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layout: documentation title: Skylark Rules

Rules

Status: Experimental. We may make breaking changes to the API, but we will help you update your code.

Rule creation

In a Skylark extension, use the rule function to create a new rule and store it in a global variable. See example.

A custom rule can be used just like a native rule. It has a mandatory name attribute, you can refer to it with a label, and you can see it in bazel query.

The rule is analyzed when you explictly build it, or if it is a dependency of the build. In this case, Bazel will execute its implementation function. This function decides what the outputs of the rule are and how to build them (using actions). During analysis, no external command can be executed: actions will be run in the execution phase.

Attributes

An attribute is a rule argument, such as srcs or deps. You must list the attributes and their types when you define a rule.

sum = rule(
    implementation=impl,
    attrs={
        "number": attr.int(default=1),
        "deps": attr.label_list(),
    },
)

The following attributes are implicitly added to every rule: deprecation, features, name, tags, testonly, visibility. Test rules also have the following attributes: args, flaky, local, shard_count, size, timeout.

To access an attribute, use ctx.attr.<attribute_name>. The name and the package of a rule are available with ctx.label.name and ctx.label.package.

See example.

Private Attributes

If an attribute name starts with _ it is private and users cannot set it. It is useful in particular for label attributes (your rule will have an implicit dependency on this label).

metal_compile = rule(
    implementation=impl,
    attrs={
        "srcs": attr.label_list(),
        "_compiler": attr.label(
            default=Label("//tools:metalc"),
            single_file=True,
            executable=True,
        ),
    },
)

Implementation function

Every rule requires an implementation function. It contains the actual logic of the rule and is executed strictly in the Analysis Phase. The function has exactly one input parameter, ctx, and it may return the runfiles and providers of the rule. The input parameter ctx can be used to access attribute values, outputs and dependent targets, and files. It also has some helper functions. See the library for more context. Example:

def impl(ctx):
  ...
  return struct(
      runfiles=...,
      my_provider=...,
      ...
  )

my_rule = rule(
    implementation=impl,
    ...
)

Files

There are two kinds of files: files stored in the file system and generated files. For each generated file, there must be one and only one generating action, and each action must generate one or more output files. Bazel will throw an error otherwise.

Targets

Every build rule corresponds to exactly one target. A target can create actions, can have dependencies (which can be files or other build rules), output files (generated by its actions), and providers.

A target y depends on target x if y has a label or label list type attribute where x is declared:

my_rule(
    name = "x",
)

my_rule(
    name = "y",
    deps = [":x"],
)

In the above case, it's possible to access targets declared in my_rule.deps:

def impl(ctx):
  for dep in ctx.attr.deps:
    # Do something with dep
  ...

my_rule = rule(
    implementation=impl,
    attrs={
        "deps": attr.label_list(),
    },
    ...
)

Output files

A target can declare output files, which must be generated by the target's actions. There are three ways to create output files in Skylark:

• If the rule is marked executable, it creates an output file of the same name as the rule's. See example

• The rule can declare default outputs, which are always generated. See example

• The rule can have output or output list type attributes. In that case the output files come from the actual attribute values. See example

All output files must have exactly one generating action. See the library for more context.

Default outputs

Every rule has a set of default outputs. This is used:

• When the user runs bazel build on your target. Bazel will build the default outputs of the rule.

• When the target is used as a dependency of another rule. A rule can access the default outputs by using target.files. This is the case, for example, if you use a rule in the srcs attribute of a genrule.

Use the files provider to specify the default outputs of a rule. If left unspecified, it will contain all the declared outputs.

def _impl(ctx):
  # ...
  return struct(files=set([file1, file2]))

This can be useful for exposing files generated with ctx.new_file. You can also have “implicit outputs”, i.e., files that are declared in the rule, but not in the default outputs (like _deploy.jar in java_binary).

Actions

There are three ways to create actions:

• ctx.action, to run a command.
• ctx.file_action, to write a string to a file.
• ctx.template_action, to generate a file from a template.

Actions take a set (which can be empty) of input files and generate a (non-empty) set of output files. The set of input and output files must be known during the analysis phase. It might depend on the value of attributes and information from dependencies, but it cannot depend on the result of the execution. For example, if your action runs the unzip command, you must specify which files you expect to be inflated (before running unzip).

Actions are comparable to pure functions: They should depend only on the provided inputs, and avoid accessing computer information, username, clock, network, or I/O devices (except for reading inputs and writing outputs).

If an action generates a file that is not listed in its outputs: This is fine, but the file will be ignored and cannot be used by other rules.

If an action does not generate a file that is listed in its outputs: This is an execution error and the build will fail. This happens for instance when a compilation fails.

If an action generates an unknown number of outputs and you want to keep them all, you may group them in a zip file. This way, you will be able to declare your output.

If an action does not list a file it uses as an input, the action execution will most likely result in an error. The file is not guaranteed to be available to the action, so if it is there, it's due to coincidence or error.

If an action lists a file as an input, but does not use it: This is fine. However, it can affect action execution order, resulting in sub-optimal performance.

Dependencies are resolved by Bazel, which will decide which actions are executed. It is an error if there is a cycle in the dependency graph. Creating an action does not guarantee that it will be executed: It depends on whether its outputs are needed for the build.

Configurations

By default, a target is built in the target configuration. For each label attribute, you can decide whether the dependency should be built in the same configuration, or in the host configuration.

In general, sources, dependent libraries, and executables that will be needed at runtime can use the same configuration.

Tools that are executed as part of the build (e.g., compilers, code generators) should be built for the host configuration. In this case, specify cfg=HOST_CFG in the attribute.

DATA_CFG is present for legacy reasons and should be used for data attributes.

Configuration Fragments

Rules may access configuration fragments such as cpp, java and jvm. However, all required fragments must be declared in order to avoid access errors:

def impl(ctx):
    # Using ctx.fragments.cpp would lead to an error since it was not declared.
    x = ctx.fragments.java
    ...

my_rule = rule(
    implementation=impl,
    fragments=["java"],      # Required fragments of the target configuration
    host_fragments=["java"], # Required fragments of the host configuration
    ...
)

ctx.fragments only provides configuration fragments for the target configuration. If you want to access fragments for the host configuration, use ctx.host_fragments instead.

Providers

Providers are used to access information from other rules. A rule depending on another rule has access to the data the latter provides. These data can be e.g. output files, the libraries the dependent rule is using to link or compile, or anything the depending rule should know about. Using providers is the only way to exchange data between rules.

A rule can only access data provided by its direct dependencies, not that of transitive dependencies: if rule top depends on middle, and middle depends on bottom, then middle is a direct dependency of top and bottom is a transitive dependency of top. In this scenario top can only access data provided by middle. If middle also provides the data that bottom provided to it, then and only then can top access it.

The following data types can be passed using providers:

• bool
• integer
• string
• file
• label
• None
• anything composed of these types and lists, dicts, sets or structs

Providers are created from the return value of the rule implementation function:

def rule_implementation(ctx):
  ...
  return struct(
    transitive_data=set(["a", "b", "c"])
  )

A dependent rule might access these data as struct fields of the target being dependened upon:

def dependent_rule_implementation(ctx):
  ...
  s = set()
  for dep_target in ctx.attr.deps:
    # Use `print(dir(dep_target))` to see the list of providers.
    s += dep_target.transitive_data
  ...

Providers are only available during the analysis phase. Examples of usage:

• mandatory providers
• optional providers

Runfiles

Runfiles are a set of files used by the (often executable) output of a rule during runtime (as opposed to build time, i.e. when the binary itself is generated). During execution, Bazel creates a directory tree containing symlinks pointing to the runfiles, staging the environment for the binary so it can access the runfiles during runtime.

Runfiles can be added manually during rule creation and/or collected transitively from the rule's dependencies:

def rule_implementation(ctx):
  ...
  transitive_runfiles = set()
  for dep in ctx.attr.special_dependencies:
     transitive_runfiles += dep.transitive_runtime_files

  runfiles = ctx.runfiles(
      # Add some files manually.
      files=[ctx.file.some_data_file],
      # Add transitive files from dependencies manually.
      transitive_files=transitive_runfiles,
      # Collect runfiles from the common locations: transitively from srcs,
      # deps and data attributes.
      collect_default=True,
  )
  # Add a field named "runfiles" to the return struct in order to actually
  # create the symlink tree.
  return struct(runfiles=runfiles)

Note that non-executable rule outputs can also have runfiles. For example, a library might need some external files during runtime, and every dependent binary should know about them.

Also note that if an action uses an executable, the executable's runfiles can be used when the action executes.

Normally, the relative path of a file in the runfiles tree is the same as the relative path of that file in the source tree or generated output tree. If these need to be different for some reason, you can specify the root_symlinks or symlinks arguments. The root_symlinks is a dictionary mapping paths to files, where the paths are relative to the root of the runfiles directory. The symlinks dictionary is the same, but paths are implicitly prefixed with the name of the workspace.

    ...
    runfiles = ctx.runfiles(
        root_symlinks={"some/path/here.foo": ctx.file.some_data_file2}
        symlinks={"some/path/here.bar": ctx.file.some_data_file3}
    )
    # Creates something like:
    # sometarget.runfiles/
    #     some/
    #         path/
    #             here.foo -> some_data_file2
    #     <workspace_name>/
    #         some/
    #             path/
    #                 here.bar -> some_data_file3

If symlinks or root_symlinks is used, be careful not to map two different files to the same path in the runfiles tree. This will cause the build to fail with an error describing the conflict. To fix, you will need to modify your ctx.runfiles arguments to remove the collision. This checking will be done for any targets using your rule, as well as targets of any kind that depend on those targets.

Instrumented files

Instrumented files are a set of files used by the coverage command. A rule can use the instrumented_files provider to provide information about which files should be used for measuring coverage.

def rule_implementation(ctx):
  ...
  return struct(instrumented_files=struct(
      # Optional: File extensions used to filter files from source_attributes.
      # If not provided, then all files from source_attributes will be
      # added to instrumented files, if an empty list is provided, then
      # no files from source attributes will be added.
      extensions=["ext1", "ext2"],
      # Optional: Attributes that contain source files for this rule.
      source_attributes=["srcs"],
      # Optional: Attributes for dependencies that could include instrumented
      # files.
      dependency_attributes=["data", "deps"]))

Executable rules

An executable rule is a rule that users can run using bazel run.

To make a rule executable, set executable=True in the rule function. During the analysis phase, the rule must generate the output file ctx.outputs.executable. See example

Test rules

Test rules are run using bazel test.

To create a test rule, set test=True in the rule function. The name of the rule must also end with _test. Test rules are implicitly executable, which means they must generate the output file ctx.outputs.executable.

Test rules inherit the following attributes: args, flaky, local, shard_count, size, timeout.