Clone this repo:
  1. 357ad91 [git] Remove GIT_HTTP_LOW_SPEED_* environment. by Nathan Mulcahey · 49 minutes ago master
  2. e672a16 [affected_tests] Also upload the output of ninja dry run by Shai Barack · 2 days ago
  3. 185d316 [swarming_retry] Refactor: Make TaskTracker's "attempts" field private by Mark Seaborn · 2 days ago
  4. 717d5e7 [ninja] Wrap build failure reason in backticks by Oliver Newman · 2 days ago
  5. 12d0906 [recipe] Don't capitalize step name by Shai Barack · 2 days ago

Fuchsia Recipes

This repository contains recipes for Fuchsia.

A recipe is a Python script that runs a series of commands, using the recipe engine framework from the LUCI project. We use recipes to automatically check out, build, and test Fuchsia in continuous integration jobs. The commands the recipes use are very similar to the ones you would use as a developer to check out, build, and test Fuchsia in your local environment.

Getting the code and setting up your environment

The recommended way to get the source code is with jiri. A recipe will not run without vpython and cipd, and using these recommended jiri manifests will ensure that you have these tools.

You can use the fuchsia infra Jiri manifest or the internal version (Googlers-only). Once that manifest is imported in your local jiri manifest, jiri update should download vpython and cipd into <JIRI ROOT>/fuchsia-infra/prebuilt/tools/. If you add that directory to your PATH, you should be good to go.

Recipe concepts

Properties

Recipes are parameterized using properties. The values for these properties can be set in the Buildbucket configuration. In the recipe code itself, they are specified in a global dictionary named PROPERTIES and passed as arguments to a function named RunSteps. The recipes engine automatically looks for these two objects at the top level of the Python file containing the recipe.

When writing a recipe, you can make your properties whatever you want, but if you plan to run your recipe on the Gerrit commit queue, there will be some standard ones starting with patch_, which give information about the commit being tested, and which you can see in the existing recipe code.

Steps

When a recipe executes, it interacts with the underlying machine by running steps.

A step is basically just a command, represented as a Python list of the arguments. You give the step a name, specify the arguments, and the recipe engine will run it in a subprocess, capture its output, and mark the job as as failed if the command fails.

Here's an example:

api.step("list temporary files", ["ls", "/tmp"])

This will execute the command ls /tmp on the machine where the recipe is running, and it will cause a failure if, for example, there is no /tmp directory. When the recipe gets run on Swarming (which is the scheduling system we use to run Fuchsia continuous integration jobs) this step will show up with the label “list temporary files” in a list of all the steps that ran.

Modules

Code is reused across recipes in the form of modules, which live either in the recipe_modules directory of this repo, or in the same directory of the recipe engine repo. The recipe engine's modules provide general functionality, and we have some modules specific to Fuchsia in this repo, such as wrappers for QEMU and Jiri.

The recipe engine looks for a list named DEPS at the top level of the Python file containing the recipe, where you can specify the modules you want to use. Each item in DEPS is a string in the form “repo_name/module_name”, where the repo name is “recipe_engine” to get the dependency from the recipe engine repo, or “infra” to get it from this repo.

Unit tests

The reason it's important to only interact with the underlying machine via steps is for testing. The recipes framework provides a way to fake the results of the steps when testing the recipe, instead of actually running the commands. It produces an “expected” JSON file, which shows exactly what commands would have run, along with context such as working directory and environment variables.

You write tests using the GenTests function. Inside GenTests, you can use the yield statement to declare individual test cases. GenTests takes an API object, which has functions on it allowing you to specify the properties to pass to the recipe, as well as mock results for individual steps.

Here's an example test case for a recipe that accepts input properties “manifest”, “remote”, “target”, and “tests”:

yield (
    api.test("failed_tests")
    + api.properties(
        manifest="fuchsia",
        remote="https://fuchsia.googlesource.com/manifest",
        target="x64",
        tests="tests.json",
    )
    + api.step_data("run tests", retcode=1)
)

In this example:

  • api.test simply gives the test case a name, which will be used to name the generated JSON “expected” file.
  • api.properties specifies the properties that will be passed to RunSteps.
  • api.step_data takes the name of one of the steps in the recipe, in this case “run tests”, and specifies how it should behave. This is where you can make the fake commands produce your choice of fake output. Or, as in this example, you can specify a return code, in order to cover error-handling code branches in the recipe.

To run the unit tests and generate the “expected” data, run the following command from the root of this repo:

python recipes.py test train

# Optionally specify a configuration file with --package
# (default is infra/config/recipes.cfg)
python recipes.py --package infra/config/recipes.cfg test train

The name of the recipe is simply the name of the recipe's Python file minus the .py extension. So, for example, the recipe in recipes/fuchsia.py is called “fuchsia”.

After you run the test train command, the JSON files with expectations will be either generated or updated. Look at diff in Git, and make sure you didn't make any breaking changes.

To just run the tests without updating the expectation files:

python recipes.py test run --filter [recipe_name]

To debug a single test, you can do this, which limits the test run to a single test and runs it in pdb:

python recipes.py debug [recipe_name] [test_name]

Choosing unit test cases

When you write new recipes or change existing recipes, your basic goal with unit testing should be to cover all of your code and to check the expected output to see if it makes sense. So if you create a new conditional, you should add a new test case.

For example, let‘s say you’re adding a feature to a simple recipe:

PROPERTIES = {
    "word": Property(kind=str, default=None),
}

def RunSteps(api, word):
    api.step("say the word", ["echo", word])

def GenTests(api):
    yield api.test("hello", word="hello")

And let's say you want to add a feature where it refuses to say “goodbye”. So you change it to look like this:

def RunSteps(api, word):
    if word == "goodbye":
        word = "farewell"
    api.step("say the word", ["echo", word])

To make sure everything works as expected, you should add a new test case for your new conditional:

def GenTests(api):
    yield api.test("hello", word="hello")
    yield api.test("no_goodbye", word="goodbye")

There will now be two generated files when you run test train: one called hello.json and one called no_goodbye.json, each showing what commands the recipe would have run depending on how the word property is set.

End-to-end testing (internal only)

To run recipe changes on actual infra bots we use led (short for LUCI Editor), a tool for retrieving completed Buildbucket builds and relaunching them with changes to the recipe code and/or properties.

led is one of the tools included in the infra prebuilts, so you'll already have it in your $PATH if you added the prebuilt directory to $PATH.

See the internal infra cookbook for more details.

Debugging

To run a test under PDB (the Python DeBugger), run: sh python recipes.py debug [recipe_name]

Developer workflow

Formatting

We format python code using Black, an open-source Python autoformatter. It should be in your PATH if you followed the instructions for setting up your environment.

After committing recipe changes, you can format the files in your commit by running this in your recipes project root:

git diff --name-only HEAD^ | grep -E '.py$' | xargs black
  • --name-only tells git to list file paths instead of contents.
  • HEAD^ specifies only files that have changed in the latest commit.
  • -E enables regular expressions for grep.

Many editors also have a setting to run Black automatically whenever you save a Python file (or on a keyboard shortcut). For VS Code, add the following to your workspace settings.json to make your editor compatible with Black and turn on auto-formatting on save:

{
    "python.formatting.provider": "black",
    "python.formatting.blackPath": "<absolute path to the black executable>",
    "[python]": {
        "editor.formatOnSave": true,
        "editor.rulers": [88], // Black enforces a line length of 88 characters.
    },
    ...
}

Naming steps

Occasionally you‘ll be confronted with the task of naming a step. It’s important that this name is informative as it will appear within the UI. Other than that, there are only two rules:

  1. Do not use the “.” character in step names. Currently that is used for indicating step nesting in the UI, but should hopefully change in the future.
  2. Step names must be unique within a single execution of a recipe. The reason for this is because recipe engine relies on unique step names for mocking out step data when testing. For this reason, step names will then be extended with a number such as “(3)” which generally isn't very useful to a reader. This is also subject to change in the future.