SRE
Distributed systems fail in ways that single-process systems don't. A network timeout doesn't crash your process — it makes your thread hang. A slow dependency doesn't just slow one request — it can exhaust your connection pool and bring down the whole service. Retries, timeouts,
SRE
Chaos engineering is the practice of intentionally breaking production systems to find weaknesses before they find you. Done wrong, it's just causing outages on purpose. Done right, it's a systematic method for building confidence in your system's resilience. The difference between chaos that helps
Testing
Chaos engineering and SLO management seem to be in conflict. SLOs protect reliability commitments. Chaos engineering deliberately breaks things. How do you test resilience without burning your error budget and violating your SLAs? The answer is planning — scoping experiments to stay within budget, baselining before you inject faults, and defining
Chaos Engineering
Chaos engineering without SLOs is just breaking things. You inject failures, watch dashboards, and write post-mortems. Chaos engineering with SLOs is different — it's a structured way to answer a specific question: "Does my system maintain its reliability commitments under real-world failure conditions?" This guide
Canary Releases
Canary deployments are only as good as the analysis behind them. Shipping 5% of traffic to a new version and watching a dashboard manually is not a canary strategy — it's a slow, manual deployment. Real canary testing uses automated analysis: metrics-based promotion gates, statistical significance checks, and
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
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
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
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