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This article shows you what are the common cache problems in java high concurrency scenarios, which are concise and easy to understand, which can definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

Cache consistency

When the real-time requirement of data is very high, it is necessary to ensure that the data in the cache is consistent with the data in the database, the cache node is consistent with the data in the copy, and there can be no difference, which depends on the cache expiration and update strategy. Generally, when the data changes, the data in the new cache will be actively followed or the corresponding cache will be removed. In this case, the problem of cache consistency may arise.

Phenomenon 1: update the database successfully, update the cache failed, the data is inconsistent.

Phenomenon 2: update the cache successfully, update the database failed, the data is inconsistent.

Phenomenon 3: the database was updated successfully, the cache was eliminated and the data was inconsistent.

Phenomenon 4: successful elimination of cache, failure to update database, query cache miss.

Cache concurrency problem

After the cache expires, an attempt will be made to obtain data from the backend database, which is a seemingly reasonable process, but in a high concurrency scenario, multiple requests may request data from the database, causing a great impact on the backend database and even causing avalanches. In addition, when a cached Key is updated, it may also be fetched by a large number of requests, which can also lead to consistency problems. How to avoid similar problems? It can be avoided through the locking mechanism. In the case of caching updates or updates, try to acquire the lock first, and release the lock when the update or data acquisition from the database is complete. Other requests can continue to retrieve data from the cache at the expense of a certain amount of waiting time.

Cache penetration problem

In a high concurrency scenario, if a Key is accessed with high concurrency and missed, it will try to get it from the back-end database for the sake of fault tolerance, resulting in a large number of requests reaching the database, and the corresponding data of the Key is empty, which leads to a lot of unnecessary query operations in the database concurrently, resulting in huge impact and pressure. In this case, cache penetration problems can be avoided in the following ways:

Cache empty objects: also cache objects whose query results are empty. If it is a collection, you can cache an empty collection.

Separate filtering processing: all possible data is empty Key, unified storage. Intercept before the request to prevent the request from penetrating into the back-end database. This approach is relatively complex to implement. It is more suitable for data with low hit but frequently updated.

Avalanche phenomenon of cache

All requests to the background database cause the system to crash.

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