Selecting a Database for Your Data Needs
Data requirements vary, and with so many different types of databases, selecting which to use can be a tricky choice. Let’s look at the popular database types so you can make the decision about which would be best for your situation.
In a relational database, data is stored in a table format with rows and columns. A table schema typically defines the data definitions, including column names, column types, primary key, foreign key, and any constraints, like that a column value must be unique or must not be null. Select a relational database, such as MySQL or PostgreSQL, if the data to be stored is structured and has relationships with other data.
If the data to be stored is not relational, a NoSQL database is the best option. NoSQL does not imply no SQL is used at all, but instead refers to the non-relational aspect of the database, with no support for a foreign key, joins, or referential integrity. Most NoSQL databases support a SQL-like query language. A NoSQL database typically has features such as schema-free and unstructured data, and they’re further categorized based on the type of storage, such as document and key-value. An example of a document database is MongoDB, and an example of a key-value database is Apache Cassandra.
A search engine is a database that stores and indexes data to make it queryable. Search engines are also typically non-relational, but their salient characteristic is that they are optimized for search, with features such as support for complex search expressions using regular expressions, full text search, and grouping of search results. Apache Lucene is an open source, full-text search library commonly used by search engines, including Elasticsearch and Apache Solr.
A graph database stores data in a graph structure, which has nodes and edges representing relationships between nodes. Graphs are used for data that has several relationships because they provide simple and efficient querying of highly interconnected data. The query language used by graph databases is different from SQL because of the complexity of the queries, and some languages have been developed just for querying a graph databases, such as Cypher. Examples of graph databases are Neo4j and ArangoDB.
An in-memory database stores data in the process memory instead of on a disk, which allows for faster response time. But a disadvantage is that a server or process failure would cause the data to be lost. Examples of open source in-memory databases are Redis and ArangoDB.
A multi-model database supports more than one data storage model, offering flexibility in storage. Some of the databases mentioned previously are multi-model databases: MySQL and PostgreSQL are also document stores, and ArangoDB is a multi-model database with support for in-memory, document, and key-value storage.
Choose which database to use based on what your database will be used for, the data structure, and the scale of your data.