A twelve-part series on building self-serve chaos engineering labs on Azure, where every injected failure has a matching signal, alert, and recovery path.
This series is about building self-serve failure labs on Azure: one overview post sets up the harness and philosophy, then eleven deep-dives take individual failure modes from cause to symptom to alert to recovery.
Every article returns to one claim: injecting failure is the easy half, and the hard half is proving you would have noticed. Each lab pairs a fault with an independent signal that fires when the fault is live, plus a clean recovery path that reverses it.
The labs use a repeated operating model: a Logic App controller invoked by a signed callback URL, a managed identity that applies the fault, one shared Log Analytics workspace, one shared action group, and an inject/recover contract for every scenario.
Read the harness overview first, then move through infrastructure, application, and integration failures before ending with the AKS capstone, which proves service health from outside the cluster.
Introduce the failure-lab harness
How a repeatable Azure failure-lab harness turns one-off chaos experiments into self-serve drills with inject contracts, alerts, and recovery paths.
Demonstrate resource saturation drills
How a CPU stress lab proves the difference between a running host and a healthy service by pairing resource saturation with alertable telemetry.
Expose reachability failures
How an Azure network isolation lab separates host liveness from reachability by breaking the path while the target keeps running.
Rehearse controlled application faults
How to inject deterministic 500s, exceptions, and latency into App Service so application failure can be rehearsed and recovered on demand.
Test downstream dependency failure
How a dependency failure lab shows that an application is only as healthy as the downstream call it relies on.
Validate identity and secret failure handling
How identity and Key Vault failures can masquerade as application bugs, and how to drill them with a controlled inject and recover path.
Exercise database failure modes
How bad credentials, slow queries, and blocked database paths surface in an Azure failure lab, and what telemetry proves the symptom.
Practice stability failure drills
How memory pressure and crash loops reveal the difference between gradual instability and instant restart storms.
Validate throttling and retry behavior
How self-imposed throttling and 429 responses expose retry behavior, alert quality, and load-amplification risks.
Test certificate trust failures
How TLS trust-chain failures fail closed and quietly, and how to rehearse them with a controlled certificate swap.
Prove queue backlog signals
How queue backlog and dead-letter counts become first-class reliability signals when there may be no immediate application error.
Complete the series with external health proof
How an AKS failure lab uses independent synthetic observers to prove service health from outside the cluster control plane.