SQL Databases

NoSQL Databases

Types

One type (SQL database) with minor variations

Many different types including key-value stores, document databases, wide-column stores, and graph databases

Development History

Developed in 1970s to deal with first wave of data storage applications

Developed in late 2000s to deal with limitations of SQL databases, especially scalability, multi-structured data, geo-distribution and agile development sprints

Examples

MySQL, Postgres, Microsoft SQL Server, Oracle Database

MongoDB, Cassandra, HBase, Neo4j

Data Storage Model

Individual records (e.g., ’employees’) are stored as rows in tables, with each column storing a specific piece of data about that record (e.g., ‘manager,’ ‘date hired,’ etc.), much like a spreadsheet. Related data is stored in separate tables, and then joined together when more complex queries are executed. For example, ‘offices’ might be stored in one table, and ’employees’ in another. When a user wants to find the work address of an employee, the database engine joins the ’employee’ and ‘office’ tables together to get all the information necessary.

Varies based on database type. For example, key-value stores function similarly to SQL databases, but have only two columns (‘key’ and ‘value’), with more complex information sometimes stored as BLOBs within the ‘value’ columns. Document databases do away with the table-and-row model altogether, storing all relevant data together in single ‘document’ in JSON, XML, or another format, which can nest values hierarchically.

Schemas

Structure and data types are fixed in advance. To store information about a new data item, the entire database must be altered, during which time the database must be taken offline.

Typically dynamic, with some enforcing data validation rules. Applications can add new fields on the fly, and unlike SQL table rows, dissimilar data can be stored together as necessary. For some databases (e.g., wide-column stores), it is somewhat more challenging to add new fields dynamically.

Scaling

Vertically, meaning a single server must be made increasingly powerful in order to deal with increased demand. It is possible to spread SQL databases over many servers, but significant additional engineering is generally required, and core relational features such as JOINs, referential integrity and transactions are typically lost.

Horizontally, meaning that to add capacity, a database administrator can simply add more commodity servers or cloud instances. The database automatically spreads data across servers as necessary.

Development Model

Mix of open-source (e.g., Postgres, MySQL) and closed source (e.g., Oracle Database)

Open-source

Supports multi-record ACID transactions

Yes

Mostly no. MongoDB 4.0 and beyond support multi-document ACID transactions. Learn more

Data Manipulation

Specific language using Select, Insert, and Update statements, e.g. SELECT fields FROM table WHERE…

Through object-oriented APIs

Consistency

Can be configured for strong consistency

Depends on product. Some provide strong consistency (e.g., MongoDB, with tunable consistency for reads) whereas others offer eventual consistency (e.g., Cassandra).