HAProxy is an open source software that provides high availability, load balancing, and proxying for TCP and HTTP-based applications. It is a fast and reliable solution used by millions of websites, including some of the world’s largest and most visited sites.
The prerequisites for monitoring HAProxy with Netdata are to have HAProxy and Netdata installed on your system.
Netdata auto discovers hundreds of services, and for those it doesn’t turning on manual discovery is a one line configuration. For more information on configuring Netdata for HAProxy monitoring please read the collector documentation.
You should now see the HAProxy section on the Overview tab in Netdata Cloud already populated with charts about all the metrics you care about.
Netdata has a public demo space (no login required) where you can explore different monitoring use-cases and get a feel for Netdata.
Backend current sessions metric measures the number of active sessions for each backend. Monitoring this metric allows us to identify problems with the server’s capacity to handle concurrent requests, as well as any potential performance bottlenecks. High values may indicate a lack of resources, while low values may suggest potential configuration issues.
Backend sessions metric measures the rate of incoming requests for each backend. Monitoring this metric can help identify any potential performance issues, as well as help determine if requests are being sent to the correct backend. High values may indicate a peak in traffic, while low values may suggest that the backend is not being fully utilized.
Backend response time average metric measures the average response time for requests sent to each backend. Monitoring this metric can help identify any potential performance issues, such as slow page loading times or slow request processing. Normal values for this metric should be around 200 milliseconds or less.
Backend queue time average metric measures the average wait time for requests sent to each backend. Monitoring this metric can help identify any potential performance issues, such as slow page loading times or slow request processing. Normal values for this metric should be around 100 milliseconds or less.
Backend current queue metric measures the number of requests queued for each backend. Monitoring this metric can help identify any potential performance issues, such as requests being blocked due to an overloaded backend. High values for this metric may indicate a lack of resources, while low values may suggest potential configuration issues.
Backend HTTP responses metric measures the rate of HTTP responses for each backend. Monitoring this metric can help identify any potential performance issues, as well as help determine if requests are being sent to the correct backend. High values may indicate a peak in traffic, while low values may suggest that the backend is not being fully utilized.
Backend network IO metric measures the rate of data in and out of each backend. Monitoring this metric can help identify any potential performance issues, such as slow page loading times or slow request processing. High values may indicate a peak in traffic, while low values may suggest that the backend is not being fully utilized.