NoSQL Database definition
A NoSQL database is a non-relational database that stores data in models other than fixed tables, such as documents, key-value pairs, wide columns or graphs. NoSQL databases, including MongoDB, Redis, Cassandra, DynamoDB and Neo4j, offer flexible schemas and easy horizontal scaling, making them suited to large volumes, varied data and specific access patterns.
Types of NoSQL databases
NoSQL is an umbrella term rather than a single technology. Each family optimizes for a different data shape and access pattern, so choosing well means understanding how the application reads and writes data first. A key-value store and a graph database are as different from each other as either is from a relational database, despite sharing the NoSQL label.
- Document databases: JSON-like documents, such as MongoDB, Couchbase and Firestore.
- Key-value stores: fast lookups by key, such as Redis and Amazon DynamoDB.
- Wide-column stores: massive write-heavy tables, such as Apache Cassandra and ScyllaDB.
- Graph databases: nodes and relationships, such as Neo4j and Amazon Neptune.
- Time-series and search engines, such as InfluxDB and Elasticsearch, are often grouped here too.
How do NoSQL databases work?
Most NoSQL databases relax some constraints of relational systems to gain flexibility or scale. Schemas are flexible, so records in the same collection can have different fields. Data is usually denormalized: related information is stored together, such as an order with its line items in one document, so it can be read in a single fast operation without joins across many tables.
Many NoSQL systems are designed to scale horizontally, partitioning data across many nodes and replicating it for availability. Some favor availability over immediate consistency, letting replicas briefly disagree during failures, while others offer tunable or strong consistency. Understanding these guarantees, often discussed through the CAP theorem, is essential before storing critical data such as balances or bookings.
When to use a NoSQL database
NoSQL databases shine for specific workloads: caching, sessions and leaderboards in Redis; product catalogs and content with varied attributes in MongoDB; huge volumes of time-ordered writes, such as IoT telemetry or messaging, in Cassandra; serverless applications with predictable key-based access in DynamoDB; and relationship-heavy problems, such as fraud rings or recommendations, in graph databases. In each case the data model fits the access pattern naturally.
A useful rule is to list the application's main queries before choosing. If they are mostly lookups by a known key or document ID at high volume, NoSQL fits naturally. If they involve ad hoc filtering, reporting and joins across many entities, a relational database will usually be easier to live with.
NoSQL vs SQL databases
Relational SQL databases use fixed schemas, joins and ACID transactions across tables, which suits structured, interrelated business data and flexible ad hoc querying. NoSQL databases trade some of that flexibility and consistency for scale, speed on known access patterns or schema flexibility. The line has blurred: PostgreSQL stores JSON efficiently, MongoDB supports multi-document transactions, and distributed SQL databases scale horizontally.
Common NoSQL pitfalls
Teams sometimes choose NoSQL for perceived scalability, then struggle with reporting queries, duplicated data that drifts out of sync, or transactions the database handles poorly. DynamoDB and Cassandra in particular require designing tables around queries upfront. Nexzem typically uses a relational database as the system of record and adds NoSQL stores where a clear access pattern or scale requirement justifies them.