How to solve the problem of redis cache breakdown

Redis cache breakdown solution: Add a mutex: acquire a distributed lock before querying the database to prevent concurrent queries. Asynchronous update cache: Put cache update operations into the queue to execute asynchronously to avoid concurrent updates. Hotspot data never expires: Set a long expiration time for hotspot data or never expires to prevent cache breakdown. Current limit: Control the number of requests to access the database to prevent concurrent access from causing excessive database pressure. Use Bloom filter: Quickly determine whether the value exists. If it exists, it returns cached data. If it does not exist, it query the database.

Redis cache breakdown solution

Redis cache breakdown refers to the fact that when the data in the cache fails, multiple requests access the database at the same time, resulting in excessive database pressure. To solve the cache breakdown problem, the following measures can be taken:

1. Add a mutex lock

Before querying the database, try to acquire the distributed lock. If the acquisition is successful, no other request is querying the database, and the query can be safely performed and the cache is updated. If the acquisition fails, wait for a while before trying again.

2. Asynchronously update the cache

Do not update the cache immediately when the cache fails. Instead, the update cache operation is placed in the queue and executed asynchronously. This can prevent multiple requests from updating the cache at the same time, resulting in excessive database pressure.

3. Hot data never expires

For hotspot data, you can set it to a longer expiration time, or even never expire. This can avoid cache breakdown problems, but you need to pay attention to data consistency.

4. Current limit

After the cache fails, the number of requests to access the database can be controlled by flow limiting. This prevents too many requests to access the database simultaneously, resulting in excessive database pressure.

5. Use a Bloom filter

The Bloom filter can quickly determine whether a value exists in the set. Before querying the database, you can first use the Bloom filter to determine whether the value exists. If it exists, the cached data is returned directly. If it does not exist, then query the database.

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