# NoSQL

NoSQL (originally referring to "non-SQL" or "non-relational") is an approach to database design that focuses on providing a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Instead of the typical tabular structure of a relational database, NoSQL databases house data within one data structure. Since this non-relational database design does not require a schema, it offers rapid scalability to manage large and typically unstructured data sets. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.
Non-relational databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 2000s, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications.
Motivations for this approach include simplicity of design, simpler "horizontal" scaling to clusters of machines (which is a problem for relational databases), finer control over availability, and limiting the object-relational impedance mismatch. The data structures used by NoSQL databases (e.g. key–value pair, 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), 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 like MongoDB 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 read. 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. Relational databases "do not allow referential integrity constraints to span databases". Few systems maintain both ACID transactions and X/Open XA standards for distributed transaction processing. Interactive relational databases share conformational relay analysis techniques as a common feature. Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to most operating systems.

## History

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 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'", referring to "not 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.

## Types and examples

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:
| Type | Notable examples of this type |
| Key–value cache | Apache Ignite, Couchbase, Coherence, eXtreme Scale, Hazelcast, Infinispan, Memcached, Redis, Velocity |
| Key–value store | Azure Cosmos DB, ArangoDB, Amazon DynamoDB, Aerospike, Couchbase, ScyllaDB |
| Key–value store (eventually consistent) | Azure Cosmos DB, Oracle NoSQL Database, Riak, Voldemort |
| Key–value store (ordered) | FoundationDB, InfinityDB, LMDB, MemcacheDB |
| Tuple store | Apache River, GigaSpaces, Tarantool, TIBCO ActiveSpaces, OpenLink Virtuoso |
| Triplestore | AllegroGraph, MarkLogic, Ontotext-OWLIM, Oracle NoSQL database, Profium Sense, Virtuoso Universal Server |
| Object database | Objectivity/DB, Perst, ZODB, db4o, GemStone/S, InterSystems Caché, JADE, ObjectDatabase++, ObjectDB, ObjectStore, ODABA, Realm, OpenLink Virtuoso, Versant Object Database |
| Document store | Azure Cosmos DB, ArangoDB, BaseX, Clusterpoint, Couchbase, CouchDB, DocumentDB, eXist-db, Google Cloud Firestore, IBM Domino, MarkLogic, MongoDB, RavenDB, Qizx, RethinkDB, Elasticsearch, OrientDB |
| Wide-column store | Azure Cosmos DB, Amazon DynamoDB, Bigtable, Cassandra, Google Cloud Datastore, HBase, Hypertable, ScyllaDB |
| Native multi-model database | ArangoDB, Azure Cosmos DB, OrientDB, MarkLogic, Apache Ignite, Couchbase, FoundationDB, Oracle Database, AgensGraph |
| Graph database | Azure Cosmos DB, AllegroGraph, ArangoDB, Apache Giraph, GUN (Graph Universe Node), InfiniteGraph, MarkLogic, Neo4J, OrientDB, Virtuoso |
| Multivalue database | D3 Pick database, Extensible Storage Engine (ESE/NT), InfinityDB, InterSystems Caché, jBASE Pick database, mvBase Rocket Software, mvEnterprise Rocket Software, Northgate Information Solutions Reality (the original Pick/MV Database), OpenQM, Revelation Software's OpenInsight (Windows) and Advanced Revelation (DOS), UniData Rocket U2, UniVerse Rocket U2 |

### Key–value store

Key–value (KV) stores use the associative 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.
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 store data in memory (RAM), while others on solid-state drives (SSD) or rotating disks (aka hard disk drive (HDD)).

### Document store

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 include XML, YAML, and JSON and binary forms like BSON. Documents are addressed in the database via a unique key that represents that document. Another defining characteristic of a document-oriented database is an API or query language to retrieve documents based on their contents.
Different implementations offer different ways of organizing and/or grouping documents:
* Collections
* Tags
* Non-visible metadata
* Directory hierarchies
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

Graph databases are designed for data whose relations are well represented as a graph consisting of elements connected by a finite number of relations. Examples of data include social relations, public transport links, road maps, network topologies, etc.

#### Graph databases and their query language

| Name | Language(s) | Notes |
| AgensGraph | Cypher | Multi-model graph database |
| AllegroGraph | SPARQL | RDF triple store |
| Amazon Neptune | Gremlin, SPARQL | Graph database |
| ArangoDB | AQL, JavaScript, GraphQL | Multi-model DBMS Document, Graph database and Key-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-model document database and RDF triple store |
| Neo4j | Cypher | Graph database |
| 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 |
| RedisGraph | Cypher | Graph database |
| Sqrrl Enterprise | Java | Graph database |
| TerminusDB | JavaScript, Python, datalog | Open source RDF triple-store and document store |

## Performance

The performance of NoSQL databases is usually evaluated using the metric of throughput, which is measured as operations per second. Performance evaluation must pay attention to the right benchmarks such as production configurations, parameters of the databases, anticipated data volume, and concurrent user workloads.
Ben Scofield rated different categories of NoSQL databases as follows:
| 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 the YCSB benchmark.

## Handling relational data

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.)

### Multiple queries

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.

### Caching, replication and non-normalized data

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.

### Nesting data

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.

## ACID and join support

A database is marked as supporting ACID properties (atomicity, consistency, isolation, durability) or join 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 |
| MongoDB | Yes | Yes |
| OrientDB | Yes | Yes |
* ↑ Joins do not necessarily apply to document databases, but MarkLogic can do joins using semantics.
* ↑ MongoDB did not support joining from a sharded collection until version 5.1.
* ↑ OrientDB can resolve 1:1 joins using links by storing direct links to foreign records.

## Query optimization and indexing in NoSQL databases

Different NoSQL databases, such as DynamoDB, 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 basic CRUD 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.