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.
Postgresql
Definition
Postgresql coverage in this archive spans 11 posts from Apr 2016 to May 2022 and centers on data correctness and operability under real production constraints. The strongest adjacent threads are databases, architecture, and performance. Recurring title motifs include database, postgresql, 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 sharding, while newer posts lean on database and postgresql as constraints shifted.
- This topic repeatedly intersects with databases, architecture, and performance, 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 databases 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 2022 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): PostgreSQL Performance: Measure First, Tune Second
- Then read (operating middle): The PostgreSQL Tuning Playbook I Actually Use
- Finish with (foundational context): Postgres vs MySQL in 2016: A Practical Comparison
Related posts
- 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
- The PostgreSQL Tuning Playbook I Actually Use
- Database Sharding: You Probably Don’t Need It Yet
- Stop Guessing: How I Fix Slow Databases
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.
The PostgreSQL Tuning Playbook I Actually Use
Battle-tested PostgreSQL tuning from running fintech and startup workloads: connection pooling, memory sizing, index discipline, vacuum management, and the queries that tell you what's broken.
Database Sharding: You Probably Don't Need It Yet
Most teams shard too early. Here's how we thought about it at the fintech startup, when it actually makes sense, and the SQL-level decisions that matter most.
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.