Chaos Engineering
Database failures are uniquely destructive. Most applications can degrade gracefully when a downstream API is slow — retry logic, circuit breakers, and fallback responses let users continue with reduced functionality. But when the database fails, most applications fail completely. This makes the database the highest-value target for chaos engineering. Understanding exactly
Chaos Engineering
Traditional chaos engineering focuses on infrastructure failure: pods terminating, networks degrading, databases slowing down. Security chaos engineering extends this to adversarial conditions: authentication systems under attack, authorization bypasses attempted, API rate limits hammered. The question isn't just "does the system survive?" — it's "does
Chaos Engineering
A chaos engineering runbook is the document that makes your experiments repeatable, learnable, and defensible. Without runbooks, chaos experiments are one-off events — knowledge that lives in one engineer's head and disappears when they leave. With runbooks, experiments become institutional memory and the foundation of a mature resilience program.
Chaos Engineering
Chaos engineering is not about breaking systems randomly. It's about running controlled experiments to validate hypotheses about system behavior under failure conditions. Without observability, you can't form those hypotheses, can't detect the blast radius of an experiment, and can't determine whether your
Chaos Engineering
Most teams run chaos experiments manually — a team gathers, runs an experiment, watches dashboards, and documents findings. This works, but it doesn't scale. Manual chaos experiments happen infrequently, require coordination, and catch problems after code has already been deployed to production. Chaos engineering in CI/CD changes this.
Testing
Internal developer platforms (IDPs) are infrastructure for your engineers. When they break, all development stops. Yet most platform teams define SLOs for their user-facing products while running their own IDPs on vibes and hope. This guide covers how to define meaningful SLOs for developer platforms, test them with chaos experiments,
Observability
Prometheus alerting rules can fail silently for years: a typo in a PromQL expression, a label mismatch in Alertmanager routing, or a threshold that never actually fires. promtool provides a native unit testing framework for alert rules—YAML-defined tests that validate expressions against synthetic time-series data without running a live
SRE
SLI, SLO, and error budget are the vocabulary of reliability engineering. They give you a rigorous way to answer "is our service reliable enough?" and "how much risk can we take with this deployment?" without relying on gut feeling or incident retrospectives. This guide covers how
SRE
Synthetic monitoring runs scripted tests against your application continuously — from multiple geographic locations, using real browsers or API clients — to detect issues before users report them. Combined with canary deployments, it becomes your automated deployment safety net. Synthetic Monitoring Fundamentals Synthetic monitors are different from real user monitoring (RUM): Synthetic
SRE
When an incident happens at 3 AM, the quality of your response depends on two things: the quality of your runbooks and how many times your team has practiced them. Testing runbook automation and running incident drills converts theory into muscle memory. Automated Runbooks vs. Reference Runbooks Reference runbooks are
SRE
High availability is a promise — that your system will remain operational when components fail. Multi-region failover is how you keep that promise when an entire cloud region becomes unavailable. Testing your HA architecture requires deliberately causing the failures your system claims to handle. HA Architecture Patterns Active-passive (warm standby): Traffic
SRE
Chaos engineering is the practice of deliberately introducing failures into a system to discover weaknesses before they cause incidents. Netflix coined the term with Chaos Monkey — a tool that randomly terminates EC2 instances to force engineers to build resilient services. For Kubernetes workloads, Litmus Chaos has become the most widely