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How and Why We Migrated Airbnb’s Giant-Scale Internet Monorepo to Bazel
Nov 12, 2024
By: Brie Bunge and Sharmila Jesupaul
At Airbnb, we’ve lately adopted Bazel — Google’s open supply construct device–as our common construct system throughout backend, net, and iOS platforms. This submit will cowl our expertise adopting Bazel for Airbnb’s large-scale (over 11 million traces of code) net monorepo. We’ll share how we ready the code base, the ideas that guided the migration, and the method of migrating chosen CI jobs. Our aim is to share data that might have been beneficial to us after we launched into this journey and to contribute to the rising dialogue round Bazel for net growth.
Traditionally, we wrote bespoke construct scripts and caching logic for varied steady integration (CI) jobs that proved difficult to take care of and constantly reached scaling limits because the repo grew. For instance, our linter, ESLint, and TypeScript’s sort checking didn’t help multi-threaded concurrency out-of-the-box. We prolonged our unit testing device, Jest, to be the runner for these instruments as a result of it had an API to leverage a number of employees.
It was not sustainable to repeatedly create workarounds to beat the inefficiencies of our tooling which didn’t help concurrency and we had been incurring a long-run upkeep price. To sort out these challenges and to greatest help our rising codebase, we discovered that Bazel’s sophistication, parallelism, caching, and efficiency fulfilled our wants.
Moreover, Bazel is language agnostic. This facilitated consolidation onto a single, common construct system throughout Airbnb and allowed us to share widespread infrastructure and experience. Now, an engineer who works on our backend monorepo can change to the online monorepo and know the best way to construct and check issues.
Once we started the migration in 2021, there was no publicized business precedent for integrating Bazel with net at scale outdoors of Google. Open supply tooling didn’t work out-of-the-box, and leveraging distant construct execution (RBE) launched further challenges. Our net codebase is giant and accommodates many unfastened information, which led to efficiency points when transmitting them to the distant atmosphere. Moreover, we established migration ideas that included bettering or sustaining total efficiency and lowering the affect on builders contributing to the monorepo in the course of the transition. We successfully achieved each of those objectives. Learn on for extra particulars.
We did some work up entrance to make the repository Bazel-ready–specifically, cycle breaking and automatic BUILD.bazel file technology.
Cycle Breaking
Our monorepo is laid out with tasks underneath a top-level frontend/ listing. To start out, we wished so as to add BUILD.bazel information to every of the ~1000 top-level frontend directories. Nevertheless, doing so created cycles within the dependency graph. This isn’t allowed in Bazel as a result of there must be a DAG of construct targets. Breaking these usually felt like battling a hydra, as eradicating one cycle spawns extra instead. To speed up the method, we modeled the issue as discovering the minimal suggestions arc set (MFAS)¹ to establish the minimal set of edges to take away leaving a DAG. This set offered the least disruption, stage of effort, and surfaced pathological edges.
Automated BUILD.bazel Technology
We robotically generate BUILD.bazel information for the next causes:
- Most contents are knowable from statically analyzable import / require statements.
- Automation allowed us to shortly iterate on BUILD.bazel modifications as we refined our rule definitions.
- It could take time for the migration to finish and we didn’t wish to ask customers to maintain these information up-to-date after they weren’t but gaining worth from them.
- Manually preserving these information up-to-date would represent a further Bazel tax, regressing the developer expertise.
We’ve a CLI device known as sync-configs that generates dependency-based configurations within the monorepo (e.g., tsconfig.json, mission configuration, now BUILD.bazel). It makes use of jest-haste-map and watchman with a customized model of the dependencyExtractor to find out the file-level dependency graph and a part of Gazelle to emit BUILD.bazel information. This CLI device is much like Gazelle but in addition generates further net particular configuration information akin to tsconfig.json information utilized in TypeScript compilation.
With preparation work full, we proceeded emigrate CI jobs to Bazel. This was a large enterprise, so we divided the work into incremental milestones. We audited our CI jobs and selected emigrate those that might profit essentially the most: sort checking, linting, and unit testing². To cut back the burden on our builders, we assigned the central Internet Platform staff the duty for porting CI jobs to Bazel. We proceeded one job at a time to ship incremental worth to builders sooner, achieve confidence in our strategy, focus our efforts, and construct momentum. With every job, we ensured that the developer expertise was high-quality, that efficiency improved, CI failures had been reproducible regionally, and that the tooling Bazel changed was totally deprecated and eliminated.
We began with the TypeScript (TS) CI job. We first tried the open supply ts_project rule³. Nevertheless, it didn’t work nicely with RBE because of the sheer variety of inputs, so we wrote a customized rule to scale back the quantity and dimension of the inputs.
The most important supply of inputs got here from node_modules. Previous to this, the information for every npm bundle had been being uploaded individually. Since Bazel works nicely with Java, we packaged up a full tar and a TS-specific tar (solely containing the *.ts and bundle.json) for every npm bundle alongside the traces of Java JAR information (basically zips).
One other supply of inputs got here by way of transitive dependencies. Transitive node_modules and d.ts information within the sandbox had been being included as a result of technically they are often wanted for subsequent mission compilations. For instance, suppose mission foo is dependent upon bar, and kinds from bar are uncovered in foo’s emit. Consequently, mission baz which is dependent upon foo would additionally want bar’s outputs within the sandbox. For lengthy chains of dependencies, this may bloat the inputs considerably with information that aren’t truly wanted. TypeScript has a — listFiles flag that tells us which information are a part of the compilation. We are able to bundle up this restricted set of information together with the emitted d.ts information into an output tsc.tar.gz file⁴. With this, targets want solely embrace direct dependencies, fairly than all transitive dependencies⁵.
This practice rule unblocked switching to Bazel for TypeScript, because the job was now nicely underneath our CI runtime funds.
We migrated the ESLint job subsequent. Bazel works greatest with actions which can be unbiased and have a slender set of inputs. A few of our lint guidelines (e.g., particular inside guidelines, import/export, import/extensions) inspected information outdoors of the linted file. We restricted our lint guidelines to those who may function in isolation as a approach of lowering enter dimension and having solely to lint straight affected information. This meant transferring or deleting lint guidelines (e.g., people who had been made redundant with TypeScript). Consequently, we lowered CI instances by over 70%.
Our subsequent problem was enabling Jest. This offered distinctive challenges, as we wanted to deliver alongside a a lot bigger set of first and third-party dependencies, and there have been extra Bazel-specific failures to repair.
Employee and Docker Cache
We tarred up dependencies to scale back enter dimension, however extraction was nonetheless sluggish. To deal with this, we launched caching. One layer of cache is on the distant employee and one other is on the employee’s Docker container, baked into the picture at construct time. The Docker layer exists to keep away from shedding our cache when distant employees are auto-scaled. We run a cron job as soon as every week to replace the Docker picture with the latest set of cached dependencies, hanging a stability of preserving them contemporary whereas avoiding picture thrashing. For extra particulars, try this Bazel Neighborhood Day discuss.
This added caching offered us with a ~25% velocity up of our Jest unit testing CI job total and lowered the time to extract our dependencies from 1–3 minutes to three–7 seconds per goal. This implementation required us to allow the NodeJS preserve-symlinks choice and patch a few of our instruments that adopted symlinks to their actual paths. We prolonged this caching technique to our Babel transformation cache, one other supply of poor efficiency.
Implicit Dependencies
Subsequent, we wanted to repair Bazel-specific check failures. Most of those had been attributable to lacking information. For any inputs not statically analyzable (e.g., referenced as a string with out an import, babel plugin string referenced in .babelrc), we added help for a Bazel hold remark (e.g., // bazelKeep: path/to/file) which acts as if the file had been imported. The benefits of this strategy are:
1. It’s colocated with the code that makes use of the dependency,
2. BUILD.bazel information don’t must be manually edited so as to add/transfer # hold feedback,
3. There isn’t any impact on runtime.
A small variety of assessments had been unsuitable for Bazel as a result of they required a big view of the repository or a dynamic and implicit set of dependencies. We moved these assessments out of our unit testing job to separate CI checks.
Stopping Backsliding
With over 20,000 check information and a whole lot of individuals actively working in the identical repository, we wanted to pursue check fixes such that they might not be undone as product growth progressed.
Our CI has three forms of construct queues:
1. “Required”, which blocks modifications,
2. “Elective”, which is non-blocking,
3. “Hidden”, which is non-blocking and never proven on PRs.
As we mounted assessments, we moved them from “hidden” to “required” through a rule attribute. To make sure a single supply of fact, assessments run in “required” underneath Bazel weren’t run underneath the Jest setup being changed.
# frontend/app/script/__tests__/BUILD.bazel
jest_test(
title = "jest_test",
is_required = True, # makes this goal a required verify on pull requests
deps = [
":source_library",
],
)
Instance jest_test rule. This signifies that this goal will run on the “required” construct queue.
We wrote a script evaluating earlier than and after Bazel to find out migration-readiness, utilizing the metrics of check runtime, code protection stats, and failure price. Thankfully, the majority of assessments could possibly be enabled with out further modifications, so we enabled these in batches. We divided and conquered the remaining burndown checklist of failures with the central staff, Internet Platform, fixing and updating assessments in Bazel to keep away from placing this burden on our builders. After a grace interval, we totally disabled and deleted the non-Bazel Jest infrastructure and eliminated the is_required param.
In tandem with our CI migration, we ensured that builders can run Bazel regionally to breed and iterate on CI failures. Our migration ideas included delivering solely what was on par with or superior to the present developer expertise and efficiency. JavaScript instruments have developer-friendly CLI experiences (e.g., watch mode, concentrating on choose information, wealthy interactivity) and IDE integrations that we wished to retain. By default, frontend builders can proceed utilizing the instruments they know and love, and in circumstances the place it’s useful they will decide into Bazel. Discrepancies between Bazel and non-Bazel are uncommon and after they do happen, builders have a method of resolving the problem. For instance, builders can run a single script, failed-on-pr which is able to re-run any targets failing CI regionally to simply reproduce points.
We additionally do some normalization of platform particular binaries in order that we are able to reuse the cache between Linux and MacOS builds. This quickens native growth and CI jobs by sharing cache between a neighborhood developer’s macbook and linux machines in CI. For native npm packages (node-gyp dependencies) we exclude platform-specific information and construct the bundle on the execution machine. The execution machine would be the machine executing the check or construct course of. We additionally use “common binaries” (e.g., for node and zstd), the place all platform binaries are included as inputs (in order that inputs are constant regardless of which platform the motion is run from) and the correct binary is chosen at runtime.
Adopting Bazel for our core CI jobs yielded important efficiency enhancements for TypeScript sort checking (34% sooner), ESLint linting (35% sooner), and Jest unit assessments (42% sooner incremental runs, 29% total). Furthermore, our CI can now higher scale because the repo grows.
Subsequent, to additional enhance Bazel efficiency, we will likely be specializing in persisting a heat Bazel host throughout CI runs, taming our construct graph, powering CI jobs that don’t use Bazel with the Bazel construct graph, and probably exploring SquashFS to additional compress and optimize our Bazel sandboxes.
We hope that sharing our journey has offered insights for organizations contemplating a Bazel migration for net.
Thanks Madison Capps, Meghan Dow, Matt Insler, Janusz Kudelka, Joe Lencioni, Rae Liu, James Robinson, Joel Snyder, Elliott Sprehn, Fanying Ye, and varied different inside and exterior companions who helped deliver Bazel to Airbnb.
We’re additionally grateful to the broader Bazel group for being welcoming and sharing concepts.
[1]: This downside is NP-complete, although approximation algorithms have been devised that also assure no cycles; we selected the implementation outlined in “Breaking Cycles in Noisy Hierarchies”.
[2]: After preliminary analysis, we thought of migrating net asset bundling as out of scope (although we might revisit this sooner or later) attributable to excessive stage of effort, unknowns within the bundler panorama, and impartial return on funding given our current adoption of Metro, as Metro’s structure already elements in scalability options (e.g. parallelism, native and distant caching, and incremental builds).
[3]: There are newer TS guidelines that will work nicely for you right here.
[4]: We later switched to utilizing zstd as a substitute of gzip as a result of it produces archives which can be higher compressed and extra deterministic, preserving tarballs constant throughout totally different platforms.
[5]: Whereas pointless information should still be included, it’s a a lot narrower set (and could possibly be pruned as an additional optimization).
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