Data types

Valkey is a data structure server. At its core, Valkey provides a collection of native data types that help you solve a wide variety of problems, from caching to queuing to event processing. Below is a short description of each data type, with links to broader overviews and command references.

If you’d like to try a comprehensive tutorial for each data structure, see their overview pages below.

Strings

Strings are the most basic Valkey data type, representing a sequence of bytes. For more information, see:

• Overview of Strings
• String command reference

Lists

Lists are lists of strings sorted by insertion order. For more information, see:

• Overview of Lists
• List command reference

Sets

Sets are unordered collections of unique strings that act like the sets from your favorite programming language (for example, Java HashSets, Python sets, and so on). With a Set, you can add, remove, and test for existence in O(1) time (in other words, regardless of the number of set elements). For more information, see:

• Overview of Sets
• Set command reference

Hashes

Hashes are record types modeled as collections of field-value pairs. As such, Hashes resemble Python dictionaries, Java HashMaps, and Ruby hashes. For more information, see:

• Overview of Hashes
• Hashes command reference

Sorted sets

Sorted Sets are collections of unique strings that maintain order by each string’s associated score. For more information, see:

• Overview of Sorted Sets
• Sorted Set command reference

Streams

A Stream is a data structure that acts like an append-only log. Streams help record events in the order they occur and then syndicate them for processing. For more information, see:

• Overview of Streams
• Streams command reference

Geospatial indexes

Geospatial indexes are useful for finding locations within a given geographic radius or bounding box. For more information, see:

• Overview of Geospatial indexes
• Geospatial indexes command reference

Bitmaps

Bitmaps let you perform bitwise operations on strings. For more information, see:

• Overview of Bitmaps
• Bitmap command reference

Bitfields

Bitfields efficiently encode multiple counters in a string value. Bitfields provide atomic get, set, and increment operations and support different overflow policies. For more information, see:

• Overview of Bitfields
• The BITFIELD command.

HyperLogLog

The HyperLogLog data structures provide probabilistic estimates of the cardinality (i.e., number of elements) of large sets. For more information, see:

• Overview of HyperLogLog
• HyperLogLog command reference

Bloom Filter

Bloom filters are a space efficient probabilistic data structure that allows adding elements and checking if item/s are definitely not present, or if there is a possibility they exist (with the configured false positive rate).

The Bloom filter data type / command support is provided by the valkey-bloom module. For more information, see:

• Overview of Bloom Filters
• Bloom filter command reference
• Valkey-bloom module on GitHub

Extensions

To extend the features provided by the included data types, use one of these options:

1. Write your own custom server-side functions in Lua.
2. Write your own Valkey module using Modules Introduction and Modules API.