Home Database Redis What are the 8 data types of redis

What are the 8 data types of redis

Apr 07, 2024 am 10:27 AM
geographical location key value pair

Redis provides 8 data types: string (text, number, binary), hash (key-value pair), list (ordered set), set (unordered unique element), ordered set ( Sort by score), Geospatial (geographic location), HyperLogLog (estimate big data cardinality), and Bitmap (bit sequence storage).

What are the 8 data types of redis

Redis’s 8 data types

Redis provides a variety of powerful data types to meet various needs. application scenario requirements. The following are the 8 data types supported by Redis:

1. String (String)

  • It is composed of byte array
  • Yes Store text, numeric or binary data
  • Support connections and operations with other data types

2. Hash

  • Stores a collection of key-value pairs
  • Values ​​can be strings, hashes, lists, or sets
  • Allows fast access and modification of values ​​in the hash table

3. List

  • Ordered collection of elements
  • Supports inserting, deleting or obtaining elements at both ends of the list
  • Can store various data types

4. Set

  • Unordered and unique set of elements
  • Automatically delete duplicate elements
  • Supports union, intersection and difference operations

5. Sorted Set

  • Element collection based on score sorting
  • Supports obtaining elements by score range and quantity range
  • Commonly used to implement rankings and priority queues

6. Geospatial (Geo)

  • Type of storing geospatial data
  • Supports geographical location operations such as points, circles and rectangles
  • Used to build location-based applications

7. HyperLogLog (HyperLogLog)

  • Used to estimate the cardinality of large data collections
  • The occupied space has a logarithmic relationship with the base number
  • Commonly used to calculate the number of website visitors or deduplication statistics

8. Bitmap(Bitmap)

  • Type for storing bit sequences
  • Supports setting, getting and manipulating bit values
  • Suitable for space-intensive applications such as log analysis and user tracking

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