Isolated Testing Infrastructure
This page is obsolete, need to be migrated to markdown (https://crbug.com/1246778)
"Build and test at scale". The goal is to drastically reduce whole test cycle time by scaling building and test sharding across multiple bots seamlessly. It does so by integrating Swarming within the Try Server and the Continuous Integration system.
This page is about the Chromium specific Swarming infrastructure. For the general Swarming Design, see its documentation.
- The Chromium waterfall used to use completely manual test sharding. A "builder" 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 hundreds of 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:
- Number of platforms to test on.
- Number of test executables.
- Number of test cases.
- Duration of each test cases, especially in the worse case.
- Size of the data required to run the test.
- 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 worked around the manual sharding that buildbot requires.
To recapitulate the Isolated design doc,
isolateserver.py 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 Swarming 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 worker using Swarming:
- Checks out sources.
- Runs 'isolate tests'. This archives the builds on https://isolateserver.appspot.com.
- Triggers Swarming tasks.
- Runs anything that needs to run locally.
- 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 the LUCI
scheduler 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
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.
How the Try Server is using Swarming:
Chromium Try Server Swarming Infrastructure
How using Swarming directly looks like
Using Swarming directly
- 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.
- Code: https://chromium.googlesource.com/infra/luci/luci-py.git.
- chromium-swarm.appspot.com: manages task execution
- isolateserver.appspot.com: build output cache
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;
- Time to archive the dependencies to the Isolate Server.
- Time to trigger a Swarming run.
- Time for the workers to react to a Swarming run request.
- Time for the workers to fetch the dependencies, map them in a temporary directory.
- Time for the workers to cleanup the temporary directory and report back stdout/stderr to the Swarming master.
- Time for the Swaming master to react and return the information to the Swarming client running on the buildbot worker.
All servers run on AppEngine. It scales just fine.
Redundancy and Reliability
There are multiple single points of failures
- The Isolate Server which is hosted on AppEngine.
- The Swarming master, which is also hosted on AppEngine.
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.
Since the whole infrastructure is visible from the internet, like this design doc, proper DACL need to be used. Both the Swarming and the Isolate servers require valid Google accounts. The credential verification is completely managed by auth_service.
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.