Buglyst Blog
Learn to debug under pressure.
Playbooks for fast pattern recognition, guides for the full investigation, and articles for the engineering judgment around the edges.
17 playbooks · 509 guides · 95 articles · 12 linked practice labs ·skip to practice
Fast pattern recognition for the production failures engineers see most often.
17 playbooks
Debugging Env Var Issues
A concise checklist for incidents where local works but CI, staging, or production changes behavior.
Debugging Cache Stampedes
How to recognize and contain request spikes when cached data expires.
Debugging CORS Origin Bugs
A practical way to inspect origin parsing, credentials, and header behavior.
Debugging Retry Bugs
Retry issues often hide the first failure and create duplicate work.
Debugging Timezone Bugs
Time bugs usually live at day boundaries, DST changes, and unit conversions.
Debugging Pagination Bugs
Pagination bugs hide in boundary math, cursor reuse, and new records arriving between pages.
Structured investigations for the failure modes engineers meet in real systems.
Flaky tests in CI: how to debug and fix intermittent test failures
A test passes nine times out of ten but randomly fails in CI with no code changes. Flaky tests destroy trust in the test suite — here is how to find and fix them.
Stale cache key bug: how to debug cache key collisions
Your cache returns data belonging to a different user, tenant, or request. The cache key is not unique enough — it is missing a dimension.
Distributed lock not releasing: how to debug stuck locks
Your distributed lock is acquired but never released. The lock holder crashed, the TTL expired but the key lingers, or the unlock logic has a bug.
Idempotency key not preventing duplicate payment: how to debug it
Your payment API accepts an idempotency key but still processes the same charge twice. The key is not being checked, stored, or matched correctly.
Slow API response: how to debug latency issues
An API endpoint that used to return in 50ms now takes 3 seconds. Find the bottleneck before your users notice.
N+1 query slowing down your API: how to find and fix it
Your API makes one query to get a list, then one more query for every item in the list. That is the N+1 pattern — it turns 1 query into 101 and kills performance.
Long-form thinking on debugging habits, observability, and the systems around the bug.
Tracking Down a 200 MB Leak with Python Memory Profilers
A production API was silently leaking 200 MB of RAM every hour. Here's how memory profilers found the culprit—a forgotten NumPy array reference—and how you can apply the same techniques.
Reading CPU Flame Graphs: What the Hot Colors Actually Tell You
Flame graphs are everywhere, but most engineers read them wrong. Here's how to identify real bottlenecks, avoid common misinterpretations, and turn profile data into actionable fixes.
When Logs Lie: The Gaps Between What You Log and What Actually Happened
Logs are the first thing we reach for during an incident. But they can be misleading, incomplete, or outright wrong. Here's when to trust them and when not to.
Adding Observability to a 500K-Line Monolith Without a Rewrite
Adding structured logging, distributed tracing, and metrics to a legacy monolith without a full rewrite. Real code examples and a war story from a 500K-line codebase.
Distributed Tracing: Following a Single Request Across Microservices
Distributed tracing lets you follow a request as it hops across services. I'll show you how trace context propagates, why sampling matters, and how tracing helped us debug a 5-second latency spike in production.
Structured Logging in JSON: Fields, Schemas, and Pitfalls from Production
A practical guide to designing JSON log schemas that are queryable, consistent, and actually useful in production — with field recommendations and a war story.