NoSQL Database Comparison

Store Categories

NoSQL databases are grouped by their data model, which determines query patterns and consistency guarantees. Document stores like MongoDB store flexible JSON-like documents with dynamic schemas. Key-value stores like Redis map keys to opaque values with minimal query capability. Column-family stores like Cassandra organize data into wide rows optimized for time-series. Graph stores like Neo4j excel at relationship traversal queries.

document

Flexible JSON documents, queryable fields

MongoDB, Couchbase

key-value

Opaque values keyed by identifier

Redis, DynamoDB

column-family

Wide rows, partitioned by row key

Cassandra, HBase

graph

Nodes and edges with property traversal

Neo4j, Dgraph

Consistency Models

NoSQL stores often trade strong consistency for availability and partition tolerance per the CAP theorem. Eventual consistency allows replicas to diverge temporarily and converge via anti-entropy. Tunable consistency lets the client request a quorum or strong read on a per-operation basis. Understanding the default consistency level is critical because weak defaults can expose stale reads and lost updates in subtle ways.

When to Choose NoSQL

NoSQL is appropriate when the data model is naturally non-relational, the scale exceeds single-node relational capacity, or flexible schemas accelerate development. Document stores suit content management and catalogs with varying attributes. Key-value stores excel at caching and session storage. Column-family stores handle high-volume time-series and write-heavy workloads. Graph stores solve relationship-heavy domains like social networks and recommendation engines. For transactional data with complex joins, relational databases remain the default choice.