PostgreSQL

Why PostgreSQL

PostgreSQL is our default database. Not because it’s trendy. Because it works.

We’ve used MySQL, MariaDB, and various NoSQL options. PostgreSQL consistently wins on three fronts: reliability, features, and community. When something goes wrong at 2 AM, you want a database with thirty years of battle-tested behavior. PostgreSQL gives you that.

The feature set matters too. Most startups don’t need five different databases. PostgreSQL handles relational data, JSON documents, full-text search, geospatial queries, and time-series data. One database. One operational burden. One team that knows it deeply.

The extension ecosystem seals the deal. PostGIS for location features. TimescaleDB for metrics. pg_cron for scheduled jobs. pgvector for AI embeddings. You get specialized capabilities without specialized infrastructure.

What We Build With It

Every product we build starts with PostgreSQL until there’s a reason to add something else. That reason rarely comes.

E-commerce platforms with complex inventory and order management. Multi-tenant SaaS applications where data isolation matters. Financial systems that need ACID guarantees on every transaction. Content platforms with millions of rows and sub-second queries.

We’ve built analytics dashboards that aggregate billions of events using PostgreSQL’s window functions and CTEs. We’ve built search features that handle typo tolerance and ranking without Elasticsearch. We’ve built real-time leaderboards using LISTEN/NOTIFY instead of adding Redis.

The pattern is clear: understand PostgreSQL deeply before reaching for another tool.

Our Experience Level

We’ve been running PostgreSQL in production for over a decade. Not just writing queries. Operating databases.

Schema design for complex domains is where we start. We know when third normal form helps and when denormalization makes sense. We understand the trade-offs between foreign keys and application-level constraints.

Query optimization is second nature. EXPLAIN ANALYZE is our first debugging tool. We read execution plans like code reviews. We know the difference between sequential scans and index scans, and when each is actually faster.

We’ve handled migrations across major versions, across hosting providers, and across schema rewrites. We’ve set up read replicas, configured logical replication, and implemented zero-downtime deployments.

Backup and recovery aren’t afterthoughts. We configure WAL archiving, test restores regularly, and build point-in-time recovery into our runbooks.

When to Use It (And When Not To)

PostgreSQL is the right choice for most applications. Structured data with relationships between entities. Systems that need transactions. Products where data integrity matters more than write throughput.

Use PostgreSQL when you need:

• Complex queries with joins across multiple tables
• Strong consistency guarantees
• Mature tooling and widespread expertise
• A single database that handles multiple data patterns

Don’t use PostgreSQL when you need:

• Sub-millisecond key-value lookups at extreme scale (use Redis)
• Full-text search with complex linguistic analysis (use Elasticsearch)
• Graph traversals across deeply connected data (use Neo4j)
• Analytics over petabytes of data (use ClickHouse or BigQuery)

We’ll tell you when PostgreSQL isn’t the answer. But that conversation happens less often than you’d think.

Common Challenges and How We Solve Them

Slow queries that worked fine in development. This is usually missing indexes or query plans that change with data volume. We use EXPLAIN ANALYZE, add appropriate indexes, and sometimes rewrite queries to help the planner.

Connection exhaustion under load. PostgreSQL creates a process per connection. That’s expensive. We implement connection pooling with PgBouncer and design applications to release connections quickly.

Schema migrations on large tables. Adding a column with a default used to lock tables for hours. We use techniques like adding nullable columns, backfilling in batches, and leveraging PostgreSQL 11+ features that make this instant.

Replication lag during traffic spikes. Read replicas fall behind when the primary is under heavy write load. We configure streaming replication properly, monitor lag actively, and design applications to tolerate eventual consistency where appropriate.

Runaway queries consuming resources. One bad query can tank your whole database. We set statement timeouts, use pg_stat_statements to find problematic queries, and implement query review processes.

PostgreSQL rewards those who understand it. We do.