database-patterns

PostgreSQL indexing strategies depend on query patterns. B-tree indexes work for range queries, GIN indexes optimize JSONB searches, partial indexes reduce size by filtering rows, and expression indexes speed computed columns. Choose based on your access patterns and data distribution.

Cache-aside and write-through are the primary Redis caching patterns. Cache-aside checks Redis first, loads from PostgreSQL on miss, and updates the cache. Write-through writes to both systems simultaneously. Select based on consistency requirements and acceptable staleness.

JSONB columns store semi-structured data with GIN indexes for fast lookups. Use JSONB for flexible metadata, nested attributes, or variable schemas while maintaining queryability. Combine with partial indexes to optimize specific JSONB paths.

Denormalize to JSONB when data is read-heavy, rarely updated independently, or has variable structure. Avoid JSONB for frequently joined data or strict transactional consistency needs. Evaluate query patterns and update frequency before deciding.

Cache invalidation strategies include time-based expiration (TTL), event-driven invalidation on database writes, and manual cache clearing. Design based on consistency tolerance and update frequency. OLTP systems typically use shorter TTLs or event-driven approaches.

Yes. PostgreSQL indexing and JSONB support analytical queries on OLTP data. Redis caching accelerates aggregations and dashboards. However, dedicated analytical databases often outperform PostgreSQL for large-scale analytics—evaluate scale and query complexity first.