How to solve redis cache penetration

Cache penetration means that malicious users continuously query data that does not exist in the database, resulting in performance degradation. Solutions include: setting default values, using bloom filters, using verification code mechanisms to limit traffic, slow query log analysis, and strengthening data verification

Redis cache traversal Penetration solution

#What is cache penetration?

Cache penetration means that malicious users or crawlers continuously query data that does not exist in the database, resulting in a large number of database queries and performance degradation.

Solution:

1. Set default value:

For non-existent data, you can set a default value , such as null values ​​or error messages, and cache them. When the user queries the data, the default value in the cache is returned directly to avoid database queries.

2. Bloom filter:

The Bloom filter is a probabilistic data structure that can quickly determine whether an element is in a set. In the cache penetration scenario, all possible data queries can be converted into keys in the Bloom filter. When the user queries for non-existent data, the Bloom filter returns negative results and directly returns null values ​​or error messages to avoid Database query.

3. Verification code mechanism:

For high-frequency malicious requests, the verification code mechanism can be used to filter. When users query a large amount of non-existent data, they are required to provide a verification code to verify their identity and prevent malicious attacks.

4. Current limiting:

For specific users or IP addresses, you can set request current limiting. When it exceeds a certain threshold, an error message will be returned directly to avoid database queries. .

5. Slow query log analysis:

By analyzing the slow query log, find out the requests that frequently query non-existent data, and optimize or repair them accordingly.

6. Strengthen data verification:

Verify the input data at the application code layer to ensure that non-existent data will not be queried. For example, determine whether the ID is a positive integer, whether the timestamp is within a reasonable range, etc.

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