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'Structured storage' redirects here. For the Microsoft technology also known as structured storage, see.
A NoSQL (originally referring to 'non ' or 'non relational') provides a mechanism for and of data that is modeled in means other than the tabular relations used in. 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 companies. NoSQL databases are increasingly used in and applications. NoSQL systems are also sometimes called 'Not only SQL' to emphasize that they may support -like query languages, or sit alongside SQL database in a architecture. Motivations for this approach include: simplicity of design, simpler to 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 (in the sense of the ) 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 across tables), lack of standardized interfaces, and huge previous investments in existing relational databases. Most NoSQL stores lack true transactions, although a few databases have made them central to their designs. Instead, most NoSQL databases offer a concept of ' 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. Some NoSQL systems provide concepts such as to avoid data loss. For 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 transactions and standards for distributed transaction processing. Contents.
History The term NoSQL was used by in 1998 to name his lightweight that did not expose the standard (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, reintroduced the term NoSQL in early 2009 when he organized an event to discuss 'open source '. 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 guarantees, contrary to the prevailing practice among relational database systems. Types and examples 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:.:,.:,.:, SDBM/Flat File,.:, A more detailed classification is the following, based on one from Stephen Yen: Type Notable examples of this type Key-Value Cache, Key-Value Store, Key-Value Store (Eventually-Consistent), Key-Value Store (Ordered), Data-Structures Server Tuple Store, Object Database, Document Store, Native Multi-model Database, are model-independent, and instead of row-based or column-based storage, use value-based storage. Key-value store.
Main article: Key-value (KV) stores use the (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. This extension is computationally powerful, in that it can efficiently retrieve selective key ranges. Key-value stores can use ranging from to. Some databases support ordering of keys.
There are various hardware implementations, and some users maintain data in memory (RAM), while others employ (SSD) or (aka Hard Disk Drive (HDD)). Document store. Main articles: and 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, and as well as binary forms like.
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:. Collections.
Tags. 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. Main article: This kind of database is designed for data whose relations are well represented as a consisting of elements interconnected with a finite number of relations between them.
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The type of data could be social relations, public transport links, road maps, network topologies, etc. Graph databases and their query language Name Language(s) Notes triple store AQL, Multi-model DBMS, and, triple store added in DB2 10, Multi-model and triple store, and hybrid triple store added in 11g, SQL Multi-model and, triple store, triple store Object database.