A NoSQL (originally referring to "non SQL" or "non relational") 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 a surge of popularity in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.
Motivations for this approach include: simplicity of design, simpler "horizontal" scaling to clusters of machines (which is a problem for relational databases), and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as "more flexible" than relational database tables.
Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases. Most NoSQL stores lack true ACID transactions, although a few databases have made them central to their designs.
Instead, most NoSQL databases offer a concept of "eventual consistency" in which database changes are propagated to all nodes "eventually" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads. Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss. Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss. For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases "do not allow referential integrity constraints to span databases." There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing.
The term NoSQL was used by Carlo Strozzi in 1998 to name his lightweight Strozzi NoSQL open-source relational database that did not expose the standard Structured Query Language (SQL) interface, but was still relational. His NoSQL RDBMS is distinct from the circa-2009 general concept of NoSQL databases. Strozzi suggests that, because the current NoSQL movement "departs from the relational model altogether, it should therefore have been called more appropriately 'NoREL', referring to 'No Relational'.
Johan Oskarsson, then a developer at Last.fm, reintroduced the term NoSQL in early 2009 when he organized an event to discuss "open source distributed, non relational databases". The name attempted to label the emergence of an increasing number of non-relational, distributed data stores, including open source clones of Google's Bigtable/MapReduce and Amazon's DynamoDB. Most of the early NoSQL systems did not attempt to provide atomicity, consistency, isolation and durability guarantees, contrary to the prevailing practice among relational database systems.
Types and examplesEdit
There are various ways to classify NoSQL databases, with different categories and subcategories, some of which overlap. What follows is a basic classification by data model, with examples:
- Column: Accumulo, Cassandra, Scylla, Apache Druid, HBase, Vertica.
- Document: Apache CouchDB, ArangoDB, BaseX, Clusterpoint, Couchbase, Cosmos DB, IBM Domino, MarkLogic, MongoDB, OrientDB, Qizx, RethinkDB
- Key-value: Aerospike, Apache Ignite, ArangoDB, Berkeley DB, Couchbase, Dynamo, FoundationDB, InfinityDB, MemcacheDB, MUMPS, Oracle NoSQL Database, OrientDB, Redis, Riak, SciDB, SDBM/Flat File dbm, ZooKeeper
- Graph: AllegroGraph, ArangoDB, InfiniteGraph, Apache Giraph, MarkLogic, Neo4J, OrientDB, Virtuoso
|Type||Notable examples of this type|
|Key-Value Cache||Apache Ignite, Coherence, eXtreme Scale, Hazelcast, Infinispan, Memcached, Velocity|
|Key-Value Store||ArangoDB, Aerospike|
|Key-Value Store (Eventually-Consistent)||Oracle NoSQL Database, Dynamo, Riak, Voldemort|
|Key-Value Store (Ordered)||FoundationDB, InfinityDB, LMDB, MemcacheDB|
|Tuple Store||Apache River, GigaSpaces|
|Object Database||Objectivity/DB, Perst, ZopeDB|
|Document Store||ArangoDB, BaseX, Clusterpoint, Couchbase, CouchDB, DocumentDB, IBM Domino, MarkLogic, MongoDB, Qizx, RethinkDB|
|Wide Column Store||Amazon DynamoDB, Bigtable, Cassandra, Scylla, Apache Druid, HBase, Hypertable|
|Native Multi-model Database||ArangoDB, Cosmos DB, OrientDB, MarkLogic|
Correlation databases are model-independent, and instead of row-based or column-based storage, use value-based storage.
Key-value (KV) stores use the associative array (also known as a map or dictionary) as their fundamental data model. In this model, data is represented as a collection of key-value pairs, such that each possible key appears at most once in the collection.
The key-value model is one of the simplest non-trivial data models, and richer data models are often implemented as an extension of it. The key-value model can be extended to a discretely ordered model that maintains keys in lexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective key ranges.
Key-value stores can use consistency models ranging from eventual consistency to serializability. Some databases support ordering of keys. There are various hardware implementations, and some users maintain data in memory (RAM), while others employ solid-state drives (SSD) or rotating disks (aka Hard Disk Drive (HDD)).
The central concept of a document store is the notion of a "document". While each document-oriented database implementation differs on the details of this definition, in general, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use include XML, YAML, and JSON as well as binary forms like BSON. Documents are addressed in the database via a unique key that represents that document. One of the other defining characteristics of a document-oriented database is that in addition to the key lookup performed by a key-value store, the database also offers an API or query language that retrieves documents based on their contents.
Different implementations offer different ways of organizing and/or grouping documents:
- Non-visible metadata
- Directory hierarchies
Compared to relational databases, for example, collections could be considered analogous to tables and documents analogous to records. But they are different: every record in a table has the same sequence of fields, while documents in a collection may have fields that are completely different.
This kind of database is designed for data whose relations are well represented as a graph consisting of elements interconnected with a finite number of relations between them. The type of data could be social relations, public transport links, road maps, network topologies, etc.
- Graph databases and their query language
|AllegroGraph||SPARQL||RDF triple store|
|DEX/Sparksee||C++, Java, .NET, Python||Graph database|
|IBM DB2||SPARQL||RDF triple store added in DB2 10|
|OpenLink Virtuoso||C++, C#, Java, SPARQL||Middleware and database engine hybrid|
|Oracle||SPARQL 1.1||RDF triple store added in 11g|
|OrientDB||Java, SQL||Multi-model document and graph database|
|OWLIM||Java, SPARQL 1.1||RDF triple store|
|Profium Sense||Java, SPARQL||RDF triple store|
|Sqrrl Enterprise||Java||Graph database|
- InterSystems Caché
- OpenLink Virtuoso
- Versant Object Database
Triple/quad store (RDF) databaseEdit
- Apache JENA (It is a framework, not a database)
- Oracle NoSQL database
- Profium Sense
- Virtuoso Universal Server
- Amazon DynamoDB
- Amazon SimpleDB
- Datastore on Google Appengine
- Clusterpoint database
- Cloudant Data Layer (CouchDB)
- Microsoft Azure Tables
- Microsoft Azure DocumentDB
- OpenLink Virtuoso
- D3 Pick database
- Extensible Storage Engine (ESE/NT)
- InterSystems Caché
- jBASE Pick database
- mvBase Rocket Software
- mvEnterprise Rocket Software
- Northgate Information Solutions Reality, the original Pick/MV Database
- Revelation Software's OpenInsight
- UniData Rocket U2
- UniVerse Rocket U2
Ben Scofield rated different categories of NoSQL databases as follows:
|Key–value store||high||high||high||none||variable (none)|
|Document-oriented store||high||variable (high)||high||low||variable (low)|
|Graph database||variable||variable||high||high||graph theory|
|Relational database||variable||variable||low||moderate||relational algebra|
Performance and scalability comparisons are sometimes done with the YCSB benchmark.
Handling relational dataEdit
Since most NoSQL databases lack ability for joins in queries, the database schema generally needs to be designed differently. There are three main techniques for handling relational data in a NoSQL database. (See table Join and ACID Support for NoSQL databases that support joins.)
Instead of retrieving all the data with one query, it is common to do several queries to get the desired data. NoSQL queries are often faster than traditional SQL queries so the cost of having to do additional queries may be acceptable. If an excessive number of queries would be necessary, one of the other two approaches is more appropriate.
Caching, replication and non-normalized dataEdit
Instead of only storing foreign keys, it is common to store actual foreign values along with the model's data. For example, each blog comment might include the username in addition to a user id, thus providing easy access to the username without requiring another lookup. When a username changes however, this will now need to be changed in many places in the database. Thus this approach works better when reads are much more common than writes.
With document databases like MongoDB it is common to put more data in a smaller number of collections. For example, in a blogging application, one might choose to store comments within the blog post document so that with a single retrieval one gets all the comments. Thus in this approach a single document contains all the data you need for a specific task.
ACID and join supportEdit
A database is marked as supporting ACID properties (Atomicity, Consistency, Isolation, Durability) or join operations if the documentation for the database makes that claim. The degree to which the capability is fully supported in a manner similar to most SQL databases or the degree to which it meets the needs of a specific application is left up to the reader to assess.
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