Redis implements common caching strategies

With the continuous development of Internet technology, data processing and transmission have become more and more important, and caching technology, as an important means of optimizing performance, has attracted more and more attention. As a high-performance cache database, Redis is often used to improve the performance and efficiency of web applications. This article will introduce how Redis implements common caching strategies.

1. Cache invalidation strategy

Cache invalidation means that the data stored in the cache has expired due to time or other reasons. In order to ensure the real-time nature of the data, we must set up a cache invalidation policy. Redis provides several cache invalidation strategies, including time invalidation strategy, spatial invalidation strategy and active invalidation strategy.

Time expiration policy: This expiration policy specifies the timeout period for cached data. In Redis, we can use the Expire command to set the expiration time for cached data. When the cached data exceeds the expiration time, Redis will automatically delete the data from the cache.

Space invalidation policy: This invalidation policy specifies the space occupied by cached data. In Redis, we can use the MaxMemory command to set the maximum memory usage of the cache. When Redis memory usage reaches the maximum memory setting, Redis will automatically delete the least recently used cache data according to the LRU algorithm.

Active invalidation strategy: This invalidation strategy is a developer-defined invalidation strategy. In practical applications, we can formulate targeted failure strategies based on business needs to achieve active failure. For example, when a user modifies certain data, we can notify Redis through the program to delete the corresponding cached data to ensure the real-time nature of the cached data.

2. Cache breakdown strategy

Cache breakdown refers to a large number of concurrent requests accessing data that does not exist in the cache but exists in the database at the same time. In this case, the database will be severely stressed, causing performance degradation. In order to avoid cache breakdown, we can use the following strategies:

Lazy loading strategy: This strategy divides the setting of data cache into two steps. First, search the corresponding cache data in Redis. If not found, return a null value. Then, the background task will asynchronously query the data from the database and write the queried data into the cache. This strategy can avoid cache penetration, but can lead to cache penetration problems.

Preloading strategy: This strategy is to load the data cache in advance, that is, when the application starts, the data is preloaded into the cache. This strategy can effectively avoid cache penetration, but will result in higher initialization cost.

3. Cache penetration strategy

Cache penetration refers to querying data that does not exist. In this case, the query will go directly to the database without going through the cache. Since the cache cannot return any data, such a query will cause a heavy load on the database. In order to avoid cache penetration, we can adopt the following strategy:

Empty cache strategy: This strategy is to return a null value in Redis when querying data that does not exist, so as to avoid cache penetration. But it will cause problems with the cache penetration strategy.

Bloom filter strategy: This strategy is based on the principle of Bloom filter and uses a bit array to record whether the data exists in the cache. When querying for non-existent data, if the data does not exist in the bit array, a null value will be returned directly. Because Bloom filters can determine whether data exists with a low error rate, cache penetration can be effectively avoided.

4. Cache avalanche strategy

Cache avalanche means that when the cache fails, a large number of concurrent requests access the cache at the same time, causing the database to bear excessive pressure and ultimately causing the system to collapse. In order to avoid cache avalanche, we can adopt the following strategy:

Distributed cache strategy: This strategy is to spread the cache pressure through multiple Redis nodes. In a distributed cache, adjacent nodes are responsible for different sets of data, thus avoiding single points of failure and cache avalanches.

Refined time expiration strategy: This strategy is to disperse the expiration time of cached data, that is, set different expiration times for different cached data to reduce the cache expiration time window. For example, if there are 1,000 cached data, and the expiration time of each data is randomly set within a certain range, then even if a large amount of cached data fails at a certain point in time, it will not cause a cache avalanche problem.

To sum up, Redis provides a variety of caching strategy implementation methods. In actual applications, we can choose appropriate caching strategies based on business needs to optimize application performance and efficiency.

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