Key-value database

A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary or hash table. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data. These records are stored and retrieved using a key that uniquely identifies the record, and is used to quickly find the data within the database.

Key-value databases work in a very different fashion from the better known relational databases (RDB). RDBs pre-define the data structure in the database as a series of tables containing fields with well defined data types. Exposing the data types to the database program allows it to apply a number of optimizations. In contrast, key-value systems treat the data as a single opaque collection, which may have different fields for every record. This offers considerable flexibility and more closely follows modern concepts like object-oriented programming. Because optional values are not represented by placeholders or input parameters, as in most RDBs, key-value databases often use far less memory to store the same database, which can lead to large performance gains in certain workloads.

Performance, a lack of standardization and other issues limited key-value systems to niche uses for many years, but the rapid move to cloud computing after 2010 has led to a renaissance as part of the broader NoSQL movement. Some graph databases are also key-value databases internally, adding the concept of the relationships (pointers) between records as a first class data type.

Types and notable examples

Key-value databases can use consistency models ranging from eventual consistency to serializability. Some support ordering of keys. Some maintain data in memory (RAM), while others employ solid-state drives or rotating disks.

Redis was the most popular implementation of a key-value database as of May 2019, according to DB-Engines ranking.[1]

Another example of key-value database is Oracle NoSQL Database. Oracle NoSQL Database provides a key-value paradigm to the application developer. Every entity (record) is a set of key-value pairs. A key has multiple components, specified as an ordered list. The major key identifies the entity and consists of the leading components of the key. The subsequent components are called minor keys. This organization is similar to a directory path specification in a file system (e.g., /Major/minor1/minor2/). The “value” part of the key-value pair is simply an uninterpreted string of bytes of arbitrary length.[2]

The Unix system provides dbm (database manager) which is a library originally written by Ken Thompson. Also ported to Windows OS platforms, provided through programming languages such as Perl for Win32. The dbm manages associative arrays of arbitrary data by use of a single key (a primary key). Modern implementations include sdbm and GNU dbm.

Provider License eventually consistent ordered RAM
Aerospike AGPL Yes
Apache Ignite AL2 Yes
ArangoDB AL2 Yes
Berkeley DB AGPL, Proprietary Yes
CDB free
Clusterpoint Database Server Proprietary
Couchbase Server Proprietary
Dynamo Proprietary Yes
etcd AL2
FoundationDB AL2 Yes
XAP Proprietary Yes
GridGain Systems Proprietary Yes
GT.M[3] AGPL, Proprietary
Hazelcast AL2, Proprietary Yes
Hibari AL2
IBM Informix C-ISAM Proprietary Yes
InfinityDB Proprietary Yes
Keyspace Proprietary
LevelDB BSD3
LMDB free Yes
memcached BSD3 Yes
MemcacheDB BSD Yes
NoSQLz free
Coherence Proprietary Yes
Oracle NoSQL Database AL2, Proprietary Yes
Project Voldemort AL2 Yes
Redis BSD3 Yes
Riak[4] AL2 Yes
RocksDB AL2 or GPL 2
Tarantool BSD Yes
Tokyo Cabinet and Kyoto Cabinet GPL
OpenLink Virtuoso GPLv2, proprietary Yes Yes

See also


  2. "Oracle NoSQL Database"
  3. Tweed, Rob; James, George (2010). "A Universal NoSQL Engine, Using a Tried and Tested Technology" (PDF). p. 25. Without exception, the most successful and well-known of the NoSQL databases have been developed from scratch, all within just the last few years. Strangely, it seems that nobody looked around to see whether there were any existing, successfully implemented database technologies that could have provided a sound foundation for meeting Web-scale demands. Had they done so, they might have discovered two products, GT.M and Caché.....*
  4. "Riak: An Open Source Scalable Data Store". 28 November 2010. Archived from the original on 31 December 2010. Retrieved 28 November 2010.
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