Isolated Testing Infrastructure


"Build and test at scale". This is focusing on the test part. Drastically reduce whole test cycle time by scaling test sharding across multiple slaves seamlessly. It does so by integrating Swarming within the Try Server and the Continuous Integration masters.

This page is about the Chromium specific Swarming infrastructure. For the general Swarming Design, see


  • The Chromium waterfall used completely manual test sharding. A "builder" slave compiles and creates a .zip of the build output. Then "testers" download the zip, checkout the sources, unpack the zip inside the source checkout and run a few tests.
  • While we can continue throwing more faster hardware at the problem, the fundamental issue remains; as tests gets larger and slower, the end-to-end test latency will continue to increase, slowing down developer productivity.
  • This is a natural extension of the Chromium Try Server (initiated and written by maruel@ in 2008) that scaled up through the years and the Commit Queue (initiated and written by maruel@ in 2011).
  • Before the Try Server, team members were not testing on other platforms than the one they were developing on, causing constant breakage. This helped getting at 50 commits/day.
  • Before the Commit Queue, the overhead of manually triggered proper tests on all the important configuration was becoming increasingly cumbersome. This could be automated and was done. This helped sustain 200 commits/day.

But these are not sufficient to scale the team velocity at over 200 commits per day. Big design flaws remain in the way the team is working. In particular, to scale the Chromium team productivity, significant changes in the infrastructure need to happen. In particular, the latency of the testing across platforms need to be drastically reduced. That requires getting the test result in O(1) time, independent of:

  1. Number of platforms to test on.
  2. Number of test executables.
  3. Number of test cases.
  4. Duration of each test cases, especially in the worse case.
  5. Size of the data required to run the test.
  6. Size of the checkout.

To achieve this, sharding a test must be a constant cost. This is what the Swarming integration is about.


Using Swarming works around Buildbot's limitations and permits sharding automatically and in an unlimited way. For example, it permits sharding the test cases on a large smoke test across multiple bots to reduce the latency of running it. Buildbot on the other hand requires manual configuration to shard the tests and is not very efficient at large scale.

By reusing the Isolated testing effort, we're going to be able to shard efficiently the Swarming bots. By integrating Swarming infrastructure inside Buildbot, we work around the manual sharding that buildbot requires.

To recapitulate the Isolated design doc, is used to archive all the run time dependencies of a unit tests on the "builder" to Isolate Server. Since the content store is content-addressed by the SHA-1 of the content, only new contents are archived. Then only the SHA-1 of the manifest describing the whole dependency is sent to the Swaming bots, with an index of the shards that it needs to run. That is, 40 bytes for the hash plus 2 integers is all that is required to know what OS is needed and what files are needed to run a shard of test cases along

How the infrastructure works

For each buildbot slave using Swarming:
  1. Checks out sources.
  2. Compile
  3. Runs 'isolate tests'. This archives the builds on
  4. Triggers Swarming tasks.
  5. Runs anything that needs to run locally.
  6. Collects Swarming tasks results.
The Commit Queue uses Swarming indirectly via the Try Server.

So there is really 2 layers of control involved. The first being Buildbot master which controls the overall "build", which includes syncing the sources, compiling, requesting the test to be run on Swarming and asking it to report success or failure. The second layer is the Swarming server itself which "micro-distribute" test shards. Each test shard is actually a subset of the test cases for a single unit test executable. All the unit tests are run concurrently. So for example for a Try Job that requests base_unittestsnet_unittestsunit_tests and browser_tests to be run, they are all run simultaneously on different Swarming bot, and slow tests, like browser_tests, are further sharded across multiple bots, all simultaneously.

Here are pretty graphs:

How the Try Server is using Swarming

Chromium Try Server Swarming Infrastructure

How using Swarming directly looks like

Using Swarming directly

Project information

  • This project is an integral part of the Chromium Continuous Integration infrastructure and the Chromium Try Server.
  • While this project will greatly improve the Chromium Commit Queue performance, it has no direct relationship and the performance improvement, while we're aiming for it, is totally a side-effect of the reduced Try Server testing latency.
  • Active project members: maruel@, tandrii@, vadimsh@.
  • Code:

Appengine Servers

Canary Setup


  1. Adding more tests and more configurations. The .isolate format itself is evolving to improve the N-dimension matrix that needs to be reduced.
    1. For example, Debug, Chromium on ChromiumOS, ASAN, Component builds, etc.

Utilization Stats


The overhead becomes large at around ~6Gib of archived data per build.


This project is primarily aimed at reducing the overall latency from "ask for green light signal for a CL" to getting the signal. The CL can be "not committed yet" or "just committed", the former being the Try Server, the later the Continuous Integration servers. The latency is reduced by enabling a higher of parallel shard execution and removing the constant costs of syncing the sources and zipping the test executables, both which are extremely slow, in the orders of minutes.
Other latencies includes;
  1. Time to archive the dependencies to the Isolate Server.
  2. Time to trigger a Swarming run.
  3. Time for the slaves to react to a Swarming run request.
  4. Time for the slaves to fetch the dependencies, map them in a temporary directory.
  5. Time for the slaves to cleanup the temporary directory and report back stdout/stderr to the Swarming master.
  6. Time for the Swaming master to react and return the information to the Swarming client running on the buildbot slave.


All servers run on AppEngine. It scales just fine.

Redundancy and Reliability

There are multiple single points of failures
  1. The Isolate Server which is hosted on AppEngine.
  2. The Swarming master, which is also hosted on AppEngine.
  3. The buildbot masters, which are single-threaded processes written in python.
There is currently no redundancy for the buildbot infrastructure, if a VM dies, it is simply replaced right away by a sysadmin. The swarming bots are intrinsically redundant. The Isolate Server data store isn't redundant or reliable, it can be rebuilt from sources if needed. If it fails, it will block the infrastructure.

Security Consideration

Since the whole infrastructure is visible from the internet, like this design doc, proper DACL need to be used. Both the Swarming master and the Isolate Server require valid Google accounts. The credential verification is completely managed by auth_service.

Testing Plan

All the code (Swarming master, Isolate Server and swarming_client code) are tested in canary before being rolled out to prod. See the Canary Setup above.


Why not a faulty file system like FUSE?

Faulty file systems are inherently slow: every time a file is missing, the whole process hangs, the FUSE adapter downloads the file synchronously, then the process resume. Multiply 8000x; that's what browser_tests lists. With a pre-loaded content-addressed file-system, all the files can be cached safely locally and be downloaded simultaneously. The saving and speed improvement is enormous.