Hibernate - Cache Eviction with Example

Cache eviction is the process of removing data from the cache to make space for new or updated data. It is important for maintaining cache performance and ensuring data consistency.

Key reasons why cache eviction is important:

• Limited cache size: Caches have finite space. Eviction ensures outdated or unused data is removed to make room for new data.
• Avoiding stale data: Data in a cache can become outdated over time. Eviction ensures only fresh data is stored.
• Improving performance: By keeping only relevant and frequently accessed data, eviction increases the cache hit rate and reduces access time.
• Ensuring consistency: Cached data may be modified elsewhere. Eviction helps maintain the latest version in the cache.
• Managing dynamic data: Data that changes frequently should be evicted to avoid storing obsolete values.

Different Types of Cache Eviction

Cache eviction is the process of removing data from a cache when the cache becomes full or when the data is no longer needed. There are several different types of cache eviction algorithms used by computer systems, including:

1. Time-Based Eviction: Removes cached data after a fixed period (TTL – Time To Live) or a period of inactivity (TTI – Time To Idle), ensuring stale data is cleared automatically.

2. Count-Based Eviction: Evicts items based on access frequency; less frequently accessed items are removed first to prioritize important data.

3. Query Result Eviction: Removes cached query results when they expire or when underlying data changes, ensuring queries return up-to-date results.

4. Cache Region Clearing: Clears all cached data in a specific cache region, useful for bulk updates or manual cache management.

Cache Eviction Strategies in Hibernate

Hibernate supports these cache eviction strategies through its second-level cache, which is a shared cache that is used to store entities and collections across multiple sessions. The second-level cache can be configured with different eviction policies for each cache region, allowing developers to optimize the caching behavior for different parts of their applications.

1. Least Recently Used (LRU): Least Recently Unused (LRU) is an algorithm that removes data from the cache that has not been used for the longest period. LRU is based on the premise that data that has been unutilized for a long period is unlikely to be required in the future.
2. First-In-First-Out (FIFO): First-in-first-out (FIFO) is an algorithm that prioritizes the removal of previously added data from the cache. The rationale behind this is that the longer the data has resided in the cache, the less likely it is to be required.
3. Least Frequently Used (LFU): Least Frequently Utilized (LFU) is an algorithm that removes data from the cache based on the premise that the least frequently accessed data is unlikely to be required in the future.

Implementation of Cache Eviction in Hibernate

The following is an example code snippet that demonstrates how to implement cache eviction in Hibernate using the second-level cache.

Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();

// Enable second-level cache for the entity
sessionFactory.getCache().evictEntityRegion(User.class);

// Perform some database operation
User user = session.get(User.class, 1L);
user.setName("John Doe");
session.update(user);

// Evict the cache for the entity
sessionFactory.getCache().evictEntity(User.class, user.getId());

tx.commit();
session.close();

Note: You can also configure cache eviction policies using third-party libraries like Ehcache or Hazelcast. These libraries provide more advanced caching options such as eviction policies based on the time-to-live or time-to-idle.