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.
6 playbooks in Distributed Systems
Debugging Cache Stampedes
How to recognize and contain request spikes when cached data expires.
Debugging Stale Locks
Stale locks turn a previous failure into a new outage.
Debugging Tenant Cache Bugs
Tenant cache bugs are both correctness and security incidents.
Debugging Distributed Locks
A checklist for stale locks, duplicate workers, TTL drift, and clock-skew ownership bugs.
Debugging Queue Consumer Failures
How to debug missing retries, duplicate workers, and failed jobs that vanish from queues.
Debugging Performance Backpressure
How to diagnose resource exhaustion in streams, queues, and hot paths.
Structured investigations for the failure modes engineers meet in real systems.
WebSocket Reconnect Storm & Thundering Herd Debugging
Diagnose and fix cascading WebSocket reconnection storms triggered by thundering herd patterns in distributed systems.
Istio Sidecar Envoy 503: Debugging Upstream Connection Failures
A systematic guide to diagnosing 503 responses from Istio sidecar proxies, covering upstream cluster issues, TLS mismatch, and routing misconfigurations.
Linkerd mTLS Handshake Failures: A Practical Debugging Guide
Linkerd mTLS connections failing? This guide covers diagnosing broken TLS handshakes, certificate mismatches, and policy misconfigurations in Kubernetes.
Long-form thinking on debugging habits, observability, and the systems around the bug.
When caches lie: debugging stale data in distributed systems
Cache invalidation is often cited as one of the two hard problems in CS, but the daily reality is subtler: partial staleness, clock drift, and silent evictions. This post walks through real debugging techniques for stale data in Redis, Memcached, and CDN layers.
Retry Storms: When Retries Make a Cascading Failure Worse
Retries seem like a safety net, but in a distributed system under load they can turn a small hiccup into a full outage. Here's how retry storms form and what to do about them.
Caching Bugs Are the Worst: A Postmortem on Stale Data Disasters
A deep dive into the most insidious caching bugs, with real postmortems and practical patterns to avoid stale-data disasters.
Timeouts Every Engineer Gets Wrong (and How to Fix Them)
Most timeout bugs aren't logic errors — they're configuration failures. Here's how to stop treating timeouts as magic numbers and start engineering them deliberately.
Backpressure in Streaming Systems: When Your Pipeline Fights Back
Backpressure is the system's way of saying 'slow down.' Ignore it and you get OOM crashes, silent data loss, or cascading failures. Here's how to design for it.