What data does redis cache?
Redis’ cache data types include: string, hash, list, set, ordered set, geographical location, stream, HyperLogLog, Bitmap, modular data types (such as RedisJSON, RedisGraph, RedisTimeSeries).
Redis cached data types
Redis is a high-performance key-value database with powerful caching capabilities make it ideal for a variety of application scenarios. For the data types of Redis cache, we can mainly divide them into the following categories:
1. String (String)
String is the most basic data Type that can store any text, numeric, or binary data.
2. Hash
Hash is a data type of key-value pair structure, in which each key is associated with a value. It is very suitable for storing objects or structured data.
3. List (List)
A list is an ordered sequence data type in which the elements are arranged in insertion order. It provides fast insertion, deletion and access operations.
4. Set
A set is an unordered collection of non-repeating elements. It can efficiently perform set operations such as intersection, union and complement.
5. Sorted Set
An ordered set is a special set whose elements are sorted according to a specific score. It supports range queries and sorting operations on scores.
6. Geographical location (Geo)
The geographical location type is used to store and operate geospatial data, such as latitude and longitude coordinates. It supports distance queries and range searches for geographical locations.
7. Stream
Stream is an infinite, append-based data type used to store time series data. It provides efficient event ingestion and streaming.
8. HyperLogLog
HyperLogLog is an approximate cardinality estimation data type used to count the number of unique elements in large data sets. It can process large amounts of data efficiently, but at the expense of accuracy.
9. Bitmap
Bitmap is a space-efficient data type used to represent an array of Boolean values. It's great for tracking status, counts, or collection membership.
10. Modular data types
In addition to the above core data types, Redis also supports various modular data types, such as RedisJSON, RedisGraph and RedisTimeSeries. These modules provide additional functionality such as JSON document storage, graph databases, and time series management.
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Redis improves application performance and scalability by caching data, implementing distributed locking and data persistence. 1) Cache data: Use Redis to cache frequently accessed data to improve data access speed. 2) Distributed lock: Use Redis to implement distributed locks to ensure the security of operation in a distributed environment. 3) Data persistence: Ensure data security through RDB and AOF mechanisms to prevent data loss.

Redis's data model and structure include five main types: 1. String: used to store text or binary data, and supports atomic operations. 2. List: Ordered elements collection, suitable for queues and stacks. 3. Set: Unordered unique elements set, supporting set operation. 4. Ordered Set (SortedSet): A unique set of elements with scores, suitable for rankings. 5. Hash table (Hash): a collection of key-value pairs, suitable for storing objects.

Redis's database methods include in-memory databases and key-value storage. 1) Redis stores data in memory, and reads and writes fast. 2) It uses key-value pairs to store data, supports complex data structures such as lists, collections, hash tables and ordered collections, suitable for caches and NoSQL databases.

Redis is a powerful database solution because it provides fast performance, rich data structures, high availability and scalability, persistence capabilities, and a wide range of ecosystem support. 1) Extremely fast performance: Redis's data is stored in memory and has extremely fast read and write speeds, suitable for high concurrency and low latency applications. 2) Rich data structure: supports multiple data types, such as lists, collections, etc., which are suitable for a variety of scenarios. 3) High availability and scalability: supports master-slave replication and cluster mode to achieve high availability and horizontal scalability. 4) Persistence and data security: Data persistence is achieved through RDB and AOF to ensure data integrity and reliability. 5) Wide ecosystem and community support: with a huge ecosystem and active community,

Key features of Redis include speed, flexibility and rich data structure support. 1) Speed: Redis is an in-memory database, and read and write operations are almost instantaneous, suitable for cache and session management. 2) Flexibility: Supports multiple data structures, such as strings, lists, collections, etc., which are suitable for complex data processing. 3) Data structure support: provides strings, lists, collections, hash tables, etc., which are suitable for different business needs.

The core function of Redis is a high-performance in-memory data storage and processing system. 1) High-speed data access: Redis stores data in memory and provides microsecond-level read and write speed. 2) Rich data structure: supports strings, lists, collections, etc., and adapts to a variety of application scenarios. 3) Persistence: Persist data to disk through RDB and AOF. 4) Publish subscription: Can be used in message queues or real-time communication systems.

Redis supports a variety of data structures, including: 1. String, suitable for storing single-value data; 2. List, suitable for queues and stacks; 3. Set, used for storing non-duplicate data; 4. Ordered Set, suitable for ranking lists and priority queues; 5. Hash table, suitable for storing object or structured data.

Redis counter is a mechanism that uses Redis key-value pair storage to implement counting operations, including the following steps: creating counter keys, increasing counts, decreasing counts, resetting counts, and obtaining counts. The advantages of Redis counters include fast speed, high concurrency, durability and simplicity and ease of use. It can be used in scenarios such as user access counting, real-time metric tracking, game scores and rankings, and order processing counting.


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