Rules

A rule defines a series of actions that Bazel performs on inputs to produce a set of outputs. For example, a C++ binary rule might:

1. Take a set of .cpp files (the inputs)
2. Run g++ on them (the action)
3. Return an executable file (the output).

From Bazel’s perspective, g++ and the standard C++ libraries are also inputs to this rule. As a rule writer, you must consider not only the user-provided inputs to a rule, but also all of the tools and libraries required to execute the actions.

Before creating or modifying any rule, ensure you are familiar with Bazel’s build phases. It will be important to understand the three phases of a build (loading, analysis and execution). It will also be useful to learn about macros to understand the difference between rules and macros.

A few rules are built into Bazel itself. These native rules, such as cc_library and java_binary, provide some core support for certain languages. By defining your own rules, you can add similar support for languages and tools that Bazel does not support natively.

Bazel provides an extensibility model for writing rules using the Starlark language. These rules are written in .bzl files, which can be loaded directly from BUILD files.

When defining your own rule, you get to decide what attributes it supports and how it generates its outputs.

The rule’s implementation function defines its exact behavior during the analysis phase. This function does not run any external commands. Rather, it registers actions that will be used later during the execution phase to build the rule’s outputs, if they are needed.

Rules also produce and pass along information that may be useful to other rules in the form of providers.

Rule creation

In a .bzl file, use the rule function to create a new rule and store it in a global variable:

my_rule = rule(...)

The rule can then be loaded in BUILD files:

load('//some/pkg:whatever.bzl', 'my_rule')

See example.

Attributes

An attribute is a rule argument, such as srcs or deps. You must list the names and schemas of all attributes when you define a rule. Attribute schemas are created using the attr module.

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

In a BUILD file, call the rule to create targets of this type:

sum(
    name = "my-target",
    deps = [":other-target"],
)

sum(
    name = "other-target",
)

Here other-target is a dependency of my-target, and therefore other-target will be analyzed first.

There are two special kinds of attributes:

• Dependency attributes, such as attr.label and attr.label_list, declare a dependency from the target that owns the attribute to the target whose label appears in the attribute’s value. This kind of attribute forms the basis of the target graph.

• Output attributes, such as attr.output and attr.output_list, declare an output file that the target generates. Although they refer to the output file by label, they do not create a dependency relationship between targets. Output attributes are used relatively rarely, in favor of other ways of declaring output files that do not require the user to specify a label.

Both dependency attributes and output attributes take in label values. These may be specified as either Label objects or as simple strings. If a string is given, it will be converted to a Label using the constructor. The repository, and possibly the path, will be resolved relative to the defined target.

If an attribute schema is defined in the rule but no value for that attribute is given when the rule is instantiated, then the rule implementation function will see a placeholder value in ctx.attr. The placeholder value depends on the type of attribute. If the schema specifies a default value, that value will be used instead of the placeholder. The schema may also specify mandatory=True, in which case it is illegal for the user to not give an explicit value. It is not useful for an attribute schema with mandatory to also have a default.

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

Private attributes and implicit dependencies

A dependency attribute with a default value is called an implicit dependency. The name comes from the fact that it is a part of the target graph that the user does not specify in a BUILD file. Implicit dependencies are useful for hard-coding a relationship between a rule and a tool (such as a compiler), since most of the time a user is not interested in specifying what tool the rule uses. From the rule’s point of view, the tool is still an input, just like any source file or other dependency.

Sometimes we want to not only provide a default value, but prevent the user from overriding this default. To do this, you can make the attribute private by giving it a name that begins with an underscore (_). Private attributes must have default values. It generally only makes sense to use private attributes for implicit dependencies.

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

In this example, every target of type metal_binary will have an implicit dependency on the compiler //tools:metalc. This allows metal_binary’s implementation function to generate actions that invoke the compiler, even though the user did not pass its label as an input. Since _compiler is a private attribute, we know for sure that ctx.attr._compiler will always point to //tools:metalc in all targets of this rule type. Alternatively, we could have named the attribute compiler without the underscore and kept the default value. This lets users substitute a different compiler if necessary, but requires no awareness of the compiler’s label otherwise.

Implementation function

Every rule requires an implementation function. This function contains the actual logic of the rule and is executed strictly in the analysis phase. As such, the function is not able to actually read or write files. Rather, its main job is to emit actions that will run later during the execution phase.

Implementation functions take exactly one parameter: a rule context, conventionally named ctx. It can be used to:

• access attribute values and obtain handles on declared input and output files;

• create actions; and

• pass information to other targets that depend on this one, via providers.

The most common way to access attribute values is by using ctx.attr.<attribute_name>, though there are several other fields besides attr that provide more convenient ways of accessing file handles, such as ctx.file and ctx.outputs. The name and the package of a rule are available with ctx.label.name and ctx.label.package. The ctx object also contains some helper functions. See its documentation for a complete list.

Rule implementation functions are usually private (i.e., named with a leading underscore) because they tend not to be reused. Conventionally, they are named the same as their rule, but suffixed with _impl.

See an example of declaring and accessing attributes.

Targets

Each call to a build rule returns no value but has the side effect of defining a new target; this is called instantiating the rule. The dependencies of the new target are any other targets whose labels are mentioned in its dependency attributes. In the following example, the target //mypkg:y depends on the targets //mypkg:x and //mypkg:z.foo.

# //mypkg:BUILD

my_rule(
    name = "x",
)

# Assuming that my_rule has attributes "deps" and "srcs",
# of type attr.label_list()
my_rule(
    name = "y",
    deps = [":x"],
    srcs = [":z.foo"],
)

Dependencies are represented at analysis time as Target objects. These objects contain the information produced by analyzing a target – in particular, its providers. The current target can access its dependencies’ Target objects within its rule implementation function by using ctx.attr.

Files

Files are represented by the File type. Since Bazel does not perform file I/O during the analysis phase, these objects cannot be used to directly read or write file content. Rather, they are passed to action-emitting functions to construct pieces of the action graph. See ctx.actions for the available kinds of actions.

A file can either be a source file or a generated file. Each generated file must be an output of exactly one action. Source files cannot be the output of any action.

Some files, including all source files, are addressable by labels. These files have Target objects associated with them. If a file’s label appears within a dependency attribute (for example, in a srcs attribute of type attr.label_list), the ctx.attr.<attr_name> entry for it will contain the corresponding Target. The File object can be obtained from this Target’s files field. This allows the file to be referenced in both the target graph and the action graph.

Outputs

A generated file that is addressable by a label is called a predeclared output. Rules can specify predeclared outputs via output or output_list attributes. In that case, the user explicitly chooses labels for outputs when they instantiate the rule. To obtain file objects for output attributes, use the corresponding attribute of ctx.outputs.

During the analysis phase, a rule’s implementation function can create additional outputs. Since all labels have to be known during the loading phase, these additional outputs have no labels. Non-predeclared outputs are created using ctx.actions.declare_file, ctx.actions.write, and ctx.actions.declare_directory.

All outputs can be passed along in providers to make them available to a target’s consumers, whether or not they have a label. A target’s default outputs are specified by the files parameter of DefaultInfo. If DefaultInfo is not returned by a rule implementation or the files parameter is not specified, DefaultInfo.files defaults to all predeclared outputs.

There are also two deprecated ways of using predeclared outputs:

• The outputs parameter of rule specifies a mapping between output attribute names and string templates for generating predeclared output labels. Prefer using non-predeclared outputs and explicitly adding outputs to DefaultInfo.files. Use the rule target’s label as input for rules which consume the output instead of a predeclared output’s label.

• For executable rules, ctx.outputs.executable refers to a predeclared executable output with the same name as the rule target. Prefer declaring the output explicitly, for example with ctx.actions.declare_file(ctx.label.name), and ensure that the command that generates the executable sets its permissions to allow execution. Explicitly pass the executable output to the executable parameter of DefaultInfo.

See example of predeclared outputs

Actions

An action describes how to generate a set of outputs from a set of inputs, for example “run gcc on hello.c and get hello.o”. When an action is created, Bazel doesn’t run the command immediately. It registers it in a graph of dependencies, because an action can depend on the output of another action (e.g. in C, the linker must be called after compilation). In the execution phase, Bazel decides which actions must be run and in which order.

All functions that create actions are defined in ctx.actions:

• ctx.actions.run, to run an executable.
• ctx.actions.run_shell, to run a shell command.
• ctx.actions.write, to write a string to a file.
• ctx.actions.expand_template, 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). This is important because the output will be cached and reused.

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 must group them in a single file (e.g., a zip, tar, or other archive format). This way, you will be able to deterministically declare your outputs.

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

Imagine that you want to build a C++ binary for a different architecture. The build can be complex and involve multiple steps. Some of the intermediate binaries, like compilers and code generators, have to run on your machine (the host). Some binaries like the final output must be built for the target architecture.

For this reason, Bazel has a concept of “configurations” and transitions. The topmost targets (the ones requested on the command line) are built in the “target” configuration, while tools that should run locally on the host are built in the “host” configuration. Rules may generate different actions based on the configuration, for instance to change the cpu architecture that is passed to the compiler. In some cases, the same library may be needed for different configurations. If this happens, it will be analyzed and potentially built multiple times.

By default, Bazel builds a target’s dependencies in the same configuration as the target itself, in other words without transitions. When a dependency is a tool that’s needed to help build the target, the corresponding attribute should specify a transition to the host configuration. This causes the tool and all its dependencies to build for the host machine.

For each dependency attribute, you can use cfg to decide if dependencies should build in the same configuration or transition to the host configuration. If a dependency attribute has the flag executable=True, cfg must be set explicitly. This is to guard against accidentally building a host tool for the wrong configuration. See example

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" in the attribute.

Otherwise, executables that are used at runtime (e.g. as part of a test) should be built for the target configuration. In this case, specify cfg="target" in the attribute.

cfg="target" doesn’t actually do anything: it’s purely a convenience value to help rule designers be explicit about their intentions. When executable=False, which means cfg is optional, only set this when it truly helps readability.

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 pieces of information that a rule exposes to other rules that depend on it. This data can include output files, libraries, parameters to pass on a tool’s command line, or anything else the depending rule should know about. Providers are the only mechanism to exchange data between rules, and can be thought of as part of a rule’s public interface (loosely analogous to a function’s return value).

A rule can only see the providers of its direct dependencies. If there is a rule top that depends on middle, and middle depends on bottom, then we say that middle is a direct dependency of top, while bottom is a transitive dependency of top. In this case, top can see the providers of middle. The only way for top to see any information from bottom is if middle re-exports this information in its own providers; this is how transitive information can be accumulated from all dependencies. In such cases, consider using depsets to hold the data more efficiently without excessive copying.

Providers can be declared using the provider() function:

TransitiveDataInfo = provider(fields=["value"])

Rule implementation function can then construct and return provider instances:

def rule_implementation(ctx):
  ...
  return [TransitiveDataInfo(value=5)]

TransitiveDataInfo acts both as a constructor for provider instances and as a key to access them. A target serves as a map from each provider that the target supports, to the target’s corresponding instance of that provider. A rule can access the providers of its dependencies using the square bracket notation ([]):

def dependent_rule_implementation(ctx):
  ...
  n = 0
  for dep_target in ctx.attr.deps:
    n += dep_target[TransitiveDataInfo].value
  ...

All targets have a DefaultInfo provider that can be used to access some information relevant to all targets.

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

• mandatory providers
• optional providers
• providers with depsets This examples shows how a library and a binary rule can pass information.

Migrating from legacy providers

Historically, Bazel providers were simple fields on the Target object. They were accessed using the dot operator, and they were created by putting the field in a struct returned by the rule’s implementation function.

This style is deprecated and should not be used in new code; see below for information that may help you migrate. The new provider mechanism avoids name clashes. It also supports data hiding, by requiring any code accessing a provider instance to retrieve it using the provider symbol.

For the moment, legacy providers are still supported. A rule can return both legacy and modern providers as follows:

def _myrule_impl(ctx):
  ...
  legacy_data = struct(x="foo", ...)
  modern_data = MyInfo(y="bar", ...)
  # When any legacy providers are returned, the top-level returned value is a struct.
  return struct(
      # One key = value entry for each legacy provider.
      legacy_info = legacy_data,
      ...
      # All modern providers are put in a list passed to the special "providers" key.
      providers = [modern_data, ...])

If dep is the resulting Target object for an instance of this rule, the providers and their contents can be retrieved as dep.legacy_info.x and dep[MyInfo].y.

In addition to providers, the returned struct can also take several other fields that have special meaning (and that do not create a corresponding legacy provider).

• The fields files, runfiles, data_runfiles, default_runfiles, and executable correspond to the same-named fields of DefaultInfo. It is not allowed to specify any of these fields while also returning a DefaultInfo modern provider.

• The field output_groups takes a struct value and corresponds to an OutputGroupInfo.

In provides declarations of rules, and in providers declarations of dependency attributes, legacy providers are passed in as strings and modern providers are passed in by their *Info symbol. Be sure to change from strings to symbols when migrating. For complex or large rule sets where it is difficult to update all rules atomically, you may have an easier time if you follow this sequence of steps:

1. Modify the rules that produce the legacy provider to produce both the legacy and modern providers, using the above syntax. For rules that declare they return the legacy provider, update that declaration to include both the legacy and modern providers.

2. Modify the rules that consume the legacy provider to instead consume the modern provider. If any attribute declarations require the legacy provider, also update them to instead require the modern provider. Optionally, you can interleave this work with step 1 by having consumers accept/require either provider: Test for the presence of the legacy provider using hasattr(target, 'foo'), or the new provider using FooInfo in target.

3. Fully remove the legacy provider from all rules.

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 the execution phase, Bazel creates a directory tree containing symlinks pointing to the runfiles. This stages the environment for the binary so it can access the runfiles during runtime.

Runfiles can be added manually during rule creation. runfiles objects can be created by the runfiles method on the rule context, ctx.runfiles.

Basic usage

Use runfiles objects to specify a set of files that are needed in an executable’s environment at runtime. Do this by passing a runfiles object to the runfiles parameter of the DefaultInfo object returned by your rule.

Construct runfiles objects using ctx.runfiles with parameters files and transitive_files.

Example:

def _rule_implementation(ctx):
  ...
  runfiles = ctx.runfiles(
      files = [ctx.file.some_data_file],
      transitive_files = ctx.attr.something[SomeProviderInfo].depset_of_files,
  )

  return [DefaultInfo(runfiles=runfiles)]

The specified files and transitive_files will be available to the executable’s runtime environment. The location of these files relative to the execution root may be obtained in a couple of ways. Note that the following recommendations only work for obtaining relative runfiles paths when running an executable on the command line with bazel run:

• If relevant files are themselves valid targets (and thus have a corresponding label), you may use ctx.expand_location.
• Use file.short_path.

See basic example.

Libraries with runfiles

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. In such cases, it’s recommended to propagate these files via a custom provider; propagate the files themselves via a depset; avoid propagating the runfiles object type in anything other than DefaultInfo, as it generally adds unnecessary complexity. (There are exceptions listed later!)

See example.

Tools with runfiles

A build action might use an executable that requires runfiles (such executables are nicknamed “tools”). For such cases, depend on this executable target via an attribute which has executable = True specified. The executable file will then be available under ctx.executable.<attr_name>. By passing this file to the executable parameter of the action registration function, the executable’s runfiles will be implicitly added to the execution environment.

The runfiles directory structure for tools is different than for basic executables (executables simply run with bazel run).

• The tool executable file exists in a root-relative path derived from its label. This full relative path can be obtained via ctx.executable.<attr_name>.path.
• The runfiles for the tool exist in a .runfiles directory which resides adjacent to the tool’s path. An individual runfile can thus be found at the following path relative to the execution root.

# Given executable_file and runfile_file:
runfiles_root = executable_file.path + ".runfiles"
workspace_name = ctx.workspace_name
runfile_path = runfile_file.short_path
execution_root_relative_path = "%s/%s/%s" % (runfiles_root, workspace_name, runfile_path)

See example.

Runfiles symlinks

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. This is especially risky if your tool is likely to be used transitively by another tool; symlink names must be unique across the runfiles of a tool and all of its dependencies!

Tools depending on tools

A tool (executable used for action registration) may depend on another tool with its own runfiles. (For purposes of this explanation, we nickname the primary tool the “root tool” and the tool it depends on a “subtool”.)

Merge the runfiles of subtools with the root tool by using runfiles.merge. Acquire the runfiles of subtools via DefaultInfo.default_runfiles

Example code:

def _mytool_impl(ctx):
  ...
  my_runfiles = ctx.runfiles(files = mytool_files)
  for subtool in ctx.attr.subtools:
    subtool_runfiles = subtool[DefaultInfo].default_runfiles
    my_runfiles = my_runfiles.merge(subtool_runfiles)
  ...
  return [DefaultInfo(runfiles = my_runfiles))]

The runfiles directory structure is a bit more difficult to manage for subtools. The runfiles directory is always adjacent to the root tool being run – not an individual subtool. To simplify subtool tool logic, it’s recommended that each subtool optionally accept its runfiles root as a parameter (via environment or command line argument/flag). A root tool can thus pass the correct canonical runfiles root to any of its subtools.

This scenario is complex and thus best demonstrated by an example.

Runfiles features to avoid

ctx.runfiles and the runfiles type have a complex set of features, many of which are kept for legacy reasons. We make the following recommendations to reduce complexity:

• Avoid use of the collect_data and collect_default modes of ctx.runfiles. These modes implicitly collect runfiles across certain hardcoded dependency edges in confusing ways. Instead, manually collect files along relevant dependency edges and add them to your runfiles using files or transitive_files parameters of ctx.runfiles.
• Avoid use of the data_runfiles and default_runfiles of the DefaultInfo constructor. Specify DefaultInfo(runfiles = ...) instead. The distinction between “default” and “data” runfiles is maintained for legacy reasons, but is unimportant for new usage.
• When retrieving runfiles from DefaultInfo (generally only for merging runfiles between the current rule and its dependencies), use DefaultInfo.default_runfiles. not DefaultInfo.data_runfiles.

Requesting output files

A single target can have several output files. When a bazel build command is run, some of the outputs of the targets given to the command are considered to be requested. Bazel only builds these requested files and the files that they directly or indirectly depend on. (In terms of the action graph, Bazel only executes the actions that are reachable as transitive dependencies of the requested files.)

Every target has a set of default outputs, which are the output files that normally get requested when that target appears on the command line. For example, a target //pkg:foo of java_library type has in its default outputs a file foo.jar, which will be built by the command bazel build //pkg:foo.

Any predeclared output can be explicitly requested on the command line. This can be used to build outputs that are not default outputs, or to build some but not all default outputs. For example, bazel build //pkg:foo_deploy.jar and bazel build //pkg:foo.jar will each just build that one file (along with its dependencies). See an example of a rule with non-default predeclared outputs.

In addition to default outputs, there are output groups, which are collections of output files that may be requested together. For example, if a target //pkg:mytarget is of a rule type that has a debug_files output group, these files can be built by running bazel build //pkg:mytarget --output_groups=debug_files. See the command line reference for details on the --output_groups argument. Since non-predeclared outputs don’t have labels, they can only be requested by appearing in the default outputs or an output group.

You can specify the default outputs and output groups of a rule by returning the DefaultInfo and OutputGroupInfo providers from its implementation function.

def _myrule_impl(ctx):
  name = ...
  binary = ctx.actions.declare_file(name)
  debug_file = ctx.actions.declare_file(name + ".pdb")
  # ... add actions to generate these files
  return [DefaultInfo(files = depset([binary])),
          OutputGroupInfo(debug_files = depset([debug_file]),
                          all_files = depset([binary, debug_file]))]

These providers can also be retrieved from dependencies using the usual syntax <target>[DefaultInfo] and <target>[OutputGroupInfo], where <target> is a Target object.

Note that even if a file is in the default outputs or an output group, you may still want to return it in a custom provider in order to make it available in a more structured way. For instance, you could pass headers and sources along in separate fields of your provider.

Code coverage instrumentation

A rule can use the InstrumentedFilesInfo provider to provide information about which files should be measured when code coverage data collection is enabled. That provider can be created with coverage_common.instrumented_files_info and included in the list of providers returned by the rule’s implementation function:

def _rule_implementation(ctx):
  ...
  instrumented_files_info = coverage_common.instrumented_files_info(
      ctx,
      # 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"])
  return [..., instrumented_files_info]

ctx.configuration.coverage_enabled notes whether coverage data collection is enabled for the current run in general (but says nothing about which files specifically should be instrumented). If a rule implementation needs to add coverage instrumentation at compile-time, it can determine if its sources should be instrumented with ctx.coverage_instrumented:

# Are this rule's sources instrumented?
if ctx.coverage_instrumented():
  # Do something to turn on coverage for this compile action

Note that function will always return false if ctx.configuration.coverage_enabled is false, so you don’t need to check both.

If the rule directly includes sources from its dependencies before compilation (e.g. header files), it may also need to turn on compile-time instrumentation if the dependencies’ sources should be instrumented. In this case, it may also be worth checking ctx.configuration.coverage_enabled so you can avoid looping over dependencies unnecessarily:

# Are this rule's sources or any of the sources for its direct dependencies
# in deps instrumented?
if ctx.configuration.coverage_enabled:
    if (ctx.coverage_instrumented() or
        any([ctx.coverage_instrumented(dep) for dep in ctx.attr.deps]):
        # Do something to turn on coverage for this compile action

Executable rules and test rules

Executable rules define targets that can be invoked by a bazel run command. Test rules are a special kind of executable rule whose targets can also be invoked by a bazel test command. Executable and test rules are created by setting the respective executable or test argument to true when defining the rule.

Test rules (but not necessarily their targets) must have names that end in _test. Non-test rules must not have this suffix.

Both kinds of rules must produce an executable output file (which may or may not be predeclared) that will be invoked by the run or test commands. To tell Bazel which of a rule’s outputs to use as this executable, pass it as the executable argument of a returned DefaultInfo provider.

The action that generates this file must set the executable bit on the file. For a ctx.actions.run() or ctx.actions.run_shell() action this should be done by the underlying tool that is invoked by the action. For a ctx.actions.write() action it is done by passing the argument is_executable=True.

As legacy behavior, executable rules have a special ctx.outputs.executable predeclared output. This file serves as the default executable if you do not specify one using DefaultInfo; it must not be used otherwise. This output mechanism is deprecated because it does not support customizing the executable file’s name at analysis time.

See examples of an executable rule and a test rule.

Test rules inherit the following attributes: args, flaky, local, shard_count, size, timeout. The defaults of inherited attributes cannot be changed, but you can use a macro with default arguments:

def example_test(size="small", **kwargs):
  _example_test(size=size, **kwargs)

_example_test = rule(
 ...
)