippet:

<dependency>
  <groupId>com.hazelcast</groupId>
  <artifactId>hazelcast-jet</artifactId>
  <version>5.1</version>
</dependency>
<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka-clients</artifactId>
  <version>3.3.1</version>
</dependency>

Integrating Kafka with Hazelcast Jet for Stream Processing

Hazelcast Jet provides native Kafka connectors to consume and produce Kafka events with minimal configuration.

Example pipeline snippet consuming from Kafka:

Pipeline pipeline = Pipeline.create();

pipeline.readFrom(KafkaSources.kafka(
        PropertiesBuilder.buildKafkaProperties(),
        KafkaProcessors.topic("input-topic")))
    .map(event -> processEvent(event))
    .writeTo(Sinks.kafka(
        PropertiesBuilder.buildKafkaProperties(),
        KafkaProcessors.topic("output-topic")));

JetInstance jet = Hazelcast.newJetInstance();
jet.newJob(pipeline).join();

This enables real-time stream processing with Hazelcast Jet acting as a Kafka consumer and producer. Internally, Jet manages event ordering, fault tolerance, and state snapshots, making it ideal for event-driven architectures requiring exactly-once processing guarantees.

Using Hazelcast Kafka Connectors for Data Synchronization

Hazelcast Kafka connectors allow syncing data between Kafka topics and Hazelcast distributed data structures like IMap or IQueue. This is useful for scenarios where you want to:

  • Cache Kafka event data in Hazelcast for low-latency access.
  • Use Hazelcast as a buffer or state store for Kafka streams.
  • Trigger Hazelcast distributed computations from Kafka event ingestion.

Configuration typically involves specifying topic names, serializers, and Hazelcast cluster connection details. The connector handles the serialization/deserialization and event mapping transparently.

Handling State and Fault Tolerance

A critical aspect of stream processing is managing state and fault tolerance. Hazelcast Jet supports stateful transformations with local state stored in-memory and checkpoints backed up in durable storage, including Kafka changelog topics.

This design enables:

  • Exactly-once processing semantics even in failure scenarios.
  • Fast recovery by replaying events from Kafka and restoring local state.
  • Scalability through partitioned state stores aligned with Kafka partitions.

Developers can implement custom stateful processors or use built-in operators to maintain counts, aggregates, windows, or session states seamlessly.

Best Practices for Performance and Scalability

To optimize the Hazelcast-Kafka integration:

  • Partition Alignment: Ensure Hazelcast Jet’s parallelism matches Kafka partitions to maximize throughput and minimize data shuffling.
  • Backpressure Handling: Use Jet’s built-in backpressure mechanisms to prevent overwhelming Hazelcast or Kafka brokers.
  • Serialization: Choose efficient serializers (e.g., Hazelcast’s Portable or Kafka’s Avro/Protobuf) to reduce data size and parsing overhead.
  • Resource Allocation: Allocate sufficient memory and CPU for Hazelcast nodes to handle in-memory state and computation.
  • Monitoring and Metrics: Utilize Hazelcast Management Center and Kafka monitoring tools to track lag, throughput, and resource utilization.

Real World Use Cases

  • Real-time fraud detection: Process transactional events from Kafka with Hazelcast Jet’s streaming pipeline and maintain stateful anomaly detection models.
  • IoT data aggregation: Ingest sensor data streams into Hazelcast for low-latency analytics and write aggregated insights back to Kafka.
  • Microservices Event Bus: Use Kafka as the event backbone and Hazelcast as a distributed cache and computation engine for responsive microservice orchestration.

Conclusion

Integrating Hazelcast with Apache Kafka provides a powerful foundation for building scalable, fault-tolerant, and real-time event-driven systems. By leveraging Hazelcast Jet’s stream processing capabilities alongside Kafka’s robust messaging infrastructure, developers can design architectures that meet demanding latency and throughput requirements.

Whether you are building complex event processors, real-time analytics platforms, or distributed caches synchronized with event streams, this integration unlocks new possibilities for handling big data workloads efficiently.

Embrace this synergy to accelerate your distributed applications and harness the full potential of event-driven messaging and stream processing today.