Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

Launch Week Day 1: Introducing Discover Services Picture this: It's 2 AM, alerts are firing, and you're staring at a dashboard trying to figure out which service is causing the cascade of failures. Your service map is a six-month-old Miro board, and you have no idea what's actually talking to what in production right now. If you've been there, you're not alone. In fast-moving teams, new services get deployed faster than you can track them.

Your application is under constant pressure to deliver low latency, high reliability, and a smooth user experience isn’t optional. When performance drops, every second matters. Application Performance Monitoring (APM) gives you the visibility to spot issues before your users feel the impact. It also helps you understand what’s happening inside your stack, so you can track resource usage, pinpoint bottlenecks, and keep things running at peak performance.

Your log format defines how your application records events. The structure you choose shapes how logs get parsed, indexed, and queried. It affects how quickly you can debug issues, build alerts, or control storage usage. In this guide, we'll take a look at the log formats developers typically use, the essential fields to include, and what trade-offs to consider before locking down a format for your system.

A PostgreSQL setup that performed well with 10,000 users starts to show strain at 100,000. Queries that once returned in under 50ms now take over 2 seconds. The connection pool regularly hits its limit during peak usage, leading to timeouts and degraded performance. This blog focuses on practical ways to reduce query latency by 50–80% and increase throughput for high-concurrency environments.

Application metrics are structured, quantifiable signals that reflect how your software behaves in production. They capture key aspects of performance, response times, error rates, throughput, and resource usage, giving you a real-time view into the health of your system. Tracking the right metrics helps detect regressions early, surface latent issues before they impact users, and guide optimization decisions based on hard data, not guesswork.

Your Jaeger setup is running. Traces are coming in, and the UI is helping you spot slow services or debug broken flows. But just like any part of your observability stack, Jaeger needs some basic monitoring to stay reliable. If the collector starts queueing spans or the agent runs out of buffer, it can lead to dropped traces, sometimes without any obvious sign in the UI. This blog focuses on the operational side of Jaeger.