Fast Counterfactuals for Go Cache Hit Rates

CloudX reported that 86% of actions/setup-go test package runs are pre-empted by its improved GitHub Actions caching strategy, according to the company's post on scaling Go CI. Because the company never benchmarked cloudx-io/setup-go against actions/setup-go simultaneously, it backtested the claim over a 4,000-commit range using go list build IDs to detect test package changes without running tests. CloudX said replaying or simulating 4,000 commits was infeasible because running the suite at an optimistic two minutes per commit would take more than five days per tested treatment.

The pièce de résistance in ‘Scaling Golang CI by Replacing actions/setup-go ’ https://www.cloudx.ai/posts/setup-go is the conclusion that 86% of actions/setup-go test package runs are pre-empted by our improved caching strategy. To summarize the CloudX article, the puzzle is to optimize use of a fine-grain cache we don’t control the GOCACHE which is saved and loaded through a coarse-grain cache where we can control cache keys that determine which instances of the GOCACHE are loaded and saved. CloudX makes that outer, coarse-grained GitHub Actions cache more granular by writing to it often and restoring fresher entries. This comparison between our key strategy and GitHub’s is a counterfactual comparison: the course of action we took against a hypothetical alternative. These are tricky We didn’t foresee open-sourcing cloudx-io/setup-go , so we never ran it alongside actions/setup-go simultaneously to benchmark their relative performance; we just switched, realized savings, and never looked back. To claim a general improvement — not just an improvement for a short, potentially unrepresentative period — I needed to show an advantage over an extended interval, including different paces of development on different kinds of features. I picked a 4,000-commit range, our latest few months of development. How would you backtest GitHub Actions performance for 4,000 commits? Maybe replay comes to mind. We have the full commit history; we could create a repository using each Actions strategy and apply each monorepo commit one at a time, recording cache-hit rates at each commit. Alternatively, we could simulate the Actions behavior locally and using the GOCACHE environment variable to point go test runs at the faux-Actions cache to “restore.” Neither of these approaches works because running tests is slow. Even if the suite takes two minutes to run per commit on average optimistic , testing each of 4,000 commits in series would take more than five days per tested treatment.<sup 1</sup For the moment, let’s set aside GitHub and focus on the fine-grained record of test runs in GOCACHE . What causes a test cache miss? When you change a test package from whatever version populated the cache, the next go test will miss the cache and every test case will run anew. When the tests run, the command output notes how long they took; when they don’t run because the cache held a prior result, the output notes that instead: bash $ go test ./... ok lukasschwab.me/demo/pkg/place 0.319s ok lukasschwab.me/demo/pkg/time cached Because a change that requires a fresh test run could occur in the test package itself or in any of its transitive dependencies, predicting test reruns isn’t as simple as checking whether the source files were updated. Thankfully, we can detect package changes without running go test or modeling package dependencies ourselves The go list tool emits a build ID that uniquely represents the test package logic, transitive dependencies included: go list -export -json -test ./... list-output.json I recommend poking around that output if you’re curious how the Go build tools process the code you feed them. For our purposes, we just need to know the build IDs for test packages, which have import paths ending in .test : bash $ jq -cr 'select .ImportPath | endswith ".test" | .BuildID' list-output.json IbwMxcbMdIQEtYuf-iSx/4BSEJASXWdJsxiNf26cO wIuIuummvmYTa4K267jr/weIdkEXe9zK5LWUoPeyV … This command builds the test binaries and exactly identifies them with build IDs, but never actually runs any tests. When the build ID for a given test package changes, the cached output no longer attests to the validity of the current build. go test misses the cache and runs the full test package from scratch. bash Get some baseline build IDs for a demo module. $ go list -export -json -test ./... \ | jq -cr 'select .ImportPath | endswith ".test" | { .ImportPath : .BuildID}' {"lukasschwab.me/pkg/place.test":"IbwMxcbMdIQEtYuf-iSx/4BSEJASXWdJsxiNf26cO"} {"lukasschwab.me/pkg/time.test":"wIuIuummvmYTa4K267jr/weIdkEXe9zK5LWUoPeyV"} Run tests to populate the GOCACHE. $ go test ./... ok lukasschwab.me/pkg/place 0.319s ok lukasschwab.me/pkg/time 0.313s Modify one test package. $ echo "const A = iota" pkg/time/time test.go The build ID for time.test is changed... $ go list -export -json -test ./... \ | jq -cr 'select .ImportPath | endswith ".test" | { .ImportPath : .BuildID}' {"lukasschwab.me/pkg/place.test":"IbwMxcbMdIQEtYuf-iSx/4BSEJASXWdJsxiNf26cO"} {"lukasschwab.me/pkg/time.test":"12RAxSa7r2gXeNxG9oHO/HDMEw48BALCftV8lUnlj"} ...so it gets a fresh test run $ go test ./... ok lukasschwab.me/pkg/place cached ok lukasschwab.me/pkg/time 0.386s In short, we can use go list outputs to take one commit and understand what test results it’ll store in the GOCACHE ; then we can use go list to see, for another commit, which test packages can be skipped on the basis of those stored results and which test packages, changed between the two commits, need fresh runs. Realizing this fast approach for counting test package runs without running tests was the crux of the CloudX backtest: I generated test build ID sets for each of the 4000 commits. <sup 2</sup fn2 By modeling the actions/setup-go and cloudx-io/setup-go key-lookups in the coarse-grained GitHub Actions cache, I identified which pairs of commits would write and restore that cache, then I used build IDs to estimate the 86% improvement. We can assess further changes to the cache key scheme by backtesting it against the same build ID dataset — these are just more counterfactuals. Close-readers of ‘Poisoning a Go Cache’ ./poisoning-go-cache.html might be surprised by my focus on build IDs here. Build IDs don’t appear in the Go cache, it’s true The cache stores test outcomes under a test action ID. The built test packages are one factor in the action ID hash, but a change in any factor can — if we’re being precise — cause a cache miss, and therefore a test rerun. In practice, this means our backtest underestimates the number of test runs for both the control and treatment key schemes. The absolute measurement effect should be roughly the same for both branches, but underestimation slightly skews percentage-improvement estimates. Sue me This backtest also ignores timing effects: it assumes the GitHub Actions cache includes all prior commits’ outputs before the next commit’s setup-go step picks one of those outputs to load. Depending on how you configure your repository, commits may merge in quick succession and their CI may run in parallel, using staler caches than our idealized model suggests. It’s harder to say how this affects our measurements. Loading a staler cache typically prolongs a CI job, predisposing the next job in the sequence to also load a staler cache. The two setup-go strategies also affect job runtimes through factors other than test-cache hits, because they save and load caches of different sizes see discussion of ‘pruning’ in the CloudX post . Nevertheless, I’m pretty satisfied with the experimental method here, especially because it should allow another team to make an adoption decision tailored to their codebase: different Go dependency trees, subject to different development patterns, will exhibit different cache invalidation patterns even with an ideal cache. cloudx-io/setup-go works for us Your mileage may vary, and now you can measure by how much. Instead, one could sample commits from the range and extrapolate from there. setup-go cache keys for each commit in the range cheap . GOCACHE=/tmp/cacheB go test /b/... for GOCACHE=/tmp/cacheC go test /c/... for GOCACHE=/tmp/cacheB go test /a/... against GOCACHE=/tmp/cacheC go test /a/... . You could test samples in parallel, but those cold builds in step 3 are expensive. There may be some mark-and-sweep approach for isolating cacheB and cacheC contents from a shared cache, but at some point these optimizations introduce measurement risk. I didn’t bother. ↩︎ fnref1 Originally I wanted a script I could run with git rebase --exec great trick for running code against every commit in a range , but our real repo history proved too complicated. Occasionally main includes a failing build, for example. Also, I wanted parallelism. My bodge looped over commits in a range and fed them to a workerpool. Each worker managed a worktree, checked out a commit, ran go list -trimpath the -trimpath term is necessary for comparing build IDs between worktrees , and recorded the results. Every several commits, the script cleared and re-warmed the shared GOCACHE to prevent it exhausting available storage. My work laptop was essentially unusable the whole time. ↩︎ fnref2