NoSQL (originally meaning "NotonlySQL" or "non-relational")[1] refers to a type ofdatabase design that stores and retrieves data differently from the traditional table-based structure ofrelational databases. Unlike relational databases, which organize data into rows and columns like a spreadsheet, NoSQL databases use a single data structure—such askey–value pairs,wide columns,graphs, ordocuments—to hold information. Since this non-relational design does not require a fixedschema, it scales easily to manage large, oftenunstructured datasets.[2] NoSQL systems are sometimes called"Not only SQL" because they can supportSQL-like query languages or work alongside SQL databases inpolyglot-persistent setups, where multiple database types are combined.[3][4] Non-relational databases date back to the late 1960s, but the term "NoSQL" emerged in the early 2000s, spurred by the needs ofWeb 2.0 companies like social media platforms.[5][6]
NoSQL databases are popular inbig data andreal-time web applications due to their simple design, ability to scale acrossclusters of machines (calledhorizontal scaling), and precise control over dataavailability.[7][8] These structures can speed up certain tasks and are often considered more adaptable than fixed database tables.[9] However, many NoSQL systems prioritize speed and availability over strict consistency (per theCAP theorem), usingeventual consistency—where updates reach all nodes eventually, typically within milliseconds, but may cause brief delays in accessing the latest data, known asstale reads.[10] While most lack fullACID transaction support, some, likeMongoDB, include it as a key feature.[11]
Barriers to wider NoSQL adoption include their use of low-levelquery languages instead of SQL, inability to perform ad hocjoins across tables, lack of standardized interfaces, and significant investments already made in relational databases.[12] Some NoSQL systems risklosing data through lost writes or other forms, though features likewrite-ahead logging—a method to record changes before they’re applied—can help prevent this.[13][14] Fordistributed transaction processing across multiple databases, keeping data consistent is a challenge for both NoSQL and relational systems, as relational databases cannot enforce rules linking separate databases, and few systems support bothACID transactions andX/Open XA standards for managing distributed updates.[15][16] Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to mostoperating systems.[17]
The termNoSQL was used byCarlo Strozzi in 1998 to name his lightweightStrozzi NoSQL open-source relational database that did not expose the standardStructured Query Language (SQL) interface, but was still relational.[18] His NoSQLRDBMS is distinct from the around-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'",[19] referring to "not relational".
Johan Oskarsson, then a developer atLast.fm, reintroduced the termNoSQL in early 2009 when he organized an event to discuss "open-sourcedistributed, non-relational databases".[20] The name attempted to label the emergence of an increasing number of non-relational, distributed data stores, including open source clones of Google'sBigtable/MapReduce and Amazon'sDynamoDB.
There are various ways to classify NoSQL databases, with different categories and subcategories, some of which overlap. What follows is a non-exhaustive classification by data model, with examples:[21]
Key–value (KV) stores use theassociative array (also called 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.[24][25]
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 inlexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective keyranges.[26]
Key–value stores can useconsistency models ranging fromeventual consistency toserializability. Some databases support ordering of keys. There are various hardware implementations, and some users store data in memory (RAM), while others onsolid-state drives (SSD) orrotating disks (aka hard disk drive (HDD)).
The central concept of a document store is that of a "document". While the details of this definition differ among document-oriented databases, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use includeXML,YAML, andJSON andbinary forms likeBSON. Documents are addressed in the database via a uniquekey that represents that document. Another defining characteristic of a document-oriented database is anAPI or query language to retrieve documents based on their contents.
Different implementations offer different ways of organizing and/or grouping documents:
Compared to relational databases, 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.
Graph databases are designed for data whose relations are well represented as agraph consisting of elements connected by a finite number of relations. Examples of data includesocial relations, public transport links, road maps, network topologies, etc.
| Name | Language(s) | Notes |
|---|---|---|
| AgensGraph | Cypher | Multi-modelgraph database |
| AllegroGraph | SPARQL | RDF triple store |
| Amazon Neptune | Gremlin,SPARQL | Graph database |
| ArangoDB | AQL,JavaScript,GraphQL | Multi-model DBMSDocument,Graph database andKey-value store |
| Azure Cosmos DB | Gremlin | Graph database |
| DEX/Sparksee | C++,Java,C#,Python | Graph database |
| FlockDB | Scala | Graph database |
| GUN (Graph Universe Node) | JavaScript | Graph database |
| IBM Db2 | SPARQL | RDF triple store added in DB2 10 |
| InfiniteGraph | Java | Graph database |
| JanusGraph | Java | Graph database |
| MarkLogic | Java,JavaScript,SPARQL,XQuery | Multi-modeldocument database andRDF triple store |
| Neo4j | Cypher | Graph database |
| OpenLink Virtuoso | C++,C#,Java,SPARQL | Middleware anddatabase engine hybrid |
| Oracle | SPARQL 1.1 | RDF triple store added in 11g |
| OrientDB | Java, SQL | Multi-modeldocument andgraph database |
| OWLIM | Java,SPARQL 1.1 | RDF triple store |
| Profium Sense | Java,SPARQL | RDF triple store |
| RedisGraph | Cypher | Graph database |
| Sqrrl Enterprise | Java | Graph database |
| TerminusDB | JavaScript,Python,datalog | Open source RDF triple-store and document store[27] |
The performance of NoSQL databases is usually evaluated using the metric ofthroughput, which is measured as operations per second. Performance evaluation must pay attention to the rightbenchmarks such as production configurations, parameters of the databases, anticipated data volume, and concurrent userworkloads.
Ben Scofield rated different categories of NoSQL databases as follows:[28]
| Data model | Performance | Scalability | Flexibility | Complexity | Data integrity | Functionality |
|---|---|---|---|---|---|---|
| Key–value store | high | high | high | none | low | variable (none) |
| Column-oriented store | high | high | moderate | low | low | minimal |
| Document-oriented store | high | variable (high) | high | low | low | variable (low) |
| Graph database | variable | variable | high | high | low-med | graph theory |
| Relational database | variable | variable | low | moderate | high | relational algebra |
Performance and scalability comparisons are most commonly done using theYCSB benchmark.
Since most NoSQL databases lack ability for joins in queries, thedatabase schema generally needs to be designed differently. There are three main techniques for handling relational data in a NoSQL database. (Seetable 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 additional queries may be acceptable. If an excessive number of queries would be necessary, one of the other two approaches is more appropriate.
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.[29]
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 needed for a specific task.
A database is marked as supportingACID properties (atomicity, consistency, isolation, durability) orjoin operations if the documentation for the database makes that claim. However, this doesn't necessarily mean that the capability is fully supported in a manner similar to most SQL databases.
| Database | ACID | Joins |
|---|---|---|
| Aerospike | Yes | No |
| AgensGraph | Yes | Yes |
| Apache Ignite | Yes | Yes |
| ArangoDB | Yes | Yes |
| Amazon DynamoDB | Yes | No |
| Couchbase | Yes | Yes |
| CouchDB | Yes | Yes |
| IBM Db2 | Yes | Yes |
| InfinityDB | Yes | No |
| LMDB | Yes | No |
| MarkLogic | Yes | Yes[nb 1] |
| MongoDB | Yes | Yes[nb 2] |
| OrientDB | Yes | Yes[nb 3] |
Different NoSQL databases, such asDynamoDB,MongoDB,Cassandra,Couchbase, HBase, and Redis, exhibit varying behaviors when querying non-indexed fields. Many perform full-table or collection scans for such queries, applying filtering operations after retrieving data. However, modern NoSQL databases often incorporate advanced features to optimize query performance. For example, MongoDB supports compound indexes and query-optimization strategies, Cassandra offers secondary indexes and materialized views, and Redis employs custom indexing mechanisms tailored to specific use cases. Systems like Elasticsearch use inverted indexes for efficient text-based searches, but they can still require full scans for non-indexed fields. This behavior reflects the design focus of many NoSQL systems on scalability and efficient key-based operations rather than optimized querying for arbitrary fields. Consequently, while these databases excel at basicCRUD operations and key-based lookups, their suitability for complex queries involving joins or non-indexed filtering varies depending on the database type—document, key–value, wide-column, or graph—and the specific implementation.[33]
NoSQL database, also called Not Only SQL
many advocates of NoSQL say that it does not mean a "no" to SQL, rather it means Not Only SQL
Key–value stores allow the application developer to store schema-less data. This data usually consists of a string that represents the key, and the actual data that is considered the value in the "key–value" relationship. The data itself is usually some kind of primitive of the programming language (a string, an integer, or an array) or an object that is being marshaled by the programming language's bindings to the key-value store. This structure replaces the need for a fixed data model and allows proper formatting.
Key–value stores provide a high-performance alternative to relational database systems with respect to storing and accessing data. This paper provides a short overview of some of the currently available key–value stores and their interface to the Ruby programming language.
| Types | |
|---|---|
| Concepts | |
| Objects | |
| Components | |
| Functions | |
| Related topics | |
