Operations | Monitoring | ITSM | DevOps | Cloud

When a container starts consuming excessive memory or becomes unresponsive, you need a way to shut it down. The two primary options — docker stop and docker kill,both terminate containers, but they operate differently and have different implications. The key difference: docker stop sends SIGTERM for a graceful shutdown, then escalates to SIGKILL if the process doesn’t exit in time. docker kill skips straight to SIGKILL, terminating the container immediately.

Your server's been logging everything—it’s just easy to overlook until something breaks. Every incoming request, database call, or auth check ends up in your access logs. They’re not flashy, but they quietly document every interaction your system handles. For developers, they’re often the most reliable starting point when things go wrong. In this blog, we'll take a look at what an access log is, its format, types, and a few best practices.

When troubleshooting with the Elastic Stack, Kibana is often the interface you’ll rely on to query and visualize logs. It doesn’t change the data—it just makes it searchable and a bit easier to work with under pressure. If you’re investigating an outage, tracking performance issues, or trying to correlate events across services, Kibana’s log exploration tools can speed up the process, assuming they’re configured and used well.

Sometimes your API call takes a few seconds longer than expected. Or users start reporting slow page loads. One of the most common reasons? Network latency.

If your users are spread across the globe but your servers are sitting in Virginia, you’ll probably hear complaints about slow load times, especially from places like Australia. CDNs fix this by caching static assets closer to where your users are. Azure CDN does exactly that, and it fits well if you're already using Azure services. You can hook it up to Blob Storage, App Services, or your origin. This guide covers how to set it up, what to expect, and how to know it’s working.

Your code passes locally, CI is green, and the deploy goes through. Then production throws a 500, and the trace isn’t helpful. And here, event logs help. A log captures timestamped records of what the app did HTTP requests, DB queries, cache misses, retries, failures. These entries give you enough context to debug without reproducing the issue locally. Especially when dealing with distributed systems, logs are often the only consistent source of truth.

Collecting logs in Kubernetes often starts as a simple goal, and quickly turns into a game of “where did that log line go?” Between sidecars, DaemonSets, and countless config options, it’s easy to get lost. Fluent Bit helps cut through the noise. It's fast, lightweight, and plays well with Kubernetes. And when you deploy it using Helm charts? The setup becomes way more manageable. This guide covers the how and the why, without overcomplicating the what.

Code in production will break. Maybe a request takes too long, maybe it fails quietly, or maybe it works fine one minute and falls over the next. Logs can help, sure—but they don’t always show the full picture, especially when performance issues are involved. Elastic APM gives you a clearer view. It traces what your application is doing from incoming requests to database queries and everything in between.

Your Kafka producer pushed a million messages yesterday. Nice. But can you tell if they all made it? Or why did latency spike at 2 PM? Producer metrics help you determine that. They expose how long messages take to send, whether messages are getting stuck, and whether retries are piling up. Let’s go over which ones help while debugging and how to monitor them.

Pulling observability data together is rarely clean. Metrics come from everywhere, formats vary, and making sense of it takes some work. OpenTelemetry Collector and Prometheus fit perfectly here. The Collector handles ingestion and processing from different sources, while Prometheus stores and queries the data. Simple, effective, and no vendor lock-in. In this blog, we cover how to integrate the Collector with Prometheus, common pitfalls, and ways to control costs.