Measuring CPU performance with X-Ray and pgbench.

An X-ray workload for measuring application density

Nutanix X-Ray is well known for being able to model IO/Storage workloads, but what about workloads that are CPU bound?

X-Ray can run Ansible scripts on the X-Ray worker VMs, and by doing so we are able to provision almost any application. For our purposes we are going to use Postgres DB and the built-in benchmarking tool PGbench. I have deliberately created a very small DB which fits into the VM memory and does almost no IO.

The X-ray workload files can be found here.

X-Ray interface to pgbench

Using standard X-Ray YAML we are able to pass custom parameters such as how many Postgres VMs to deploy, how many clients and how many threads for pgbench to run.

When X-Ray runs the workload the results are displayed in the X-Ray UI. This time though the metric is Database transactions per second not IOPS or Storage throughput.

PGbench Transactions per second are displayed in the X-ray UI.

By running a variety of experiments, altering the number of VMs running the workload, I was able to plot the aggregate transactions/s and the per-VM value, which as expected decreases once the host platform CPU is saturated.

I was able to use the CPU bound nature of this particular workload to take a look at scheduling and CPU usage characteristics of different hypervisors and CPU types. I found that one combination gave better performance at low loads, but the other combination generated better performance under higher loads.

Per VM transaction count drops sharply once the number of VMs exceeds the CPU capacity of the host.

These sorts of experiments are quite straight-forward using X-Ray and Ansible. A special shout-out goes to GV who created the custom exporter to send the postgres transaction/s results back to X-ray in realtime.