Approach to Chaos Engineering: Chaos engineering is very important in terms of testing a system robustness when failure occurs in the controlled environment. My process begins by identifying significant system dependencies and then building a "steady state" – metrics like response time, throughput or error rate that reflect normal performance. Then, based on hypotheses I'll be formulating around possible weak spots such as, "if the database becomes unavailable does the cache handle the load?", these hypotheses can actually help to observe how a system reacts with various failure modes in operation, and through such repeated iterations, one would incrementally introduce faults within the safe environment (normally through restricted traffic and so forth) and hence work all the way toward actual deployment.
Tools: I use Netflix's Simian Army suite Gremlin and Chaos Monkey to perform general-purpose chaos testing, which allows for injection of failures such as instance terminations, network latency, or CPU spikes. For Kubernetes, I utilize LitmusChaos and PowerfulSeal to simulate disruptions at the container, node, and pod levels in order to test resilience. AWS Fault Injection Simulator (FIS) is also valuable for performing controlled chaos experiments in AWS environments, helping to ensure that failover and redundancy mechanisms respond appropriately.
