reliability

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.

We practiced deployments religiously but never tested a rollback. When a release broke checkout and we hit the big red button, we found out half the system couldn't actually go backward.

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'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.

Most healthcheck endpoints return 200 OK as long as the process is running. That's not a healthcheck — it's a pulse check. Here's what happened when we confused the two, and what a real healthcheck should verify.

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's staging environment had drifted so far from production that developers stopped using it. Tests passed in staging and failed in prod. Tests failed in staging and passed in prod. Eventually the team just stopped looking.

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 single slow database query triggered aggressive retries across four microservices. Within minutes, the entire order pipeline was down. Here's how we traced it and what we changed.

We ran blameless postmortems after every incident. We wrote detailed action items with owners and deadlines. Six months later, 78% of those items were still open — and the same incidents kept happening.

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.