performance

A customer clicked "Export to CSV" on 380,000 records. The server loaded them all into memory, OOMed, and took the API offline for every tenant. The fix was straightforward. The real question is why nobody caught it sooner.

A client's 45-minute data import gradually became a 14-hour ordeal that bled into business hours, tanked dashboard performance, and made the analytics team distrust their own numbers. The fix wasn't what anyone expected.

A client's API had rate limiting configured and enforced. It still couldn't prevent a single customer from tanking performance for everyone else. The problem wasn't the limiter — it was what we were counting.

A Node.js service kept getting OOMKilled, but only on Tuesdays. The batch job was a red herring. The real problem was a lazy cache eviction strategy that nobody thought to question.

A client's pods were getting OOMKilled during peak traffic, but the team spent days chasing application bugs. The real problem was resource limits that nobody had revisited since the initial cluster setup.

A client's PostgreSQL writes were getting slower every quarter. The table had 57 indexes. Only 14 of them were ever used. Every INSERT and UPDATE was paying a tax nobody had thought to audit.

A client's API was getting measurably slower every week. The dashboards were green, the alerts were silent, and the database looked healthy. The problem was hiding in plain sight — on the container's local disk.

A client's API started throwing 500s every weekday afternoon like clockwork. The database was fine. The queries were fast. The problem was a reporting job that quietly hogged every available connection during peak traffic.

A client's dashboard took 11 seconds to render. Everyone blamed the database. The real problem was an ORM doing exactly what we told it to — we just never looked at what that meant.

We ran load tests before a big product launch, got green across the board, and watched the system buckle under real traffic two days later. The tests weren't wrong — they just weren't testing reality.

We added Redis to fix slow API responses. Instead we got stale data, thundering herds, and a system that was harder to debug than the original problem.