Operations | Monitoring | ITSM | DevOps | Cloud

Chaos Monkey is an open-source tool. Its primary use is to check system reliability against random instance failures. Chaos Monkey follows the testing concept of chaos engineering, which prepares networked systems for resilience against random and unpredictable chaotic conditions. Let’s take a deeper look.

tldr: This post discusses how to measure CDN request locality without indexing a single log. The function of a CDN is to bring cacheable data, like pictures & videos, physically closer to your customers. This reduces latencies, and load on your backend servers. When measuring CDNs, there are some very common metrics.

Ralph Marsten once said, “Don't lower your expectations to meet your performance. Raise your level of performance to meet your expectations.” Many organizations today seem to follow the opposite. If everything looks green on a dashboard, they assume all is well. But is it?

I run a small Kubernetes cluster at home, which I originally set up as somewhere to experiment. Because it started as a playground, I never bothered to set up monitoring. However, as time passed, I’ve ended up dropping more production-esque workloads onto it, so I decided I should probably put some observability in place. Not having visibility into the cluster was actually a little odd, considering that even my fish tank can page me.

Grafana is a widely used open-source platform for monitoring and visualization. Grafana has a lot of built-in functionality and also provides a large amount of community templates that can improve your overall experience. However, Grafana requires quite a lot of configuration and the documentation can be a bit overwhelming for beginners. In this article, we explore seven alternatives that can be simpler to use and can provide seamless integration of traces, logs, and metrics.

At this point, it’s almost passé to write a blog post comparing events to the three pillars. Nobody really wants to give up their position. Regardless, I’m going to talk about how great events are and use some analogies to try to get that across. Maybe these will help folks learn to really appreciate them and to depreciate a certain understanding of the three pillars. Or maybe not.

Today, we’re excited to unveil the Anomaly Detection feature. It will enable users to create smarter alerts based on dynamic metrics, moving beyond traditional fixed-threshold alerts. It will soon be available to all our users and is currently undergoing beta testing with select users. By detecting deviations from expected patterns, Anomaly Detection will help you stay informed about critical issues without getting overwhelmed by irrelevant alerts. Let’s dig in deeper.

Remember the Tabs vs. Spaces arguments? It seems that observability has grown up enough that we are arguing over which signals are the “best” signals for observability. Often referred to as the Pillars of Observability, Metrics, Logs, and Traces (sometimes adding Events for MELT) each provide a unique perspective on a system. What happens when we change our perspective from finding the “best” telemetry format to finding the telemetry that aligns with the problems we need to solve?