Hazelcast: In-Memory Data Grid
Master Hazelcast for distributed computing, real-time processing, and scalable data management
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
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
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
Feature | Hazelcast | Redis | Apache Ignite | Coherence |
---|---|---|---|---|
Deployment | Embedded/Client-Server | Client-Server only | Embedded/Client-Server | Embedded/Client-Server |
Stream Processing | ✓ Jet Engine | ✗ Limited | ✓ Built-in | ○ Limited |
SQL Support | ✓ Full SQL | ✗ No SQL | ✓ ANSI SQL | ✓ SQL-like |
Ease of Use | High | High | Medium | Medium |
Computing | ✓ Distributed | ✗ Limited | ✓ Distributed | ✓ Distributed |
License | Apache 2.0 | BSD 3-Clause | Apache 2.0 | Commercial |
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