Redis as a consistency strategy for cache database

Redis has become more and more popular as a cache database. In order to ensure high availability and high performance of applications, consistency strategies should be adopted to ensure data consistency and reliability.

Consistency strategy refers to data consistency between applications, persistent storage and cache databases. In a distributed system, data inconsistency may occur due to the message passing and synchronization mechanisms between computers. Therefore, we need to adopt a consistency strategy to avoid this happening.

The consistency strategy of Redis as a cache database mainly includes:

1. Data writing and reading

When the application needs to write data, it will The data is written to the cache database and then waits for the cache database to confirm that the data has been written. If the cache database confirmation is successful, the application can consider that the data has been written to the cache database, otherwise the write should be retried after the cache database confirmation.

When the application needs to read data, it will first query the cache database. If the data does not exist in the cache database, the application will retrieve the data from the persistent storage and write the data to the cache. database for subsequent reading. If the data already exists in the cache database, the application can read the data directly from the cache database.

2. Cleaning of cached data

When the data in the cache database is no longer needed, it should be cleaned up from the cache database in time to free up storage space. Strategies for clearing cached data can use time- and space-based mechanisms.

The time-based mechanism means that when the storage time of cached data exceeds a certain period of time, it will be automatically cleared. This mechanism does not require application intervention, but may result in untimely updates of data.

The space-based mechanism means that when the data in the cache database takes up more than a certain size, some data that takes up a lot of space will be cleared to free up space. This mechanism requires application intervention, but can ensure timely updating of data.

3. Expiration time of cached data

In order to prevent cached data from being stored for too long and becoming outdated, you can set an expiration time to automatically clean up outdated data. This mechanism can ensure timely updating of cached data, but requires the application to manage the expiration time.

4. Cache database sharding strategy

When the cache database capacity cannot meet the needs of the application, the cache database sharding strategy can be used to expand the capacity. The sharding strategy of the cache database can use a hash value-based mechanism to disperse data to different nodes to improve the concurrency and availability of the system.

5. Backup and recovery strategy

In order to ensure the reliability of data, when the data in the cache database fails, backup and recovery are required. Backup and recovery strategies can use cold backup and hot backup mechanisms. The former needs to stop the cache database service and back up the data to other storage media; the latter needs to ensure that the data in the cache database can continue to be accessed while backing up.

Summary:

As a cache database, Redis needs to adopt a consistency strategy to ensure data consistency and reliability. These strategies include data writing and reading, cache data cleaning, cache data expiration time, cache database sharding strategy, and backup and recovery strategies. Through reasonable consistency strategies, the concurrency and availability of the system can be improved, and the user experience can be improved.

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