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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'",^[21] 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".^[22] 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.

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:^[23]

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.^[26][27]

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.^[28]

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)).

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 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

The performance of NoSQL databases is usually evaluated using the metric of throughput, which is measured as operations/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:^[30]

Performance and scalability comparisons are most commonly done using the YCSB benchmark.

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 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.^[31]

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.

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.

• CAP theorem
• Comparison of object database management systems
• Comparison of structured storage software
• C++
• Database scalability
• Distributed cache
• Faceted search
• MultiValue database
• Multi-model database
• Schema-agnostic databases
• Triplestore
• Vector database

• Sadalage, Pramod; Fowler, Martin (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley. ISBN 978-0-321-82662-6.
• McCreary, Dan; Kelly, Ann (2013). Making Sense of NoSQL: A guide for managers and the rest of us. Manning. ISBN 9781617291074.
• Wiese, Lena (2015). Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. DeGruyter/Oldenbourg. ISBN 978-3-11-044140-6.
• Strauch, Christof (2012). "NoSQL Databases" (PDF).
• Moniruzzaman, A. B.; Hossain, S. A. (2013). "NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison". arXiv:1307.0191 [cs.DB].
• Orend, Kai (2013). "Analysis and Classification of NoSQL Databases and Evaluation of their Ability to Replace an Object-relational Persistence Layer". CiteSeerX 10.1.1.184.483.
• Krishnan, Ganesh; Kulkarni, Sarang; Dadbhawala, Dharmesh Kirit. "Method and system for versioned sharing, consolidating and reporting information".
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