Hazelcast is a powerful in-memory data grid that excels in distributed computing and caching. While its out-of-the-box data structures like IMap, IQueue, and MultiMap satisfy many scenarios, advanced use cases often require custom data structures tailored to specific application needs. Leveraging Hazelcast’s advanced APIs allows intermediate and advanced users to implement these custom solutions with scalability, fault tolerance, and high throughput in mind.

In this post, we will explore how to use Hazelcast’s internal APIs and extension points to build custom data structures optimized for your unique distributed system requirements.

Why Build Custom Data Structures in Hazelcast?

Default Hazelcast data structures cover common patterns, but some scenarios demand specialized behavior:

  • Domain-specific indexing or querying: Build structures optimized for complex queries beyond standard map operations.
  • Custom serialization and partitioning: Improve performance by controlling how data is distributed and serialized.
  • Enhanced consistency or transactional semantics: Implement data structures with fine-grained control over concurrency.
  • Tailored eviction and expiration policies: For use cases with strict memory or lifecycle constraints.

By creating custom data structures, you obtain greater control over performance, scalability, and resource utilization in your distributed system.

Understanding Hazelcast SPI for Custom Data Structures

At the core of building custom data structures in Hazelcast is the Service Provider Interface (SPI). Hazelcast SPI lets you plug in new distributed data services that integrate seamlessly with the Hazelcast infrastructure.

Key SPI components include:

  • PartitionAwareService: Manage partition-specific data and operations.
  • ManagedService: Lifecycle management hooks for your custom service.
  • QuorumAwareService: Integrate quorum-based consistency.
  • SplitBrainHandlerService: Handle cluster split-brain scenarios cleanly.
  • Operation and OperationFactory: Define custom operations executed on cluster members.

Implementing these interfaces allows your data structure to participate in Hazelcast’s partitioning, replication, and cluster management mechanisms.

Step-by-Step Guide to Implementing a Custom Hazelcast Data Structure

  1. Define Your Data Structure API

    Begin by designing an interface exposing the operations your data structure supports. This ensures a clean API for clients.

    public interface DistributedCustomStructure<E> {
    void add(E element);
    boolean contains(E element);
    void remove(E element);
    // Add domain-specific methods
}

  2. Create the Service Implementation

    Implement the SPI interfaces to manage data, lifecycle, and cluster events.

    public class CustomStructureService implements PartitionAwareService, ManagedService {
    // Store data per partition
    private final ConcurrentMap<Integer, Set<Object>> partitionData = new ConcurrentHashMap<>();

    @Override
    public void init(NodeEngine nodeEngine, Properties properties) {
        // Initialize service resources
    }

    @Override
    public void reset() {
        partitionData.clear();
    }

    // PartitionAwareService methods
    @Override
    public void beforeMigration(PartitionMigrationEvent event) { /*...*/ }

    @Override
    public void commitMigration(PartitionMigrationEvent event) { /*...*/ }

    @Override
    public void rollbackMigration(PartitionMigrationEvent event) { /*...*/ }
}

  3. Implement Operation Classes

    Define custom operations to be executed on cluster members, encapsulating behavior like add or contains.

    public class AddOperation extends Operation {
    private Object element;

    @Override
    public void run() {
        // Access partition data and add element
    }

    @Override
    public void writeData(ObjectDataOutput out) throws IOException {
        out.writeObject(element);
    }

    @Override
    public void readData(ObjectDataInput in) throws IOException {
        element = in.readObject();
    }
}

  4. Register Your Service

    Integrate your custom service with Hazelcast by adding it in the configuration.

    Config config = new Config();
config.getServicesConfig().addServiceConfig(
    new ServiceConfig()
        .setEnabled(true)
        .setName("custom-structure-service")
        .setClassName(CustomStructureService.class.getName())
);

  5. Create Client-Side Proxy

    Implement a proxy that interacts with your service via Hazelcast’s InvocationService or custom operations.

    public class CustomStructureProxy<E> implements DistributedCustomStructure<E> {
    private final NodeEngine nodeEngine;

    @Override
    public void add(E element) {
        // Invoke AddOperation on appropriate partition
    }
}

Optimizing Performance and Scalability

  • Partition Awareness: Ensure data is partitioned based on your data structure’s access patterns. Implement PartitionAware interface if needed.
  • Custom Serialization: Use Hazelcast’s IdentifiedDataSerializable or Portable interfaces for fast serialization.
  • Asynchronous Operations: Leverage ICompletableFuture for non-blocking calls to improve throughput.
  • Backups and Consistency: Control backup counts and consistency models depending on the criticality of your data.
  • Near Cache Integration: Combine with Hazelcast Near Cache for low-latency reads in read-heavy use cases.

Use Case Example: Distributed Priority Queue with Custom Ordering

Suppose you need a distributed priority queue with a specialized comparator that Hazelcast’s default PriorityQueue does not support. Implementing a custom data structure via SPI allows:

  • Partitioning queue elements to distribute load.
  • Custom serialization of queue entries.
  • Fine control over ordering logic on cluster members.
  • Seamless integration with Hazelcast cluster lifecycle.

This approach scales better than a centralized queue and supports advanced ordering semantics.

Testing and Debugging Custom Hazelcast Data Structures

  • Use Hazelcast’s Test HazelcastInstanceFactory for unit and integration testing.
  • Enable hazelcast.logging.type=log4j or slf4j for detailed logs.
  • Validate partition migrations and split-brain recovery scenarios.
  • Profile serialization and network overhead with Hazelcast Management Center or JVisualVM.

Conclusion

Implementing custom data structures with Hazelcast’s advanced APIs unlocks powerful capabilities for distributed applications requiring specific behaviors beyond out-of-the-box offerings. By leveraging Hazelcast SPI, operation classes, and client proxies, developers can build scalable, fault-tolerant, and efficient distributed data solutions tailored precisely to their use cases.

This technical approach not only enhances application performance but also allows deep integration with Hazelcast’s ecosystem, making it an essential skill for intermediate and advanced Hazelcast users aiming to maximize their distributed system’s potential.

Boost your distributed applications today by mastering Hazelcast’s advanced APIs and crafting custom data structures that scale!