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Hazelcast

Databases & CachingPerformance / In-Memory Data GridJava
Official docs ↗GitHub ↗

What it is

Hazelcast is a distributed in-memory data grid and caching platform for Java. It provides high-performance storage for key-value data, distributed maps, queues, topics, and support for distributed computing, clustering, and transactions.

Hazelcast provides distributed collections such as maps, sets, queues, lists, and topics. It supports automatic clustering, distributed computation, transactions, and persistence to ensure data reliability. Developers can integrate Hazelcast as a cache, compute grid, or in-memory database.

Installation

<dependency> <groupId>com.hazelcast</groupId> <artifactId>hazelcast</artifactId> <version>5.3.2</version> </dependency>

Getting started

The smallest useful thing you can do with it, and what each part means.

Starting a Hazelcast instance and using a map
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.map.IMap;

HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<Integer, String> map = hz.getMap("myMap");
map.put(1, "Hello Hazelcast");
System.out.println(map.get(1));
hz.shutdown();
Starts a Hazelcast node, creates a distributed map, stores and retrieves a value, and shuts down the instance.

Advanced usage

Where the library earns its place over a simpler alternative.

Using distributed queue
import com.hazelcast.collection.IQueue;
IQueue<String> queue = hz.getQueue("myQueue");
queue.add("Task1");
System.out.println(queue.poll());
Demonstrates using a distributed queue for inter-node task processing.
Executing distributed computation
hz.getExecutorService("exec").submit(() -> System.out.println("Running task across cluster"));
Executes a task across cluster nodes using Hazelcast's distributed executor service.
Transactions
import com.hazelcast.transaction.TransactionContext;
TransactionContext context = hz.newTransactionContext();
context.beginTransaction();
try {
    IMap<Integer, String> mapTx = context.getMap("myMap");
    mapTx.put(2, "Transactional Value");
    context.commitTransaction();
} catch(Exception e) {
    context.rollbackTransaction();
}
Performs atomic operations on distributed data using transactions.
Event listeners
map.addEntryListener(entryEvent -> System.out.println("Entry updated: " + entryEvent), true);
Adds a listener to monitor map entry events across the cluster.

Errors and fixes

The failures you are most likely to hit, and what actually resolves them.

HazelcastInstanceNotActiveException
Occurs if instance is shut down or not active. Ensure the Hazelcast instance is running.
TransactionException
Thrown if transaction fails. Handle commit/rollback appropriately.
IllegalStateException
Occurs if configuration is invalid or cluster is unstable. Validate cluster setup and configuration.

Best practices

  • Use appropriate collection types (map, queue, set) based on use case.
  • Enable backups for high availability and fault tolerance.
  • Use transactions for critical operations to ensure consistency.
  • Monitor cluster performance and adjust partitions and backups accordingly.
  • Leverage Hazelcast management center for monitoring and tuning cluster.

Background

Why it exists, and what it was reacting to.

Hazelcast was developed to enable real-time, scalable, and fault-tolerant data processing across clusters. It supports in-memory computing, distributed caching, and event-driven architectures, making it ideal for microservices, high-throughput applications, and low-latency systems.

Related libraries

Others in Databases & Caching for Java.

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