Java framework cache invalidation and solutions

Common reasons for Java framework cache invalidation include data changes, TTL expiration, manual invalidation, and concurrent updates. The processing solutions include: incremental update (for frequently updated data); cache penetration protection (to prevent directly bypassing the cache to query the database); manual invalidation (to invalidate data immediately); distributed lock (to prevent concurrent update data from being inconsistent).

Java framework cache invalidation and processing solution

Introduction

The caching mechanism is It is crucial in modern web development and can greatly improve the performance and responsiveness of applications. However, cached data may become invalid, which requires the application to take appropriate measures to handle it. In this article, we will explore common causes of cache invalidation in Java frameworks and common solutions.

Cause invalidation reasons

• Data changes: When the data in the cache is updated in the database or other persistent storage, the cache Data will become stale.
• TTL Expiration: Most caching systems set a time-to-live (TTL) value for each cache item after which the item will expire from the cache.
• Manual invalidation: There are times when applications need to proactively invalidate caches, such as when updating sensitive information or performing breaking changes.
• Concurrent updates: In a concurrent environment, multiple threads may update the same cache item at the same time, causing one of the updates to be overwritten.

Processing plan

1. Incremental update

For frequently updated data, use incremental update The mechanism can effectively reduce cache invalidation. When data changes, only the affected portion of the cache is updated, not the entire item. For example, you can use the @CachePut annotation to implement Spring's incremental updates.

@CachePut(value = "userCache", key = "#user.id")
public User updateUser(User user) {
    // ... 更新数据库并返回更新后的用户
}

2. Cache penetration protection

Cache penetration refers to directly bypassing the cache and querying the database when the target data cannot be queried. To prevent cache penetration, bloom filters or second-level caches can be used to intercept such requests.

3. Manual invalidation

When you need to invalidate cached data immediately, you can use the manual invalidation method provided by the cache API. For example, the Cache.evict method in Spring Cache can be used to explicitly invalidate a cache entry.

4. Distributed lock

In a concurrent environment, distributed locks can be used to ensure that the same cache item is not updated by multiple threads at the same time. By obtaining the lock before updating the cache item, you can prevent data inconsistencies caused by concurrent updates.

Practical Case

Consider the shopping basket example of an e-commerce website. When a user adds or removes items from the shopping basket, the website needs to update the shopping basket cache. Since the shopping basket data is updated frequently, using the incremental update mechanism can optimize cache performance.

@Cacheable(value = "shoppingCartCache", key = "#userId")
public ShoppingCart getShoppingCartForUser(Long userId) {
    // ... 查询数据库并返回购物车
}

@CachePut(value = "shoppingCartCache", key = "#userId")
public ShoppingCart updateShoppingCart(Long userId, ShoppingCart cart) {
    // ... 更新数据库和购物篮缓存
}

This example uses Spring Cache to implement incremental updates, which only updates the affected part of the shopping cart cache (that is, the items that have been added or deleted), without affecting the entire shopping cart.

Conclusion

Cache invalidation is a common problem in the Java framework caching mechanism. Understanding the causes of failures and adopting appropriate solutions are critical to ensuring application performance and data consistency.

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