Automatic Resource Optimization

Posted on June 25, 2020 by Rickard Nilsson

As of today, will automatically select resources (CPU count and memory amount) for builds submitted to it. Based on historic build data, calculates a resource allocation that will make your build as performant as possible, while wasting minimal CPU time. This means users get faster and cheaper builds, while also taking away the user’s burden of figuring out what resource settings to use for each individual build.

Previously, all builds were assigned 4 CPUs unless the user configured resource selection differently. However, configuring different resource settings for individual builds was difficult, since Nix has no notion of such settings. Additionally, it is really tricky to know wether a build will gain anything from being allocated many CPUs, or if it just makes the build more expensive. It generally requires the user to try out the build with different settings, which is time-consuming for a single build and almost insurmountable for a large set of builds with different characteristics.

Now, each individual build will be analyzed and can be assigned between 1 and 16 CPUs, depending on how well the build utilizes multiple CPUs. The memory allocation will be adapted to minimize the amount of unused memory.

The automatic resource optimization has been tested both internally and by a selected number of beta users, and the results have been very positive so far. We’re happy to make this feature available to all users, since it aligns perfectly with the service’s core idea of being simple, cost-effective and performant.

How Does it Work?

The automatic resource optimization works in two steps:

  1. When a Nix derivation is submitted to, we look for similar derivations that have been built on before. A heuristic approach is used, where derivations are compared based on package names and version numbers. This approach can be improved in the future, by looking at more parts of the derivations, like dependencies and build scripts.

  2. A number of the most recent, most similar derivations are selected. We then analyze the build data of those derivations. Since we have developed a secure sandbox specifically for running Nix builds, we’re also able to collect a lot of data about the builds. One metric that is collected is CPU utilization, and that lets us make predictions about how well a build would scale, performance-wise, if it was given more CPUs.

    We also look at metrics about the historic memory usage, and make sure the new build is allocated enough memory.