Kubernetes workload troubleshooting with metrics, logs, and traces

Dynatrace uses its deterministic AI, Davis, to monitor Kubernetes workloads and recently identified a problem in a Keptn instance due to a 33% increase in failure rate in the mongodb-datastore workload. The AI not only alerts teams to anomalies by automatically baselining service endpoints but also identifies root causes by analyzing container logs, as demonstrated by detecting an unhandled error in the code interacting with a MongoDB instance. This capability allows for rapid problem resolution, as evidenced by the Keptn development team's swift fix after receiving detailed insights, including log data and distributed traces, which provide additional context like timing and API endpoints. The blog post underscores the effectiveness of Dynatrace's monitoring tools in enhancing operational efficiency and problem-solving speed.