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.

Data types stored in Redis cache

Redis is an in-memory database widely used for caching. Known for its excellent performance and diverse data structure storage capabilities. The following are the data types commonly stored in the Redis cache:

• String (String) : Strings in Redis are concise data structures that can store text, numbers, or other type of data. They are typically used to store simple information or configuration settings that are obtained once, such as a user session or application state.
• Hash: Hashes allow you to store related data using key-value pairs. Each hash has a set of keys, each key mapping to a specific value. Hashes are ideal for storing user profiles, product information, or other data that needs to be grouped logically.
• List (List) : A list is a collection of values ​​stored in order. You can access elements in a list by index, and you can use commands to perform operations on the list, such as appending, inserting, and deleting. Lists are often used to store event logs, shopping lists, or task queues.
• Set (Set) : A set is an unordered collection that stores unique elements. They can efficiently check membership and be used to store tags, interests, or other non-duplicate data.
• Ordered Set (Sorted Set) : An ordered set is similar to a set, but the elements are sorted according to a specific score. They can be used to store rankings, scores, or other data that needs to be sorted.
• Bitmaps: Bitmap is a compact data structure used to efficiently represent a set of Boolean values. Each bit represents a flag, which is set to true or false. Bitmaps are very useful for storing large amounts of binary data.
• Geo Data (Geo Data) : Redis supports geospatial data types, allowing you to store and manipulate geographic locations. You can store shapes such as points, polygons, and circles in Redis and perform geographic queries, such as finding locations within a specific area.
• HyperLogLog: HyperLogLog is a probabilistic data structure used to estimate the number of unique entries in large data sets. It provides accurate estimation by taking up less space and is suitable for scenarios where counting a large number of unique elements.

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