Yichun Zhao's blog

SpringBoot: Redis Keeping Cached Consistent With Underlying

Yichun Zhao's photo
Yichun Zhao
·

3 min read

Maintaining consistency between the cache and the underlying database is a common challenge in cache-aided application design. Here are some strategies and best practices to keep the cache consistent with the database:

1. Cache Invalidation

The most straightforward approach is invalidating (clearing) cache entries when the corresponding data in the database changes. This can be done using cache eviction techniques.

  • Use @CacheEvict in Spring: When you perform an update or delete operation, use the @CacheEvict annotation to remove outdated entries from the cache.

      @CacheEvict(value = "users", key = "#userId")
  public void updateUser(String userId, User updatedUser) {
      // Update user in the database
  }

  @CacheEvict(value = "users", key = "#userId")
  public void deleteUser(String userId) {
      // Delete user from the database
  }

2. Time-to-Live (TTL)

Set a TTL (time-to-live) for cache entries. After the TTL expires, the cache entry is either automatically removed or marked as stale, forcing a fresh load from the database on the next request.

  • Configuring TTL in Redis: Most cache systems, including Redis, allow you to set a TTL for cache entries.

3. Write-Through and Write-Behind Caching

  • Write-Through Caching: In this approach, writes are immediately written to both the cache and the database. This ensures consistency but might have a performance impact due to the latency of writing to the database.

  • Write-Behind Caching: Here, writes are first written to the cache, and the cache asynchronously writes these changes to the database. This approach improves write performance but has a window of inconsistency between the cache and the database.

4. Transactional Caching

If your cache and database operations are part of a transaction, ensure that the cache is updated only after the transaction successfully commits. This is complex to implement and often requires advanced features of the caching framework.

5. Cache Aside Pattern

With this pattern, the application code is responsible for maintaining the cache. It works as follows:

  • Read: Check the cache first; if a miss occurs, read from the database and update the cache.
  • Write: Write to the database first, then invalidate the related cache entry.

6. Eventual Consistency

In some use cases, it's acceptable to have the cache be slightly out of sync with the database for a short period. This approach, known as eventual consistency, can be a good trade-off between performance and consistency.

7. Use of Distributed Caching Solutions

Distributed caching solutions (like Hazelcast, Apache Ignite) offer advanced features for maintaining consistency across multiple nodes and can handle synchronization with the database.

8. Monitoring and Alerts

Regularly monitor cache and database synchronization. Set up alerts for cache misses or synchronization issues to detect and resolve inconsistencies quickly.

Conclusion

Choosing the right strategy depends on the specific requirements and constraints of your application, such as the need for real-time consistency versus performance trade-offs. In many cases, a combination of these strategies is used to achieve the desired level of consistency and performance.

SpringbootRedis