---
layout: documentation
title: Rules
---
# Rules

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

A rule defines a series of [actions](#actions) that Bazel should perform on
inputs to get a set of outputs. For example, a C++ binary rule might take a set
of `.cpp` files (the inputs), run `g++` on them (the action), and return an
executable file (the output).

Note that, 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 (called _implicit inputs_).

Before creating or modifying any rule, make sure you are familiar with the
[extensibility model](concepts.md) (understand the three phases and the
differences between macros and rules).

## Rule creation

In a `.bzl` file, use the [rule](lib/globals.html#rule)
function to create a new rule and store it in a global variable:

```python
my_rule = rule(...)
```

See [the cookbook](cookbook.md#empty) for examples. The rule can then be
loaded by BUILD files:

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

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 explicitly 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](#actions)). During the [analysis phase](concepts.md#evaluation-model),
no external command can be executed. Instead, actions are registered and
will be run in the execution phase, if their output is needed for the build.

## 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.

```python
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`.

Labels listed in `attr` will be inputs to the rule.

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

See [an example](cookbook.md#attr) of using `attr` in a rule.

### <a name="private-attributes"></a> Private Attributes

In Python, we use one leading underscore(`_`) for non-public methods and
instance variables (see [PEP-8][1]).

Similarly, 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).

```python
metal_compile = rule(
    implementation = _impl,
    attrs = {
        "srcs": attr.label_list(),
        "_compiler": attr.label(
            default = Label("//tools:metalc"),
            allow_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](concepts.md#evaluation-model). The function has exactly one
input parameter, `ctx`, and it may return the [runfiles](#runfiles)
and [providers](#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](lib/ctx.html) for more context.
Example:

```python
def _impl(ctx):
  ...
  return [DefaultInfo(runfiles=...),  MyInfo(...)]

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](#actions), can have dependencies (which can be files or
other build rules), [output files](#output-files) (generated by
its actions), and [providers](#providers).

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

```python
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`:

```python
def _impl(ctx):
  for dep in ctx.attr.deps:
    # Do something with dep
  ...

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

## <a name="output-files"></a> 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:

* If the rule is marked `executable`, it creates an output file of the same name
  as the rule's. [See example](cookbook.md#outputs-executable)

* The rule can declare default `outputs`, which are always generated.
  [See example](cookbook.md#outputs-default)

* The rule can have output or output list type attributes. In that case the
  output files come from the actual attribute values.
  [See example](cookbook.md#outputs-custom)

Each output file must have exactly one generating action. See the
[library](lib/ctx.html#outputs) 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](lib/Target.html#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.

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

This can be useful for exposing files generated with
[ctx.new_file](lib/ctx.html#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

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.

There are three ways to create actions:

* [ctx.action](lib/ctx.html#action), to run a command.
* [ctx.file_action](lib/ctx.html#file_action), to write a string to a file.
* [ctx.template_action](lib/ctx.html#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](concepts.md#evaluation-model). 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 and target a different architecture.
The build can be complex and involve multiple steps. Some of the intermediate
binaries, like the compilers and code generators, have to run on your machine
(the host); some of the binaries such 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 the dependencies of a target in the same configuration
as the target itself, i.e. without transitioning. When a target depends on a
tool, the label attribute will specify a transition to the host configuration.
This causes the tool and all of its dependencies to be built for the host
machine, assuming those dependencies do not themselves have transitions.

For each [label attribute](lib/attr.html#label), you can decide whether the
dependency should be built in the same configuration, or transition to the host
configuration (using `cfg`). If a label attribute has the flag
`executable=True`, the configuration must be set explicitly.
[See example](cookbook.html#execute-a-binary)

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.

The configuration `"data"` is present for legacy reasons and should be used for
the `data` attributes.

## <a name="fragments"></a> Configuration Fragments

Rules may access [configuration fragments](lib/skylark-configuration-fragment.html)
such as `cpp`, `java` and `jvm`. However, all required fragments must be
declared in order to avoid access errors:

```python
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](depsets.md) to hold the data more efficiently without excessive
copying.

Providers can be declared using the [provider()](lib/globals.html#provider) function:

```python
TransitiveDataInfo = provider()
```

Rule implementation function can then construct and return provider instances:

```python
def rule_implementation(ctx):
  ...
  return [TransitiveDataInfo(value = ["a", "b", "c"])]
```

`TransitiveDataInfo` acts both as a constructor for provider instances and as a key to access them.
A [target](lib/Target.html) 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 (`[]`):

```python
def dependent_rule_implementation(ctx):
  ...
  s = depset()
  for dep_target in ctx.attr.deps:
    s += dep_target[TransitiveDataInfo].value
  ...
```

All targets have a [`DefaultInfo`](lib/globals.html#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](cookbook.md#mandatory-providers)
* [optional providers](cookbook.md#optional-providers)

> *Note:*
> Historically, Bazel also supported provider instances that are identified by strings and
> accessed as fields on the `target` object instead of as keys. This style is deprecated
> but still supported. Return legacy providers as follows:
>
```python
def rule_implementation(ctx):
  ...
  modern_provider = TransitiveDataInfo(value = ["a", "b", "c"])
  # Legacy style.
  return struct(legacy_provider = struct(...),
                another_legacy_provider = struct(...),
                # The `providers` field contains provider instances that can be accessed
                # the "modern" way.
                providers = [modern_provider])
```
> To access legacy providers, use the dot notation.
> Note that the same target can define both modern and legacy providers:
>
```python
def dependent_rule_implementation(ctx):
  ...
  s = depset()
  for dep_target in ctx.attr.deps:
    x = dep_target.legacy_provider            # legacy style
    s += dep_target[TransitiveDataInfo].value # modern style
  ...
```
> **We recommend using modern providers for all future code.**

## 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](concepts.md#evaluation-model), 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 and/or collected
transitively from the rule's dependencies:

```python
def _rule_implementation(ctx):
  ...
  transitive_runfiles = depset()
  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 DefaultInfo provider in order to actually
  # create the symlink tree.
  return [DefaultInfo(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.

```python
    ...
    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.

## Code coverage instrumentation

A rule can use the `instrumented_files` provider to provide information about
which files should be measured when code coverage data collection is enabled:

```python
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"]))
```

[ctx.config.coverage_enabled](lib/configuration.html#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](lib/ctx.html#coverage_instrumented):

```python
# 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.config.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.config.coverage_enabled` so you can avoid looping
over dependencies unnecessarily:

```python
# Are this rule's sources or any of the sources for its direct dependencies
# in deps instrumented?
if ctx.config.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

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](lib/globals.html#rule). The `implementation` function of the
rule must generate the output file `ctx.outputs.executable`.
[See example](cookbook.md#outputs-executable)

## Test rules

Test rules are run using `bazel test`.

To create a test rule, set `test=True` in the
[rule function](lib/globals.html#rule). The name of the rule must
also end with `_test`. Test rules are implicitly executable, which means that
the `implementation` function of the rule must generate the output file
`ctx.outputs.executable`.

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

[1]: https://www.python.org/dev/peps/pep-0008/#id46
