A framework for evaluating NoSQL databases against eight practical criteria: data model fit, consistency model, query language, horizontal scalability, performance under load, multicloud/edge deployment, operational burden, and migration path. Covers the five NoSQL families (document, key-value, wide-column, graph, vector), when to choose NoSQL over SQL, and walks through scoring Couchbase against the rubric, noting it excels broadly but is not the best fit for deep graph traversal workloads.
Table of contents
What is a NoSQL database, and when does it fit?NoSQL vs. SQL: When to choose a non-relational databaseTypes of NoSQL databases and their trade-offsThe eight-criteria evaluation frameworkScoring real candidates: The evaluation scorecardApplying the framework: Where Couchbase landsNoSQL database FAQsQuestions this post answers
What are the main types of NoSQL databases and what is each one best for?
There are four established NoSQL families plus an emerging fifth. Document databases (Couchbase, MongoDB) store JSON for hierarchical, object-like data. Key-value databases (Redis, DynamoDB) do single-key lookups for caching and sessions. Wide-column databases (Cassandra, HBase) handle write-heavy, time-series workloads. Graph databases (Neo4j) excel at multi-hop relationship traversal. Vector databases handle similarity search over embeddings and are increasingly built into multi-model platforms. Comparing document, key-value, wide-column, graph, and vector databases gets easier with real-world evaluations on daily.dev.
What criteria should I use to evaluate a NoSQL database for my application?
Score candidates 1-5 across eight weighted criteria: data model fit, consistency model, query language capability, horizontal scalability, performance under load (especially p99 latency), multicloud and edge deployment support, operational burden, and migration path (including vendor lock-in risk). Weight each criterion based on your specific workload rather than relying on generic vendor benchmarks or synthetic YCSB numbers. Developers weighing database trade-offs for a new project can track evaluation frameworks like this on daily.dev.
Is Couchbase a good fit for graph traversal workloads?
No, Couchbase is not optimized for deep graph traversal. While it can store and query relationships, pure graph traversal workloads such as multi-hop relationship queries at depth are better served by a dedicated graph database like Neo4j. Couchbase's strengths lie instead in document, key-value, and vector workloads within a single multi-model platform. Anyone choosing between multi-model and graph-native databases can follow these comparisons on daily.dev.