database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the "NoSQL" moniker until their surge in popularity in the early twenty-first century,[3] triggered by the storage needs of Web 2.0 companies such as Facebook, Google and Amazon.com.[4]
Motivations for this approach include simplicity of design, presumed better "horizontal" scaling to clusters of machine, which is a problem for relational databases,[3] and presumed finer control over availability. The data structures used by NoSQL databases (e.g. key-value, graph, or document) differ slightly from those used by default in relational databases, making some operations faster in NoSQL and others faster in relational databases. The particular suitability of a given NoSQL database depends on the problem it must solve.
NoSQL databases are increasingly used in big data and real-time web applications.[5] NoSQL systems are also called "Not only SQL" to emphasize that they may also support SQL-like query languages.
Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability and partition tolerance. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages, the lack of standardized interfaces, and huge investments in existing SQL.[6] Most NoSQL stores lack true ACID transactions, although a few recent systems, such as FairCom c-treeACE, Google Spanner (though technically a NewSQL database), FoundationDB and OrientDB have made them central to their designs. (See ACID and JOIN Support.)
Unfortunately, not all NoSQL systems live up to the promised "eventual consistency" and partition tolerance, but in experiments with network partitioning often exhibited lost writes and other forms of data loss.