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.
18 playbooks · 509 guides · 96 articles · 13 linked practice labs ·skip to practice
Fast pattern recognition for the production failures engineers see most often.
18 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.
Jest Fake Timers Not Working: A Debugging Guide
A practical guide to debugging when Jest's fake timers (jest.useFakeTimers) don't behave as expected—covering async race conditions, timer scope, and common pitfalls.
Thread Pool Starvation Deadlock: How to Detect, Diagnose, and Fix It
Learn how to identify and resolve thread pool starvation deadlocks where waiting tasks block worker threads, causing cascading failures.
GC Thrashing Causes High CPU — How to Diagnose and Stop It
A practical guide to identifying and resolving garbage collection thrashing that spikes CPU usage in JVM and .NET applications.
Debugging Pandas Merge Producing Duplicate Rows
A targeted guide for diagnosing and fixing unexpected row explosions when merging DataFrames, covering key mismatches, duplicate keys, and index bleed.
Pandas SettingWithCopyWarning: How to Find and Fix the Real Source
A practical guide to eliminating SettingWithCopyWarning in pandas by understanding chain indexing, copy vs. view semantics, and using .loc and .copy() correctly.
RabbitMQ Connection Churn Spiking CPU: Debugging Guide
When clients rapidly open/close connections, RabbitMQ's Erlang VM burns CPU managing channel and process overhead. This guide covers the real causes, from client misconfigurations to rogue heartbeat timeouts.
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.