The table below summarizes the main differences between SQL and NoSQL databases.
|SQL Databases||NoSQL Databases|
|Data Storage Model||Tables with fixed rows and columns||Document: JSON documents, Key-value: key-value pairs, Wide-column: tables with rows and dynamic columns, Graph: nodes and edges|
|Development History||Developed in the 1970s with a focus on reducing data duplication||Developed in the late 2000s with a focus on scaling and allowing for rapid application change driven by agile and DevOps practices.|
|Examples||Oracle, MySQL, Microsoft SQL Server, and PostgreSQL||Document: MongoDB and CouchDB, Key-value: Redis and DynamoDB, Wide-column: Cassandra and HBase, Graph: Neo4j and Amazon Neptune|
|Primary Purpose||General purpose||Document: general purpose, Key-value: large amounts of data with simple lookup queries, Wide-column: large amounts of data with predictable query patterns, Graph: analyzing and traversing relationships between connected data|
|Scaling||Vertical (scale-up with a larger server)||Horizontal (scale-out across commodity servers)|
|Multi-Record ACID Transactions||Supported||Most do not support multi-record ACID transactions. However, some—like MongoDB—do.|
|Joins||Typically required||Typically not required|
|Data to Object Mapping||Requires ORM (object-relational mapping)||Many do not require ORMs. MongoDB documents map directly to data structures in most popular programming languages.|
NoSQL databases offer many benefits over relational databases. NoSQL databases have flexible data models, scale horizontally, have incredibly fast queries, and are easy for developers to work with.
Flexible data models
NoSQL databases typically have very flexible schemas. A flexible schema allows you to easily make changes to your database as requirements change. You can iterate quickly and continuously integrate new application features to provide value to your users faster.
Most SQL databases require you to scale-up vertically (migrate to a larger, more expensive server) when you exceed the capacity requirements of your current server. Conversely, most NoSQL databases allow you to scale-out horizontally, meaning you can add cheaper, commodity servers whenever you need to.
Queries in NoSQL databases can be faster than SQL databases. Why? Data in SQL databases is typically normalized, so queries for a single object or entity require you to join data from multiple tables. As your tables grow in size, the joins can become expensive. However, data in NoSQL databases is typically stored in a way that is optimized for queries. The rule of thumb when you use MongoDB is Data is that is accessed together should be stored together. Queries typically do not require joins, so the queries are very fast.
Easy for developers
Some NoSQL databases like MongoDB map their data structures to those of popular programming languages. This mapping allows developers to store their data in the same way that they use it in their application code. While it may seem like a trivial advantage, this mapping can allow developers to write less code, leading to faster development time and fewer bugs.
One of the most frequently cited drawbacks of NoSQL databases is that they don’t support ACID (atomicity, consistency, isolation, durability) transactions across multiple documents. With appropriate schema design, single record atomicity is acceptable for lots of applications. However, there are still many applications that require ACID across multiple records.
To address these use cases MongoDB added support for multi-document ACID transactions in the 4.0 release, and extended them in 4.2 to span sharded clusters.
Since data models in NoSQL databases are typically optimized for queries and not for reducing data duplication, NoSQL databases can be larger than SQL databases. Storage is currently so cheap that most consider this a minor drawback, and some NoSQL databases also support compression to reduce the storage footprint.
Depending on the NoSQL database type you select, you may not be able to achieve all of your use cases in a single database. For example, graph databases are excellent for analyzing relationships in your data but may not provide what you need for everyday retrieval of the data such as range queries. When selecting a NoSQL database, consider what your use cases will be and if a general purpose database like MongoDB would be a better option.
Now that you understand the basics of NoSQL databases, you’re ready to give them a shot.
You can check out the Where to Use MongoDB whitepaper to help you determine if MongoDB or another database is right for your use case. Then hop on over to What is a Document Database? to learn about the document model and how it compares to the relational model.
For those who like to jump right in and learn by doing, one of the easiest ways to get started with NoSQL databases is to use MongoDB Atlas. Atlas is MongoDB’s fully managed, global database service that is available on all of the leading cloud providers. One of the many handy things about Atlas is that it has a generous, forever-free tier so you can create a database and discover all of the benefits of NoSQL databases first hand without providing your credit card. If you’d like to try a paid tier, apply code NOSQLEXPLAINED for $200 of Atlas credits.
For those who prefer structured learning, MongoDB University is completely free online training that will walk you step-by-step through the process of learning MongoDB.
When you’re ready to interact with MongoDB using your favorite programming language, check out the Quick Start Tutorials. These tutorials will help you get up and running as quickly as possible in the language of your choice.