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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:
.cpp source files (inputs).g++ on the source files (action).DefaultInfo provider with the executable output and other files to make available at runtime.CcInfo provider 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.
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.
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.
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.
Rules that process source code usually define the following attributes to handle various types of dependencies:
srcs specifies 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 separate hdrs attribute for headers processed by a target and its consumers.deps specifies code dependencies for a target. The attribute schema should specify which providers those dependencies must provide. (For example, cc_library provides CcInfo.)data specifies 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.
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, such as attr.output and attr.output_list, declare an output file that the target generates. These differ from dependency attributes in two ways:
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.
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.
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 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.
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 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.
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 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.
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 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), ... ]
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 ], ), ) ]
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 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{: .external} and a test rule{: .external}.
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( ... )
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 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)
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.
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.
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{: .external}
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. Because of this, there is a single “host” configuration, and a “host” transition that can be used to build a dependency in the host configuration. Many rules still use the “host” transition for their tools, but this is currently deprecated and being migrated to use “exec” transitions where possible.
There are numerous differences between the “host” and “exec” configurations:
Both the “exec” and “host” configurations apply the same option changes, (for example, set --compilation_mode from --host_compilation_mode, set --cpu from --host_cpu, etc). The difference is that the “host” configuration starts with the default values of all other flags, whereas the “exec” configuration starts with the current values of flags, based on the target configuration.
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 ... )
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.
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.
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 explcit coverage configuration for all rules in the dependency chain.)
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.
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:
It is assumed that these targets have their own separate builds and tests that would uncover any validation failures.
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)
Running validation actions is controlled by the --run_validations command line flag, which defaults to true.
There are 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.
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_data and collect_default modes of ctx.runfiles. These modes implicitly collect runfiles across certain hardcoded dependency edges in confusing ways. Instead, add files using the files or transitive_files parameters of ctx.runfiles, or by merging in runfiles from dependencies with runfiles = runfiles.merge(dep[DefaultInfo].default_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. For example, some rules put their default outputs in data_runfiles, but not default_runfiles. Instead of using data_runfiles, rules should both include default outputs and merge in default_runfiles from attributes which provide runfiles (often data).
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.
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, 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 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:
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 using FooInfo in target.
Fully remove the legacy provider from all rules.