Operations | Monitoring | ITSM | DevOps | Cloud

When application performance monitoring detects a spike in latency or error rates, the immediate challenge is determining the underlying cause. APM logs address this by correlating performance metrics with the specific log events that occurred at the same time. Instead of switching between monitoring dashboards and manually searching through log files, APM log correlation consolidates both views.

Azure Kubernetes Service (AKS) continuously generates a high volume of telemetry, ranging from node-level CPU and memory usage to request latencies and error rates within individual pods and services. Without a structured monitoring strategy, this flood of metrics can easily become noise, leaving teams blind to early warning signs. Effective monitoring in AKS is about identifying the right signals, correlating them across layers, and acting before they impact application performance or cluster stability.

Launch Week Day 3: Introducing Discover Infrastructure Your dashboard looks perfect. APIs responding in 80ms, background jobs processing smoothly, error rates at 0.02%. Everything's green. Then production breaks. "Why is checkout so slow?" "The payment service keeps timing out!" You run kubectl get pods and discover payment-service pods restarting every 3 minutes due to OOM kills. Then you check your database host—CPU at 98% because someone forgot the new ML training job runs there too.

Launch Week Day 2: Introducing Discover Jobs Your dashboard looks perfect. APIs responding in 80ms. Error rates at 0.02%. Kubernetes pods healthy. Everything's green. Then Slack explodes: "Why didn't my invoice generate?" "Where's my password reset email?" "The data export I requested yesterday is still processing?" You check your job queue. Sidekiq dashboard shows 47,000 jobs processed today. Redis looks fine. Workers are running. But somehow, your business logic is silently falling apart.

Real User Monitoring (RUM) measures how real users interact with your application in production. Unlike synthetic monitoring, which relies on scripted tests, RUM collects data from actual sessions. This means performance is observed across different devices, networks, and usage patterns. The result is a clear view of how the application behaves under real conditions, where latency is introduced, which features take longer to load, and at what points users drop off.