A non-relational database is a type of database that stores data in non-tabular formats. Also known as NoSQL (Not Only SQL) databases, they are typically considered to be more flexible and scalable compared to traditional relational databases like MySQL and PostgreSQL. Non-relational databases do not need predefined schemas and can handle both unstructured and semi-structured data.
Compared to relational databases, non-relational databases provide the following benefits:
- Flexibility: NoSQL databases can handle various data formats ranging from JSON/XML documents to relationship data represented as graphs. Also, NoSQL databases don’t require a predefined schema to store data like relational databases, allowing for quick, iterative development. .
- Scalability: Many NoSQL databases scale out horizontally by adding multiple distributed servers. This pattern generally scales better than adding read-replicas or implementing a sharding mechanism commonly employed by relational databases.
- Performance: Different types of NoSQL databases are optimized for different data models. For certain use cases, these optimizations allow for faster query times and lower disk I/O, leading to higher performance compared to a generic relational data model.
For these reasons, NoSQL databases are popular for applications that require flexible yet scalable data management. Examples include social media, web/mobile gaming, and multimedia content.
NoSQL databases have limitations that may not make it suitable for certain use cases:
- Limited transactionality: Unlike relational databases that provide ACID compliance, NoSQL databases prioritize availability and scalability, falling back on eventual consistency guarantees instead. While some NoSQL databases offer limited transactional features, it does not provide the same level of consistency guarantees as relational databases.
- Limited querying capability: While some NoSQL databases may offer close to full SQL compatibility, depending on the type of NoSQL databases, querying capabilities may be limited to simple access patterns. A lack of enforced schema may also reduce support for complex transactions that require joins and aggregations.
- Lack of standardization: The differences in the APIs, SDKs, and interfaces for NoSQL databases are starker compared to minor syntactic variations across relational databases.
- Key-value: Key-value stores data as a collection of key-value pairs. These are optimized for quick reads over complex calculations. Examples of key-value databases include Redis, Couchbase, Riak, and etcd.
- Document: Document databases store data as a collection of objects (documents), often in JSON or XML format. Document databases do not enforce the structure of each document, but can support hierarchical access and robust query operations. Examples of document databases include MongoDB, Amazon DynamoDB, and Google Firebase.
- Graph: Graph databases utilize graph structures to represent and store highly connected data. Relationships are encapsulated by nodes, edges, and properties. Graph structures are popular for storing social media, recommendation engines, and knowledge graph data. Examples of graph databases include Neo4j, Amazon Neptune, and ArangoDB.
- Search: Search databases index and aggregate semi-structured text for quick and efficient search operations. Popular examples of search databases include Elasticsearch and Apache Solr.
- Time-series: Traditionally, time-series databases(TSDB) have been considered as non-relational as the first products did not support tabular data or querying via SQL. However, modern time-series databases such as QuestDB support both although storage and access patterns behind the scenes differ from traditional relational databases.
The table below compares the traits of relational and non-relational databases:
|Non-relational Databases||Relational Databases|
|Data Model||Key-value, document, graph, search, time-series||Relational|
|Query Language||Varies, including SQL||SQL|
|Use Cases||Unstructured or semi-structured data, high write-throughput||Structured data, complex querying|
|Data Consistency||Eventual consistency||Strong consistency (ACID)|
|Examples||MongoDB, Cassandra, Redis, DynamoDB||MySQL, PostgreSQL, SQL Server|