|Aug 08, 2017||Capturing Perfmon Counters With Telegraf|
|Aug 07, 2017||InfluxDB and Annotations|
|May 24, 2017||Running InfluxDB as a service in Windows|
|May 17, 2017||Setting Up InfluxDb, Chronograf, and Grafana for the SqlServer Dev|
This post assumes you’ve already setup InfluxDB and have Grafana running.
Annotations are not a special type of resource, instead it’s just another metric that you query with a feature in Grafana to display on other metrics. This means the same insert Line Protocol applies to the Annotation.
This post on maxchadwick.xyz greatly helped me get started: Creating Grafana Annotations with InfluxDb Max Chadwick
Per Max’s original post it supports html as well, so you could link for example to a build, test result, or anything else you want to link to from your performance statistics.
This provides an annotation on your timeline in a nice format for browsing through the timeline. I can see usage cases for identifying specific activity or progress in tests, helping coorelate the performance metrics with known activity steps from a build, script, or other related tasks. You could have an type of activity trigger this powershell insert, providing a lot of flexibility to help relate useful metrics to your monitoring.
My personal use case has been to ensure load testing start/end times and other significant points of time in a test are easily visible in the same timeline I’m reviewing metrics on.
Warning: I did experience performance degradation with Grafana and many annotations on a timeline. I found just disabling the annotations kept this from occurring, so you only pull them when youd them.
Adding Annotations to Grafana
Now that you have the results being inserted into InfluxDB, you can query these in Grafana as annonations to overlay your graphs.
I could see a whole lot of uses for this!
- insert at build related activity
- Windows update
- Specific Database Related Maintenance like Ola Hallengren’s index optimize or database integrity check
Monitoring always loses it’s value when you have a limited picture of what is happening. Triggering relevant details for stuff that might help analyze activity might be the key to immediately gaining an understanding on what is causing a spike of activity, or of better evaluating the timeline of a load test.