Understanding Redict Data Types #
Redict is a data structure server. At its core, Redict provides a collection of native data types that help you solve a wide variety of problems, from 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.
Core #
Strings #
Redict strings are the most basic Redict data type, representing a sequence of bytes. For more information, see:
Lists #
Redict lists are lists of strings sorted by insertion order. For more information, see:
Sets #
Redict 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 Redict 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:
Hashes #
Redict hashes are record types modeled as collections of field-value pairs. As such, Redict hashes resemble Python dictionaries, Java HashMaps, and Ruby hashes. For more information, see:
Sorted sets #
Redict sorted sets are collections of unique strings that maintain order by each string’s associated score. For more information, see:
Streams #
A Redict 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:
Geospatial indexes #
Redict geospatial indexes are useful for finding locations within a given geographic radius or bounding box. For more information, see:
Bitmaps #
Redict bitmaps let you perform bitwise operations on strings. For more information, see:
Bitfields #
Redict 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:
HyperLogLog #
The Redict HyperLogLog data structures provide probabilistic estimates of the cardinality (i.e., number of elements) of large sets. For more information, see: