About MAAS performance

The MAAS engineering team actively works to improve the performance of MAAS. This article explains how performance measurements work, and provides reference tables of MAAS metrics.

Recent performance measurements

We improved MAAS API performance by testing with simulated loads. We made the following assumptions:

  • five rack controllers
  • 48 machines per fabric
  • five VMs per LXD host
  • three different architectures
  • six disks per machine, randomly defined as flat, RAID, LVM, and BCACHE disks
  • five network interfaces per machine
  • machines in a random status

To measure performance, we use continuous performance monitoring, arranged like this:

On a daily basis, we generate simulation data based on the assumptions above, for 10, 100, and 1000 machines. These three datapoints help us get a sense of how our performance improvements scale. A Jenkins tool exercises both the REST API and the WebSocket API, trapping the results in a database, from which we can build a dashboard:

Note that we always compare stable release with the development release, before issue become harder to find and fix. We also capture profiling data that allows us to find bottlenecks, generating a heatmap that shows which parts of the code are causing issues at the moment.

For example, comparing MAAS 3.2 to MAAS 3.1, machine listings load 32% faster (on average) for the datasets we’re using.

Performance efforts to date

Here’s a short history of our performance efforts to date:

Note that this list captures only bigger, sustained efforts, although we are continuously improving performance as issues crop up.

Collecting your own metrics

You can collect your own MAAS metrics – and even share them with the MAAS engineering team. We are keen to know as much as possible about machine counts, network sizes, and MAAS performance in all areas. Please use the discourse performance forum to share your feedback and observations.

Recent developments

As part of the MAAS 3.2 development effort, we have taken steps to improve the performance of machine listings. To date, we have measured the speed of listing a large number (100-1000) of machines via the REST API to be 32% faster, on average.

Next steps

We are actively working to improve MAAS performance for other operations, such as search.

Last updated 6 days ago.