site/en/docs/persistent-workers.md - bazel - Git at Google

 Project: /_project.yaml
 Book: /_book.yaml

 # Persistent Workers

 This page covers how to use persistent workers, the benefits, requirements, and
 how workers affect sandboxing.

 A persistent worker is a long-running process started by the Bazel server, which
 functions as a *wrapper* around the actual *tool* (typically a compiler), or is
 the *tool* itself. In order to benefit from persistent workers, the tool must
 support doing a sequence of compilations, and the wrapper needs to translate
 between the tool's API and the request/response format described below. The same
 worker might be called with and without the `--persistent_worker` flag in the
 same build, and is responsible for appropriately starting and talking to the
 tool, as well as shutting down workers on exit. Each worker instance is assigned
 (but not chrooted to) a separate working directory under
 `<outputBase>/bazel-workers`.

 Using persistent workers is an
 [execution strategy](/docs/user-manual#execution-strategy) that decreases
 start-up overhead, allows more JIT compilation, and enables caching of for
 example the abstract syntax trees in the action execution. This strategy
 achieves these improvements by sending multiple requests to a long-running
 process.

 Persistent workers are implemented for multiple languages, including Java,
 [Scala](https://github.com/bazelbuild/rules_scala){: .external},
 [Kotlin](https://github.com/bazelbuild/rules_kotlin){: .external}, and more.

 Programs using a NodeJS runtime can use the
 [@bazel/worker](https://www.npmjs.com/package/@bazel/worker) helper library to
 implement the worker protocol.

 ## Using persistent workers {:#usage}

 [Bazel 0.27 and higher](https://blog.bazel.build/2019/06/19/list-strategy.html)
 uses persistent workers by default when executing builds, though remote
 execution takes precedence. For actions that do not support persistent workers,
 Bazel falls back to starting a tool instance for each action. You can explicitly
 set your build to use persistent workers by setting the `worker`
 [strategy](/docs/user-manual#execution-strategy) for the applicable tool
 mnemonics. As a best practice, this example includes specifying `local` as a
 fallback to the `worker` strategy:

 ```posix-terminal
 bazel build //{{ '<var>' }}my:target{{ '</var>' }} --strategy=Javac=worker,local
 ```

 Using the workers strategy instead of the local strategy can boost compilation
 speed significantly, depending on implementation. For Java, builds can be 2–4
 times faster, sometimes more for incremental compilation. Compiling Bazel is
 about 2.5 times as fast with workers. For more details, see the
 "[Choosing number of workers](#number-of-workers)" section.

 If you also have a remote build environment that matches your local build
 environment, you can use the experimental
 [*dynamic* strategy](https://blog.bazel.build/2019/02/01/dynamic-spawn-scheduler.html){: .external},
 which races a remote execution and a worker execution. To enable the dynamic
 strategy, pass the
 [--experimental_spawn_scheduler](/reference/command-line-reference#flag--experimental_spawn_scheduler)
 flag. This strategy automatically enables workers, so there is no need to
 specify the `worker` strategy, but you can still use `local` or `sandboxed` as
 fallbacks.

 ## Choosing number of workers {:#number-of-workers}

 The default number of worker instances per mnemonic is 4, but can be adjusted
 with the
 [`worker_max_instances`](/reference/command-line-reference#flag--worker_max_instances)
 flag. There is a trade-off between making good use of the available CPUs and the
 amount of JIT compilation and cache hits you get. With more workers, more
 targets will pay start-up costs of running non-JITted code and hitting cold
 caches. If you have a small number of targets to build, a single worker may give
 the best trade-off between compilation speed and resource usage (for example,
 see [issue #8586](https://github.com/bazelbuild/bazel/issues/8586){: .external}.
 The `worker_max_instances` flag sets the maximum number of worker instances per
 mnemonic and flag set (see below), so in a mixed system you could end up using
 quite a lot of memory if you keep the default value. For incremental builds the
 benefit of multiple worker instances is even smaller.

 This graph shows the from-scratch compilation times for Bazel (target
 `//src:bazel`) on a 6-core hyper-threaded Intel Xeon 3.5 GHz Linux workstation
 with 64 GB of RAM. For each worker configuration, five clean builds are run and
 the average of the last four are taken.

 ![Graph of performance improvements of clean builds](/docs/images/workers-clean-chart.png "Performance improvements of clean builds")

 **Figure 1.** Graph of performance improvements of clean builds.

 For this configuration, two workers give the fastest compile, though at only 14%
 improvement compared to one worker. One worker is a good option if you want to
 use less memory.

 Incremental compilation typically benefits even more. Clean builds are
 relatively rare, but changing a single file between compiles is common, in
 particular in test-driven development. The above example also has some non-Java
 packaging actions to it that can overshadow the incremental compile time.

 Recompiling the Java sources only
 (`//src/main/java/com/google/devtools/build/lib/bazel:BazelServer_deploy.jar`)
 after changing an internal string constant in
 [AbstractContainerizingSandboxedSpawn.java](https://github.com/bazelbuild/bazel/blob/master/src/main/java/com/google/devtools/build/lib/sandbox/AbstractContainerizingSandboxedSpawn.java){: .external}
 gives a 3x speed-up (average of 20 incremental builds with one warmup build
 discarded):

 ![Graph of performance improvements of incremental builds](/docs/images/workers-incremental-chart.png "Performance improvements of incremental builds")

 **Figure 2.** Graph of performance improvements of incremental builds.

 The speed-up depends on the change being made. A speed-up of a factor 6 is
 measured in the above situation when a commonly used constant is changed.

 ## Modifying persistent workers {:#options}

 You can pass the
 [`--worker_extra_flag`](/reference/command-line-reference#flag--worker_extra_flag)
 flag to specify start-up flags to workers, keyed by mnemonic. For instance,
 passing `--worker_extra_flag=javac=--debug` turns on debugging for Javac only.
 Only one worker flag can be set per use of this flag, and only for one mnemonic.
 Workers are not just created separately for each mnemonic, but also for
 variations in their start-up flags. Each combination of mnemonic and start-up
 flags is combined into a `WorkerKey`, and for each `WorkerKey` up to
 `worker_max_instances` workers may be created. See the next section for how the
 action configuration can also specify set-up flags.

 You can use the
 [`--high_priority_workers`](/reference/command-line-reference#flag--high_priority_workers)
 flag to specify a mnemonic that should be run in preference to normal-priority
 mnemonics. This can help prioritize actions that are always in the critical
 path. If there are two or more high priority workers executing requests, all
 other workers are prevented from running. This flag can be used multiple times.

 Passing the
 [`--worker_sandboxing`](/reference/command-line-reference#flag--worker_sandboxing)
 flag makes each worker request use a separate sandbox directory for all its
 inputs. Setting up the [sandbox](/docs/sandboxing) takes some extra time,
 especially on macOS, but gives a better correctness guarantee.

 The
 [`--worker_quit_after_build`](/reference/command-line-reference#flag--worker_quit_after_build)
 flag is mainly useful for debugging and profiling. This flag forces all workers
 to quit once a build is done. You can also pass
 [`--worker_verbose`](/reference/command-line-reference#flag--worker_verbose) to
 get more output about what the workers are doing. This flag is reflected in the
 `verbosity` field in `WorkRequest`, allowing worker implementations to also be
 more verbose.

 Workers store their logs in the `<outputBase>/bazel-workers` directory, for
 example
 `/tmp/_bazel_larsrc/191013354bebe14fdddae77f2679c3ef/bazel-workers/worker-1-Javac.log`.
 The file name includes the worker id and the mnemonic. Since there can be more
 than one `WorkerKey` per mnemonic, you may see more than `worker_max_instances`
 log files for a given mnemonic.

 For Android builds, see details at the
 [Android Build Performance page](/docs/android-build-performance).

 ## Implementing persistent workers {:#implementation}

 See the [creating persistent workers](/docs/creating-workers) page for
 information on how to make a worker.

 This example shows a Starlark configuration for a worker that uses JSON:

 ```python
 args_file = ctx.actions.declare_file(ctx.label.name + "_args_file")
 ctx.actions.write(
     output = args_file,
     content = "\n".join(["-g", "-source", "1.5"] + ctx.files.srcs),
 )
 ctx.actions.run(
     mnemonic = "SomeCompiler",
     executable = "bin/some_compiler_wrapper",
     inputs = inputs,
     outputs = outputs,
     arguments = [ "-max_mem=4G",  "@%s" % args_file.path],
     execution_requirements = {
         "supports-workers" : "1", "requires-worker-protocol" : "json" }
 )
 ```

 With this definition, the first use of this action would start with executing
 the command line `/bin/some_compiler -max_mem=4G --persistent_worker`. A request
 to compile `Foo.java` would then look like:

 ```prototext
 arguments: [ "-g", "-source", "1.5", "Foo.java" ]
 inputs: [
   {path: "symlinkfarm/input1" digest: "d49a..." },
   {path: "symlinkfarm/input2", digest: "093d..."},
 ]
 ```

 The worker receives this on `stdin` in newline-delimited JSON format (because
 `requires-worker-protocol` is set to JSON). The worker then performs the action,
 and sends a JSON-formatted `WorkResponse` to Bazel on its stdout. Bazel then
 parses this response and manually converts it to a `WorkResponse` proto. To
 communicate with the associated worker using binary-encoded protobuf instead of
 JSON, `requires-worker-protocol` would be set to `proto`, like this:

 ```
   execution_requirements = {
     "supports-workers" : "1" ,
     "requires-worker-protocol" : "proto"
   }
 ```

 If you do not include `requires-worker-protocol` in the execution requirements,
 Bazel will default the worker communication to use protobuf.

 Bazel derives the `WorkerKey` from the mnemonic and the shared flags, so if this
 configuration allowed changing the `max_mem` parameter, a separate worker would
 be spawned for each value used. This can lead to excessive memory consumption if
 too many variations are used.

 Each worker can currently only process one request at a time. The experimental
 [multiplex workers](/docs/multiplex-worker) feature allows using multiple
 threads, if the underlying tool is multithreaded and the wrapper is set up to
 understand this.

 In
 [this GitHub repo](https://github.com/Ubehebe/bazel-worker-examples){: .external},
 you can see example worker wrappers written in Java as well as in Python. If you
 are working in JavaScript or TypeScript, the
 [@bazel/worker package](https://www.npmjs.com/package/@bazel/worker){: .external}
 and
 [nodejs worker example](https://github.com/bazelbuild/rules_nodejs/tree/stable/examples/worker){: .external}
 might be helpful.

 ## How do workers affect sandboxing? {:#sandboxing}

 Using the `worker` strategy by default does not run the action in a
 [sandbox](/docs/sandboxing), similar to the `local` strategy. You can set the
 `--worker_sandboxing` flag to run all workers inside sandboxes, making sure each
 execution of the tool only sees the input files it's supposed to have. The tool
 may still leak information between requests internally, for instance through a
 cache. Using `dynamic` strategy
 [requires workers to be sandboxed](https://github.com/bazelbuild/bazel/blob/master/src/main/java/com/google/devtools/build/lib/exec/SpawnStrategyRegistry.java){: .external}.

 To allow correct use of compiler caches with workers, a digest is passed along
 with each input file. Thus the compiler or the wrapper can check if the input is
 still valid without having to read the file.

 Even when using the input digests to guard against unwanted caching, sandboxed
 workers offer less strict sandboxing than a pure sandbox, because the tool may
 keep other internal state that has been affected by previous requests.

 Multiplex workers can only be sandboxed if the worker implementation support it,
 and this sandboxing must be separately enabled with the
 `--experimental_worker_multiplex_sandboxing` flag. See more details in
 [the design doc](https://docs.google.com/document/d/1ncLW0hz6uDhNvci1dpzfEoifwTiNTqiBEm1vi-bIIRM/edit)).

 ## Further reading {:#further-reading}

 For more information on persistent workers, see:

 *   [Original persistent workers blog post](https://blog.bazel.build/2015/12/10/java-workers.html)
 *   [Haskell implementation description](https://www.tweag.io/blog/2019-09-25-bazel-ghc-persistent-worker-internship/)
     {: .external}
 *   [Blog post by Mike Morearty](https://medium.com/@mmorearty/how-to-create-a-persistent-worker-for-bazel-7738bba2cabb)
     {: .external}
 *   [Front End Development with Bazel: Angular/TypeScript and Persistent Workers
     w/ Asana](https://www.youtube.com/watch?v=0pgERydGyqo) {: .external}
 *   [Bazel strategies explained](https://jmmv.dev/2019/12/bazel-strategies.html) {: .external}
 *   [Informative worker strategy discussion on the bazel-discuss mailing list](https://groups.google.com/forum/#!msg/bazel-discuss/oAEnuhYOPm8/ol7hf4KWJgAJ)
     {: .external}
	Project: /_project.yaml
	Book: /_book.yaml

	# Persistent Workers

	This page covers how to use persistent workers, the benefits, requirements, and
	how workers affect sandboxing.

	A persistent worker is a long-running process started by the Bazel server, which
	functions as a wrapper around the actual tool (typically a compiler), or is
	the tool itself. In order to benefit from persistent workers, the tool must
	support doing a sequence of compilations, and the wrapper needs to translate
	between the tool's API and the request/response format described below. The same
	worker might be called with and without the `--persistent_worker` flag in the
	same build, and is responsible for appropriately starting and talking to the
	tool, as well as shutting down workers on exit. Each worker instance is assigned
	(but not chrooted to) a separate working directory under
	`<outputBase>/bazel-workers`.

	Using persistent workers is an
	[execution strategy](/docs/user-manual#execution-strategy) that decreases
	start-up overhead, allows more JIT compilation, and enables caching of for
	example the abstract syntax trees in the action execution. This strategy
	achieves these improvements by sending multiple requests to a long-running
	process.

	Persistent workers are implemented for multiple languages, including Java,
	[Scala](https://github.com/bazelbuild/rules_scala){: .external},
	[Kotlin](https://github.com/bazelbuild/rules_kotlin){: .external}, and more.

	Programs using a NodeJS runtime can use the
	[@bazel/worker](https://www.npmjs.com/package/@bazel/worker) helper library to
	implement the worker protocol.

	## Using persistent workers {:#usage}

	[Bazel 0.27 and higher](https://blog.bazel.build/2019/06/19/list-strategy.html)
	uses persistent workers by default when executing builds, though remote
	execution takes precedence. For actions that do not support persistent workers,
	Bazel falls back to starting a tool instance for each action. You can explicitly
	set your build to use persistent workers by setting the `worker`
	[strategy](/docs/user-manual#execution-strategy) for the applicable tool
	mnemonics. As a best practice, this example includes specifying `local` as a
	fallback to the `worker` strategy:

	```posix-terminal
	bazel build //{{ '<var>' }}my:target{{ '</var>' }} --strategy=Javac=worker,local
	```

	Using the workers strategy instead of the local strategy can boost compilation
	speed significantly, depending on implementation. For Java, builds can be 2–4
	times faster, sometimes more for incremental compilation. Compiling Bazel is
	about 2.5 times as fast with workers. For more details, see the
	"[Choosing number of workers](#number-of-workers)" section.

	If you also have a remote build environment that matches your local build
	environment, you can use the experimental
	[dynamic strategy](https://blog.bazel.build/2019/02/01/dynamic-spawn-scheduler.html){: .external},
	which races a remote execution and a worker execution. To enable the dynamic
	strategy, pass the
	[--experimental_spawn_scheduler](/reference/command-line-reference#flag--experimental_spawn_scheduler)
	flag. This strategy automatically enables workers, so there is no need to
	specify the `worker` strategy, but you can still use `local` or `sandboxed` as
	fallbacks.

	## Choosing number of workers {:#number-of-workers}

	The default number of worker instances per mnemonic is 4, but can be adjusted
	with the
	[`worker_max_instances`](/reference/command-line-reference#flag--worker_max_instances)
	flag. There is a trade-off between making good use of the available CPUs and the
	amount of JIT compilation and cache hits you get. With more workers, more
	targets will pay start-up costs of running non-JITted code and hitting cold
	caches. If you have a small number of targets to build, a single worker may give
	the best trade-off between compilation speed and resource usage (for example,
	see [issue #8586](https://github.com/bazelbuild/bazel/issues/8586){: .external}.
	The `worker_max_instances` flag sets the maximum number of worker instances per
	mnemonic and flag set (see below), so in a mixed system you could end up using
	quite a lot of memory if you keep the default value. For incremental builds the
	benefit of multiple worker instances is even smaller.

	This graph shows the from-scratch compilation times for Bazel (target
	`//src:bazel`) on a 6-core hyper-threaded Intel Xeon 3.5 GHz Linux workstation
	with 64 GB of RAM. For each worker configuration, five clean builds are run and
	the average of the last four are taken.

	![Graph of performance improvements of clean builds](/docs/images/workers-clean-chart.png "Performance improvements of clean builds")

	Figure 1. Graph of performance improvements of clean builds.

	For this configuration, two workers give the fastest compile, though at only 14%
	improvement compared to one worker. One worker is a good option if you want to
	use less memory.

	Incremental compilation typically benefits even more. Clean builds are
	relatively rare, but changing a single file between compiles is common, in
	particular in test-driven development. The above example also has some non-Java
	packaging actions to it that can overshadow the incremental compile time.

	Recompiling the Java sources only
	(`//src/main/java/com/google/devtools/build/lib/bazel:BazelServer_deploy.jar`)
	after changing an internal string constant in
	[AbstractContainerizingSandboxedSpawn.java](https://github.com/bazelbuild/bazel/blob/master/src/main/java/com/google/devtools/build/lib/sandbox/AbstractContainerizingSandboxedSpawn.java){: .external}
	gives a 3x speed-up (average of 20 incremental builds with one warmup build
	discarded):

	![Graph of performance improvements of incremental builds](/docs/images/workers-incremental-chart.png "Performance improvements of incremental builds")

	Figure 2. Graph of performance improvements of incremental builds.

	The speed-up depends on the change being made. A speed-up of a factor 6 is
	measured in the above situation when a commonly used constant is changed.

	## Modifying persistent workers {:#options}

	You can pass the
	[`--worker_extra_flag`](/reference/command-line-reference#flag--worker_extra_flag)
	flag to specify start-up flags to workers, keyed by mnemonic. For instance,
	passing `--worker_extra_flag=javac=--debug` turns on debugging for Javac only.
	Only one worker flag can be set per use of this flag, and only for one mnemonic.
	Workers are not just created separately for each mnemonic, but also for
	variations in their start-up flags. Each combination of mnemonic and start-up
	flags is combined into a `WorkerKey`, and for each `WorkerKey` up to
	`worker_max_instances` workers may be created. See the next section for how the
	action configuration can also specify set-up flags.

	You can use the
	[`--high_priority_workers`](/reference/command-line-reference#flag--high_priority_workers)
	flag to specify a mnemonic that should be run in preference to normal-priority
	mnemonics. This can help prioritize actions that are always in the critical
	path. If there are two or more high priority workers executing requests, all
	other workers are prevented from running. This flag can be used multiple times.

	Passing the
	[`--worker_sandboxing`](/reference/command-line-reference#flag--worker_sandboxing)
	flag makes each worker request use a separate sandbox directory for all its
	inputs. Setting up the [sandbox](/docs/sandboxing) takes some extra time,
	especially on macOS, but gives a better correctness guarantee.

	The
	[`--worker_quit_after_build`](/reference/command-line-reference#flag--worker_quit_after_build)
	flag is mainly useful for debugging and profiling. This flag forces all workers
	to quit once a build is done. You can also pass
	[`--worker_verbose`](/reference/command-line-reference#flag--worker_verbose) to
	get more output about what the workers are doing. This flag is reflected in the
	`verbosity` field in `WorkRequest`, allowing worker implementations to also be
	more verbose.

	Workers store their logs in the `<outputBase>/bazel-workers` directory, for
	example
	`/tmp/_bazel_larsrc/191013354bebe14fdddae77f2679c3ef/bazel-workers/worker-1-Javac.log`.
	The file name includes the worker id and the mnemonic. Since there can be more
	than one `WorkerKey` per mnemonic, you may see more than `worker_max_instances`
	log files for a given mnemonic.

	For Android builds, see details at the
	[Android Build Performance page](/docs/android-build-performance).

	## Implementing persistent workers {:#implementation}

	See the [creating persistent workers](/docs/creating-workers) page for
	information on how to make a worker.

	This example shows a Starlark configuration for a worker that uses JSON:

	```python
	args_file = ctx.actions.declare_file(ctx.label.name + "_args_file")
	ctx.actions.write(
	output = args_file,
	content = "\n".join(["-g", "-source", "1.5"] + ctx.files.srcs),
	)
	ctx.actions.run(
	mnemonic = "SomeCompiler",
	executable = "bin/some_compiler_wrapper",
	inputs = inputs,
	outputs = outputs,
	arguments = [ "-max_mem=4G", "@%s" % args_file.path],
	execution_requirements = {
	"supports-workers" : "1", "requires-worker-protocol" : "json" }
	)
	```

	With this definition, the first use of this action would start with executing
	the command line `/bin/some_compiler -max_mem=4G --persistent_worker`. A request
	to compile `Foo.java` would then look like:

	```prototext
	arguments: [ "-g", "-source", "1.5", "Foo.java" ]
	inputs: [
	{path: "symlinkfarm/input1" digest: "d49a..." },
	{path: "symlinkfarm/input2", digest: "093d..."},
	]
	```

	The worker receives this on `stdin` in newline-delimited JSON format (because
	`requires-worker-protocol` is set to JSON). The worker then performs the action,
	and sends a JSON-formatted `WorkResponse` to Bazel on its stdout. Bazel then
	parses this response and manually converts it to a `WorkResponse` proto. To
	communicate with the associated worker using binary-encoded protobuf instead of
	JSON, `requires-worker-protocol` would be set to `proto`, like this:

	```
	execution_requirements = {
	"supports-workers" : "1" ,
	"requires-worker-protocol" : "proto"
	}
	```

	If you do not include `requires-worker-protocol` in the execution requirements,
	Bazel will default the worker communication to use protobuf.

	Bazel derives the `WorkerKey` from the mnemonic and the shared flags, so if this
	configuration allowed changing the `max_mem` parameter, a separate worker would
	be spawned for each value used. This can lead to excessive memory consumption if
	too many variations are used.

	Each worker can currently only process one request at a time. The experimental
	[multiplex workers](/docs/multiplex-worker) feature allows using multiple
	threads, if the underlying tool is multithreaded and the wrapper is set up to
	understand this.

	In
	[this GitHub repo](https://github.com/Ubehebe/bazel-worker-examples){: .external},
	you can see example worker wrappers written in Java as well as in Python. If you
	are working in JavaScript or TypeScript, the
	[@bazel/worker package](https://www.npmjs.com/package/@bazel/worker){: .external}
	and
	[nodejs worker example](https://github.com/bazelbuild/rules_nodejs/tree/stable/examples/worker){: .external}
	might be helpful.

	## How do workers affect sandboxing? {:#sandboxing}

	Using the `worker` strategy by default does not run the action in a
	[sandbox](/docs/sandboxing), similar to the `local` strategy. You can set the
	`--worker_sandboxing` flag to run all workers inside sandboxes, making sure each
	execution of the tool only sees the input files it's supposed to have. The tool
	may still leak information between requests internally, for instance through a
	cache. Using `dynamic` strategy
	[requires workers to be sandboxed](https://github.com/bazelbuild/bazel/blob/master/src/main/java/com/google/devtools/build/lib/exec/SpawnStrategyRegistry.java){: .external}.

	To allow correct use of compiler caches with workers, a digest is passed along
	with each input file. Thus the compiler or the wrapper can check if the input is
	still valid without having to read the file.

	Even when using the input digests to guard against unwanted caching, sandboxed
	workers offer less strict sandboxing than a pure sandbox, because the tool may
	keep other internal state that has been affected by previous requests.

	Multiplex workers can only be sandboxed if the worker implementation support it,
	and this sandboxing must be separately enabled with the
	`--experimental_worker_multiplex_sandboxing` flag. See more details in
	[the design doc](https://docs.google.com/document/d/1ncLW0hz6uDhNvci1dpzfEoifwTiNTqiBEm1vi-bIIRM/edit)).

	## Further reading {:#further-reading}

	For more information on persistent workers, see:

	* [Original persistent workers blog post](https://blog.bazel.build/2015/12/10/java-workers.html)
	* [Haskell implementation description](https://www.tweag.io/blog/2019-09-25-bazel-ghc-persistent-worker-internship/)
	{: .external}
	* [Blog post by Mike Morearty](https://medium.com/@mmorearty/how-to-create-a-persistent-worker-for-bazel-7738bba2cabb)
	{: .external}
	* [Front End Development with Bazel: Angular/TypeScript and Persistent Workers
	w/ Asana](https://www.youtube.com/watch?v=0pgERydGyqo) {: .external}
	* [Bazel strategies explained](https://jmmv.dev/2019/12/bazel-strategies.html) {: .external}
	* [Informative worker strategy discussion on the bazel-discuss mailing list](https://groups.google.com/forum/#!msg/bazel-discuss/oAEnuhYOPm8/ol7hf4KWJgAJ)
	{: .external}