The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.
It’s the year 2009, a nice weekend in late spring and a small group of monitoring enthusiasts comes together to discuss how to move forward with the idea of forking Nagios. The Icinga team in 2009, just to set the mood. Plans were made to make it faster, easier, more scalable, and simply better. Of course, such a project has a lot of hurdles to take – the most important one was of course: the name.
Distributed tracing enables you to monitor and observe requests as they flow through your distributed systems to understand whether these requests are behaving properly. You can compare tiny differences between multiple traces coming through your microservices-based applications every day to pinpoint areas that are affecting performance. As a result, debugging and troubleshooting are simpler and faster.
As the world’s leading local delivery platform, Delivery Hero brings groceries and household goods to customers in more than 70 countries. Their technology stack comprises over 200 services across 20 Kubernetes clusters running on Amazon EKS. This cloud-based, containerized infrastructure enabled them to scale their operation to support increasing demand as the volume of orders placed on their platform doubled during the pandemic.
Blocked queries are one of the key issues faced by database analysts, engineers, and anyone managing database performance at scale. Blocking can be caused by inefficient query or database design as well as resource saturation, and can lead to increased latency, errors, and user frustration. Pinpointing root blockers—the underlying problematic queries that set off cascading locks on database resources—is key to troubleshooting and remediating database performance issues.
As part of the InfluxDB Cloud, powered by IOx launch, Paul Dix and Balaji Palani provided an InfluxDB Cloud overview and demo. In case you missed it, this blog is a quick 5 minute read summarizing the webinar. We shared the recording and the slides from the presentation for everyone to review and watch at your leisure.
We have recently extended the native machine learning (ML) based anomaly detection capabilities of Netdata to support all metrics, regardless on their collection frequency (update every). Previously only metrics collected every second were supported, but now Netdata can run anomaly detection out of the box with zero config on metrics with any collection frequency.
Late last year we announced improvements to our public dashboards that included a revamped dashboard design that allowed users to see monitoring data in a more easily-digestible way, on any device. We improved performance across the board, and also introduced new incident management functionality—available for paid plans only—that allows users to more easily communicate scheduled maintenance notices and alert developers to minor and major incidents.
