# Precompiling

Precompiling is compiling Python source files (`.py` files) into byte code
(`.pyc` files) at build time instead of runtime. Doing it at build time can
improve performance by skipping that work at runtime.

Precompiling is disabled by default, so you must enable it using flags or
attributes to use it.

## Overhead of precompiling

While precompiling helps runtime performance, it has two main costs:
1. Increasing the size (count and disk usage) of runfiles. It approximately
   double the count of the runfiles because for every `.py` file, there is also
   a `.pyc` file. Compiled files are generally around the same size as the
   source files, so it approximately doubles the disk usage.
2. Precompiling requires running an extra action at build time. While
   compiling itself isn't that expensive, the overhead can become noticable
   as more files need to be compiled.

## Binary-level opt-in

Because of the costs of precompiling, it may not be feasible to globally enable it
for your repo for everything. For example, some binaries may be
particularly large, and doubling the number of runfiles isn't doable.

If this is the case, there's an alternative way to more selectively and
incrementally control precompiling on a per-binry basis.

To use this approach, the two basic steps are:
1. Disable pyc files from being automatically added to runfiles:
   `--@rules_python//python/config_settings:precompile_add_to_runfiles=decided_elsewhere`,
2. Set the `pyc_collection` attribute on the binaries/tests that should or should
   not use precompiling.

The default for the `pyc_collection` attribute is controlled by a flag, so you
can use an opt-in or opt-out approach by setting the flag:
* targets must opt-out: `--@rules_python//python/config_settings:pyc_collection=include_pyc`,
* targets must opt-in: `--@rules_python//python/config_settings:pyc_collection=disabled`,

## Advanced precompiler customization

The default implementation of the precompiler is a persistent, multiplexed,
sandbox-aware, cancellation-enabled, json-protocol worker that uses the same
interpreter as the target toolchain. This works well for local builds, but may
not work as well for remote execution builds. To customize the precompiler, two
mechanisms are available:

* The exec tools toolchain allows customizing the precompiler binary used with
  the `precompiler` attribute. Arbitrary binaries are supported.
* The execution requirements can be customized using
  `--@rules_python//tools/precompiler:execution_requirements`. This is a list
  flag that can be repeated. Each entry is a key=value that is added to the
  execution requirements of the `PyPrecompile` action. Note that this flag
  is specific to the rules_python precompiler. If a custom binary is used,
  this flag will have to be propagated from the custom binary using the
  `testing.ExecutionInfo` provider; refer to the `py_interpreter_program` an

The default precompiler implementation is an asynchronous/concurrent
implementation. If you find it has bugs or hangs, please report them. In the
meantime, the flag `--worker_extra_flag=PyPrecompile=--worker_impl=serial` can
be used to switch to a synchronous/serial implementation that may not perform
as well, but is less likely to have issues.

The `execution_requirements` keys of most relevance are:
* `supports-workers`: 1 or 0, to indicate if a regular persistent worker is
  desired.
* `supports-multiplex-workers`: 1 o 0, to indicate if a multiplexed persistent
  worker is desired.
* `requires-worker-protocol`: json or proto; the rules_python precompiler
  currently only supports json.
* `supports-multiplex-sandboxing`: 1 or 0, to indicate if sanboxing is of the
  worker is supported.
* `supports-worker-cancellation`: 1 or 1, to indicate if requests to the worker
  can be cancelled.

Note that any execution requirements values can be specified in the flag.

## Known issues, caveats, and idiosyncracies

* Precompiling requires Bazel 7+ with the Pystar rule implementation enabled.
* Mixing rules_python PyInfo with Bazel builtin PyInfo will result in pyc files
  being dropped.
* Precompiled files may not be used in certain cases prior to Python 3.11. This
  occurs due Python adding the directory of the binary's main `.py` file, which
  causes the module to be found in the workspace source directory instead of
  within the binary's runfiles directory (where the pyc files are). This can
  usually be worked around by removing `sys.path[0]` (or otherwise ensuring the
  runfiles directory comes before the repos source directory in `sys.path`).
* The pyc filename does not include the optimization level (e.g.
  `foo.cpython-39.opt-2.pyc`). This works fine (it's all byte code), but also
  means the interpreter `-O` argument can't be used -- doing so will cause the
  interpreter to look for the non-existent `opt-N` named files.