Today we are excited to launch one of our flagship ML assisted troubleshooting features in Netdata – the Anomaly Advisor. The Anomaly Advisor builds on earlier work to introduce unsupervised anomaly detection capabilities into the Netdata Agent from v1.32.0 onwards.
In the Kubernetes (K8s) community, there is a huge misconception about CPU allocation and utilization. Even highly experienced SREs find themselves struggling with the way Kubernetes allocates CPU resources, leading to misconfigured CPU allocations and extremely negative outcomes. For starters, this results in significant quality degradation on important service components, introduced by behind-the-scenes CPU limiting (or throttling).
At Netdata, we love two things more than anything else: Our goal is to make troubleshooting and monitoring as seamless as possible with the open-source Agent. This includes giving you pre-configured alerts so that you get notified immediately when a disruption occurs. The Netdata Agent comes with over 250 pre-configured and optimized alerts.
Our Analytics & ML lead Andrew Maguire recently had a chance to share our new Anomaly Advisor feature with the wider CNCF community. In his demonstration he did some light chaos engineering (using Gremlin and stress-ng) to generate some real anomalies on his infrastructure and watch how it all played out in the Anomaly Advisor in Netdata Cloud. There were also some great questions and discussion from the audience around ML in general and in the observability space itself.
Together with you, our fabulous community, Netdata is changing the way the world thinks of high fidelity monitoring – and we are gaining momentum. Our chief troublemaker and CEO, Costa Tsaousis, is the pioneer and architect of this revolution that’s brewing in the monitoring and troubleshooting space. Watch him explain the Netdata way of troubleshooting.
There is a lot of buzz in the world of machine learning (ML) and as a layperson it can be hard to keep up with it all. Therefore, we decided to write down some of our thoughts and musings on how we are approaching ML at Netdata. We’ll touch on the current state of applied ML in industry in general, and zoom in on ML in the monitoring industry.
In version v1.32 of Netdata, we announced a remarkable new update that we are extremely proud of; Netdata Cloud now runs on the most reliable and stable backend that we’ve ever built. The architectural changes allow us to deliver new features, such as parent-child relationships and improved alert handling.
Netdata excels in collecting, storing, and organizing metrics in out-of-the-box dashboards for powerful troubleshooting. We are now doubling down on this by transforming data into even more effective visualizations, helping you make the most sense out of all your metrics for increased observability. The new Netdata Charts provide a ton of useful information and we invite you to further explore our new charts from a design and development perspective.
This is the the last of a 2-part blog post series regarding Netdata and Geth. If you missed the first, be sure to check it out here. Geth is short for Go-Ethereum and is the official implementation of the Ethereum Client in Go. Currently it’s one of the most widely used implementations and a core piece of infrastructure for the Ethereum ecosystem. With this proof of concept I wanted to showcase how easy it really is to gather data from any Prometheus endpoint and visualize them in Netdata.
As complexity of systems and applications continue to evolve and change, the number of metrics that need to be monitored grows in parallel. Whether you’re on a DevOps team, an SRE, or a developer building the code yourself, many of these components may be fragmented across your infrastructure, making it increasingly difficult to identify the root cause when experiencing downtime or abnormal behavior.
