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What data does redis cache generally store?
Data types stored in the redis cache include: strings, hashes, lists, sets, ordered sets, bitmaps, geospatial data, and hyperloglog. These data types are suitable for storing a variety of data, from simple information to complex objects and geographic locations. This topic provides you with relevant articles, downloads, and course content for you to download and experience for free.
What data does redis cache generally store?Data types stored in the Redis cache include: strings, hashes, lists, sets, ordered sets, bitmaps, geospatial data, and HyperLogLog. These data types are suitable for storing a variety of data, from simple information to complex objects and geographic locations.
What is the redis caching mechanism?Redis' caching mechanism speeds up access and improves application performance by storing copies of data in memory. The core steps include: storing data in memory when writing; first checking the memory when reading, returning directly if it exists, and loading from persistent storage if it does not exist; setting the expiration time (TTL) to achieve cache invalidation; when there is insufficient memory space Use elimination strategies (such as LRU, LFU) to remove data. This mechanism effectively implements data caching, improving application response time and performance.
How to clear redis cacheThe method of clearing the cache in Redis is as follows: Clear all keys: Use the FLUSHALL command to clear all keys. Clear keys in a specific pattern: Use the DEL command to clear keys matching a specific pattern. Clear expired keys: Use the EXPIRE command to set the expiration time for the key, and it will be automatically cleared after expiration. Clear lazy delete keys: Use the BGREWRITEAOF or BGSAVE command to manually clear lazy delete keys. Clear keys in a specific database: Use SELECT to select a database, and then use the FLUSHALL command to clear the keys in that database.
What data structure is used for redis cache?Redis cache supports a variety of data structures, including: strings, hashes, lists, sets, sorted sets, geospatial data types, HyperLogLog, and bitmaps. Each data structure is optimized for specific application scenarios, improving the performance and efficiency of Redis caching.
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