Operations | Monitoring | ITSM | DevOps | Cloud

Everything looks fine—dashboards are green, logs are quiet. But users start reporting slow response times. No errors, no traffic spikes. Just a general slowdown. It’s a common situation. Not all problems show up as crashes or clear failures. Sometimes, performance degrades quietly, and standard metrics don’t catch it early. But that's where Prometheus alerting can help, if you're monitoring the right signals.

When requests slow down across your microservices, tracing helps you understand where time is spent. Jaeger and Zipkin are two popular tools for distributed tracing, built to answer a simple question: where did the request go? If you're choosing between them or just exploring options, this guide breaks down the differences and when each one might be a better fit.

In a microservices setup, tracking a single request across services quickly gets complex. One service calls another, then a third, and your logs don’t line up. The traceparent header carries context between services, so all parts of a request connect back to the start. For example, when a frontend sends a request to an API, which then calls a database service, traceparent it links those calls in the trace. Without it, you’re left guessing how requests flow.

When an application functions flawlessly in your environment but crashes unpredictably on a client’s Windows server, the root cause is often buried in system logs—logs many developers overlook. Windows maintains comprehensive error records that document crashes, failures, and system events with precise detail. These Windows error logs serve as an invaluable resource for diagnosing issues in production environments.

If you manage a Linux server and notice something unusual, auditd logs can help you track exactly what’s happening. This built-in audit system records who accessed the system and what actions they performed. In this guide, we’ll cover setting up auditd, reading the logs, and using them to detect potential security issues early.

You’ve probably run a lot of Docker containers, but do you know what happens behind the scenes? The Docker container lifecycle is the path a container follows from being created to running, stopping, and finally getting removed. Understanding these steps helps you figure out why a container might not start or when to restart it instead of creating a new one.

If you’ve worked with Kubernetes, you know logs are essential for understanding what’s happening inside your clusters. However, unlike traditional servers, Kubernetes logs present their unique challenges. Pods frequently start and stop, containers restart regularly, and logs stored locally can be lost quickly. Because of this, managing logs in Kubernetes requires a different approach.

Server performance metrics help you figure out what’s going wrong, where your bottlenecks are, and how your system handles load. They give you the data to plan capacity, fix issues before they escalate, and build more reliable infrastructure. In this guide, we’ll go over the core metrics that matter, how to monitor them effectively, and the tools that can help along the way.

Logs are a key part of building and running software, but managing them can get complicated fast. As your apps grow and generate logs from many sources, choosing the right tool to store, search, and analyze those logs becomes important. Graylog and Loki are two popular options, each with a different way of handling logs. In this blog, we’ll break down the main differences between Graylog and Loki, how they work, and which types of projects they suit best.