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A brief introduction to Redis cache update consistency

2025-01-16 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Database >

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This article mainly introduces "brief introduction of Redis cache update consistency". In daily operation, I believe many people have doubts about the brief introduction of Redis cache update consistency. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "brief introduction of Redis cache update consistency". Next, please follow the editor to study!

When designing an update strategy, we need to consider a number of issues:

Impact on system throughput: for example, the database load generated by updating the cache policy is less than that of deleting the cache policy.

Concurrent security: some abnormal operations may cause data inconsistency when reading and writing concurrently, such as keeping outdated data in cache for a long time.

Impact of update failure: how to minimize business impact if an operation fails

Difficulty in detecting and fixing failures: errors caused by failed operations are recorded in the log and are easy to detect and fix. Data errors caused by concurrency problems are difficult to find without obvious traces, and concurrency errors are more likely to occur during peak traffic periods, resulting in greater business risk.

There are two ways to update the cache:

Delete invalid cache: when reading, new data is read from the database and updated to the cache because the cache is missed

Update cache: write new data directly to the cache to overwrite expired data

There are two orders to update the cache and the database:

Database before caching

Cache first and then database

There are four update strategies in pairwise combinations, and now we will analyze them one by one.

Concurrency problems are usually caused by the thread that starts later but finishes the operation first. we call this phenomenon "snatching". Let's analyze the errors caused by "snatching" one by one of the four strategies.

Update the database before deleting the cache

If the database update is successful and the delete cache operation fails, all the data that has expired in the cache will be read since then, resulting in inconsistencies.

Possible concurrency errors:

Update the database before updating the cache

As with the delete cache policy, if the database update succeeds and fails to cache the update, it will cause data inconsistency.

Possible concurrency errors:

When two write threads collide, thread A can be prevented from writing old data by comparing data versions.

Delete the cache before updating the database

Possible concurrency errors:

Update the cache before updating the database

If the cache update succeeds and the database update fails, then all the data you read since then are unpersisted data. Because the data in the cache is volatile, this state is very dangerous.

This strategy is risky because the database is more likely to fail writes due to key constraints.

Possible concurrency errors:

Asynchronous update

The logic of double write update is complex, and there are many consistency problems. Now we can update the cache by subscribing to database updates.

We can adopt the strategy that the API server only writes to the database, while another thread subscribes to the database binlog increment for cache updates.

This problem can also be solved by updating the cache by asynchronous threads and comparing data versions when writing to the cache.

At this point, the study on "a brief introduction to the consistency of Redis cache updates" 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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