A rule defines a series of actions that Bazel performs on inputs to produce a set of outputs, which are referenced in providers returned by the rule's implementation function. For example, a C++ binary rule might:
- Take a set of
.cppsource files (inputs). - Run
g++on the source files (action). - Return the
DefaultInfoprovider with the executable output and other files to make available at runtime. - Return the
CcInfoprovider with C++-specific information gathered from the target and its dependencies.
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 is important to understand the three phases of a build (loading, analysis, and execution). It is also useful to learn about macros to understand the difference between rules and macros. To get started, first review the Rules Tutorial. Then, use this page as a reference.
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.
Rule creation
In a .bzl file, use the rule function to define a new
rule, and store the result in a global variable. The call to rule specifies
attributes and an
implementation function:
example_library = rule(
implementation = _example_library_impl,
attrs = {
"deps": attr.label_list(),
...
},
)
This defines a kind of rule named example_library.
The call to rule also must specify if the rule creates an
executable output (with executable=True), or specifically
a test executable (with test=True). If the latter, the rule is a test rule,
and the name of the rule must end in _test.
Target instantiation
Rules can be loaded and called in BUILD files:
load('//some/pkg:rules.bzl', 'example_library')
example_library(
name = "example_target",
deps = [":another_target"],
...
)
Each call to a build rule returns no value, but has the side effect of defining a target. This is called instantiating the rule. This specifies a name for the new target and values for the target's attributes.
Rules can also be called from Starlark functions and loaded in .bzl files.
Starlark functions that call rules are called Starlark macros.
Starlark macros must ultimately be called from BUILD files, and can only be
called during the loading phase, when BUILD
files are evaluated to instantiate targets.
Attributes
An attribute is a rule argument. Attributes can provide specific values to a target's implementation, or they can refer to other targets, creating a graph of dependencies.
Rule-specific attributes, such as srcs or deps, are defined by passing a map
from attribute names to schemas (created using the attr
module) to the attrs parameter of rule.
Common attributes, such as
name and visibility, are implicitly added to all rules. Additional
attributes are implicitly added to
executable and test rules specifically. Attributes which
are implicitly added to a rule cannot be included in the dictionary passed to
attrs.
Dependency attributes
Rules that process source code usually define the following attributes to handle various types of dependencies:
srcsspecifies source files processed by a target's actions. Often, the attribute schema specifies which file extensions are expected for the sort of source file the rule processes. Rules for languages with header files generally specify a separatehdrsattribute for headers processed by a target and its consumers.depsspecifies code dependencies for a target. The attribute schema should specify which providers those dependencies must provide. (For example,cc_libraryprovidesCcInfo.)dataspecifies files to be made available at runtime to any executable which depends on a target. That should allow arbitrary files to be specified.
example_library = rule(
implementation = _example_library_impl,
attrs = {
"srcs": attr.label_list(allow_files = [".example"]),
"hdrs": attr.label_list(allow_files = [".header"]),
"deps": attr.label_list(providers = [ExampleInfo]),
"data": attr.label_list(allow_files = True),
...
},
)
These are examples of dependency attributes. Any attribute that specifies
an input label (those defined with
attr.label_list,
attr.label, or
attr.label_keyed_string_dict)
specifies dependencies of a certain type
between a target and the targets whose labels (or the corresponding
Label objects) are listed in that attribute when the target
is defined. The repository, and possibly the path, for these labels is resolved
relative to the defined target.
example_library(
name = "my_target",
deps = [":other_target"],
)
example_library(
name = "other_target",
...
)
In this example, other_target is a dependency of my_target, and therefore
other_target is analyzed first. It is an error if there is a cycle in the
dependency graph of targets.
Private attributes and implicit dependencies
A dependency attribute with a default value creates an implicit dependency. It
is implicit because it's 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 (a build-time dependency, such as a
compiler), since most of the time a user is not interested in specifying what
tool the rule uses. Inside the rule's implementation function, this is treated
the same as other dependencies.
If you want to provide an implicit dependency without allowing the user to
override that value, 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.
example_library = rule(
implementation = _example_library_impl,
attrs = {
...
"_compiler": attr.label(
default = Label("//tools:example_compiler"),
allow_single_file = True,
executable = True,
cfg = "exec",
),
},
)
In this example, every target of type example_library has an implicit
dependency on the compiler //tools:example_compiler. This allows
example_library'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, it follows that ctx.attr._compiler
will always point to //tools:example_compiler in all targets of this rule
type. Alternatively, you can name the attribute compiler without the
underscore and keep the default value. This allows users to substitute a
different compiler if necessary, but it requires no awareness of the compiler's
label.
Implicit dependencies are generally used for tools that reside in the same repository as the rule implementation. If the tool comes from the execution platform or a different repository instead, the rule should obtain that tool from a toolchain.
Output attributes
Output attributes, such as attr.output and
attr.output_list, declare an output file that the
target generates. These differ from dependency attributes in two ways:
- They define output file targets instead of referring to targets defined elsewhere.
- The output file targets depend on the instantiated rule target, instead of the other way around.
Typically, output attributes are only used when a rule needs to create outputs
with user-defined names which cannot be based on the target name. If a rule has
one output attribute, it is typically named out or outs.
Output attributes are the preferred way of creating predeclared outputs, which can be specifically depended upon or requested at the command line.
Implementation function
Every rule requires an implementation function. These functions are executed
strictly in the analysis phase and transform the
graph of targets generated in the loading phase into a graph of
actions to be performed during the execution phase. As such,
implementation functions can not actually read or write files.
Rule implementation functions are usually private (named with a leading
underscore). Conventionally, they are named the same as their rule, but suffixed
with _impl.
Implementation functions take exactly one parameter: a
rule context, conventionally named ctx. They return a list of
providers.
Targets
Dependencies are represented at analysis time as Target
objects. These objects contain the providers generated when the
target's implementation function was executed.
ctx.attr has fields corresponding to the names of each
dependency attribute, containing Target objects representing each direct
dependency via that attribute. For label_list attributes, this is a list of
Targets. For label attributes, this is a single Target or None.
A list of provider objects are returned by a target's implementation function:
return [ExampleInfo(headers = depset(...))]
Those can be accessed using index notation ([]), with the type of provider as
a key. These can be custom providers defined in Starlark or
providers for native rules available as Starlark
global variables.
For example, if a rule takes header files via a hdrs attribute and provides
them to the compilation actions of the target and its consumers, it could
collect them like so:
def _example_library_impl(ctx):
...
transitive_headers = [hdr[ExampleInfo].headers for hdr in ctx.attr.hdrs]
For the legacy style in which a struct is returned from a
target's implementation function instead of a list of provider objects:
return struct(example_info = struct(headers = depset(...)))
Providers can be retrieved from the corresponding field of the Target object:
transitive_headers = [hdr.example_info.headers for hdr in ctx.attr.hdrs]
This style is strongly discouraged and rules should be migrated away from it.
Files
Files are represented by File objects. 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 (see ctx.actions) to construct pieces of the
action graph.
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.
For each dependency attribute, the corresponding field of
ctx.files contains a list of the default outputs of all
dependencies via that attribute:
def _example_library_impl(ctx):
...
headers = depset(ctx.files.hdrs, transitive=transitive_headers)
srcs = ctx.files.srcs
...
ctx.file contains a single File or None for
dependency attributes whose specs set allow_single_file=True.
ctx.executable behaves the same as ctx.file, but only
contains fields for dependency attributes whose specs set executable=True.
Declaring outputs
During the analysis phase, a rule's implementation function can create outputs.
Since all labels have to be known during the loading phase, these additional
outputs have no labels. File objects for outputs can be created using
ctx.actions.declare_file and
ctx.actions.declare_directory. Often,
the names of outputs are based on the target's name,
ctx.label.name:
def _example_library_impl(ctx):
...
output_file = ctx.actions.declare_file(ctx.label.name + ".output")
...
For predeclared outputs, like those created for
output attributes, File objects instead can be retrieved
from the corresponding fields of ctx.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. For example, in C, the linker must be called after the compiler.
General-purpose 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.
ctx.actions.args can be used to efficiently
accumulate the arguments for actions. It avoids flattening depsets until
execution time:
def _example_library_impl(ctx):
...
transitive_headers = [dep[ExampleInfo].headers for dep in ctx.attr.deps]
headers = depset(ctx.files.hdrs, transitive=transitive_headers)
srcs = ctx.files.srcs
inputs = depset(srcs, transitive=[headers])
output_file = ctx.actions.declare_file(ctx.label.name + ".output")
args = ctx.actions.args()
args.add_joined("-h", headers, join_with=",")
args.add_joined("-s", srcs, join_with=",")
args.add("-o", output_file)
ctx.actions.run(
mnemonic = "ExampleCompile",
executable = ctx.executable._compiler,
arguments = [args],
inputs = inputs,
outputs = [output_file],
)
...
Actions take a list or depset of input files and generate a (non-empty) list 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, including providers 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 which create a variable number of files internally can wrap those in a single file (such as a zip, tar, or other archive format).
Actions must list all of their inputs. Listing inputs that are not used is permitted, but inefficient.
Actions must create all of their outputs. They may write other files, but anything not in outputs will not be available to consumers. All declared outputs must be written by some action.
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.
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, that depends on whether its outputs are needed for the build.
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 a target's consumers should know about.
Since a rule's implementation function can only read providers from the
instantiated target's immediate dependencies, rules need to forward any
information from a target's dependencies that needs to be known by a target's
consumers, generally by accumulating that into a depset.
A target's providers are specified by a list of Provider objects returned by
the implementation function.
Old implementation functions can also be written in a legacy style where the
implementation function returns a struct instead of list of
provider objects. This style is strongly discouraged and rules should be
migrated away from it.
Default outputs
A target's default outputs are the outputs that are requested by default when
the target is requested for build at the command line. For example, a
java_library target //pkg:foo has foo.jar as a default output, so that
will be built by the command bazel build //pkg:foo.
Default outputs are specified by the files parameter of
DefaultInfo:
def _example_library_impl(ctx):
...
return [
DefaultInfo(files = depset([output_file]), ...),
...
]
If DefaultInfo is not returned by a rule implementation or the files
parameter is not specified, DefaultInfo.files defaults to all
predeclared outputs (generally, those created by output
attributes).
Rules that perform actions should provide default outputs, even if those outputs are not expected to be directly used. Actions that are not in the graph of the requested outputs are pruned. If an output is only used by a target's consumers, those actions will not be performed when the target is built in isolation. This makes debugging more difficult because rebuilding just the failing target won't reproduce the failure.
Runfiles
Runfiles are a set of files used by a target at runtime (as opposed to build time). 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 and passed to the
runfiles parameter on DefaultInfo. The executable output of
executable rules is implicitly added to the runfiles.
Some rules specify attributes, generally named
data, whose outputs are added to
a targets' runfiles. Runfiles should also be merged in from data, as well as
from any attributes which might provide code for eventual execution, generally
srcs (which might contain filegroup targets with associated data) and
deps.
def _example_library_impl(ctx):
...
runfiles = ctx.runfiles(files = ctx.files.data)
transitive_runfiles = []
for runfiles_attr in (
ctx.attr.srcs,
ctx.attr.hdrs,
ctx.attr.deps,
ctx.attr.data,
):
for target in runfiles_attr:
transitive_runfiles.append(target[DefaultInfo].default_runfiles)
runfiles = runfiles.merge_all(transitive_runfiles)
return [
DefaultInfo(..., runfiles = runfiles),
...
]
Custom providers
Providers can be defined using the provider
function to convey rule-specific information:
ExampleInfo = provider(
"Info needed to compile/link Example code.",
fields={
"headers": "depset of header Files from transitive dependencies.",
"files_to_link": "depset of Files from compilation.",
})
Rule implementation functions can then construct and return provider instances:
def _example_library_impl(ctx):
...
return [
...
ExampleInfo(
headers = headers,
files_to_link = depset(
[output_file],
transitive = [
dep[ExampleInfo].files_to_link for dep in ctx.attr.deps
],
),
)
]
Custom initialization of providers
It's possible to guard the instantiation of a provider with custom preprocessing and validation logic. This can be used to ensure that all provider instances obey certain invariants, or to give users a cleaner API for obtaining an instance.
This is done by passing an init callback to the
provider function. If this callback is given, the
return type of provider() changes to be a tuple of two values: the provider
symbol that is the ordinary return value when init is not used, and a "raw
constructor".
In this case, when the provider symbol is called, instead of directly returning
a new instance, it will forward the arguments along to the init callback. The
callback's return value must be a dict mapping field names (strings) to values;
this is used to initialize the fields of the new instance. Note that the
callback may have any signature, and if the arguments do not match the signature
an error is reported as if the callback were invoked directly.
The raw constructor, by contrast, will bypass the init callback.
The following example uses init to preprocess and validate its arguments:
# //pkg:exampleinfo.bzl
_core_headers = [...] # private constant representing standard library files
# It's possible to define an init accepting positional arguments, but
# keyword-only arguments are preferred.
def _exampleinfo_init(*, files_to_link, headers = None, allow_empty_files_to_link = False):
if not files_to_link and not allow_empty_files_to_link:
fail("files_to_link may not be empty")
all_headers = depset(_core_headers, transitive = headers)
return {'files_to_link': files_to_link, 'headers': all_headers}
ExampleInfo, _new_exampleinfo = provider(
...
init = _exampleinfo_init)
export ExampleInfo
A rule implementation may then instantiate the provider as follows:
ExampleInfo(
files_to_link=my_files_to_link, # may not be empty
headers = my_headers, # will automatically include the core headers
)
The raw constructor can be used to define alternative public factory functions
that do not go through the init logic. For example, in exampleinfo.bzl we
could define:
def make_barebones_exampleinfo(headers):
"""Returns an ExampleInfo with no files_to_link and only the specified headers."""
return _new_exampleinfo(files_to_link = depset(), headers = all_headers)
Typically, the raw constructor is bound to a variable whose name begins with an
underscore (_new_exampleinfo above), so that user code cannot load it and
generate arbitrary provider instances.
Another use for init is to simply prevent the user from calling the provider
symbol altogether, and force them to use a factory function instead:
def _exampleinfo_init_banned(*args, **kwargs):
fail("Do not call ExampleInfo(). Use make_exampleinfo() instead.")
ExampleInfo, _new_exampleinfo = provider(
...
init = _exampleinfo_init_banned)
def make_exampleinfo(...):
...
return _new_exampleinfo(...)
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 in the call to rule:
example_binary = rule(
implementation = _example_binary_impl,
executable = True,
...
)
example_test = rule(
implementation = _example_binary_impl,
test = True,
...
)
Test rules must have names that end in _test. (Test target names also often
end in _test by convention, but this is not required.) 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. That executable is added to the default outputs of the rule (so you
don't need to pass that to both executable and files). It's also implicitly
added to the runfiles:
def _example_binary_impl(ctx):
executable = ctx.actions.declare_file(ctx.label.name)
...
return [
DefaultInfo(executable = executable, ...),
...
]
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, pass 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.
Executable rules and test rules have additional attributes implicitly defined, in addition to those added for all rules. The defaults of implicitly-added attributes cannot be changed, though this can be worked around by wrapping a private rule in a Starlark macro which alters the default:
def example_test(size="small", **kwargs):
_example_test(size=size, **kwargs)
_example_test = rule(
...
)
Runfiles location
When an executable target is run with bazel run (or test), the root of the
runfiles directory is adjacent to the executable. The paths relate as follows:
# Given launcher_path and runfile_file:
runfiles_root = launcher_path.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)
The path to a File under the runfiles directory corresponds to
File.short_path.
The binary executed directly by bazel is adjacent to the root of the
runfiles directory. However, binaries called from the runfiles can't make
the same assumption. To mitigate this, each binary should provide a way to
accept its runfiles root as a parameter using an environment or command line
argument/flag. This allows binaries to pass the correct canonical runfiles root
to the binaries it calls. If that's not set, a binary can guess that it was the
first binary called and look for an adjacent runfiles directory.
Advanced topics
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.)
In addition to default outputs, any predeclared output can
be explicitly requested on the command line. Rules can specify predeclared
outputs via output 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. Rules can
implicitly define predeclared outputs based
on the target name as well, but this feature is deprecated.
In addition to default outputs, there are output groups, which are collections
of output files that may be requested together. These can be requested with
--output_groups. 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. Since non-predeclared outputs don't have labels,
they can only be requested by appearing in the default outputs or an output
group.
Output groups can be specified with the
OutputGroupInfo provider. Note that unlike many
built-in providers, OutputGroupInfo can take parameters with arbitrary names
to define output groups with that name:
def _example_library_impl(ctx):
...
debug_file = ctx.actions.declare_file(name + ".pdb")
...
return [
DefaultInfo(files = depset([output_file]), ...),
OutputGroupInfo(
debug_files = depset([debug_file]),
all_files = depset([output_file, debug_file]),
),
...
]
Also unlike most providers, OutputGroupInfo can be returned by both an
aspect and the rule target to which that aspect is applied, as
long as they do not define the same output groups. In that case, the resulting
providers are merged.
Note that OutputGroupInfo generally shouldn't be used to convey specific sorts
of files from a target to the actions of its consumers. Define
rule-specific providers for that instead.
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 the execution platform (which could be your host, or a remote executor). 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 on the execution platform are built in an "exec" 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 an exec configuration. This causes the tool and all its dependencies to build for the execution platform.
For each dependency attribute, you can use cfg to decide if dependencies
should build in the same configuration or transition to an exec configuration.
If a dependency attribute has the flag executable=True, cfg must be set
explicitly. This is to guard against accidentally building a 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 (such as compilers or code generators)
should be built for an exec configuration. In this case, specify cfg="exec" in
the attribute.
Otherwise, executables that are used at runtime (such as 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.
You can also use cfg=my_transition to use
user-defined transitions, which allow
rule authors a great deal of flexibility in changing configurations, with the
drawback of
making the build graph larger and less comprehensible.
Note: Historically, Bazel didn't have the concept of execution platforms, and instead all build actions were considered to run on the host machine. Bazel versions before 6.0 created a distinct "host" configuration to represent this. If you see references to "host" in code or old documentation, that's what this refers to. We recommend using Bazel 6.0 or newer to avoid this extra conceptual overhead.
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 leads 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
...
)
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 main workspace (not the name of the repository containing the
current target).
...
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.
Code coverage
When the coverage command is run,
the build may need to add coverage instrumentation for certain targets. The
build also gathers the list of source files that are instrumented. The subset of
targets that are considered is controlled by the flag
--instrumentation_filter.
Test targets are excluded, unless
--instrument_test_targets
is specified.
If a rule implementation adds coverage instrumentation at build time, it needs to account for that in its implementation function. ctx.coverage_instrumented returns true in coverage mode if a target's sources should be instrumented:
# Are this rule's sources instrumented?
if ctx.coverage_instrumented():
# Do something to turn on coverage for this compile action
Logic that always needs to be on in coverage mode (whether a target's sources specifically are instrumented or not) can be conditioned on ctx.configuration.coverage_enabled.
If the rule directly includes sources from its dependencies before compilation (such as header files), it may also need to turn on compile-time instrumentation if the dependencies' sources should be instrumented:
# Are this rule's sources or any of the sources for its direct dependencies
# in deps instrumented?
if (ctx.configuration.coverage_enabled and
(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
Rules also should provide information about which attributes are relevant for
coverage with the InstrumentedFilesInfo provider, constructed using
coverage_common.instrumented_files_info.
The dependency_attributes parameter of instrumented_files_info should list
all runtime dependency attributes, including code dependencies like deps and
data dependencies like data. The source_attributes parameter should list the
rule's source files attributes if coverage instrumentation might be added:
def _example_library_impl(ctx):
...
return [
...
coverage_common.instrumented_files_info(
ctx,
dependency_attributes = ["deps", "data"],
# Omitted if coverage is not supported for this rule:
source_attributes = ["srcs", "hdrs"],
)
...
]
If InstrumentedFilesInfo is not returned, a default one is created with each
non-tool dependency attribute that doesn't set
cfg to "host" or "exec" in the attribute schema) in
dependency_attributes. (This isn't ideal behavior, since it puts attributes
like srcs in dependency_attributes instead of source_attributes, but it
avoids the need for explicit coverage configuration for all rules in the
dependency chain.)
Validation Actions
Sometimes you need to validate something about the build, and the information required to do that validation is available only in artifacts (source files or generated files). Because this information is in artifacts, rules cannot do this validation at analysis time because rules cannot read files. Instead, actions must do this validation at execution time. When validation fails, the action will fail, and hence so will the build.
Examples of validations that might be run are static analysis, linting, dependency and consistency checks, and style checks.
Validation actions can also help to improve build performance by moving parts of actions that are not required for building artifacts into separate actions. For example, if a single action that does compilation and linting can be separated into a compilation action and a linting action, then the linting action can be run as a validation action and run in parallel with other actions.
These "validation actions" often don't produce anything that is used elsewhere in the build, since they only need to assert things about their inputs. This presents a problem though: If a validation action does not produce anything that is used elsewhere in the build, how does a rule get the action to run? Historically, the approach was to have the validation action output an empty file, and artificially add that output to the inputs of some other important action in the build:
This works, because Bazel will always run the validation action when the compile action is run, but this has significant drawbacks:
The validation action is in the critical path of the build. Because Bazel thinks the empty output is required to run the compile action, it will run the validation action first, even though the compile action will ignore the input. This reduces parallelism and slows down builds.
If other actions in the build might run instead of the compile action, then the empty outputs of validation actions need to be added to those actions as well (
java_library's source jar output, for example). This is also a problem if new actions that might run instead of the compile action are added later, and the empty validation output is accidentally left off.
The solution to these problems is to use the Validations Output Group.
Validations Output Group
The Validations Output Group is an output group designed to hold the otherwise unused outputs of validation actions, so that they don't need to be artificially added to the inputs of other actions.
This group is special in that its outputs are always requested, regardless of
the value of the --output_groups flag, and regardless of how the target is
depended upon (for example, on the command line, as a dependency, or through
implicit outputs of the target). Note that normal caching and incrementality
still apply: if the inputs to the validation action have not changed and the
validation action previously succeeded, then the validation action will not be
run.
Using this output group still requires that validation actions output some file, even an empty one. This might require wrapping some tools that normally don't create outputs so that a file is created.
A target's validation actions are not run in three cases:
- When the target is depended upon as a tool
- When the target is depended upon as an implicit dependency (for example, an attribute that starts with "_")
- When the target is built in the host or exec configuration.
It is assumed that these targets have their own separate builds and tests that would uncover any validation failures.
Using the Validations Output Group
The Validations Output Group is named _validation and is used like any other
output group:
def _rule_with_validation_impl(ctx):
ctx.actions.write(ctx.outputs.main, "main output\n")
ctx.actions.write(ctx.outputs.implicit, "implicit output\n")
validation_output = ctx.actions.declare_file(ctx.attr.name + ".validation")
ctx.actions.run(
outputs = [validation_output],
executable = ctx.executable._validation_tool,
arguments = [validation_output.path])
return [
DefaultInfo(files = depset([ctx.outputs.main])),
OutputGroupInfo(_validation = depset([validation_output])),
]
rule_with_validation = rule(
implementation = _rule_with_validation_impl,
outputs = {
"main": "%{name}.main",
"implicit": "%{name}.implicit",
},
attrs = {
"_validation_tool": attr.label(
default = Label("//validation_actions:validation_tool"),
executable = True,
cfg = "exec"),
}
)
Notice that the validation output file is not added to the DefaultInfo or the
inputs to any other action. The validation action for a target of this rule kind
will still run if the target is depended upon by label, or any of the target's
implicit outputs are directly or indirectly depended upon.
It is usually important that the outputs of validation actions only go into the validation output group, and are not added to the inputs of other actions, as this could defeat parallelism gains. Note however that Bazel does not currently have any special checking to enforce this. Therefore, you should test that validation action outputs are not added to the inputs of any actions in the tests for Starlark rules. For example:
load("@bazel_skylib//lib:unittest.bzl", "analysistest")
def _validation_outputs_test_impl(ctx):
env = analysistest.begin(ctx)
actions = analysistest.target_actions(env)
target = analysistest.target_under_test(env)
validation_outputs = target.output_groups._validation.to_list()
for action in actions:
for validation_output in validation_outputs:
if validation_output in action.inputs.to_list():
analysistest.fail(env,
"%s is a validation action output, but is an input to action %s" % (
validation_output, action))
return analysistest.end(env)
validation_outputs_test = analysistest.make(_validation_outputs_test_impl)
Validation Actions Flag
Running validation actions is controlled by the --run_validations command line
flag, which defaults to true.
Deprecated features
Deprecated predeclared outputs
There are two deprecated ways of using predeclared outputs:
The
outputsparameter ofrulespecifies a mapping between output attribute names and string templates for generating predeclared output labels. Prefer using non-predeclared outputs and explicitly adding outputs toDefaultInfo.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.executablerefers to a predeclared executable output with the same name as the rule target. Prefer declaring the output explicitly, for example withctx.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 theexecutableparameter ofDefaultInfo.
Runfiles features to avoid
ctx.runfiles and the runfiles
type have a complex set of features, many of which are kept for legacy reasons.
The following recommendations help reduce complexity:
Avoid use of the
collect_dataandcollect_defaultmodes ofctx.runfiles. These modes implicitly collect runfiles across certain hardcoded dependency edges in confusing ways. Instead, add files using thefilesortransitive_filesparameters ofctx.runfiles, or by merging in runfiles from dependencies withrunfiles = runfiles.merge(dep[DefaultInfo].default_runfiles).Avoid use of the
data_runfilesanddefault_runfilesof theDefaultInfoconstructor. SpecifyDefaultInfo(runfiles = ...)instead. The distinction between "default" and "data" runfiles is maintained for legacy reasons. For example, some rules put their default outputs indata_runfiles, but notdefault_runfiles. Instead of usingdata_runfiles, rules should both include default outputs and merge indefault_runfilesfrom attributes which provide runfiles (oftendata).When retrieving
runfilesfromDefaultInfo(generally only for merging runfiles between the current rule and its dependencies), useDefaultInfo.default_runfiles, notDefaultInfo.data_runfiles.
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 _old_rule_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,
...
# Additional modern providers:
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 thus do not create a corresponding legacy
provider):
The fields
files,runfiles,data_runfiles,default_runfiles, andexecutablecorrespond to the same-named fields ofDefaultInfo. It is not allowed to specify any of these fields while also returning aDefaultInfoprovider.The field
output_groupstakes a struct value and corresponds to anOutputGroupInfo.
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:
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.
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 usingFooInfo in target.Fully remove the legacy provider from all rules.