In modern web applications, maintaining user session state reliably across multiple servers is crucial for delivering seamless user experiences. Traditional in-memory session storage becomes a bottleneck in clustered environments, causing session loss during failover or scaling events. This is where Hazelcast, an in-memory data grid, shines by providing a distributed session clustering solution that is highly available, scalable, and performant.

Hazelcast’s architecture allows sessions to be stored across multiple nodes, ensuring fault tolerance and quick recovery. This blog post targets intermediate and advanced developers looking to implement a robust session store using Hazelcast, diving deep into technical details and best practices.

Why Use Hazelcast for Session Clustering?

Session clustering demands a distributed cache that supports:

  • Fault tolerance to avoid session loss during node failures
  • Scalability to handle increasing user loads effortlessly
  • Low latency for real-time user interactions
  • Consistency to ensure session data integrity

Hazelcast meets these demands by providing a distributed, in-memory data grid with native support for session replication and partitioning. It integrates smoothly with popular web frameworks and containers, including Spring Boot and Tomcat, making it an ideal choice for session clustering.

Core Concepts of Hazelcast for Session Storage

  • Partitioning and Replication: Hazelcast partitions session data across cluster nodes for load distribution and replicates it to backup nodes for high availability.
  • Near Cache: Improves read performance by caching frequently accessed session data locally on each node.
  • Eviction Policies: Controls memory usage by removing stale or least recently used sessions.
  • Serialization: Efficient serialization mechanisms (such as Hazelcast’s IdentifiedDataSerializable) reduce overhead and improve network efficiency.
  • Cluster Membership and Failover: Hazelcast monitors cluster node health and automatically redistributes session data upon node failure to avoid session loss.

Step-by-Step Implementation of Hazelcast Session Clustering

1. Setting Up Hazelcast Cluster

Start by including Hazelcast dependencies in your project (Maven/Gradle):

<dependency>
  <groupId>com.hazelcast</groupId>
  <artifactId>hazelcast</artifactId>
  <version>5.x</version>
</dependency>

Configure Hazelcast nodes to form a cluster using multicast or TCP/IP. For production, TCP/IP with static IPs or Kubernetes discovery is preferred for stability.

2. Configuring Session Replication

Enable distributed session storage by integrating Hazelcast with your web container. For example, with Tomcat, use the Hazelcast Tomcat session manager:

<Manager className="com.hazelcast.web.tomcat.HazelcastSessionManager"
         mapName="tomcat-sessions"
         sticky="false"
         stickySession="false" />

This configuration stores HTTP sessions in the Hazelcast cluster map tomcat-sessions, replicating sessions automatically.

3. Fine-Tuning Hazelcast for Session Use Case
  • Set Backup Count: Typically 1 or 2 backups balance availability and resource usage.
<map name="tomcat-sessions">
  <backup-count>2</backup-count>
  <time-to-live-seconds>3600</time-to-live-seconds>
</map>

  • Eviction Policies: Use LRU or LFU eviction to manage memory.

  • Serialization Optimization: Implement custom serializers for session objects to reduce serialization cost.

4. Handling Session Failover and Consistency

Hazelcast ensures eventual consistency for session data. For critical applications, consider implementing session versioning or write-behind strategies to guarantee strong consistency.

5. Monitoring and Scaling

Use Hazelcast Management Center or metrics exporters to monitor cluster health, session counts, and memory usage. Hazelcast scales horizontally by adding nodes dynamically, redistributing session partitions without downtime.

Best Practices for Production-Grade Hazelcast Session Stores

  • Enable TLS and Authentication: Secure cluster communication to protect session data.
  • Use Persistent Backups: Combine Hazelcast persistence with session clustering for disaster recovery.
  • Tune Network and JVM: Optimize TCP/IP settings and JVM heap for low latency and throughput.
  • Regularly Test Failover Scenarios: Simulate node crashes to validate session continuity.
  • Leverage Hazelcast’s WAN Replication: For geo-distributed session clustering across data centers.

Performance Considerations and Benchmarks

Hazelcast’s in-memory architecture typically reduces session read/write latency to microseconds, outperforming traditional database-backed session stores. However, network overhead and serialization cost are key factors:

  • Use compact serialization wherever possible.
  • Minimize session size by storing only essential data.
  • Enable Near Cache for read-heavy workloads.

Benchmarks show Hazelcast can handle tens of thousands of concurrent session operations per second on modest hardware.

Conclusion

Implementing a highly available session store with Hazelcast empowers web applications with fault tolerance, scalability, and performance essential for modern user demands. By leveraging Hazelcast’s distributed caching, session replication, and cluster management capabilities, developers can design resilient session clustering architectures that gracefully handle node failures and dynamic scaling.

For intermediate and advanced users, optimizing serialization, tuning backup counts, and securing the cluster are key to unlocking Hazelcast’s full potential. Integrate Hazelcast today to elevate your web application’s session management to a new level of reliability and efficiency.