---
title: 'The Bazel codebase'
---


This document is a description of the codebase and how Bazel is structured. It
is intended for people willing to contribute to Bazel, not for end-users.

## Introduction {#introduction}

The codebase of Bazel is large (~350KLOC production code and ~260 KLOC test
code) and no one is familiar with the whole landscape: everyone knows their
particular valley very well, but few know what lies over the hills in every
direction.

In order for people midway upon the journey not to find themselves within a
forest dark with the straightforward pathway being lost, this document tries to
give an overview of the codebase so that it's easier to get started with
working on it.

The public version of the source code of Bazel lives on GitHub at
[github.com/bazelbuild/bazel](http://github.com/bazelbuild/bazel). This is not
the "source of truth"; it's derived from a Google-internal source tree that
contains additional functionality that is not useful outside Google. The
long-term goal is to make GitHub the source of truth.

Contributions are accepted through the regular GitHub pull request mechanism,
and manually imported by a Googler into the internal source tree, then
re-exported back out to GitHub.

## Client/server architecture {#client-architecture}

The bulk of Bazel resides in a server process that stays in RAM between builds.
This allows Bazel to maintain state between builds.

This is why the Bazel command line has two kinds of options: startup and
command. In a command line like this:

```
    bazel --host_jvm_args=-Xmx8G build -c opt //foo:bar
```

Some options (`--host_jvm_args=`) are before the name of the command to be run
and some are after (`-c opt`); the former kind is called a "startup option" and
affects the server process as a whole, whereas the latter kind, the "command
option", only affects a single command.

Each server instance has a single associated workspace (collection of source
trees known as "repositories") and each workspace usually has a single active
server instance. This can be circumvented by specifying a custom output base
(see the "Directory layout" section for more information).

Bazel is distributed as a single ELF executable that is also a valid .zip file.
When you type `bazel`, the above ELF executable implemented in C++ (the
"client") gets control. It sets up an appropriate server process using the
following steps:

1.  Checks whether it has already extracted itself. If not, it does that. This
    is where the implementation of the server comes from.
2.  Checks whether there is an active server instance that works: it is running,
    it has the right startup options and uses the right workspace directory. It
    finds the running server by looking at the directory `$OUTPUT_BASE/server`
    where there is a lock file with the port the server is listening on.
3.  If needed, kills the old server process
4.  If needed, starts up a new server process

After a suitable server process is ready, the command that needs to be run is
communicated to it over a gRPC interface, then the output of Bazel is piped back
to the terminal. Only one command can be running at the same time. This is
implemented using an elaborate locking mechanism with parts in C++ and parts in
Java. There is some infrastructure for running multiple commands in parallel,
since the inability to run `bazel version` in parallel with another command
is somewhat embarrassing. The main blocker is the life cycle of `BlazeModule`s
and some state in `BlazeRuntime`.

At the end of a command, the Bazel server transmits the exit code the client
should return. An interesting wrinkle is the implementation of `bazel run`: the
job of this command is to run something Bazel just built, but it can't do that
from the server process because it doesn't have a terminal. So instead it tells
the client what binary it should `exec()` and with what arguments.

When one presses Ctrl-C, the client translates it to a Cancel call on the gRPC
connection, which tries to terminate the command as soon as possible. After the
third Ctrl-C, the client sends a SIGKILL to the server instead.

The source code of the client is under `src/main/cpp` and the protocol used to
communicate with the server is in `src/main/protobuf/command_server.proto` .

The main entry point of the server is `BlazeRuntime.main()` and the gRPC calls
from the client are handled by `CommandServer.serveAndAwaitTermination()`.

## Directory layout {#directory-layout}

Bazel creates a somewhat complicated set of directories during a build. A full
description is available in [Output directory layout](/remote/output-directories).

The "main repo" is the source tree Bazel is run in. It usually corresponds to
something you checked out from source control. The root of this directory is
known as the "workspace root".

Bazel puts all of its data under the "output user root". This is usually
`$HOME/.cache/bazel/_bazel_${USER}`, but can be overridden using the
`--output_user_root` startup option.

The "install base" is where Bazel is extracted to. This is done automatically
and each Bazel version gets a subdirectory based on its checksum under the
install base. It's at `$OUTPUT_USER_ROOT/install` by default and can be changed
using the `--install_base` command line option.

The "output base" is the place where the Bazel instance attached to a specific
workspace writes to. Each output base has at most one Bazel server instance
running at any time. It's usually at `$OUTPUT_USER_ROOT/<checksum of the path
to the workspace>`. It can be changed using the `--output_base` startup option,
which is, among other things, useful for getting around the limitation that only
one Bazel instance can be running in any workspace at any given time.

The output directory contains, among other things:

*   The fetched external repositories at `$OUTPUT_BASE/external`.
*   The exec root, a directory that contains symlinks to all the source
    code for the current build. It's located at `$OUTPUT_BASE/execroot`. During
    the build, the working directory is `$EXECROOT/<name of main
    repository>`. We are planning to change this to `$EXECROOT`, although it's a
    long term plan because it's a very incompatible change.
*   Files built during the build.

## The process of executing a command {#command-process}

Once the Bazel server gets control and is informed about a command it needs to
execute, the following sequence of events happens:

1.  `BlazeCommandDispatcher` is informed about the new request. It decides
    whether the command needs a workspace to run in (almost every command except
    for ones that don't have anything to do with source code, such as version or
    help) and whether another command is running.

2.  The right command is found. Each command must implement the interface
    `BlazeCommand` and must have the `@Command` annotation (this is a bit of an
    antipattern, it would be nice if all the metadata a command needs was
    described by methods on `BlazeCommand`)

3.  The command line options are parsed. Each command has different command line
    options, which are described in the `@Command` annotation.

4.  An event bus is created. The event bus is a stream for events that happen
    during the build. Some of these are exported to outside of Bazel under the
    aegis of the Build Event Protocol in order to tell the world how the build
    goes.

5.  The command gets control. The most interesting commands are those that run a
    build: build, test, run, coverage and so on: this functionality is
    implemented by `BuildTool`.

6.  The set of target patterns on the command line is parsed and wildcards like
    `//pkg:all` and `//pkg/...` are resolved. This is implemented in
    `AnalysisPhaseRunner.evaluateTargetPatterns()` and reified in Skyframe as
    `TargetPatternPhaseValue`.

7.  The loading/analysis phase is run to produce the action graph (a directed
    acyclic graph of commands that need to be executed for the build).

8.  The execution phase is run. This means running every action required to
    build the top-level targets that are requested are run.

## Command line options {#command-options}

The command line options for a Bazel invocation are parsed into an
`OptionsParsingResult` object, which holds instances of `OptionsBase`
subclasses populated with the parsed values. An "option class" is a subclass
of `OptionsBase` and groups related command line options together.

There are two main kinds of option classes:

1.  **Configuration options:** These options affect how targets are built,
    for example, defining the target platform or compilation mode. They are
    defined in subclasses of `FragmentOptions` (e.g., `CppOptions`,
    `JavaOptions`), which is itself a subclass of `OptionsBase`.
    `FragmentOptions` instances are collected into a `BuildOptions` object,
    which is used to create the `BuildConfiguration` for a configured target.
    These options are available during the analysis phase via
    `RuleContext.getFragment()` in Java or `ctx.fragments` in Starlark.
2.  **Other command options:** These options affect other aspects of Bazel's
    behavior. They are defined in classes that extend `OptionsBase` directly,
    but are not `FragmentOptions`. Examples include `ExecutionOptions`, which
    influences how actions are executed, and `CommonCommandOptions`, which
    contains options applicable to many commands. These are not part of
    `BuildOptions`.

Some configuration options (for example, whether to do C++ include scanning with
`--cc_include_scanning`) are read in the execution phase. But that always
requires explicit plumbing since `BuildConfiguration` is not available then. For
more information, see the section "Configurations".

**WARNING:** We like to pretend that `OptionsBase` instances are immutable and
use them that way (such as a part of `SkyKeys`). This is not the case and
modifying them is a really good way to break Bazel in subtle ways that are hard
to debug. Unfortunately, making them actually immutable is a large endeavor.
(Modifying a `FragmentOptions` immediately after construction before anyone else
gets a chance to keep a reference to it and before `equals()` or `hashCode()` is
called on it is okay.)

Bazel learns about option classes in the following ways:

1.  Some are hard-wired into Bazel (`CommonCommandOptions`).
2.  From the `@Command` annotation on each Bazel command, which
    lists option classes applicable to that command.
3.  From `ConfiguredRuleClassProvider` (these are `FragmentOptions` for
    individual programming languages that become part of `BuildOptions`).
4.  Starlark rules can also define their own options, known as build settings
    (see [here](/extending/config)).

Each option (excluding Starlark-defined options) is a member variable of an
`OptionsBase` subclass that has the `@Option` annotation, which specifies
the name and the type of the command line option along with some help text.

The Java type of the value of a command line option is usually something simple
(a string, an integer, a Boolean, a label, etc.). However, we also support
options of more complicated types; in this case, the job of converting from the
command line string to the data type falls to an implementation of
`com.google.devtools.common.options.Converter`.

## The source tree, as seen by Bazel {#source-tree}

Bazel is in the business of building software, which happens by reading and
interpreting the source code. The totality of the source code Bazel operates on
is called "the workspace" and it is structured into repositories, packages and
rules.

### Repositories {#repositories}

A "repository" is a source tree on which a developer works; it usually
represents a single project. Bazel's ancestor, Blaze, operated on a monorepo,
that is, a single source tree that contains all source code used to run the build.
Bazel, in contrast, supports projects whose source code spans multiple
repositories. The repository from which Bazel is invoked is called the "main
repository", the others are called "external repositories".

A repository is marked by a repo boundary file (`MODULE.bazel`, `REPO.bazel`, or
in legacy contexts, `WORKSPACE` or `WORKSPACE.bazel`) in its root directory. The
main repo is the source tree where you're invoking Bazel from. External repos
are defined in various ways; see [external dependencies
overview](/external/overview) for more information.

Code of external repositories is symlinked or downloaded under
`$OUTPUT_BASE/external`.

When running the build, the whole source tree needs to be pieced together; this
is done by `SymlinkForest`, which symlinks every package in the main repository
to `$EXECROOT` and every external repository to either `$EXECROOT/external` or
`$EXECROOT/..`.

### Packages {#packages}

Every repository is composed of packages, a collection of related files and
a specification of the dependencies. These are specified by a file called
`BUILD` or `BUILD.bazel`. If both exist, Bazel prefers `BUILD.bazel`; the reason
why `BUILD` files are still accepted is that Bazel's ancestor, Blaze, used this
file name. However, it turned out to be a commonly used path segment, especially
on Windows, where file names are case-insensitive.

Packages are independent of each other: changes to the `BUILD` file of a package
cannot cause other packages to change. The addition or removal of `BUILD` files
_can _change other packages, since recursive globs stop at package boundaries
and thus the presence of a `BUILD` file stops the recursion.

The evaluation of a `BUILD` file is called "package loading". It's implemented
in the class `PackageFactory`, works by calling the Starlark interpreter and
requires knowledge of the set of available rule classes. The result of package
loading is a `Package` object. It's mostly a map from a string (the name of a
target) to the target itself.

A large chunk of complexity during package loading is globbing: Bazel does not
require every source file to be explicitly listed and instead can run globs
(such as `glob(["**/*.java"])`). Unlike the shell, it supports recursive globs that
descend into subdirectories (but not into subpackages). This requires access to
the file system and since that can be slow, we implement all sorts of tricks to
make it run in parallel and as efficiently as possible.

Globbing is implemented in the following classes:

*   `LegacyGlobber`, a fast and blissfully Skyframe-unaware globber
*   `SkyframeHybridGlobber`, a version that uses Skyframe and reverts back to
    the legacy globber in order to avoid "Skyframe restarts" (described below)

The `Package` class itself contains some members that are exclusively used to
parse the "external" package (related to external dependencies) and which do not
make sense for real packages. This is
a design flaw because objects describing regular packages should not contain
fields that describe something else. These include:

*   The repository mappings
*   The registered toolchains
*   The registered execution platforms

Ideally, there would be more separation between parsing the "external" package
from parsing regular packages so that `Package` does not need to cater for the
needs of both. This is unfortunately difficult to do because the two are
intertwined quite deeply.

### Labels, Targets, and Rules {#labels-targets-rules}

Packages are composed of targets, which have the following types:

1.  **Files:** things that are either the input or the output of the build. In
    Bazel parlance, we call them _artifacts_ (discussed elsewhere). Not all
    files created during the build are targets; it's common for an output of
    Bazel not to have an associated label.
2.  **Rules:** these describe steps to derive its outputs from its inputs. They
    are generally associated with a programming language (such as `cc_library`,
    `java_library` or `py_library`), but there are some language-agnostic ones
    (such as `genrule` or `filegroup`)
3.  **Package groups:** discussed in the [Visibility](#visibility) section.

The name of a target is called a _Label_. The syntax of labels is
`@repo//pac/kage:name`, where `repo` is the name of the repository the Label is
in, `pac/kage` is the directory its `BUILD` file is in and `name` is the path of
the file (if the label refers to a source file) relative to the directory of the
package. When referring to a target on the command line, some parts of the label
can be omitted:

1.  If the repository is omitted, the label is taken to be in the main
    repository.
2.  If the package part is omitted (such as `name` or `:name`), the label is taken
    to be in the package of the current working directory (relative paths
    containing uplevel references (..) are not allowed)

A kind of a rule (such as "C++ library") is called a "rule class". Rule classes may
be implemented either in Starlark (the `rule()` function) or in Java (so called
"native rules", type `RuleClass`). In the long term, every language-specific
rule will be implemented in Starlark, but some legacy rule families (such as Java
or C++) are still in Java for the time being.

Starlark rule classes need to be imported at the beginning of `BUILD` files
using the `load()` statement, whereas Java rule classes are "innately" known by
Bazel, by virtue of being registered with the `ConfiguredRuleClassProvider`.

Rule classes contain information such as:

1.  Its attributes (such as `srcs`, `deps`): their types, default values,
    constraints, etc.
2.  The configuration transitions and aspects attached to each attribute, if any
3.  The implementation of the rule
4.  The transitive info providers the rule "usually" creates

**Terminology note:** In the codebase, we often use "Rule" to mean the target
created by a rule class. But in Starlark and in user-facing documentation,
"Rule" should be used exclusively to refer to the rule class itself; the target
is just a "target". Also note that despite `RuleClass` having "class" in its
name, there is no Java inheritance relationship between a rule class and targets
of that type.

## Skyframe {#skyframe}

The evaluation framework underlying Bazel is called Skyframe. Its model is that
everything that needs to be built during a build is organized into a directed
acyclic graph with edges pointing from any pieces of data to its dependencies,
that is, other pieces of data that need to be known to construct it.

The nodes in the graph are called `SkyValue`s and their names are called
`SkyKey`s. Both are deeply immutable; only immutable objects should be
reachable from them. This invariant almost always holds, and in case it doesn't
(such as for the individual options classes `BuildOptions`, which is a member of
`BuildConfigurationValue` and its `SkyKey`) we try really hard not to change
them or to change them in only ways that are not observable from the outside.
From this it follows that everything that is computed within Skyframe (such as
configured targets) must also be immutable.

The most convenient way to observe the Skyframe graph is to run `bazel dump
--skyframe=deps`, which dumps the graph, one `SkyValue` per line. It's best
to do it for tiny builds, since it can get pretty large.

Skyframe lives in the `com.google.devtools.build.skyframe` package. The
similarly-named package `com.google.devtools.build.lib.skyframe` contains the
implementation of Bazel on top of Skyframe. More information about Skyframe is
available [here](/reference/skyframe).

To evaluate a given `SkyKey` into a `SkyValue`, Skyframe will invoke the
`SkyFunction` corresponding to the type of the key. During the function's
evaluation, it may request other dependencies from Skyframe by calling the
various overloads of `SkyFunction.Environment.getValue()`. This has the
side-effect of registering those dependencies into Skyframe's internal graph, so
that Skyframe will know to re-evaluate the function when any of its dependencies
change. In other words, Skyframe's caching and incremental computation work at
the granularity of `SkyFunction`s and `SkyValue`s.

Whenever a `SkyFunction` requests a dependency that is unavailable, `getValue()`
will return null. The function should then yield control back to Skyframe by
itself returning null. At some later point, Skyframe will evaluate the
unavailable dependency, then restart the function from the beginning — only this
time the `getValue()` call will succeed with a non-null result.

A consequence of this is that any computation performed inside the `SkyFunction`
prior to the restart must be repeated. But this does not include work done to
evaluate dependency `SkyValues`, which are cached. Therefore, we commonly work
around this issue by:

1.  Declaring dependencies in batches (by using `getValuesAndExceptions()`) to
    limit the number of restarts.
2.  Breaking up a `SkyValue` into separate pieces computed by different
    `SkyFunction`s, so that they can be computed and cached independently. This
    should be done strategically, since it has the potential to increases memory
    usage.
3.  Storing state between restarts, either using
    `SkyFunction.Environment.getState()`, or keeping an ad hoc static cache
    "behind the back of Skyframe". With complex SkyFunctions, state management
    between restarts can get tricky, so
    [`StateMachine`s](/contribute/statemachine-guide) were introduced for a
    structured approach to logical concurrency, including hooks to suspend and
    resume hierarchical computations within a `SkyFunction`. Example:
    [`DependencyResolver#computeDependencies`][statemachine_example]
    uses a `StateMachine` with `getState()` to compute the potentially huge set
    of direct dependencies of a configured target, which otherwise can result in
    expensive restarts.

[statemachine_example]: https://developers.google.com/devsite/reference/markdown/links#reference_links

Fundamentally, Bazel need these types of workarounds because hundreds of
thousands of in-flight Skyframe nodes is common, and Java's support of
lightweight threads [does not outperform][virtual_threads] the
`StateMachine` implementation as of 2023.

[virtual_threads]: /contribute/statemachine-guide#epilogue_eventually_removing_callbacks

## Starlark {#starlark}

Starlark is the domain-specific language people use to configure and extend
Bazel. It's conceived as a restricted subset of Python that has far fewer types,
more restrictions on control flow, and most importantly, strong immutability
guarantees to enable concurrent reads. It is not Turing-complete, which
discourages some (but not all) users from trying to accomplish general
programming tasks within the language.

Starlark is implemented in the `net.starlark.java` package.
It also has an independent Go implementation
[here](https://github.com/google/starlark-go). The Java
implementation used in Bazel is currently an interpreter.

Starlark is used in several contexts, including:

1.  **`BUILD` files.** This is where new build targets are defined. Starlark
    code running in this context only has access to the contents of the `BUILD`
    file itself and `.bzl` files loaded by it.
2.  **The `MODULE.bazel` file.** This is where external dependencies are
    defined. Starlark code running in this context only has very limited access
    to a few predefined directives.
3.  **`.bzl` files.** This is where new build rules, repo rules, module
    extensions are defined. Starlark code here can define new functions and load
    from other `.bzl` files.

The dialects available for `BUILD` and `.bzl` files are slightly different
because they express different things. A list of differences is available
[here](/rules/language#differences-between-build-and-bzl-files).

More information about Starlark is available [here](/rules/language).

## The loading/analysis phase {#loading-phase}

The loading/analysis phase is where Bazel determines what actions are needed to
build a particular rule. Its basic unit is a "configured target", which is,
quite sensibly, a (target, configuration) pair.

It's called the "loading/analysis phase" because it can be split into two
distinct parts, which used to be serialized, but they can now overlap in time:

1.  Loading packages, that is, turning `BUILD` files into the `Package` objects
    that represent them
2.  Analyzing configured targets, that is, running the implementation of the
    rules to produce the action graph

Each configured target in the transitive closure of the configured targets
requested on the command line must be analyzed bottom-up; that is, leaf nodes
first, then up to the ones on the command line. The inputs to the analysis of
a single configured target are:

1.  **The configuration.** ("how" to build that rule; for example, the target
    platform but also things like command line options the user wants to be
    passed to the C++ compiler)
2.  **The direct dependencies.** Their transitive info providers are available
    to the rule being analyzed. They are called like that because they provide a
    "roll-up" of the information in the transitive closure of the configured
    target, such as all the .jar files on the classpath or all the .o files that
    need to be linked into a C++ binary)
3.  **The target itself**. This is the result of loading the package the target
    is in. For rules, this includes its attributes, which is usually what
    matters.
4.  **The implementation of the configured target.** For rules, this can either
    be in Starlark or in Java. All non-rule configured targets are implemented
    in Java.

The output of analyzing a configured target is:

1.  The transitive info providers that configured targets that depend on it can
    access
2.  The artifacts it can create and the actions that produce them.

The API offered to Java rules is `RuleContext`, which is the equivalent of the
`ctx` argument of Starlark rules. Its API is more powerful, but at the same
time, it's easier to do Bad Things™, for example to write code whose time or
space complexity is quadratic (or worse), to make the Bazel server crash with a
Java exception or to violate invariants (such as by inadvertently modifying an
`Options` instance or by making a configured target mutable)

The algorithm that determines the direct dependencies of a configured target
lives in `DependencyResolver.dependentNodeMap()`.

### Configurations {#configurations}

Configurations are the "how" of building a target: for what platform, with what
command line options, etc.

The same target can be built for multiple configurations in the same build. This
is useful, for example, when the same code is used for a tool that's run during
the build and for the target code and we are cross-compiling or when we are
building a fat Android app (one that contains native code for multiple CPU
architectures)

Conceptually, the configuration is a `BuildOptions` instance. However, in
practice, `BuildOptions` is wrapped by `BuildConfiguration` that provides
additional sundry pieces of functionality. It propagates from the top of the
dependency graph to the bottom. If it changes, the build needs to be
re-analyzed.

This results in anomalies like having to re-analyze the whole build if, for
example, the number of requested test runs changes, even though that only
affects test targets (we have plans to "trim" configurations so that this is
not the case, but it's not ready yet).

When a rule implementation needs part of the configuration, it needs to declare
it in its definition using `RuleClass.Builder.requiresConfigurationFragments()`
. This is both to avoid mistakes (such as Python rules using the Java fragment) and
to facilitate configuration trimming so that such as if Python options change, C++
targets don't need to be re-analyzed.

The configuration of a rule is not necessarily the same as that of its "parent"
rule. The process of changing the configuration in a dependency edge is called a
"configuration transition". It can happen in two places:

1.  On a dependency edge. These transitions are specified in
    `Attribute.Builder.cfg()` and are functions from a `Rule` (where the
    transition happens) and a `BuildOptions` (the original configuration) to one
    or more `BuildOptions` (the output configuration).
2.  On any incoming edge to a configured target. These are specified in
    `RuleClass.Builder.cfg()`.

The relevant classes are `TransitionFactory` and `ConfigurationTransition`.

Configuration transitions are used, for example:

1.  To declare that a particular dependency is used during the build and it
    should thus be built in the execution architecture
2.  To declare that a particular dependency must be built for multiple
    architectures (such as for native code in fat Android APKs)

If a configuration transition results in multiple configurations, it's called a
_split transition._

Configuration transitions can also be implemented in Starlark (documentation
[here](/extending/config))

### Transitive info providers {#transitive-info-providers}

Transitive info providers are a way (and the _only _way) for configured targets
to learn things about other configured targets that they depend on, and the only
way to tell things about themselves to other configured targets that depend on
them. The reason why "transitive" is in their name is that this is usually some
sort of roll-up of the transitive closure of a configured target.

There is generally a 1:1 correspondence between Java transitive info providers
and Starlark ones (the exception is `DefaultInfo` which is an amalgamation of
`FileProvider`, `FilesToRunProvider` and `RunfilesProvider` because that API was
deemed to be more Starlark-ish than a direct transliteration of the Java one).
Their key is one of the following things:

1.  A Java Class object. This is only available for providers that are not
    accessible from Starlark. These providers are a subclass of
    `TransitiveInfoProvider`.
2.  A string. This is legacy and heavily discouraged since it's susceptible to
    name clashes. Such transitive info providers are direct subclasses of
    `build.lib.packages.Info` .
3.  A provider symbol. This can be created from Starlark using the `provider()`
    function and is the recommended way to create new providers. The symbol is
    represented by a `Provider.Key` instance in Java.

New providers implemented in Java should be implemented using `BuiltinProvider`.
`NativeProvider` is deprecated (we haven't had time to remove it yet) and
`TransitiveInfoProvider` subclasses cannot be accessed from Starlark.

### Configured targets {#configured-targets}

Configured targets are implemented as `RuleConfiguredTargetFactory`. There is a
subclass for each rule class implemented in Java. Starlark configured targets
are created through `StarlarkRuleConfiguredTargetUtil.buildRule()` .

Configured target factories should use `RuleConfiguredTargetBuilder` to
construct their return value. It consists of the following things:

1.  Their `filesToBuild`, the hazy concept of "the set of files this rule
    represents." These are the files that get built when the configured target
    is on the command line or in the srcs of a genrule.
2.  Their runfiles, regular and data.
3.  Their output groups. These are various "other sets of files" the rule can
    build. They can be accessed using the output\_group attribute of the
    filegroup rule in BUILD and using the `OutputGroupInfo` provider in Java.

### Runfiles {#runfiles}

Some binaries need data files to run. A prominent example is tests that need
input files. This is represented in Bazel by the concept of "runfiles". A
"runfiles tree" is a directory tree of the data files for a particular binary.
It is created in the file system as a symlink tree with individual symlinks
pointing to the files in the source or output trees.

A set of runfiles is represented as a `Runfiles` instance. It is conceptually a
map from the path of a file in the runfiles tree to the `Artifact` instance that
represents it. It's a little more complicated than a single `Map` for two
reasons:

*   Most of the time, the runfiles path of a file is the same as its execpath.
    We use this to save some RAM.
*   There are various legacy kinds of entries in runfiles trees, which also need
    to be represented.

Runfiles are collected using `RunfilesProvider`: an instance of this class
represents the runfiles a configured target (such as a library) and its transitive
closure needs and they are gathered like a nested set (in fact, they are
implemented using nested sets under the cover): each target unions the runfiles
of its dependencies, adds some of its own, then sends the resulting set upwards
in the dependency graph. A `RunfilesProvider` instance contains two `Runfiles`
instances, one for when the rule is depended on through the "data" attribute and
one for every other kind of incoming dependency. This is because a target
sometimes presents different runfiles when depended on through a data attribute
than otherwise. This is undesired legacy behavior that we haven't gotten around
removing yet.

Runfiles of binaries are represented as an instance of `RunfilesSupport`. This
is different from `Runfiles` because `RunfilesSupport` has the capability of
actually being built (unlike `Runfiles`, which is just a mapping). This
necessitates the following additional components:

*   **The input runfiles manifest.** This is a serialized description of the
    runfiles tree. It is used as a proxy for the contents of the runfiles tree
    and Bazel assumes that the runfiles tree changes if and only if the contents
    of the manifest change.
*   **The output runfiles manifest.** This is used by runtime libraries that
    handle runfiles trees, notably on Windows, which sometimes doesn't support
    symbolic links.
*   **Command line arguments** for running the binary whose runfiles the
    `RunfilesSupport` object represents.

### Aspects {#aspects}

Aspects are a way to "propagate computation down the dependency graph". They are
described for users of Bazel
[here](/extending/aspects). A good
motivating example is protocol buffers: a `proto_library` rule should not know
about any particular language, but building the implementation of a protocol
buffer message (the "basic unit" of protocol buffers) in any programming
language should be coupled to the `proto_library` rule so that if two targets in
the same language depend on the same protocol buffer, it gets built only once.

Just like configured targets, they are represented in Skyframe as a `SkyValue`
and the way they are constructed is very similar to how configured targets are
built: they have a factory class called `ConfiguredAspectFactory` that has
access to a `RuleContext`, but unlike configured target factories, it also knows
about the configured target it is attached to and its providers.

The set of aspects propagated down the dependency graph is specified for each
attribute using the `Attribute.Builder.aspects()` function. There are a few
confusingly-named classes that participate in the process:

1.  `AspectClass` is the implementation of the aspect. It can be either in Java
    (in which case it's a subclass) or in Starlark (in which case it's an
    instance of `StarlarkAspectClass`). It's analogous to
    `RuleConfiguredTargetFactory`.
2.  `AspectDefinition` is the definition of the aspect; it includes the
    providers it requires, the providers it provides and contains a reference to
    its implementation, such as the appropriate `AspectClass` instance. It's
    analogous to `RuleClass`.
3.  `AspectParameters` is a way to parametrize an aspect that is propagated down
    the dependency graph. It's currently a string to string map. A good example
    of why it's useful is protocol buffers: if a language has multiple APIs, the
    information as to which API the protocol buffers should be built for should
    be propagated down the dependency graph.
4.  `Aspect` represents all the data that's needed to compute an aspect that
    propagates down the dependency graph. It consists of the aspect class, its
    definition and its parameters.
5.  `RuleAspect` is the function that determines which aspects a particular rule
    should propagate. It's a `Rule` -> `Aspect` function.

A somewhat unexpected complication is that aspects can attach to other aspects;
for example, an aspect collecting the classpath for a Java IDE will probably
want to know about all the .jar files on the classpath, but some of them are
protocol buffers. In that case, the IDE aspect will want to attach to the
(`proto_library` rule + Java proto aspect) pair.

The complexity of aspects on aspects is captured in the class
`AspectCollection`.

### Platforms and toolchains {#platforms-toolchains}

Bazel supports multi-platform builds, that is, builds where there may be
multiple architectures where build actions run and multiple architectures for
which code is built. These architectures are referred to as _platforms_ in Bazel
parlance (full documentation
[here](/extending/platforms))

A platform is described by a key-value mapping from _constraint settings_ (such as
the concept of "CPU architecture") to _constraint values_ (such as a particular CPU
like x86\_64). We have a "dictionary" of the most commonly used constraint
settings and values in the `@platforms` repository.

The concept of _toolchain_ comes from the fact that depending on what platforms
the build is running on and what platforms are targeted, one may need to use
different compilers; for example, a particular C++ toolchain may run on a
specific OS and be able to target some other OSes. Bazel must determine the C++
compiler that is used based on the set execution and target platform
(documentation for toolchains
[here](/extending/toolchains)).

In order to do this, toolchains are annotated with the set of execution and
target platform constraints they support. In order to do this, the definition of
a toolchain are split into two parts:

1.  A `toolchain()` rule that describes the set of execution and target
    constraints a toolchain supports and tells what kind (such as C++ or Java) of
    toolchain it is (the latter is represented by the `toolchain_type()` rule)
2.  A language-specific rule that describes the actual toolchain (such as
    `cc_toolchain()`)

This is done in this way because we need to know the constraints for every
toolchain in order to do toolchain resolution and language-specific
`*_toolchain()` rules contain much more information than that, so they take more
time to load.

Execution platforms are specified in one of the following ways:

1.  In the MODULE.bazel file using the `register_execution_platforms()` function
2.  On the command line using the --extra\_execution\_platforms command line
    option

The set of available execution platforms is computed in
`RegisteredExecutionPlatformsFunction` .

The target platform for a configured target is determined by
`PlatformOptions.computeTargetPlatform()` . It's a list of platforms because we
eventually want to support multiple target platforms, but it's not implemented
yet.

The set of toolchains to be used for a configured target is determined by
`ToolchainResolutionFunction`. It is a function of:

*   The set of registered toolchains (in the MODULE.bazel file and the
    configuration)
*   The desired execution and target platforms (in the configuration)
*   The set of toolchain types that are required by the configured target (in
    `UnloadedToolchainContextKey)`
*   The set of execution platform constraints of the configured target (the
    `exec_compatible_with` attribute), in `UnloadedToolchainContextKey`

Its result is an `UnloadedToolchainContext`, which is essentially a map from
toolchain type (represented as a `ToolchainTypeInfo` instance) to the label of
the selected toolchain. It's called "unloaded" because it does not contain the
toolchains themselves, only their labels.

Then the toolchains are actually loaded using `ResolvedToolchainContext.load()`
and used by the implementation of the configured target that requested them.

We also have a legacy system that relies on there being one single "host"
configuration and target configurations being represented by various
configuration flags, such as `--cpu` . We are gradually transitioning to the above
system. In order to handle cases where people rely on the legacy configuration
values, we have implemented
[platform mappings](https://docs.google.com/document/d/1Vg_tPgiZbSrvXcJ403vZVAGlsWhH9BUDrAxMOYnO0Ls)
to translate between the legacy flags and the new-style platform constraints.
Their code is in `PlatformMappingFunction` and uses a non-Starlark "little
language".

### Constraints {#constraints}

Sometimes one wants to designate a target as being compatible with only a few
platforms. Bazel has (unfortunately) multiple mechanisms to achieve this end:

*   Rule-specific constraints
*   `environment_group()` / `environment()`
*   Platform constraints

Rule-specific constraints are mostly used within Google for Java rules; they are
on their way out and they are not available in Bazel, but the source code may
contain references to it. The attribute that governs this is called
`constraints=` .

#### environment_group() and environment() {#environment-rule}

These rules are a legacy mechanism and are not widely used.

All build rules can declare which "environments" they can be built for, where an
"environment" is an instance of the `environment()` rule.

There are various ways supported environments can be specified for a rule:

1.  Through the `restricted_to=` attribute. This is the most direct form of
    specification; it declares the exact set of environments the rule supports.
2.  Through the `compatible_with=` attribute. This declares environments a rule
    supports in addition to "standard" environments that are supported by
    default.
3.  Through the package-level attributes `default_restricted_to=` and
    `default_compatible_with=`.
4.  Through default specifications in `environment_group()` rules. Every
    environment belongs to a group of thematically related peers (such as "CPU
    architectures", "JDK versions" or "mobile operating systems"). The
    definition of an environment group includes which of these environments
    should be supported by "default" if not otherwise specified by the
    `restricted_to=` / `environment()` attributes. A rule with no such
    attributes inherits all defaults.
5.  Through a rule class default. This overrides global defaults for all
    instances of the given rule class. This can be used, for example, to make
    all `*_test` rules testable without each instance having to explicitly
    declare this capability.

`environment()` is implemented as a regular rule whereas `environment_group()`
is both a subclass of `Target` but not `Rule` (`EnvironmentGroup`) and a
function that is available by default from Starlark
(`StarlarkLibrary.environmentGroup()`) which eventually creates an eponymous
target. This is to avoid a cyclic dependency that would arise because each
environment needs to declare the environment group it belongs to and each
environment group needs to declare its default environments.

A build can be restricted to a certain environment with the
`--target_environment` command line option.

The implementation of the constraint check is in
`RuleContextConstraintSemantics` and `TopLevelConstraintSemantics`.

#### Platform constraints {#platform-constraints}

The current "official" way to describe what platforms a target is compatible
with is by using the same constraints used to describe toolchains and platforms.
It was implemented in pull request
[#10945](https://github.com/bazelbuild/bazel/pull/10945).

### Visibility {#visibility}

If you work on a large codebase with a lot of developers (like at Google), you
want to take care to prevent everyone else from arbitrarily depending on your
code. Otherwise, as per [Hyrum's law](https://www.hyrumslaw.com/),
people _will_ come to rely on behaviors that you considered to be implementation
details.

Bazel supports this by the mechanism called _visibility_: you can limit which
targets can depend on a particular target using the
[visibility](/reference/be/common-definitions#common-attributes) attribute. This
attribute is a little special because, although it holds a list of labels, these
labels may encode a pattern over package names rather than a pointer to any
particular target. (Yes, this is a design flaw.)

This is implemented in the following places:

*   The `RuleVisibility` interface represents a visibility declaration. It can
    be either a constant (fully public or fully private) or a list of labels.
*   Labels can refer to either package groups (predefined list of packages), to
    packages directly (`//pkg:__pkg__`) or subtrees of packages
    (`//pkg:__subpackages__`). This is different from the command line syntax,
    which uses `//pkg:*` or `//pkg/...`.
*   Package groups are implemented as their own target (`PackageGroup`) and
    configured target (`PackageGroupConfiguredTarget`). We could probably
    replace these with simple rules if we wanted to. Their logic is implemented
    with the help of: `PackageSpecification`, which corresponds to a
    single pattern like `//pkg/...`; `PackageGroupContents`, which corresponds
    to a single `package_group`'s `packages` attribute; and
    `PackageSpecificationProvider`, which aggregates over a `package_group` and
    its transitive `includes`.
*   The conversion from visibility label lists to dependencies is done in
    `DependencyResolver.visitTargetVisibility` and a few other miscellaneous
    places.
*   The actual check is done in
    `CommonPrerequisiteValidator.validateDirectPrerequisiteVisibility()`

### Nested sets {#nested-sets}

Oftentimes, a configured target aggregates a set of files from its dependencies,
adds its own, and wraps the aggregate set into a transitive info provider so
that configured targets that depend on it can do the same. Examples:

*   The C++ header files used for a build
*   The object files that represent the transitive closure of a `cc_library`
*   The set of .jar files that need to be on the classpath for a Java rule to
    compile or run
*   The set of Python files in the transitive closure of a Python rule

If we did this the naive way by using, for example, `List` or `Set`, we'd end up with
quadratic memory usage: if there is a chain of N rules and each rule adds a
file, we'd have 1+2+...+N collection members.

In order to get around this problem, we came up with the concept of a
`NestedSet`. It's a data structure that is composed of other `NestedSet`
instances and some members of its own, thereby forming a directed acyclic graph
of sets. They are immutable and their members can be iterated over. We define
multiple iteration order (`NestedSet.Order`): preorder, postorder, topological
(a node always comes after its ancestors) and "don't care, but it should be the
same each time".

The same data structure is called `depset` in Starlark.

### Artifacts and Actions {#artifacts}

The actual build consists of a set of commands that need to be run to produce
the output the user wants. The commands are represented as instances of the
class `Action` and the files are represented as instances of the class
`Artifact`. They are arranged in a bipartite, directed, acyclic graph called the
"action graph".

Artifacts come in two kinds: source artifacts (ones that are available
before Bazel starts executing) and derived artifacts (ones that need to be
built). Derived artifacts can themselves be multiple kinds:

1.  **Regular artifacts.** These are checked for up-to-dateness by computing
    their checksum, with mtime as a shortcut; we don't checksum the file if its
    ctime hasn't changed.
2.  **Unresolved symlink artifacts.** These are checked for up-to-dateness by
    calling readlink(). Unlike regular artifacts, these can be dangling
    symlinks. Usually used in cases where one then packs up some files into an
    archive of some sort.
3.  **Tree artifacts.** These are not single files, but directory trees. They
    are checked for up-to-dateness by checking the set of files in it and their
    contents. They are represented as a `TreeArtifact`.
4.  **Constant metadata artifacts.** Changes to these artifacts don't trigger a
    rebuild. This is used exclusively for build stamp information: we don't want
    to do a rebuild just because the current time changed.

There is no fundamental reason why source artifacts cannot be tree artifacts or
unresolved symlink artifacts; it's just that we haven't implemented it yet. At
the moment, source symlinks are always resolved, and while source directories
are supported, their contents are entirely opaque to build rules and thus don't
support the same kind of lazy command line expansion as tree artifacts do.

Actions are best understood as a command that needs to be run, the environment
it needs and the set of outputs it produces. The following things are the main
components of the description of an action:

*   The command line that needs to be run
*   The input artifacts it needs
*   The environment variables that need to be set
*   Annotations that describe the environment (such as platform) it needs to run in
    \

There are also a few other special cases, like writing a file whose content is
known to Bazel. They are a subclass of `AbstractAction`. Most of the actions are
a `SpawnAction` or a `StarlarkAction` (the same, they should arguably not be
separate classes), although Java and C++ have their own action types
(`JavaCompileAction`, `CppCompileAction` and `CppLinkAction`).

We eventually want to move everything to `SpawnAction`; `JavaCompileAction` is
pretty close, but C++ is a bit of a special-case due to .d file parsing and
include scanning.

The action graph is mostly "embedded" into the Skyframe graph: conceptually, the
execution of an action is represented as an invocation of
`ActionExecutionFunction`. The mapping from an action graph dependency edge to a
Skyframe dependency edge is described in
`ActionExecutionFunction.getInputDeps()` and `Artifact.key()` and has a few
optimizations in order to keep the number of Skyframe edges low:

*   Derived artifacts do not have their own `SkyValue`s. Instead,
    `Artifact.getGeneratingActionKey()` is used to find out the key for the
    action that generates it
*   Nested sets have their own Skyframe key.

### Shared actions {#shared-actions}

Some actions are generated by multiple configured targets; Starlark rules are
more limited since they are only allowed to put their derived actions into a
directory determined by their configuration and their package (but even so,
rules in the same package can conflict), but rules implemented in Java can put
derived artifacts anywhere.

This is considered to be a misfeature, but getting rid of it is really hard
because it produces significant savings in execution time when, for example, a
source file needs to be processed somehow and that file is referenced by
multiple rules (handwave-handwave). This comes at the cost of some RAM: each
instance of a shared action needs to be stored in memory separately.

If two actions generate the same output file, they must be exactly the same:
have the same inputs, the same outputs and run the same command line. This
equivalence relation is implemented in `Actions.canBeShared()` and it is
verified between the analysis and execution phases by looking at every Action.
This is implemented in `SkyframeActionExecutor.findAndStoreArtifactConflicts()`
and is one of the few places in Bazel that requires a "global" view of the
build.

## The execution phase {#execution-phase}

This is when Bazel actually starts running build actions, such as commands that
produce outputs.

The first thing Bazel does after the analysis phase is to determine what
Artifacts need to be built. The logic for this is encoded in
`TopLevelArtifactHelper`; roughly speaking, it's the `filesToBuild` of the
configured targets on the command line and the contents of a special output
group for the explicit purpose of expressing "if this target is on the command
line, build these artifacts".

The next step is creating the execution root. Since Bazel has the option to read
source packages from different locations in the file system (`--package_path`),
it needs to provide locally executed actions with a full source tree. This is
handled by the class `SymlinkForest` and works by taking note of every target
used in the analysis phase and building up a single directory tree that symlinks
every package with a used target from its actual location. An alternative would
be to pass the correct paths to commands (taking `--package_path` into account).
This is undesirable because:

*   It changes action command lines when a package is moved from a package path
    entry to another (used to be a common occurrence)
*   It results in different command lines if an action is run remotely than if
    it's run locally
*   It requires a command line transformation specific to the tool in use
    (consider the difference between such as Java classpaths and C++ include paths)
*   Changing the command line of an action invalidates its action cache entry
*   `--package_path` is slowly and steadily being deprecated

Then, Bazel starts traversing the action graph (the bipartite, directed graph
composed of actions and their input and output artifacts) and running actions.
The execution of each action is represented by an instance of the `SkyValue`
class `ActionExecutionValue`.

Since running an action is expensive, we have a few layers of caching that can
be hit behind Skyframe:

*   `ActionExecutionFunction.stateMap` contains data to make Skyframe restarts
    of `ActionExecutionFunction` cheap
*   The local action cache contains data about the state of the file system
*   Remote execution systems usually also contain their own cache

### The local action cache {#cache}

This cache is another layer that sits behind Skyframe; even if an action is
re-executed in Skyframe, it can still be a hit in the local action cache. It
represents the state of the local file system and it's serialized to disk which
means that when one starts up a new Bazel server, one can get local action cache
hits even though the Skyframe graph is empty.

This cache is checked for hits using the method
`ActionCacheChecker.getTokenIfNeedToExecute()` .

Contrary to its name, it's a map from the path of a derived artifact to the
action that emitted it. The action is described as:

1.  The set of its input and output files and their checksum
2.  Its "action key", which is usually the command line that was executed, but
    in general, represents everything that's not captured by the checksum of the
    input files (such as for `FileWriteAction`, it's the checksum of the data
    that's written)

There is also a highly experimental "top-down action cache" that is still under
development, which uses transitive hashes to avoid going to the cache as many
times.

### Input discovery and input pruning {#input-discovery}

Some actions are more complicated than just having a set of inputs. Changes to
the set of inputs of an action come in two forms:

*   An action may discover new inputs before its execution or decide that some
    of its inputs are not actually necessary. The canonical example is C++,
    where it's better to make an educated guess about what header files a C++
    file uses from its transitive closure so that we don't heed to send every
    file to remote executors; therefore, we have an option not to register every
    header file as an "input", but scan the source file for transitively
    included headers and only mark those header files as inputs that are
    mentioned in `#include` statements (we overestimate so that we don't need to
    implement a full C preprocessor) This option is currently hard-wired to
    "false" in Bazel and is only used at Google.
*   An action may realize that some files were not used during its execution. In
    C++, this is called ".d files": the compiler tells which header files were
    used after the fact, and in order to avoid the embarrassment of having worse
    incrementality than Make, Bazel makes use of this fact. This offers a better
    estimate than the include scanner because it relies on the compiler.

These are implemented using methods on Action:

1.  `Action.discoverInputs()` is called. It should return a nested set of
    Artifacts that are determined to be required. These must be source artifacts
    so that there are no dependency edges in the action graph that don't have an
    equivalent in the configured target graph.
2.  The action is executed by calling `Action.execute()`.
3.  At the end of `Action.execute()`, the action can call
    `Action.updateInputs()` to tell Bazel that not all of its inputs were
    needed. This can result in incorrect incremental builds if a used input is
    reported as unused.

When an action cache returns a hit on a fresh Action instance (such as created
after a server restart), Bazel calls `updateInputs()` itself so that the set of
inputs reflects the result of input discovery and pruning done before.

Starlark actions can make use of the facility to declare some inputs as unused
using the `unused_inputs_list=` argument of
`ctx.actions.run()`.

### Various ways to run actions: Strategies/ActionContexts {#options-run-actions}

Some actions can be run in different ways. For example, a command line can be
executed locally, locally but in various kinds of sandboxes, or remotely. The
concept that embodies this is called an `ActionContext` (or `Strategy`, since we
successfully went only halfway with a rename...)

The life cycle of an action context is as follows:

1.  When the execution phase is started, `BlazeModule` instances are asked what
    action contexts they have. This happens in the constructor of
    `ExecutionTool`. Action context types are identified by a Java `Class`
    instance that refers to a sub-interface of `ActionContext` and which
    interface the action context must implement.
2.  The appropriate action context is selected from the available ones and is
    forwarded to `ActionExecutionContext` and `BlazeExecutor` .
3.  Actions request contexts using `ActionExecutionContext.getContext()` and
    `BlazeExecutor.getStrategy()` (there should really be only one way to do
    it…)

Strategies are free to call other strategies to do their jobs; this is used, for
example, in the dynamic strategy that starts actions both locally and remotely,
then uses whichever finishes first.

One notable strategy is the one that implements persistent worker processes
(`WorkerSpawnStrategy`). The idea is that some tools have a long startup time
and should therefore be reused between actions instead of starting one anew for
every action (This does represent a potential correctness issue, since Bazel
relies on the promise of the worker process that it doesn't carry observable
state between individual requests)

If the tool changes, the worker process needs to be restarted. Whether a worker
can be reused is determined by computing a checksum for the tool used using
`WorkerFilesHash`. It relies on knowing which inputs of the action represent
part of the tool and which represent inputs; this is determined by the creator
of the Action: `Spawn.getToolFiles()` and the runfiles of the `Spawn` are
counted as parts of the tool.

More information about strategies (or action contexts!):

*   Information about various strategies for running actions is available
    [here](https://jmmv.dev/2019/12/bazel-strategies.html).
*   Information about the dynamic strategy, one where we run an action both
    locally and remotely to see whichever finishes first is available
    [here](https://jmmv.dev/series.html#Bazel%20dynamic%20execution).
*   Information about the intricacies of executing actions locally is available
    [here](https://jmmv.dev/2019/11/bazel-process-wrapper.html).

### The local resource manager {#resource-manager}

Bazel _can_ run many actions in parallel. The number of local actions that
_should_ be run in parallel differs from action to action: the more resources an
action requires, the less instances should be running at the same time to avoid
overloading the local machine.

This is implemented in the class `ResourceManager`: each action has to be
annotated with an estimate of the local resources it requires in the form of a
`ResourceSet` instance (CPU and RAM). Then when action contexts do something
that requires local resources, they call `ResourceManager.acquireResources()`
and are blocked until the required resources are available.

A more detailed description of local resource management is available
[here](https://jmmv.dev/2019/12/bazel-local-resources.html).

### The structure of the output directory {#output-directory}

Each action requires a separate place in the output directory where it places
its outputs. The location of derived artifacts is usually as follows:

```
$EXECROOT/bazel-out/<configuration>/bin/<package>/<artifact name>
```

How is the name of the directory that is associated with a particular
configuration determined? There are two conflicting desirable properties:

1.  If two configurations can occur in the same build, they should have
    different directories so that both can have their own version of the same
    action; otherwise, if the two configurations disagree about such as the command
    line of an action producing the same output file, Bazel doesn't know which
    action to choose (an "action conflict")
2.  If two configurations represent "roughly" the same thing, they should have
    the same name so that actions executed in one can be reused for the other if
    the command lines match: for example, changes to the command line options to
    the Java compiler should not result in C++ compile actions being re-run.

So far, we have not come up with a principled way of solving this problem, which
has similarities to the problem of configuration trimming. A longer discussion
of options is available
[here](https://docs.google.com/document/d/1fZI7wHoaS-vJvZy9SBxaHPitIzXE_nL9v4sS4mErrG4/edit).
The main problematic areas are Starlark rules (whose authors usually aren't
intimately familiar with Bazel) and aspects, which add another dimension to the
space of things that can produce the "same" output file.

The current approach is that the path segment for the configuration is
`<CPU>-<compilation mode>` with various suffixes added so that configuration
transitions implemented in Java don't result in action conflicts. In addition, a
checksum of the set of Starlark configuration transitions is added so that users
can't cause action conflicts. It is far from perfect. This is implemented in
`OutputDirectories.buildMnemonic()` and relies on each configuration fragment
adding its own part to the name of the output directory.

## Tests {#tests}

Bazel has rich support for running tests. It supports:

*   Running tests remotely (if a remote execution backend is available)
*   Running tests multiple times in parallel (for deflaking or gathering timing
    data)
*   Sharding tests (splitting test cases in same test over multiple processes
    for speed)
*   Re-running flaky tests
*   Grouping tests into test suites

Tests are regular configured targets that have a TestProvider, which describes
how the test should be run:

*   The artifacts whose building result in the test being run. This is a "cache
    status" file that contains a serialized `TestResultData` message
*   The number of times the test should be run
*   The number of shards the test should be split into
*   Some parameters about how the test should be run (such as the test timeout)

### Determining which tests to run {#determine-test-run}

Determining which tests are run is an elaborate process.

First, during target pattern parsing, test suites are recursively expanded. The
expansion is implemented in `TestsForTargetPatternFunction`. A somewhat
surprising wrinkle is that if a test suite declares no tests, it refers to
_every_ test in its package. This is implemented in `Package.beforeBuild()` by
adding an implicit attribute called `$implicit_tests` to test suite rules.

Then, tests are filtered for size, tags, timeout and language according to the
command line options. This is implemented in `TestFilter` and is called from
`TargetPatternPhaseFunction.determineTests()` during target parsing and the
result is put into `TargetPatternPhaseValue.getTestsToRunLabels()`. The reason
why rule attributes which can be filtered for are not configurable is that this
happens before the analysis phase, therefore, the configuration is not
available.

This is then processed further in `BuildView.createResult()`: targets whose
analysis failed are filtered out and tests are split into exclusive and
non-exclusive tests. It's then put into `AnalysisResult`, which is how
`ExecutionTool` knows which tests to run.

In order to lend some transparency to this elaborate process, the `tests()`
query operator (implemented in `TestsFunction`) is available to tell which tests
are run when a particular target is specified on the command line. It's
unfortunately a reimplementation, so it probably deviates from the above in
multiple subtle ways.

### Running tests {#run-tests}

The way the tests are run is by requesting cache status artifacts. This then
results in the execution of a `TestRunnerAction`, which eventually calls the
`TestActionContext` chosen by the `--test_strategy` command line option that
runs the test in the requested way.

Tests are run according to an elaborate protocol that uses environment variables
to tell tests what's expected from them. A detailed description of what Bazel
expects from tests and what tests can expect from Bazel is available
[here](/reference/test-encyclopedia). At the
simplest, an exit code of 0 means success, anything else means failure.

In addition to the cache status file, each test process emits a number of other
files. They are put in the "test log directory" which is the subdirectory called
`testlogs` of the output directory of the target configuration:

*   `test.xml`, a JUnit-style XML file detailing the individual test cases in
    the test shard
*   `test.log`, the console output of the test. stdout and stderr are not
    separated.
*   `test.outputs`, the "undeclared outputs directory"; this is used by tests
    that want to output files in addition to what they print to the terminal.

There are two things that can happen during test execution that cannot during
building regular targets: exclusive test execution and output streaming.

Some tests need to be executed in exclusive mode, for example not in parallel with
other tests. This can be elicited either by adding `tags=["exclusive"]` to the
test rule or running the test with `--test_strategy=exclusive` . Each exclusive
test is run by a separate Skyframe invocation requesting the execution of the
test after the "main" build. This is implemented in
`SkyframeExecutor.runExclusiveTest()`.

Unlike regular actions, whose terminal output is dumped when the action
finishes, the user can request the output of tests to be streamed so that they
get informed about the progress of a long-running test. This is specified by the
`--test_output=streamed` command line option and implies exclusive test
execution so that outputs of different tests are not interspersed.

This is implemented in the aptly-named `StreamedTestOutput` class and works by
polling changes to the `test.log` file of the test in question and dumping new
bytes to the terminal where Bazel rules.

Results of the executed tests are available on the event bus by observing
various events (such as `TestAttempt`, `TestResult` or `TestingCompleteEvent`).
They are dumped to the Build Event Protocol and they are emitted to the console
by `AggregatingTestListener`.

### Coverage collection {#coverage-collection}

Coverage is reported by the tests in LCOV format in the files
`bazel-testlogs/$PACKAGE/$TARGET/coverage.dat` .

To collect coverage, each test execution is wrapped in a script called
`collect_coverage.sh` .

This script sets up the environment of the test to enable coverage collection
and determine where the coverage files are written by the coverage runtime(s).
It then runs the test. A test may itself run multiple subprocesses and consist
of parts written in multiple different programming languages (with separate
coverage collection runtimes). The wrapper script is responsible for converting
the resulting files to LCOV format if necessary, and merges them into a single
file.

The interposition of `collect_coverage.sh` is done by the test strategies and
requires `collect_coverage.sh` to be on the inputs of the test. This is
accomplished by the implicit attribute `:coverage_support` which is resolved to
the value of the configuration flag `--coverage_support` (see
`TestConfiguration.TestOptions.coverageSupport`)

Some languages do offline instrumentation, meaning that the coverage
instrumentation is added at compile time (such as C++) and others do online
instrumentation, meaning that coverage instrumentation is added at execution
time.

Another core concept is _baseline coverage_. This is the coverage of a library,
binary, or test if no code in it was run. The problem it solves is that if you
want to compute the test coverage for a binary, it is not enough to merge the
coverage of all of the tests because there may be code in the binary that is not
linked into any test. Therefore, what we do is to emit a coverage file for every
binary which contains only the files we collect coverage for with no covered
lines. The default baseline coverage file for a target is at
`bazel-testlogs/$PACKAGE/$TARGET/baseline_coverage.dat`, but rules are
encouraged to generate their own baseline coverage files with more meaningful
content than just the names of the source files.

We track two groups of files for coverage collection for each rule: the set of
instrumented files and the set of instrumentation metadata files.

The set of instrumented files is just that, a set of files to instrument. For
online coverage runtimes, this can be used at runtime to decide which files to
instrument. It is also used to implement baseline coverage.

The set of instrumentation metadata files is the set of extra files a test needs
to generate the LCOV files Bazel requires from it. In practice, this consists of
runtime-specific files; for example, gcc emits .gcno files during compilation.
These are added to the set of inputs of test actions if coverage mode is
enabled.

Whether or not coverage is being collected is stored in the
`BuildConfiguration`. This is handy because it is an easy way to change the test
action and the action graph depending on this bit, but it also means that if
this bit is flipped, all targets need to be re-analyzed (some languages, such as
C++ require different compiler options to emit code that can collect coverage,
which mitigates this issue somewhat, since then a re-analysis is needed anyway).

The coverage support files are depended on through labels in an implicit
dependency so that they can be overridden by the invocation policy, which allows
them to differ between the different versions of Bazel. Ideally, these
differences would be removed, and we standardized on one of them.

We also generate a "coverage report" which merges the coverage collected for
every test in a Bazel invocation. This is handled by
`CoverageReportActionFactory` and is called from `BuildView.createResult()` . It
gets access to the tools it needs by looking at the `:coverage_report_generator`
attribute of the first test that is executed.

## The query engine {#query-engine}

Bazel has a
[little language](/query/guide)
used to ask it various things about various graphs. The following query kinds
are provided:

*   `bazel query` is used to investigate the target graph
*   `bazel cquery` is used to investigate the configured target graph
*   `bazel aquery` is used to investigate the action graph

Each of these is implemented by subclassing `AbstractBlazeQueryEnvironment`.
Additional additional query functions can be done by subclassing `QueryFunction`
. In order to allow streaming query results, instead of collecting them to some
data structure, a `query2.engine.Callback` is passed to `QueryFunction`, which
calls it for results it wants to return.

The result of a query can be emitted in various ways: labels, labels and rule
classes, XML, protobuf and so on. These are implemented as subclasses of
`OutputFormatter`.

A subtle requirement of some query output formats (proto, definitely) is that
Bazel needs to emit _all _the information that package loading provides so that
one can diff the output and determine whether a particular target has changed.
As a consequence, attribute values need to be serializable, which is why there
are only so few attribute types without any attributes having complex Starlark
values. The usual workaround is to use a label, and attach the complex
information to the rule with that label. It's not a very satisfying workaround
and it would be very nice to lift this requirement.

## The module system {#module-system}

Bazel can be extended by adding modules to it. Each module must subclass
`BlazeModule` (the name is a relic of the history of Bazel when it used to be
called Blaze) and gets information about various events during the execution of
a command.

They are mostly used to implement various pieces of "non-core" functionality
that only some versions of Bazel (such as the one we use at Google) need:

*   Interfaces to remote execution systems
*   New commands

The set of extension points `BlazeModule` offers is somewhat haphazard. Don't
use it as an example of good design principles.

## The event bus {#event-bus}

The main way BlazeModules communicate with the rest of Bazel is by an event bus
(`EventBus`): a new instance is created for every build, various parts of Bazel
can post events to it and modules can register listeners for the events they are
interested in. For example, the following things are represented as events:

*   The list of build targets to be built has been determined
    (`TargetParsingCompleteEvent`)
*   The top-level configurations have been determined
    (`BuildConfigurationEvent`)
*   A target was built, successfully or not (`TargetCompleteEvent`)
*   A test was run (`TestAttempt`, `TestSummary`)

Some of these events are represented outside of Bazel in the
[Build Event Protocol](/remote/bep)
(they are `BuildEvent`s). This allows not only `BlazeModule`s, but also things
outside the Bazel process to observe the build. They are accessible either as a
file that contains protocol messages or Bazel can connect to a server (called
the Build Event Service) to stream events.

This is implemented in the `build.lib.buildeventservice` and
`build.lib.buildeventstream` Java packages.

## External repositories {#external-repos}

Note: The information in this section is out of date, as code in this area has
undergone extensive change in the past couple of years. Please refer to
[external dependencies overview](/external/overview) for more up-to-date
information.

Whereas Bazel was originally designed to be used in a monorepo (a single source
tree containing everything one needs to build), Bazel lives in a world where
this is not necessarily true. "External repositories" are an abstraction used to
bridge these two worlds: they represent code that is necessary for the build but
is not in the main source tree.

### The WORKSPACE file {#workspace-file}

The set of external repositories is determined by parsing the WORKSPACE file.
For example, a declaration like this:

```
    local_repository(name="foo", path="/foo/bar")
```

Results in the repository called `@foo` being available. Where this gets
complicated is that one can define new repository rules in Starlark files, which
can then be used to load new Starlark code, which can be used to define new
repository rules and so on…

To handle this case, the parsing of the WORKSPACE file (in
`WorkspaceFileFunction`) is split up into chunks delineated by `load()`
statements. The chunk index is indicated by `WorkspaceFileKey.getIndex()` and
computing `WorkspaceFileFunction` until index X means evaluating it until the
Xth `load()` statement.

### Fetching repositories {#fetch-repos}

Before the code of the repository is available to Bazel, it needs to be
_fetched_. This results in Bazel creating a directory under
`$OUTPUT_BASE/external/<repository name>`.

Fetching the repository happens in the following steps:

1.  `PackageLookupFunction` realizes that it needs a repository and creates a
    `RepositoryName` as a `SkyKey`, which invokes `RepositoryLoaderFunction`
2.  `RepositoryLoaderFunction` forwards the request to
    `RepositoryDelegatorFunction` for unclear reasons (the code says it's to
    avoid re-downloading things in case of Skyframe restarts, but it's not a
    very solid reasoning)
3.  `RepositoryDelegatorFunction` finds out the repository rule it's asked to
    fetch by iterating over the chunks of the WORKSPACE file until the requested
    repository is found
4.  The appropriate `RepositoryFunction` is found that implements the repository
    fetching; it's either the Starlark implementation of the repository or a
    hard-coded map for repositories that are implemented in Java.

There are various layers of caching since fetching a repository can be very
expensive:

1.  There is a cache for downloaded files that is keyed by their checksum
    (`RepositoryCache`). This requires the checksum to be available in the
    WORKSPACE file, but that's good for hermeticity anyway. This is shared by
    every Bazel server instance on the same workstation, regardless of which
    workspace or output base they are running in.
2.  A "marker file" is written for each repository under `$OUTPUT_BASE/external`
    that contains a checksum of the rule that was used to fetch it. If the Bazel
    server restarts but the checksum does not change, it's not re-fetched. This
    is implemented in `RepositoryDelegatorFunction.DigestWriter` .
3.  The `--distdir` command line option designates another cache that is used to
    look up artifacts to be downloaded. This is useful in enterprise settings
    where Bazel should not fetch random things from the Internet. This is
    implemented by `DownloadManager` .

Once a repository is downloaded, the artifacts in it are treated as source
artifacts. This poses a problem because Bazel usually checks for up-to-dateness
of source artifacts by calling stat() on them, and these artifacts are also
invalidated when the definition of the repository they are in changes. Thus,
`FileStateValue`s for an artifact in an external repository need to depend on
their external repository. This is handled by `ExternalFilesHelper`.

### Repository mappings {#repo-mappings}

It can happen that multiple repositories want to depend on the same repository,
but in different versions (this is an instance of the "diamond dependency
problem"). For example, if two binaries in separate repositories in the build
want to depend on Guava, they will presumably both refer to Guava with labels
starting `@guava//` and expect that to mean different versions of it.

Therefore, Bazel allows one to re-map external repository labels so that the
string `@guava//` can refer to one Guava repository (such as `@guava1//`) in the
repository of one binary and another Guava repository (such as `@guava2//`) the
repository of the other.

Alternatively, this can also be used to **join** diamonds. If a repository
depends on `@guava1//`, and another depends on `@guava2//`, repository mapping
allows one to re-map both repositories to use a canonical `@guava//` repository.

The mapping is specified in the WORKSPACE file as the `repo_mapping` attribute
of individual repository definitions. It then appears in Skyframe as a member of
`WorkspaceFileValue`, where it is plumbed to:

*   `Package.Builder.repositoryMapping` which is used to transform label-valued
    attributes of rules in the package by
    `RuleClass.populateRuleAttributeValues()`
*   `Package.repositoryMapping` which is used in the analysis phase (for
    resolving things like `$(location)` which are not parsed in the loading
    phase)
*   `BzlLoadFunction` for resolving labels in load() statements

## JNI bits {#jni-bits}

The server of Bazel is _mostly_ written in Java. The exception is the parts that
Java cannot do by itself or couldn't do by itself when we implemented it. This
is mostly limited to interaction with the file system, process control and
various other low-level things.

The C++ code lives under src/main/native and the Java classes with native
methods are:

*   `NativePosixFiles` and `NativePosixFileSystem`
*   `ProcessUtils`
*   `WindowsFileOperations` and `WindowsFileProcesses`
*   `com.google.devtools.build.lib.platform`

## Console output {#console-output}

Emitting console output seems like a simple thing, but the confluence of running
multiple processes (sometimes remotely), fine-grained caching, the desire to
have a nice and colorful terminal output and having a long-running server makes
it non-trivial.

Right after the RPC call comes in from the client, two `RpcOutputStream`
instances are created (for stdout and stderr) that forward the data printed into
them to the client. These are then wrapped in an `OutErr` (an (stdout, stderr)
pair). Anything that needs to be printed on the console goes through these
streams. Then these streams are handed over to
`BlazeCommandDispatcher.execExclusively()`.

Output is by default printed with ANSI escape sequences. When these are not
desired (`--color=no`), they are stripped by an `AnsiStrippingOutputStream`. In
addition, `System.out` and `System.err` are redirected to these output streams.
This is so that debugging information can be printed using
`System.err.println()` and still end up in the terminal output of the client
(which is different from that of the server). Care is taken that if a process
produces binary output (such as `bazel query --output=proto`), no munging of stdout
takes place.

Short messages (errors, warnings and the like) are expressed through the
`EventHandler` interface. Notably, these are different from what one posts to
the `EventBus` (this is confusing). Each `Event` has an `EventKind` (error,
warning, info, and a few others) and they may have a `Location` (the place in
the source code that caused the event to happen).

Some `EventHandler` implementations store the events they received. This is used
to replay information to the UI caused by various kinds of cached processing,
for example, the warnings emitted by a cached configured target.

Some `EventHandler`s also allow posting events that eventually find their way to
the event bus (regular `Event`s do _not _appear there). These are
implementations of `ExtendedEventHandler` and their main use is to replay cached
`EventBus` events. These `EventBus` events all implement `Postable`, but not
everything that is posted to `EventBus` necessarily implements this interface;
only those that are cached by an `ExtendedEventHandler` (it would be nice and
most of the things do; it's not enforced, though)

Terminal output is _mostly_ emitted through `UiEventHandler`, which is
responsible for all the fancy output formatting and progress reporting Bazel
does. It has two inputs:

*   The event bus
*   The event stream piped into it through Reporter

The only direct connection the command execution machinery (for example the rest of
Bazel) has to the RPC stream to the client is through `Reporter.getOutErr()`,
which allows direct access to these streams. It's only used when a command needs
to dump large amounts of possible binary data (such as `bazel query`).

## Profiling Bazel {#profile-bazel}

Bazel is fast. Bazel is also slow, because builds tend to grow until just the
edge of what's bearable. For this reason, Bazel includes a profiler which can be
used to profile builds and Bazel itself. It's implemented in a class that's
aptly named `Profiler`. It's turned on by default, although it records only
abridged data so that its overhead is tolerable; The command line
`--record_full_profiler_data` makes it record everything it can.

It emits a profile in the Chrome profiler format; it's best viewed in Chrome.
It's data model is that of task stacks: one can start tasks and end tasks and
they are supposed to be neatly nested within each other. Each Java thread gets
its own task stack. **TODO:** How does this work with actions and
continuation-passing style?

The profiler is started and stopped in `BlazeRuntime.initProfiler()` and
`BlazeRuntime.afterCommand()` respectively and attempts to be live for as long
as possible so that we can profile everything. To add something to the profile,
call `Profiler.instance().profile()`. It returns a `Closeable`, whose closure
represents the end of the task. It's best used with try-with-resources
statements.

We also do rudimentary memory profiling in `MemoryProfiler`. It's also always on
and it mostly records maximum heap sizes and GC behavior.

## Testing Bazel {#bazel-tests}

Bazel has two main kinds of tests: ones that observe Bazel as a "black box" and
ones that only run the analysis phase. We call the former "integration tests"
and the latter "unit tests", although they are more like integration tests that
are, well, less integrated. We also have some actual unit tests, where they are
necessary.

Of integration tests, we have two kinds:

1.  Ones implemented using a very elaborate bash test framework under
    `src/test/shell`
2.  Ones implemented in Java. These are implemented as subclasses of
    `BuildIntegrationTestCase`

`BuildIntegrationTestCase` is the preferred integration testing framework as it
is well-equipped for most testing scenarios. As it is a Java framework, it
provides debuggability and seamless integration with many common development
tools. There are many examples of `BuildIntegrationTestCase` classes in the
Bazel repository.

Analysis tests are implemented as subclasses of `BuildViewTestCase`. There is a
scratch file system you can use to write `BUILD` files, then various helper
methods can request configured targets, change the configuration and assert
various things about the result of the analysis.