Redis cache penetration processing: causes and solutions

Redis cache penetration refers to a situation where a malicious user or attacker bypasses the cache and directly accesses the database by sending a large number of invalid queries. When a request queries for data that does not exist in the cache, Redis will send the request to the database for query. If the query conditions are illegal, the database will return empty query results. However, due to the presence of a large number of invalid query pressures, the database Too many resources will be used to process these queries, causing system performance bottlenecks.

There are many reasons for Redis cache penetration, such as invalid query conditions, a large number of malicious requests, and cache failures. In order to solve this problem, we need to take some measures, including but not limited to the following points:

1. Bloom Filter

Bloom Filter is a typical probabilistic data Structure, it can be used to handle collection query problems of massive data. At the same time, it does not need to store all elements and can save space. In Redis, we can use Bloom Filter to quickly check whether a Key exists in the cache. If the Key does not exist in the Bloom Filter, Redis will not query the database, thus avoiding cache penetration problems.

2. Create cached null value information

When Redis queries the database for data corresponding to a Key that does not exist, we can choose to store this information in Redis, and It does not directly return a null value. In this way, when the Key is queried again, it can be quickly determined that the data corresponding to the Key does not exist in the cache. This technique is called "cache nulling".

3. Set cache expiration time

The cache expiration time is an important concept. If we set the expiration time too short, the cache effect will become insufficient. Obviously; if the expiration time is set too long, the data in the cache will not be real-time enough. So we need to set appropriate expiration time according to different business scenarios. In addition, when the cache expiration time expires, Redis will automatically delete the Key from the cache to ensure the reliability of the cache.

4. Hotspot data warm-up

In the initial stage of system operation, we can pre-load the most frequently accessed data into the cache through some means, which can reduce the load on the system. load pressure and avoid cache penetration problems. This process is called "hotspot data warm-up".

To sum up, in order to solve the problem of Redis cache penetration, we need to adopt a variety of strategies and means, including filter technology, cache null values, cache expiration time and hotspot data preheating, etc., to ensure the cache reliability and system performance.

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