Hazelcast: In-Memory Data Grid

Master Hazelcast for distributed computing, real-time processing, and scalable data management

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What is Hazelcast?

Hazelcast is an in-memory computing platform that provides distributed data structures, distributed computing, and distributed messaging. It enables applications to share data and computation across a cluster of servers, providing high performance, scalability, and fault tolerance for mission-critical applications.

Core Components

  • • Distributed Maps & Caches
  • • Distributed Computing
  • • Event Processing
  • • Stream Processing (Jet)
  • • SQL Queries

Key Benefits

  • • Linear scalability
  • • Fault tolerance
  • • Real-time processing
  • • Easy integration
  • • Cloud native

Core Features

Distributed Map

In-memory key-value store distributed across cluster nodes with automatic partitioning

Use Case: Distributed caching, session storage, and real-time data sharing

Implementation Example

// Java configuration
Config config = new Config();
config.getMapConfig("user-sessions")
  .setTimeToLiveSeconds(3600)
  .setMaxIdleSeconds(1800)
  .setEvictionConfig(new EvictionConfig()
    .setSize(1000)
    .setMaxSizePolicy(MaxSizePolicy.PER_NODE)
    .setEvictionPolicy(EvictionPolicy.LRU));

HazelcastInstance hz = Hazelcast.newHazelcastInstance(config);
IMap<String, User> userMap = hz.getMap("user-sessions");

// Store and retrieve data
userMap.put("user123", new User("John", "Doe"));
User user = userMap.get("user123");

// Near cache configuration for performance
config.getMapConfig("user-sessions")
  .setNearCacheConfig(new NearCacheConfig()
    .setInvalidateOnChange(true)
    .setTimeToLiveSeconds(60));

Key Benefits

Automatic data partitioning
Linear scalability
Built-in replication
Near cache support
SQL-like queries

Cluster Statistics

Live Cluster Metrics

Real-time view of cluster health and performance

Active Nodes

3

Map Entries

15,420

📊

Memory (GB)

2.1

💾

Ops/Sec

1250

🚀

Cluster Status

All nodes healthy
Data balanced
High throughput

Implementation Patterns

Distributed Caching Layer

High-performance caching tier between application and database

Implementation: Read-through, write-through, and write-behind caching strategies

Detailed Example

// Cache configuration with store integration
MapConfig cacheConfig = new MapConfig("product-cache")
  .setMapStoreConfig(new MapStoreConfig()
    .setClassName("com.example.ProductMapStore")
    .setEnabled(true)
    .setWriteDelaySeconds(5)
    .setWriteBatchSize(100))
  .setMaxIdleSeconds(300);

// Read-through pattern
public class ProductMapStore implements MapStore<String, Product> {
  private ProductRepository repository;
  
  @Override
  public Product load(String productId) {
    // Automatically loads from database if not in cache
    return repository.findById(productId);
  }
  
  @Override
  public void store(String key, Product product) {
    // Write-behind: batch writes to database
    repository.save(product);
  }
  
  @Override
  public Map<String, Product> loadAll(Collection<String> keys) {
    return repository.findAllById(keys);
  }
}

// Usage - cache handles database integration transparently
IMap<String, Product> productCache = hz.getMap("product-cache");
Product product = productCache.get("prod123"); // Auto-loads if missing

Key Considerations

  • Cache invalidation strategies
  • Write-behind batch sizing
  • Near cache configuration
  • Eviction policy tuning
  • Monitoring cache hit ratios

Hazelcast vs Alternatives

FeatureHazelcastRedisApache IgniteCoherence
DeploymentEmbedded/Client-ServerClient-Server onlyEmbedded/Client-ServerEmbedded/Client-Server
Stream Processing✓ Jet Engine✗ Limited✓ Built-in○ Limited
SQL Support✓ Full SQL✗ No SQL✓ ANSI SQL✓ SQL-like
Ease of UseHighHighMediumMedium
Computing✓ Distributed✗ Limited✓ Distributed✓ Distributed
LicenseApache 2.0BSD 3-ClauseApache 2.0Commercial

Best Practices

Performance

  • Configure near cache for hot data
  • Use entry processors for data locality
  • Optimize serialization with custom serializers
  • Tune JVM heap and GC settings

Reliability

  • Configure appropriate backup counts
  • Implement proper health checks
  • Use split-brain protection
  • Monitor cluster state continuously

Security

  • Enable TLS for member communication
  • Implement authentication and authorization
  • Use security interceptors
  • Audit sensitive operations

Operations

  • Use Management Center for monitoring
  • Set up proper logging and metrics
  • Plan for rolling upgrades
  • Automate cluster lifecycle management

📝 Test Your Knowledge

📝 Hazelcast Knowledge Quiz

1 of 5Current: 0/5

What is the primary advantage of Hazelcast's data partitioning strategy?