Today, we released our systemd journal plugin for Netdata, allowing you to explore, view, search, filter and analyze systemd journal logs. Like most things about Netdata, this is a zero-configuration plugin. You don’t have to do anything apart from installing Netdata on your systems.This is key design direction for Netdata, since we want Netdata to be able to help even if you install it mid-crisis, while you have an incident at hand.
“Why bother with it? I let it run in the background and focus on more important DevOps work.”— a random DevOps Engineer at Reddit r/devops In an era where technology is evolving at breakneck speeds, it's easy to overlook the tools that are right under our noses. One such underutilized powerhouse is the systemd journal. For many, it's a mere tool to check the status of systemd service units or to tail the most recent events (journalctl -f).
In this blog, we will walk you through the basics of getting Netdata, Prometheus and Grafana all working together and monitoring your application servers. This article will be using docker on your local workstation. We will be working with docker in an ad-hoc way, launching containers that run /bin/bash and attaching a TTY to them. We use docker here in a purely academic fashion and do not condone running Netdata in a container.
Netdata reads /proc/
Netdata monitors tc QoS classes for all interfaces. If you also use FireQOS it will collect interface and class names. There is a shell helper for this (all parsing is done by the plugin in C code - this shell script is just a configuration for the command to run to get tc output). The source of the tc plugin is here. It is somewhat complex, because a state machine was needed to keep track of all the tc classes, including the pseudo classes tc dynamically creates. You can see a live demo here.
Over the last few years we have slowly and methodically been building out the ML based capabilities of the Netdata agent, dogfooding and iterating as we go. To date, these features have mostly been somewhat reactive and tools to aid once you are already troubleshooting. Now we feel we are ready to take a first gentle step into some more proactive use cases, starting with a simple node level anomaly rate alert. note You can read a bit more about our ML journey in our ML related blog posts.
