We’re always looking out for new ways to visualize our performance data. Most often to aid our troubleshooting, but sometimes most for fun. This is one of those times. Let’s check out how we can visualize our compute usage across the world!
This post is a (late) follow-up on a previous post I did about exploring the monitoring endpoints of the vCenter Server Appliance (VCSA), and an addition to the vSphere Performance blog series.
Now we will add performance metrics and health status of the VCSA to our monitoring solution. We’ll utilize the REST APIs in vCenter and feed the data into our Influx database and visualize it in Grafana.
In vCenter we have the Appliance Management page also refered to as the VAMI.
Recently there was a new release of Telegraf, a monitoring agent from the guys that built InfluxDB. This new version, 1.8.0, comes with a plugin for vSphere which I’m pretty excited about!
Previously I’ve been testing Telegraf for monitoring some Linux VMs and also my InfluxDB servers and the agent works as expected and it’s as easy to use as the other products in the TICK stack from Influx.
If you’ve followed my blog series about building a monitoring solution for vSphere and other infrastructure components you know that I’ve pulled metrics with PowerCLI scripts.
I had the privilege of delivering 3 sessions at VMUG Norway this week in Oslo, Trondheim and Bergen.
With the extremely nice weather in Norway this week in mind the attendance were great and as always the discussions were valuable.
My session on vSphere Performance monitoring were the short version of the blog series I did about how we built our solution for doing performance monitoring of vSphere with InfluxDB and Grafana, and how we easily can customize with adding metrics and datasources.
In my blog series on building a solution for monitoring vSphere Performance we have scripts for pulling VM and Host performance. I did some changes to those recently, mainly by adding some more metrics for instance for VDI hosts.
This post will be about how we included our VSAN environments to the performance monitoring. This has gotten a great deal easier after the Get-VSANStat cmdlet came along in recent versions of PowerCLI.
For those of you that have read my blog you probably know I’ve done a series on performance monitoring infrastructure with the help of InfluxDB.
InfluxDB is a part of the TICK stack delivered by InfluxData. All components are open-sourced and available. The TICK stack consists of, Telegraf, InfluxDB, Chronograf and Kapacitor.
This post will do a quick review and some examples on how I have started exploring them in my Performance monitoring project.
If you’ve followed my vSphere performance data blog series you probably have noted that I used InfluxDB as the database for storing the performance data.
With over 4 months of performance data in the InfluxDB I’ve picked up some gotcha’s along the way (there’s probably more lying around which I’ve not come over yet).
In this blog post I’ll outline what I’ve learned so far
(Save) Disk space One of them is of course, and this is an obvious one, the amount of data and the corresponding disk space needed to store it.
This is Part 8 and last part (I think…) of my series on vSphere Performance data.
Part 1 discusses the project, Part 2 is about exploring how to retrieve data, Part 3 is about using Get-Stat for the retrieval. Part 4 talked about the database used to store the retrieved data, InfluxDB. Part 5 showed how data is written to the database. Part 6 was about creating dashboards to show off the data.
This is Part 7 of my series on vSphere Performance data.
Part 1 discusses the project, Part 2 is about exploring how to retrieve data, Part 3 is about using Get-Stat for the retrieval. Part 4 talked about the database used to store the retrieved data, InfluxDB. Part 5 showed how data is written to the database. Part 6 was about creating dashboards to show off the data. This post adds even more data to the project!
This is Part 6 of my series on vSphere Performance data.
Part 1 discusses the project, Part 2 is about exploring how to retrieve data, Part 3 is about using Get-Stat for the retrieval. Part 4 talked about the database used to store the retrieved data, InfluxDB. Part 5 showed how data is written to the database. This post will show some results of our work!
As I talked about in Part 1 I had decided to go with Grafana as the front-end.