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.
We recently got this great feedback from a dear user in our Discord: This is great and exactly what we want, a clear problem or improvement we could make to help make that users monitoring life a little easier. This is also where the beauty of open source comes in and being able to build on the shoulders of giants - adding such a feature turned out to be pretty easy by just extending our existing Pandas collector to support SQL queries leveraging its read_sql() capabilities.
Netdata is committed to making it simpler and easier for everyone to monitor and troubleshoot their infrastructure. With that goal in mind, we're excited to announce the launch of our new "Functions" feature, which allows Netdata Agent collectors to expose "functions" that can be executed in run-time and on-demand.
Read more about Netdata introducing paid subscriptions. All Netdata functionality is and will be available for free forever in the Community Plan. Paid tiers include features targeted for businesses and users who would need to customise their monitoring solution with different levels of user access, other notification mechanisms, etc.
We completely reworked our custom-made, time series database (dbengine), resulting in stunning improvements to performance, scalability, and stability, while at the same time significantly reducing the agent memory requirements. On production-grade hardware (e.g. 48 threads, 32GB ram) Netdata Agent Parents can easily collect 2 million points/second while servicing data queries for 10 million points / second, and running ML training and Health querying 1 million points / second each!
We have been busy at work under the hood of the Netdata agent to introduce new capabilities that let you extend the "training window" used by Netdata's native anomaly detection capabilities. This blog post will discuss one of these improvements to help you reduce "false positives" by essentially extending the training window by using the new (beautifully named) number of models per dimension configuration parameter.
Understand what makes a storage device S.M.A.R.T and how to monitor a self monitoring component using Netdata.
Find out how to effectively and easily monitor and troubleshoot BIND 9 using Netdata.
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Find out how to effectively and easily monitor and troubleshoot NTPdaemon using Netdata.
