[andrewjshults]

We have a home grown solution for this. We use pants (https://pantsbuild.github.io/) for our Python builds and phabricator for code reviews. Pants gives us a dependency graph so from which files get touched in a given diff, we can feed that back into pants to get the targets to build and the test suites to run. It's not as granular as running individual tests in a suite for small changes, but it definitely helps cut most runs down to just a small percentage of all the tests. The ability to pants to build test suites in isolation is also pretty key since it means we can run a whole bunch of suites in parallel so in many cases the total test run time can be just the time of the longest individual suite.

As others have mentioned, kicking off full runs on a regular basis is important to make sure there aren't weird cases that break and aren't caught in this approach.