PostgreSQL Performance: Measure First, Tune Second
Most Postgres performance problems are indexing problems. The rest are vacuum problems. Here's how to find and fix both.
Databases
Definition
Databases coverage in this archive spans 10 posts from Apr 2016 to Mar 2026 and centers on data correctness and operability under real production constraints. The strongest adjacent threads are postgresql, architecture, and engineering. Recurring title motifs include database, databases, migrations, and without.
Key claims
- The common theme is that schema, ownership, and query shape drive most downstream outcomes.
- Early posts lean on database and postgres, while newer posts lean on database and most as constraints shifted.
- This topic repeatedly intersects with postgresql, architecture, and engineering, so design choices here rarely stand alone.
Practical checklist
- Define freshness, correctness, and latency targets before choosing storage or pipeline patterns.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read postgresql and architecture before committing implementation details.
Failure modes
- Scaling pipelines before locking down source-of-truth and reconciliation behavior.
- Optimizing single queries while ignoring data model drift and access patterns.
- Applying guidance from 2016 to 2026 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): De-Risking the Black Swan: Red-Teaming Distributed Databases Before Production
- Then read (operating middle): Database Replication Patterns That Actually Matter
- Finish with (foundational context): Postgres vs MySQL in 2016: A Practical Comparison
Related posts
- De-Risking the Black Swan: Red-Teaming Distributed Databases Before Production
- PostgreSQL Performance: Measure First, Tune Second
- Zero-Downtime Database Migrations Without the Drama
- Database Reliability Engineering: What I’ve Learned the Hard Way
- Most Teams Should Just Use Postgres
- Database Replication Patterns That Actually Matter
- Stop Guessing: How I Fix Slow Databases
- The Economics of State: Why Scaling Up Beats Sharding (Until It Doesn’t)
References
Zero-Downtime Database Migrations Without the Drama
Database migrations are the one place where a single ALTER TABLE can ruin your weekend. Here's how to do them safely with expand-and-contract, batched backfills, and compatible deploys.
Database Reliability Engineering: What I've Learned the Hard Way
Practical database reliability from running Postgres at the fintech startup and at large enterprises. Includes config examples, migration patterns, and the operational habits that actually prevent outages.
Most Teams Should Just Use Postgres
Serverless databases are solving problems most teams don't have. Here's why Postgres with a connection pooler is still the right answer.
Database Replication Patterns That Actually Matter
A practical breakdown of replication modes, topologies, and the tradeoffs between consistency, availability, and not losing your users' data at 3am.
Stop Guessing: How I Fix Slow Databases
The repeatable process I use at the fintech startup to diagnose and fix database performance problems instead of throwing random indexes at the wall.
The Economics of State: Why Scaling Up Beats Sharding (Until It Doesn't)
A production-grounded case for exhausting single-server headroom with pooling, replicas, and partitioning before taking on sharding complexity.
Database Migrations Without Downtime
A practical guide to evolving schemas without maintenance windows by keeping old and new code compatible at every step.
Postgres vs MySQL in 2016: A Practical Comparison
A grounded look at PostgreSQL and MySQL as of April 2016, focusing on integrity, query power, and operational tradeoffs rather than benchmark hype.