PostgreSQL Performance Rescue
A PostgreSQL performance rescue starts with profiling, not with indexes. On a 109M-row telemetry table, a hardware-aware partitioning strategy cut the critical report from about one hour to under 5 seconds — a 720x speedup delivered with no downtime window, while the legacy table kept serving reads.
What you get
- Workload profile: your actual top queries, not a generic checklist
- EXPLAIN-verified fix plan ordered by expected impact (patch, index, layout, or more hardware)
- The fix implemented — usually 1–3 of: indexes, partitioning, caching layer, query rewrite
- A migration that runs on the live system without an outage window
- Guardrails: slow-query logging and alerts so the regression is caught early next time
How it runs
Day 1–3 — measure, don't guess
I take real query stats (pg_stat_statements), schema, and hardware sizes, and rank the queries where seconds can be removed. You get findings before any change is proposed.
Week 1–3 — fix in the order that pays
Cheap wins first (bad plans, missing indexes, N+1s). If the layout itself is wrong — the 109M-row case — partitioning is designed around your access patterns, not by convention or date-only.
Week 3–4 — ship it without downtime
Schema changes land as staged migrations: build the new layout, backfill incrementally, validate, flip queries in a controlled cutover. The old path keeps serving reads until the numbers confirm the win.
Case studies behind this service
FAQ
How much data needs tuning before performance work pays off?
Row count is a weak signal; the query profile is the real threshold. A 50M-row table with narrow indexed queries needs nothing, while a 10M-row table with aggregate reports can already hurt. If your critical reports run minutes instead of seconds, profiling is worth a day regardless of table size.
Can you speed up a live production table without downtime?
Yes, with a staged migration: create the new layout (indexes, partitions, or a report-optimised view), backfill incrementally while the legacy table keeps serving reads, validate the numbers, then flip queries in a controlled cutover. The 109M-row Perseus migration ran exactly this way with no outage window.
What if the database is just too small for the load?
Sometimes the honest answer is more hardware or a caching layer, and I say so on day one. Fixing the layout first still pays: right-sized queries make a modest upgrade enough, and rescue projects usually end with the existing instance doing far more work than it did.
Further reading
- PostgreSQL Partitioning at 109M RowsA critical report on 109M+ telemetry rows took an hour. Hardware-aware partitioning cut it to under 5 seconds. The real decisions behind a 720x speedup, no theory.
- Postgres Performance Tuning on 100M+ RowsPartitioning is the last resort, not the first move. A tuning playbook from real 100M+ row systems: pg_stat_statements first, indexes second, layout third — with the numbers that came out of a 109M-row rescue.
Who you work with
Eugene Zaiko
Eugene Zaiko is a senior full-stack engineer (5+ years, ex-PixelPlex, Syberry, Linnovate) specializing in event-driven backends, PostgreSQL at scale, and production AI systems. No account managers: you talk directly to the engineer whose fingerprints are on the code.