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.
Stale lock bug: how locks that are never refreshed cause outages
A lock is acquired, the operation succeeds, but the lock is never released or refreshed. Other processes wait until the lock TTL expires, causing delays or outages.
Tenant cache leak: how to debug cross-tenant data exposure through caching
A user from Tenant A sees data from Tenant B because the cache key does not include the tenant identifier. The cache is leaking data across tenants.
Observability missing logs: how to debug gaps in logging and monitoring
A critical error happened in production but there are no logs for it. The log level is too high, logs are dropped under load, or the log pipeline has a silent failure.
Log says success but the user still fails: how to debug misleading logs
Your application logs 'Operation completed successfully' but the user sees an error or gets no result. The log is lying — it is logging intent, not outcome.
Background job stuck: how to debug jobs that never complete
A background job enters the queue, a worker picks it up, and then nothing. The job never completes, never fails, and never retries. It is stuck in limbo.
Race condition in payment flow: how to debug concurrent payment issues
Two requests hit the payment endpoint at the same time. Both check the balance, both see enough funds, and both deduct. The user is charged twice.
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.