Choosing the Right Database — SQL, NoSQL, and Everything Between

There is no best database#

"Should I use PostgreSQL or MongoDB?" is the wrong question. The right question is: "What are my data access patterns?"

Every database makes trade-offs. The one that's perfect for your use case might be terrible for someone else's. Here's how to choose.

The categories#

Relational (SQL)#

PostgreSQL, MySQL, SQLite

Structured data with relationships. Tables, rows, columns, JOINs, ACID transactions.

Choose when:

• Data has clear relationships (users → orders → products)
• You need complex queries with JOINs and aggregations
• ACID transactions are required (financial data)
• Schema is known and relatively stable

Skip when: Schema changes frequently, data is deeply nested, or you need horizontal scaling beyond a single node.

Document (NoSQL)#

MongoDB, CouchDB, Firestore

JSON-like documents with flexible schemas. Each document can have different fields.

Choose when:

• Schema varies between records (CMS content, user profiles with optional fields)
• Data is naturally nested (blog post with embedded comments)
• Rapid prototyping where schema isn't finalized
• Horizontal scaling is a primary concern

Skip when: You need complex JOINs, strong transactions across documents, or strict schema enforcement.

Key-Value#

Redis, Memcached, DynamoDB

Simple: put a key, get a value. Fastest possible reads.

Choose when:

• Session storage, caching, feature flags
• Real-time leaderboards (Redis sorted sets)
• Shopping carts, rate limiters
• You need sub-millisecond reads

Skip when: You need queries beyond key lookup, complex relationships, or ad-hoc reporting.

Wide-Column#

Cassandra, ScyllaDB, HBase

Optimized for massive write throughput and time-series data.

Choose when:

• IoT sensor data, event logging, time-series metrics
• Write-heavy workloads (millions of writes/second)
• Multi-region replication with tunable consistency

Skip when: Read-heavy with complex queries, small datasets, or need for strong consistency.

Graph#

Neo4j, Amazon Neptune, Dgraph

Nodes and edges. Optimized for traversing relationships.

Choose when:

• Social networks (friends of friends)
• Recommendation engines (users who bought X also bought Y)
• Fraud detection (finding suspicious transaction patterns)
• Knowledge graphs

Skip when: Data doesn't have meaningful relationships, simple CRUD operations.

Search#

Elasticsearch, Meilisearch, Typesense

Full-text search with relevance ranking, facets, and autocomplete.

Choose when:

• Product search, log analysis, content discovery
• Autocomplete and fuzzy matching
• Faceted navigation (filter by price, category, rating)

Skip when: Primary data store (use as secondary index). Not designed for transactions.

The decision framework#

Most apps need 2-3 databases#

A typical production system might use:

1. PostgreSQL — primary data (users, orders, products)
2. Redis — caching, sessions, rate limiting
3. Elasticsearch — search and analytics

This isn't overengineering — each database handles what it's best at.

Start with PostgreSQL#

If you're unsure, start with PostgreSQL. It handles 90% of use cases well:

• JSON columns for semi-structured data
• Full-text search for basic search needs
• PostGIS for geospatial queries
• Great performance up to hundreds of millions of rows

Add specialized databases when PostgreSQL becomes the bottleneck for a specific access pattern — not before.

See databases in your architecture#

On Codelit, every generated architecture includes typed database components. Click any database node to audit its scaling characteristics and get recommendations for your specific workload pattern.

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Explore database choices: describe your system on Codelit.io and audit each database component for the right fit.