Developer(s) Salvatore Sanfilippo Written in ANSI C | Development status Active | |
Initial release May 10, 2009; 7 years ago (2009-05-10) Stable release 3.2.8 / February 12, 2017; 42 days ago (2017-02-12) Repository github.com/antirez/redis | ||
Redis is an open-source software project (sponsored by Redis Labs) that implements data structure servers. It is networked, in-memory, and stores keys with optional durability.
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History
The name Redis means REmote DIctionary Server. Salvatore Sanfilippo, the original developer of Redis, was hired by VMware in March, 2010. In May, 2013, Redis was sponsored by Pivotal Software (a VMware spin-off). In June 2015, development became sponsored by Redis Labs.
According to monthly rankings by DB-Engines.com, Redis is often ranked the most popular key-value database. Redis has also been ranked the #4 NoSQL database in user satisfaction and market presence based on user reviews, the most popular NoSQL database in containers, and the #1 NoSQL database among Top 50 Developer Tools & Services.
Supported languages
Many languages have Redis bindings, including: ActionScript, C, C++, C#, Chicken Scheme, Clojure, Common Lisp, D, Dart, Erlang, Go, Haskell, Haxe, Io, Java, JavaScript (Node.js), Julia, Lua, Objective-C, OCaml, Perl, PHP, Pure Data, Python, R, Racket, Ruby, Rust, Scala, Smalltalk and Tcl.
Data types
Redis maps keys to types of values. An important difference between Redis and other structured storage systems is that Redis supports not only strings, but also abstract data types:
The type of a value determines what operations (called commands) are available for the value itself. Redis supports high-level, atomic, server-side operations like intersection, union, and difference between sets and sorting of lists, sets and sorted sets.
Persistence
Redis typically holds the whole dataset in memory. Versions up to 2.4 could be configured to use what they refer to as virtual memory in which some of the dataset is stored on disk, but this feature is deprecated. Persistence is now achieved in two different ways: one is called snapshotting, and is a semi-persistent durability mode where the dataset is asynchronously transferred from memory to disk from time to time, written in RDB dump format. Since version 1.1 the safer alternative is AOF, an append-only file (a journal) that is written as operations modifying the dataset in memory are processed. Redis is able to rewrite the append-only file in the background in order to avoid an indefinite growth of the journal.
By default, Redis writes data to a file system at least every 2 seconds, with more or less robust options available if needed. In the case of a complete system failure on default settings, only a few seconds of data would be lost.
Replication
Redis supports master-slave replication. Data from any Redis server can replicate to any number of slaves. A slave may be a master to another slave. This allows Redis to implement a single-rooted replication tree. Redis slaves can be configured to accept writes, permitting intentional and unintentional inconsistency between instances. The Publish/Subscribe feature is fully implemented, so a client of a slave may SUBSCRIBE to a channel and receive a full feed of messages PUBLISHed to the master, anywhere up the replication tree. Replication is useful for read (but not write) scalability or data redundancy.
Performance
When the durability of data is not needed, the in-memory nature of Redis allows it to perform well compared to database systems that write every change to disk before considering a transaction committed. Redis operates as a single process and is single-threaded or double-threaded when it rewrites the AOF (append-only file). Therefore, a single Redis instance cannot utilize parallel execution of tasks such as stored procedures.
Clustering
Redis introduced clustering in April 2015 with the release of version 3.0. The cluster specification implements a subset of Redis commands: all single-key commands are available, multi-key operations (commands related to unions and intersections) are restricted to keys belonging to the same node, and commands related to database selection operations are unavailable. A Redis cluster is able to scale up to 1,000 nodes, achieve "acceptable" write safety and to continue operations when some nodes fail.
Use cases
Due to the nature of the database design, typical use cases are session caching, full page cache, message queue applications, leaderboards and counting among others. Large companies such as Twitter are using Redis.