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This article mainly introduces "how to solve the redis cache problem". In the daily operation, I believe many people have doubts about how to solve the redis cache problem. The editor consulted all kinds of data and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubts about "how to solve the redis cache problem"! Next, please follow the editor to study!

1. Cache traversal

Cache traversal refers to querying a non-existent data, there is no information about the data in the cache, and directly penetrates the cache layer to the database layer.

Solution:

Bloom filter: similar to a hash table that generates a bitmap based on all possible query conditions, this bitmap is used for filtering when querying the database, and filtering directly is not included in it, thus reducing the pressure on the database level.

Null cache: after querying non-existent data for the first time, put the key of this value and the corresponding null value into the cache, but set a short expiration time, so that you can deal with a large number of key attacks in a short time.

2. Cache avalanche:

In ordinary cache systems, such as (redis, memcache), we will set an expiration time for the cache, but if all cache expiration times are the same, then requests from all systems will be sent to the database when they expire at the same time, which may cause db to crash under such great pressure.

Solution

Thread mutual exclusion: only one thread is allowed to build the cache, while the other threads wait for the thread building the cache to finish executing. Only one thread is executing the request at a time, reducing the pressure on db, but the disadvantage is also obvious, reducing the number of requests in the system QPS (1s).

Staggered failure time: when the cache expiration time is set, a random time from an appropriate range can be used as the expiration time.

3. Cache breakdown:

A special case of cache avalanche, the difference: breakdown is for specific hot spot data, while avalanche is all data. Hot topic, when the cache reaches the expiration time, there are still a large number of requests to reach the system, without the protection of the cache layer, directly reaching the failure caused by db.

Solution:

Second-level cache: second-level cache for hot data, and set different expiration time for different levels of cache, then the request will not directly break down to reach db (how the limit of second-level cache can consider the LRU algorithm "least recently used algorithm")

At this point, the study on "how to solve the redis cache problem" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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