SQL vs NoSQL Selection
Choosing between relational and non-relational databases based on data shape, query patterns, and consistency requirements.
Description
SQL databases (PostgreSQL, MySQL, SQL Server) enforce a rigid schema with tables, rows, and relationships, providing strong ACID guarantees and powerful JOIN operations. They excel when data is highly relational, when you need complex ad-hoc queries, or when transactional integrity is non-negotiable -- think financial systems, inventory management, or user account data.
NoSQL databases span multiple paradigms: document stores (MongoDB, CouchDB), key-value stores (Redis, DynamoDB), wide-column stores (Cassandra, HBase), and graph databases (Neo4j). They trade schema rigidity and sometimes consistency for horizontal scalability, flexible data models, and optimized performance for specific access patterns. A document store is ideal when your data is naturally hierarchical and you read/write entire documents; a key-value store shines for caching and session management; a graph database handles deeply connected data like social networks.
The decision should never be ideological. Evaluate your read/write ratio, data relationships, consistency requirements (CP vs AP in CAP theorem), scaling projections, and team expertise. Many production systems use polyglot persistence -- a relational database for transactional core data alongside Redis for caching and Elasticsearch for search -- rather than forcing one engine to do everything.
Prompt Snippet
Evaluate data access patterns and consistency requirements before selecting a database engine. For transactional workloads with complex relationships, default to PostgreSQL with proper indexing. For high-throughput document storage with flexible schemas, consider MongoDB with WiredTiger. For caching and ephemeral session data, deploy Redis with RDB+AOF persistence. Document the CAP theorem trade-offs for each data store in your architecture decision record (ADR).
Tags
Related Terms
Database Indexing Strategies
Creating and managing indexes to accelerate query performance while balancing write overhead and storage costs.
ACID Transactions
The four guarantees (Atomicity, Consistency, Isolation, Durability) that ensure database transactions are processed reliably even under concurrent access and system failures.
Database Sharding
Distributing data across multiple database instances by splitting rows based on a shard key, enabling horizontal scaling beyond the capacity of a single server.
Database Replication
Copying data from one database server to another in real-time or near-real-time to provide redundancy, failover capability, and read scaling.