Hibernate - Cache Eviction with Example

Caching in Hibernate means storing and reusing frequently used data to speed up your application. There are two kinds of caching: Session-level and SessionFactory-level. Level 1 cache is a cache that stores objects that have been queried and persist to the current session. This cache helps to reduce the number of database round trips by storing objects that are frequently accessed in the session. Level 1 cache is the default cache that is stored by default in hibernate. It is related to the hibernate session object.

Hibernate’s Level 2 caching is a built-in caching mechanism that allows you to cache data across several sessions. Level 2 cache stores data in your application’s shared cache region, which is accessible by every Hibernate session in your application. Level 2 caching can help optimize your application by reducing the number of database queries needed to load data. There are several cache providers available for Hibernate. You can use any of them, including Ehcache, Infinispan, Hazelcast, and others.

Importance of Cache Eviction

The process of cache eviction involves the deletion or replacement of data stored in a cache. This is a critical component of cache management as it guarantees that the cache remains functional and efficient. Here are some reasons why cache eviction is important:

1. Maximizing cache space: Caches have a limited amount of space, and if the cache becomes full, new data cannot be stored. Eviction ensures that the cache is not filled with outdated or unused data, allowing space for new data.
2. Improved cache performance: Eviction ensures that the cache only contains relevant data, increasing the cache hit rate and improving overall performance.
3. Avoiding data staleness: Data in a cache may become stale over time, especially if it is rarely accessed. Eviction removes such data, ensuring that only fresh data is stored in the cache.
4. Reducing cache access time: Eviction ensures that the cache only contains relevant data, which reduces the time needed to access data from the cache.
5. Reducing cache misses: Eviction helps reduce the occurrence of cache misses, which are situations where requested data is not found in the cache, and must be retrieved from slower storage such as a disk.

Definition of Cache Eviction

When a cache gets full, it can't store new data without getting rid of old data. That's what cache eviction is all about. It's the process of getting rid of or swapping out data in the cache to make room for new stuff. It's done by using algorithms to figure out which data to get rid of. There are different policies for doing this, like LRU (Least Recently Used), LFU (Frequently Used), and RPP (Random Replacement). These policies are picked based on the type of data being stored and how often it's accessed. The idea is to make sure the cache only has stuff that's important and frequently accessed. That way, it'll be more efficient and work better.

Reasons for Cache Eviction

There are several reasons for cache eviction, which are as follows:

1. Limited cache size: Caches have a limited size, and if the cache becomes full, new data cannot be stored. Eviction ensures that the cache is not filled with outdated or unused data, making space for new data.
2. Stale data: Data in a cache may become stale over time, especially if it is rarely accessed. Eviction removes such data, ensuring that only fresh data is stored in the cache.
3. Access frequency: Some data may be accessed more frequently than others. Eviction algorithms can prioritize frequently accessed data and remove less frequently accessed data.
4. Cache efficiency: Caches are designed to store frequently accessed data, and eviction ensures that the cache only contains relevant data, which improves the cache hit rate and overall performance.
5. Cache consistency: Caches may hold copies of data that may be modified by other processes or threads. Eviction ensures that the cache holds the most recent version of the data, improving cache consistency.
6. Dynamic data: Some data may change over time, and caching this data may not be effective. Eviction algorithms can remove data that is likely to change frequently.

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
2. Count-Based Eviction
3. Query Result Eviction
4. Cache Region Clearing

1. Time-Based Eviction

• Time-based eviction is a cache eviction strategy that involves removing data from the cache after a certain period has elapsed since the data was last accessed or added to the cache. The idea behind this strategy is that the data in the cache may become stale or irrelevant after a certain amount of time, and it is better to remove it from the cache to make room for more relevant data.
• Depending on your system’s requirements, you may be able to implement time-based eviction in different ways. For instance, some systems use a fixed TTL for all cache data, while others use a TTI based on how long it’s been since the last time you accessed the data.

2. Count-Based Eviction

• Count-Based Eviction is a technique used in cache management, where items are removed from the cache based on the number of times they have been accessed. The basic idea is that items that are accessed frequently are more likely to be accessed again shortly, so they should be kept in the cache. On the other hand, items that are accessed less frequently are less likely to be accessed again, so they can be evicted to make room for more frequently accessed items.
• Each item in your cache is associated with a counter. Every time you access an item, the counter on that item goes up. When your cache gets too full, the item that has the lowest counter will be evicted.

3. Query Result Eviction

• Query result eviction refers to the process of removing or expiring cached query results from a database or data storage system. When a query is executed in a database, the result of that query is typically stored in a cache to improve performance. This cache can be set to expire after a certain period or when new data is added to the database.
• When the cached query result is evicted, it is removed from the cache and the next time the same query is executed, the database will have to retrieve the data again from its primary storage location.

4. Cache Region Clearing

• Cache region clearing refers to the process of deleting or emptying the cache data stored in a particular region of a device or system. A cache is a temporary storage area that stores frequently accessed data to improve the performance of the system. When cache data is cleared, it is removed from the cache memory, and the system needs to retrieve the data again from the source, which may take more time.
• Cache regions can be cleared in various ways, depending on the system or device being used. For example, web browsers usually have options to clear the cache, either for specific regions or for the entire cache. Similarly, mobile devices and computers may have built-in tools for clearing the cache, or users may need to use third-party applications or software to perform the task.

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.

Conclusion

• In conclusion, cache eviction is an essential process in Hibernate's caching mechanism that helps to maintain the accuracy and consistency of cached data. Hibernate provides a second-level cache that can be used to store entities, collections, and query results in memory. However, cached data can become stale due to database updates, and we need to remove stale data from the cache to ensure that the next retrieval of data is accurate and up-to-date.
• Cache eviction can be implemented in Hibernate using methods like evictEntityRegion() and evictEntity() to remove cached data for specific entities. Additionally, third-party libraries like Ehcache or Hazelcast can be used to configure more advanced cache eviction policies based on time-to-live or time-to-idle.
• Overall, understanding how to implement cache eviction in Hibernate is essential for developing high-performance applications that leverage Hibernate's caching mechanism to improve the speed of database operations.