debugging

A client's codebase had try-catch blocks wrapped around everything. Nothing ever crashed. Nothing ever worked correctly either. The error handling strategy was actually an error hiding strategy.

Every deploy was losing a handful of HTTP requests, but nobody noticed until a payment callback disappeared. The fix wasn't in the deployment pipeline — it was in the application code that never learned how to shut down.

A routine ANALYZE flipped a Postgres query plan from an index scan to a sequential scan, and our API went from 12ms to 8 seconds. Here's what we learned about a failure mode most teams never think about.

A client moved their reads to database replicas for performance. The latency numbers looked great — until customers started getting charged twice and inventory counts drifted from reality.

A client found one of their API keys in a public error log. Tracing where that key actually lived took longer than fixing the leak — and revealed a secrets management problem nobody wanted to own.

A client's payment provider was sending webhook notifications correctly. Their system acknowledged every one. And then quietly threw most of them away.

A client's "graceful degradation" strategy silently served stale pricing data for 11 hours. The outage would have been better.

A client's AI features were burning through their OpenAI budget 3x faster than projected. Adding OpenTelemetry's GenAI semantic conventions revealed the problem wasn't what anyone expected.

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 was confident about how their services talked to each other. Then we instrumented the system with OpenTelemetry and found out what was actually happening.

A payment provider started responding in 8 seconds instead of 200ms. It wasn't an outage — their status page stayed green. But it took out our client's entire checkout flow because nobody had configured a timeout.

A client had six monitoring tools and still couldn't diagnose a production incident in under an hour. The problem wasn't the tools — it was what happens when observability grows by accretion instead of design.

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 notification queue was draining normally and all dashboards showed green. But three weeks of transactional emails had vanished into a catch block nobody thought to monitor.

A Node.js service was writing UTC timestamps to a PostgreSQL database configured for Europe/Berlin. Nobody noticed the mismatch until a DST transition made an entire hour of orders vanish from daily reports.

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.

A consulting story about a nightly billing job that quietly started double-charging customers after a Kubernetes migration — and the boring lock that finally fixed it.

A debugging deep dive into replacing wall-of-text logs with structured logging and trace IDs — and how it cut our mean time to resolution from hours to minutes.