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Extension for the Buildbot continuous integration tool

Project description

Build Status

Ursa Labs' buildbot configuration for Apache Arrow

Ursabot is a continous integration framework based on the buildbot framework. The primary focus of ursabot is to execute various builds benchmark and packaging tasks for Apache Arrow however ursabot can be used for arbitrary projects.

Notable features

  • a standalone project abstraction to make the project configurations module and reusable, and a less verbose master configuration supporting multiple projects
  • locally reproducible builds via command line interface
  • attachable interactive shells to the docker workers in case of build failures
  • local source mounting for docker workers
  • declerative builder configuration and a docker builder which makes it easier to work with docker latent workers
  • extended github hook to drive buildbot via github comments
  • click based comment parser
  • improved change filter to filter changes based on build properties
  • reimplemented github reporters: status-, comment- and review reporters
  • easily extensible formatter classes to use with the reimplemented reporters
  • steps implemented based on new-style ShellCommand step
  • a token rotator to use multiple github tokens with github services
  • a docker image tool to maintain and build hierachical docker images
  • command line interface and additional utilities

Driving Ursabot

Allowing PR reviewers to request additional checks on demand within the review process makes it easier for us to apply extra scrutiny at review time while also conserving CI bandwidth by using human expertise to know which checks are needed.

via Comments

Ursabot receives github events through a webhook. It listens on pull request comments mentioning @ursabot. It follows the semantics of a command line interface, to see the available commands add a comment on the pull request: @ursabot --help.

The @ursabot GitHub user will respond or react that it has started a build for you. The command parser is implemented in

Currently available commands:

  • @ursabot build: Triggers all the ursabot tests. These tests are run automatically, but this is a convinient way to force a re-build.
  • @ursabot benchmark: Triggers C++ benchmarks and sends back the results as a github comment and highlights the regressions.
  • @ursabot crossbow test cpp-python: Triggers the cpp-python test group defined in test.yml and responds with a URL pointing to submitted crossbow branches at the github UI showing the build statuses.
  • @ursabot crossbow package -g wheel -g conda: Triggers the wheel and conda crossbow packaging groups defined in tasks.yml.
  • @ursabot crossbow package wheel-win-cp35m wheel-win-cp36m: Triggers only two tasks passed explicitly.

Note that the commands won't trigger any builds if the commit message contains a skip pattern, like [skip ci] or [ci skip]. In order to drive ursabot the user must have either 'OWNER', 'MEMBER' or 'CONTRIBUTOR roles.

via the Web UI

You can also initiate a build for a specific architecture/configuration in the buildbot UI. Navigate to Builds > Builders, select a builder, and click Build apache/arrow buttin at the top right. This triggers the force schedulers where you can specify a branch and/or commit to build. In the future specialized builders will have different fields to provide the neccessary information.

via CLI

Buildbot supports submitting local patches directly to the cluster and triggering specific builders. The TryScheduler is a really handy way to test local changes without polluting the git history:

buildbot try \
  --connect=pb \
  --master=... \
  --username=... \
  --passwd=... \

If someone wants to use this feature then please raise an issue, because it requires custom credentials.

Install ursabot and the CLI

Running it locally helps with the development and testing new feature and/or debugging issues without touching the production instance.

Installation requires at least Python 3.6:

cd /path/to/ursabot
pip install -e .

Now the ursabot command is available which looks for a master.cfg file in the current directory. master.cfg can be passed explicitly via the --config option:

ursabot -c path/to/master.cfg

Describe the loaded master configuration:

ursabot desc

Describe the loaded project configuration:

ursabot project desc  # for master configs with a single project
ursabot project -p arrow desc  # for master configs with multiple projects

How to validate the configurations

The checkconfig command runs sanity checks and various validations on the master configuration. Most of the time is checkconfig passes then the master can be run successfully (unless there are some variables only available at runtime).

ursabot checkconfig

ursabot command loads master.cfg from the current directory by default, but --config argument can be passed to explicitly define a configuration file.

ursabot -c arrow/master.cfg checkconfig

The top-level master.cfg contains the production configuration for so it requires additional dependencies like pass. To install pass:

which apt && sudo -H apt install -V -y pass
which brew && brew install pass

Run a local instance of Ursabot

After installation master's database must be initialized:

ursabot -v upgrade-master

Start/stop/restart the master:

ursabot -v start|stop|restart

Define the configuration environment (prod|test) and start the service:

export URSABOT_ENV=test  # this is the default
buildbot restart ursabot
tail -f ursabot/twisted.log

Then open http://localhost:8100 in the browser.

Commands for local reproducibility

Builders can be run locally without the web interface using the ursabot project build command.

Testing AMD64 Conda C++ builder on master:

ursabot project build 'AMD64 Conda C++'

Testing AMD64 Conda C++ builder with github pull request number 140:

ursabot project build -pr 140 'AMD64 Conda C++'

Testing AMD64 Conda C++ with local repository:

ursabot project build -s ~/Workspace/arrow:. 'AMD64 Conda C++'

Where ~/Workspace/arrow is the path of the local Arrow repository and . is the destination directory under the worker's build directory (in this case: /buildbot/AMD64_Conda_C__/.)

Passing multiple buildbot properties for the build:

ursabot project build -p prop=value -p myprop=myvalue 'AMD64 Conda C++'

Attach on failure

Ursabot supports debugging failed builds with attach attaching ordinary shells to the still running workers - where the build has previously failed.

Use the --attach-on-failure or -a flags.

ursabot project build --attach-on-failure `AMD64 Conda C++`

Configuring Ursabot

The buildmaster configuration happens in the master.cfg files. Originally buildbot loads the dictionary called BuildmasterConfig, but to make it more flexible and moduler ursabot introduces the ProjectConfig and MasterConfig abstractions. ProjectConfig contains all the relevant information for testing a project like Apache Arrow or Ursabot itself. ProjectConfig can be run alone, it must be passed to a MasterConfig object which provides a thin abstraction over the original buildbot BuildmasterConfig. One MasterConfig can contain multiple ProjectConfig objects. Including other project configurations makes it possible to maintain the project relevant settings within the projects' repositories instead of a decoupled one dedicated for the buildmaster.

Adding a new build(er)s

The closest abstraction to the traditional yaml based CI configs in ursabot are the Builders. In the simplest case a builder is defined by a sequence of steps which are executed as shell commands on the worker. The following example builder presumes, that apt-get and git is available on the worker.

from buildbot.plugins import util, worker
from ursabot.steps import ShellCommand
from import Builder
from ursabot.schedulers import AnyBranchScheduler

repo = ''

class TestBuilder(Builder):
    tags = ['example-build', 'arbitrary-tag']
    steps = [
            name='Clone the test repository',
            name='Install dependencies',
            command=['apt-get', 'install', '-y'],
            args=['my', 'packages']
            name='Execute tests',
            command=['my-custom-test-runner', util.Property('test-selector')]

# in the master.cfg
local_worker = worker.LocalWorker('my-local-worker')
simple_builder = TestBuilder(
        'test-selector': 'all'
scheduler = AnyBranchScheduler(

project = ProjectConfig([

master = MasterConfig(

The DockerBuilder provides more flexibility, faster builds and better worker isolation, Ursabot uses DockerBuilders extensively.

from ursabot.docker import DockerImage
from import DockerBuilder
from ursabot.workers import DockerLatentWorker

miniconda = DockerImage(

class TestDockerBuilder(DockerBuilder):
    tags = ['build-within-docker-container']
    steps = [
        # checkout the source code
        # execute arbitrary commands
        # ...

docker_worker = DockerLatentWorker(

# instantiates builders based on the available workers, the Builder's
# images and the workers are matched based on their architecture
docker_builders = TestDockerBuilder.combine_with(

scheduler = AnyBranchScheduler(

project = ProjectConfig([

master = MasterConfig(

Define docker images

Arrow supports multiple platforms, has a wide variety of features thus a lot of dependencies. Installing them in each build would be time and resource consuming, so ursabot ships docker images for reusability.

There is a small docker utility in ursabot.docker module to define hierachical images. It uses a DSL implemented in python instead of plain Dockerfiles. A small example to demonstrate it:

from ursabot.docker import DockerImage, ImageCollection
from ursabot.docker import RUN, ENV, CMD, ADD, apt, conda

miniconda = DockerImage(
pandas = DockerImage(
pyarrow = DockerImage(

images = ImageCollection([miniconda, pandas, pyarrow])

# create a docker image for each of the previous ones running jupyter notebook
jupyter_steps = [
        'jupyter', 'notebook',
        '--ip', '',
    for image in images

# build all of the images in topological order

# filter the images
print(images.filter(name='pyarrow', tag='jupyter'))

Try running jupyter with pyarrow pre-installed:

docker run -p 8888:8888 amd64-debian-9-pyarrow:jupyter

Ursabot has a CLI interface to build the docker images:

ursabot docker build --help

To list Arrow C++ amd64 conda cpp images:

ursabot --verbose docker --arch amd64 --variant conda --name cpp list

Additional filtering:

ursabot docker --arch amd64 list
ursabot docker --arch amd64 --variant conda list
ursabot docker --arch amd64 --variant conda --name cpp list
ursabot docker --arch amd64 --variant conda --name cpp --tag worker list
ursabot docker --arch amd64 --variant conda --name cpp --os debian-9 list

To build and push Arrow C++ amd64 conda cpp images:

ursabot --verbose docker --arch amd64 --variant conda --name cpp build --push

To build and push all arm64v8 alpine images:

ursabot --verbose \
  docker --docker-host tcp://arm-machine:2375 --arch arm64v8 --os alpine-3.9 \
  build --push

Developing Ursabot

Buildbot doesn't distribute its testing suite with binary wheels, so in order to run the unit tests buildbot must be installed from source:

pip install --no-binary buildbot -e .
pytest -v ursabot

Pre-commit hooks

Install pre-commit then to setup the git hooks run pre-commit install.

Adding new workers to

Adding docker latent workers requires a worker entry in the workers.yaml configuration. Name, architecture and a docker host (accessable by the buildmaster) are required, see an example in workers.yaml. Adding non-docker workers are also possible, but must register them in the master.cfg.

Possible further improvements

These have been discussed and would be valuable, but they are definitely "nice to haves" and should be deferred until the primary goals are met.

  • Database for storing benchmark results
  • Central station for hosting the build artifacts
  • Dashboard showing build health across all platforms and configurations

More closely Ursabot related:

  • Multi-master setup for scaling
  • Setup WAMP/Crossbar to restart the buildmaster without cancelling the running builds
  • Windows containers and workers (docker in virtualized nodes)

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