PEP: 690
Title: Lazy Imports
Author: Germán Méndez Bravo <german.mb@gmail.com>, Carl Meyer <carl@oddbird.net>
Sponsor: Barry Warsaw <barry@python.org>
Discussions-To: https://discuss.python.org/t/pep-690-lazy-imports/15474
Status: Rejected
Type: Standards Track
Content-Type: text/x-rst
Created: 29-Apr-2022
Python-Version: 3.12
Post-History: `03-May-2022 <https://discuss.python.org/t/pep-690-lazy-imports/15474>`__,
              `03-May-2022 <https://mail.python.org/archives/list/python-dev@python.org/thread/IHOSWMIBKCXVB46FI7NGOC2F34RUYZ5Z/>`__
Resolution: https://discuss.python.org/t/pep-690-lazy-imports-again/19661/26


Abstract
========

This PEP proposes a feature to transparently defer the finding and execution of
imported modules until the moment when an imported object is first used.  Since
Python programs commonly import many more modules than a single invocation of
the program is likely to use in practice, lazy imports can greatly reduce the
overall number of modules loaded, improving startup time and memory usage. Lazy
imports also mostly eliminate the risk of import cycles.


Motivation
==========

Common Python code style :pep:`prefers <8#imports>` imports at module
level, so they don't have to be repeated within each scope the imported object
is used in, and to avoid the inefficiency of repeated execution of the import
system at runtime. This means that importing the main module of a program
typically results in an immediate cascade of imports of most or all of the
modules that may ever be needed by the program.

Consider the example of a Python command line program (CLI) with a number of
subcommands. Each subcommand may perform different tasks, requiring the import
of different dependencies. But a given invocation of the program will only
execute a single subcommand, or possibly none (i.e. if just ``--help`` usage
info is requested). Top-level eager imports in such a program will result in the
import of many modules that will never be used at all; the time spent (possibly
compiling and) executing these modules is pure waste.

To improve startup time, some large Python CLIs make imports lazy by manually
placing imports inline into functions to delay imports of expensive subsystems.
This manual approach is labor-intensive and fragile; one misplaced import or
refactor can easily undo painstaking optimization work.

The Python standard library already includes built-in support for lazy imports,
via `importlib.util.LazyLoader
<https://docs.python.org/3/library/importlib.html#importlib.util.LazyLoader>`_.
There are also third-party packages such as `demandimport
<https://github.com/bwesterb/py-demandimport/>`_. These provide a "lazy module
object" which delays its own import until first attribute access. This is not
sufficient to make all imports lazy: imports such as ``from foo import a, b``
will still eagerly import the module ``foo`` since they immediately access an
attribute from it. It also imposes noticeable runtime overhead on every module
attribute access, since it requires a Python-level ``__getattr__`` or
``__getattribute__`` implementation.

Authors of scientific Python packages have also made extensive use of lazy
imports to allow users to write e.g. ``import scipy as sp`` and then easily
access many different submodules with e.g. ``sp.linalg``, without requiring all
the many submodules to be imported up-front. `SPEC 1
<https://scientific-python.org/specs/spec-0001/>`_ codifies this practice in the
form of a ``lazy_loader`` library that can be used explicitly in a package
``__init__.py`` to provide lazily accessible submodules.

Users of static typing also have to import names for use in type annotations
that may never be used at runtime (if :pep:`563` or possibly in future
:pep:`649` are used to avoid eager runtime evaluation of annotations). Lazy
imports are very attractive in this scenario to avoid overhead of unneeded
imports.

This PEP proposes a more general and comprehensive solution for lazy imports
that can encompass all of the above use cases and does not impose detectable
overhead in real-world use. The implementation in this PEP has already
`demonstrated
<https://github.com/facebookincubator/cinder/blob/cinder/3.8/CinderDoc/lazy_imports.rst>`_
startup time improvements up to 70% and memory-use reductions up to 40% on
real-world Python CLIs.

Lazy imports also eliminate most import cycles. With eager imports, "false
cycles" can easily occur which are fixed by simply moving an import to the
bottom of a module or inline into a function, or switching from ``from foo
import bar`` to ``import foo``. With lazy imports, these "cycles" just work.
The only cycles which will remain are those where two modules actually each use
a name from the other at module level; these "true" cycles are only fixable by
refactoring the classes or functions involved.


Rationale
=========

The aim of this feature is to make imports transparently lazy. "Lazy" means
that the import of a module (execution of the module body and addition of the
module object to ``sys.modules``) should not occur until the module (or a name
imported from it) is actually referenced during execution. "Transparent" means
that besides the delayed import (and necessarily observable effects of that,
such as delayed import side effects and changes to ``sys.modules``), there is
no other observable change in behavior: the imported object is present in the
module namespace as normal and is transparently loaded whenever first used: its
status as a "lazy imported object" is not directly observable from Python or
from C extension code.

The requirement that the imported object be present in the module namespace as
usual, even before the import has actually occurred, means that we need some
kind of "lazy object" placeholder to represent the not-yet-imported object.
The transparency requirement dictates that this placeholder must never be
visible to Python code; any reference to it must trigger the import and replace
it with the real imported object.

Given the possibility that Python (or C extension) code may pull objects
directly out of a module ``__dict__``, the only way to reliably prevent
accidental leakage of lazy objects is to have the dictionary itself be
responsible to ensure resolution of lazy objects on lookup.

When a lookup finds that the key references a lazy object, it resolves the lazy
object immediately before returning it. To avoid side effects mutating
dictionaries midway through iteration, all lazy objects in a dictionary are
resolved prior to starting an iteration; this could incur a performance penalty
when using bulk iterations (``iter(dict)``, ``reversed(dict)``,
``dict.__reversed__()``, ``dict.keys()``, ``iter(dict.keys())`` and
``reversed(dict.keys())``). To avoid this performance penalty on the vast
majority of dictionaries, which never contain any lazy objects, we steal a bit
from the ``dk_kind`` field for a new ``dk_lazy_imports`` flag to keep track of
whether a dictionary may contain lazy objects or not.

This implementation comprehensively prevents leakage of lazy objects, ensuring
they are always resolved to the real imported object before anyone can get hold
of them for any use, while avoiding any significant performance impact on
dictionaries in general.


Specification
=============

Lazy imports are opt-in, and they can be globally enabled either via a new
``-L`` flag to the Python interpreter, or via a call to a new
``importlib.set_lazy_imports()`` function. This function takes two arguments, a
boolean ``enabled`` and an ``excluding`` container. If ``enabled`` is true, lazy
imports will be turned on from that point forward. If it is false, they will be
turned off from that point forward. (Use of the ``excluding`` keyword is
discussed below under "Per-module opt-out.")

When the flag ``-L`` is passed to the Python interpreter, a new
``sys.flags.lazy_imports`` is set to ``True``, otherwise it exists as ``False``.
This flag is used to propagate ``-L`` to new Python subprocesses.

The flag in ``sys.flags.lazy_imports`` does not necessarily reflect the current
status of lazy imports, only whether the interpreter was started with the ``-L``
option. Actual current status of whether lazy imports are enabled or not at any
moment can be retrieved using ``importlib.is_lazy_imports_enabled()``, which
will return ``True`` if lazy imports are enabled at the call point or ``False``
otherwise.

When lazy imports are enabled, the loading and execution of all (and only)
top-level imports is deferred until the imported name is first used. This could
happen immediately (e.g. on the very next line after the import statement) or
much later (e.g.  while using the name inside a function being called by some
other code at some later time.)

For these top level imports, there are two contexts which will make them eager
(not lazy): imports inside ``try`` / ``except`` / ``finally`` or ``with``
blocks, and star imports (``from foo import *``.) Imports inside
exception-handling blocks (this includes ``with`` blocks, since those can also
"catch" and handle exceptions) remain eager so that any exceptions arising from
the import can be handled. Star imports must remain eager since performing the
import is the only way to know which names should be added to the namespace.

Imports inside class definitions or inside functions/methods are not "top
level" and are never lazy.

Dynamic imports using ``__import__()`` or ``importlib.import_module()`` are
also never lazy.

Lazy imports state (i.e. whether they have been enabled, and any excluded
modules; see below) is per-interpreter, but global within the interpreter (i.e.
all threads will be affected).


Example
-------

Say we have a module ``spam.py``::

    # simulate some work
    import time
    time.sleep(10)
    print("spam loaded")

And a module ``eggs.py`` which imports it::

    import spam
    print("imports done")

If we run ``python -L eggs.py``, the ``spam`` module will never be imported
(because it is never referenced after the import), ``"spam loaded"`` will never
be printed, and there will be no 10 second delay.

But if ``eggs.py`` simply references the name ``spam`` after importing it, that
will be enough to trigger the import of ``spam.py``::

    import spam
    print("imports done")
    spam

Now if we run ``python -L eggs.py``, we will see the output ``"imports done"``
printed first, then a 10 second delay, and then ``"spam loaded"`` printed after
that.

Of course, in real use cases (especially with lazy imports), it's not
recommended to rely on import side effects like this to trigger real work. This
example is just to clarify the behavior of lazy imports.

Another way to explain the effect of lazy imports is that it is as if each lazy
import statement had instead been written inline in the source code immediately
before each use of the imported name. So one can think of lazy imports as
similar to transforming this code::

    import foo

    def func1():
        return foo.bar()

    def func2():
        return foo.baz()

To this::

    def func1():
        import foo
        return foo.bar()

    def func2():
        import foo
        return foo.baz()

This gives a good sense of when the import of ``foo`` will occur under lazy
imports, but lazy import is not really equivalent to this code transformation.
There are several notable differences:

* Unlike in the latter code, under lazy imports the name ``foo`` still does
  exist in the module's global namespace, and can be imported or referenced by
  other modules that import this one. (Such references would also trigger the
  import.)

* The runtime overhead of lazy imports is much lower than the latter code; after
  the first reference to the name ``foo`` which triggers the import, subsequent
  references will have zero import system overhead; they are indistinguishable
  from a normal name reference.

In a sense, lazy imports turn the import statement into just a declaration of an
imported name or names, to later be fully resolved when referenced.

An import in the style ``from foo import bar`` can also be made lazy. When the
import occurs, the name ``bar`` will be added to the module namespace as a lazy
import. The first reference to ``bar`` will import ``foo`` and resolve ``bar``
to ``foo.bar``.


Intended usage
--------------

Since lazy imports are a potentially-breaking semantic change, they should be
enabled only by the author or maintainer of a Python application, who is
prepared to thoroughly test the application under the new semantics, ensure it
behaves as expected, and opt-out any specific imports as needed (see below).
Lazy imports should not be enabled speculatively by the end user of a Python
application with any expectation of success.

It is the responsibility of the application developer enabling lazy imports for
their application to opt-out any library imports that turn out to need to be
eager for their application to work correctly; it is not the responsibility of
library authors to ensure that their library behaves exactly the same under lazy
imports.

The documentation of the feature, the ``-L`` flag, and the new ``importlib``
APIs will be clear about the intended usage and the risks of adoption without
testing.


Implementation
--------------

Lazy imports are represented internally by a "lazy import" object. When a lazy
import occurs (say ``import foo`` or ``from foo import bar``), the key ``"foo"``
or ``"bar"`` is immediately added to the module namespace dictionary, but with
its value set to an internal-only "lazy import" object that preserves all the
necessary metadata to execute the import later.

A new boolean flag in ``PyDictKeysObject`` (``dk_lazy_imports``) is set to
signal that this particular dictionary may contain lazy import objects. This
flag is only used to efficiently resolve all lazy objects in "bulk" operations,
when a dictionary may contain lazy objects.

Anytime a key is looked up in a dictionary to extract its value, the
value is checked to see if it is a lazy import object. If so, the lazy object
is immediately resolved, the relevant imported modules executed, the lazy
import object is replaced in the dictionary (if possible) by the actual
imported value, and the resolved value is returned from the lookup function. A
dictionary could mutate as part of an import side effect while resolving a lazy
import object. In this case it is not possible to efficiently replace the key
value with the resolved object. In this case, the lazy import object will gain
a cached pointer to the resolved object. On next access that cached reference
will be returned and the lazy import object will be replaced in the dict with
the resolved value.

Because this is all handled internally by the dictionary implementation, lazy
import objects can never escape from the module namespace to become visible to
Python code; they are always resolved at their first reference. No stub, dummy
or thunk objects are ever visible to Python code or placed in ``sys.modules``.
If a module is imported lazily, no entry for it will appear in ``sys.modules``
at all until it is actually imported on first reference.

If two different modules (``moda`` and ``modb``) both contain a lazy ``import
foo``, each module's namespace dictionary will have an independent lazy import
object under the key ``"foo"``, delaying import of the same ``foo`` module. This
is not a problem. When there is first a reference to, say, ``moda.foo``, the
module ``foo`` will be imported and placed in ``sys.modules`` as usual, and the
lazy object under the key ``moda.__dict__["foo"]`` will be replaced by the
actual module ``foo``. At this point ``modb.__dict__["foo"]`` will remain a lazy
import object. When ``modb.foo`` is later referenced, it will also try to
``import foo``. This import will find the module already present in
``sys.modules``, as is normal for subsequent imports of the same module in
Python, and at this point will replace the lazy import object at
``modb.__dict__["foo"]`` with the actual module ``foo``.

There are two cases in which a lazy import object can escape a dictionary:

* Into another dictionary: to preserve the performance of bulk-copy operations
  like ``dict.update()`` and ``dict.copy()``, they do not check for or resolve
  lazy import objects. However, if the source dict has the ``dk_lazy_imports``
  flag set that indicates it may contain lazy objects, that flag will be
  passed on to the updated/copied dictionary. This still ensures that the lazy
  import object can't escape into Python code without being resolved.

* Through the garbage collector: lazy imported objects are still Python objects
  and live within the garbage collector; as such, they can be collected and seen
  via e.g. ``gc.get_objects()``. If a lazy object becomes
  visible to Python code in this way, it is opaque and inert; it has no useful
  methods or attributes. A ``repr()`` of it would be shown as something like:
  ``<lazy_object 'fully.qualified.name'>``.

When a lazy object is added to a dictionary, the flag ``dk_lazy_imports`` is set.
Once set, the flag is only cleared if *all* lazy import objects in the
dictionary are resolved, e.g. prior to dictionary iteration.

All dictionary iteration methods involving values (such as ``dict.items()``,
``dict.values()``, ``PyDict_Next()`` etc.) will attempt to resolve *all* lazy
import objects in the dictionary prior to starting the iteration. Since only
(some) module namespace dictionaries will ever have ``dk_lazy_imports`` set, the
extra overhead of resolving all lazy import objects inside a dictionary is only
paid by those dictionaries that need it. Minimizing the overhead on normal
non-lazy dictionaries is the sole purpose of the ``dk_lazy_imports`` flag.

``PyDict_Next`` will attempt to resolve all lazy import objects the first time
position ``0`` is accessed, and those imports could fail with exceptions. Since
``PyDict_Next`` cannot set an exception, ``PyDict_Next`` will return ``0``
immediately in this case, and any exception will be printed to stderr as an
unraisable exception.

For this reason, this PEP introduces ``PyDict_NextWithError``, which works in
the same way as ``PyDict_Next``, but which can set an error when returning ``0``
and this should be checked via ``PyErr_Occurred()`` after the call.

The eagerness of imports within ``try`` / ``except`` / ``with`` blocks or within
class or function bodies is handled in the compiler via a new
``EAGER_IMPORT_NAME`` opcode that always imports eagerly. Top-level imports use
``IMPORT_NAME``, which may be lazy or eager depending on ``-L`` and/or
``importlib.set_lazy_imports()``.


Debugging
---------

Debug logging from ``python -v`` will include logging whenever an import
statement has been encountered but execution of the import will be deferred.

Python's ``-X importtime`` feature for profiling import costs adapts naturally
to lazy imports; the profiled time is the time spent actually importing.

Although lazy import objects are not generally visible to Python code, in some
debugging cases it may be useful to check from Python code whether the value at
a given key in a given dictionary is a lazy import object, without triggering
its resolution. For this purpose, ``importlib.is_lazy_import()`` can be used::

    from importlib import is_lazy_import

    import foo

    is_lazy_import(globals(), "foo")

    foo

    is_lazy_import(globals(), "foo")

In this example, if lazy imports have been enabled the first call to
``is_lazy_import`` will return ``True`` and the second will return ``False``.


Per-module opt-out
------------------

Due to the backwards compatibility issues mentioned below, it may be necessary
for an application using lazy imports to force some imports to be eager.

In first-party code, since imports inside a ``try`` or ``with`` block are never
lazy, this can be easily accomplished::

    try:  # force these imports to be eager
        import foo
        import bar
    finally:
        pass

This PEP proposes to add a new ``importlib.eager_imports()`` context manager,
so the above technique can be less verbose and doesn't require comments to
clarify its intent::

    from importlib import eager_imports

    with eager_imports():
        import foo
        import bar

Since imports within context managers are always eager, the ``eager_imports()``
context manager can just be an alias to a null context manager. The context
manager's effect is not transitive: ``foo`` and ``bar`` will be imported
eagerly, but imports within those modules will still follow the usual laziness
rules.

The more difficult case can occur if an import in third-party code that can't
easily be modified must be forced to be eager. For this purpose,
``importlib.set_lazy_imports()`` takes a second optional keyword-only
``excluding`` argument, which can be set to a container of module names within
which all imports will be eager::

    from importlib import set_lazy_imports

    set_lazy_imports(excluding=["one.mod", "another"])

The effect of this is also shallow: all imports within ``one.mod`` will be
eager, but not imports in all modules imported by ``one.mod``.

The ``excluding`` parameter of ``set_lazy_imports()`` can be a container of any
kind that will be checked to see whether it contains a module name. If the
module name is contained in the object, imports within it will be eager. Thus,
arbitrary opt-out logic can be encoded in a ``__contains__`` method::

    import re
    from importlib import set_lazy_imports

    class Checker:
        def __contains__(self, name):
            return re.match(r"foo\.[^.]+\.logger", name)

    set_lazy_imports(excluding=Checker())

If Python was executed with the ``-L`` flag, then lazy imports will already be
globally enabled, and the only effect of calling ``set_lazy_imports(True,
excluding=...)`` will be to globally set the eager module names/callback. If
``set_lazy_imports(True)`` is called with no ``excluding`` argument, the
exclusion list/callback will be cleared and all eligible imports (module-level
imports not in ``try/except/with``, and not ``import *``) will be lazy from that
point forward.

This opt-out system is designed to maintain the possibility of local reasoning
about the laziness of an import. You only need to see the code of one module,
and the ``excluding`` argument to ``set_lazy_imports``, if any, to know whether
a given import will be eager or lazy.


Testing
-------

The CPython test suite will pass with lazy imports enabled (with some tests
skipped). One buildbot should run the test suite with lazy imports enabled.


C API
-----

For authors of C extension modules, the proposed public C API is as follows:

.. list-table::
   :widths: 50 50
   :header-rows: 1

   * - C API
     - Python API
   * - ``PyObject *PyImport_SetLazyImports(PyObject *enabled, PyObject *excluding)``
     - ``importlib.set_lazy_imports(enabled: bool = True, *, excluding: typing.Container[str] | None = None)``
   * - ``int PyDict_IsLazyImport(PyObject *dict, PyObject *name)``
     - ``importlib.is_lazy_import(dict: typing.Dict[str, object], name: str) -> bool``
   * - ``int PyImport_IsLazyImportsEnabled()``
     - ``importlib.is_lazy_imports_enabled() -> bool``
   * - ``void PyDict_ResolveLazyImports(PyObject *dict)``
     -
   * - ``PyDict_NextWithError()``
     -

* ``void PyDict_ResolveLazyImports(PyObject *dict)`` resolves all lazy objects
  in a dictionary, if any. To be used prior calling ``PyDict_NextWithError()``
  or ``PyDict_Next()``.

* ``PyDict_NextWithError()``, works the same way as ``PyDict_Next()``, with
  the exception it propagates any errors to the caller by returning ``0`` and
  setting an exception. The caller should use ``PyErr_Occurred()`` to check for any
  errors.


Backwards Compatibility
=======================

This proposal preserves full backwards compatibility when the feature is
disabled, which is the default.

Even when enabled, most code will continue to work normally without any
observable change (other than improved startup time and memory usage.)
Namespace packages are not affected: they work just as they do currently,
except lazily.

In some existing code, lazy imports could produce currently unexpected results
and behaviors. The problems that we may see when enabling lazy imports in an
existing codebase are related to:


Import Side Effects
-------------------

Import side effects that would otherwise be produced by the execution of
imported modules during the execution of import statements will be deferred
until the imported objects are used.

These import side effects may include:

* code executing any side-effecting logic during import;
* relying on imported submodules being set as attributes in the parent module.

A relevant and typical affected case is the `click
<https://click.palletsprojects.com/>`_ library for building Python command-line
interfaces. If e.g. ``cli = click.group()`` is defined in ``main.py``, and
``sub.py`` imports ``cli`` from ``main`` and adds subcommands to it via
decorator (``@cli.command(...)``), but the actual ``cli()`` call is in
``main.py``, then lazy imports may prevent the subcommands from being
registered, since in this case Click is depending on side effects of the import
of ``sub.py``. In this case the fix is to ensure the import of ``sub.py`` is
eager, e.g. by using the ``importlib.eager_imports()`` context manager.


Dynamic Paths
-------------

There could be issues related to dynamic Python import paths; particularly,
adding (and then removing after the import) paths from ``sys.path``::

    sys.path.insert(0, "/path/to/foo/module")
    import foo
    del sys.path[0]
    foo.Bar()

In this case, with lazy imports enabled, the import of ``foo`` will not actually
occur while the addition to ``sys.path`` is present.

An easy fix for this (which also improves the code style and ensures cleanup)
would be to place the ``sys.path`` modifications in a context manager. This
resolves the issue, since imports inside a ``with`` block are always eager.


Deferred Exceptions
-------------------

Exceptions that occur during a lazy import bubble up and erase the
partially-constructed module(s) from ``sys.modules``, just as exceptions during
normal import do.

Since errors raised during a lazy import will occur later than they would if
the import were eager (i.e. wherever the name is first referenced), it is also
possible that they could be accidentally caught by exception handlers that did
not expect the import to be running within their ``try`` block, leading to
confusion.


Drawbacks
=========

Downsides of this PEP include:

* It provides a subtly incompatible semantics for the behavior of Python
  imports. This is a potential burden on library authors who may be asked by their
  users to support both semantics, and is one more possibility for Python
  users/readers to be aware of.

* Some popular Python coding patterns (notably centralized registries populated
  by a decorator) rely on import side effects and may require explicit opt-out to
  work as expected with lazy imports.

* Exceptions can be raised at any point while accessing names representing lazy
  imports, this could lead to confusion and debugging of unexpected exceptions.

Lazy import semantics are already possible and even supported today in the
Python standard library, so these drawbacks are not newly introduced by this
PEP. So far, existing usage of lazy imports by some applications has not proven
a problem. But this PEP could make the usage of lazy imports more popular,
potentially exacerbating these drawbacks.

These drawbacks must be weighed against the significant benefits offered by this
PEP's implementation of lazy imports. Ultimately these costs will be higher if
the feature is widely used; but wide usage also indicates the feature provides a
lot of value, perhaps justifying the costs.


Security Implications
=====================

Deferred execution of code could produce security concerns if process owner,
shell path, ``sys.path``, or other sensitive environment or contextual states
change between the time the ``import`` statement is executed and the time the
imported object is first referenced.


Performance Impact
==================

The reference implementation has shown that the feature has negligible
performance impact on existing real-world codebases (Instagram Server, several
CLI programs at Meta, Jupyter notebooks used by Meta researchers), while
providing substantial improvements to startup time and memory usage.

The reference implementation shows `no measurable change
<https://gist.github.com/ericsnowcurrently/d027ff4130dedec3b58ab1f55be11e8c>`_
in aggregate performance on the `pyperformance benchmark suite
<https://github.com/python/pyperformance>`_.


How to Teach This
=================

Since the feature is opt-in, beginners should not encounter it by default.
Documentation of the ``-L`` flag and ``importlib.set_lazy_imports()`` can
clarify the behavior of lazy imports.

The documentation should also clarify that opting into lazy imports is opting
into a non-standard semantics for Python imports, which could cause Python
libraries to break in unexpected ways. The responsibility to identify these
breakages and work around them with an opt-out (or stop using lazy imports)
rests entirely with the person choosing to enable lazy imports for their
application, not with the library author. Python libraries are under no
obligation to support lazy import semantics. Politely reporting an
incompatibility may be useful to the library author, but they may choose to
simply say their library does not support use with lazy imports, and this is a
valid choice.

Some best practices to deal with some of the issues that could arise and to
better take advantage of lazy imports are:

* Avoid relying on import side effects. Perhaps the most common reliance on
  import side effects is the registry pattern, where population of some external
  registry happens implicitly during the importing of modules, often via
  decorators. Instead, the registry should be built via an explicit call that does
  a discovery process to find decorated functions or classes in explicitly
  nominated modules.

* Always import needed submodules explicitly, don't rely on some other import
  to ensure a module has its submodules as attributes. That is, unless there is an
  explicit ``from . import bar`` in ``foo/__init__.py``, always do ``import
  foo.bar; foo.bar.Baz``, not ``import foo; foo.bar.Baz``. The latter only works
  (unreliably) because the attribute ``foo.bar`` is added as a side effect of
  ``foo.bar`` being imported somewhere else. With lazy imports this may not always
  happen in time.

* Avoid using star imports, as those are always eager.


Reference Implementation
========================

The initial implementation is available as part of `Cinder
<https://github.com/facebookincubator/cinder>`_. This reference implementation
is in use within Meta and has proven to achieve improvements in startup time
(and total runtime for some applications) in the range of 40%-70%, as well as
significant reduction in memory footprint (up to 40%), thanks to not needing to
execute imports that end up being unused in the common flow.

An `updated reference implementation based on CPython main branch
<https://github.com/Kronuz/cpython/pull/17>`_ is also available.


Rejected Ideas
==============

Wrapping deferred exceptions
----------------------------

To reduce the potential for confusion, exceptions raised in the
course of executing a lazy import could be replaced by a ``LazyImportError``
exception (a subclass of ``ImportError``), with a ``__cause__`` set to the
original exception.

Ensuring that all lazy import errors are raised as ``LazyImportError`` would
reduce the likelihood that they would be accidentally caught and mistaken for a
different expected exception. However, in practice we have seen cases, e.g.
inside tests, where failing modules raise ``unittest.SkipTest`` exception and
this too would end up being wrapped in ``LazyImportError``, making such tests
fail because the true exception type is hidden. The drawbacks here seem to
outweigh the hypothetical case where unexpected deferred exceptions are caught
by mistake.


Per-module opt-in
-----------------

A per-module opt-in using future imports (i.e.
``from __future__ import lazy_imports``) does not make sense because
``__future__`` imports are not feature flags, they are for transition to
behaviors which will become default in the future. It is not clear if lazy
imports will ever make sense as the default behavior, so we should not
promise this with a ``__future__`` import.

There are other cases where a library might desire to locally opt-in to lazy
imports for a particular module; e.g. a lazy top-level ``__init__.py`` for a
large library, to make its subcomponents accessible as lazy attributes. For now,
to keep the feature simpler, this PEP chooses to focus on the "application" use
case and does not address the library use case. The underlying laziness
mechanism introduced in this PEP could be used in the future to address this use
case as well.


Explicit syntax for individual lazy imports
-------------------------------------------

If the primary objective of lazy imports were solely to work around import
cycles and forward references, an explicitly-marked syntax for particular
targeted imports to be lazy would make a lot of sense. But in practice it would
be very hard to get robust startup time or memory use benefits from this
approach, since it would require converting most imports within your code base
(and in third-party dependencies) to use the lazy import syntax.

It would be possible to aim for a "shallow" laziness where only the top-level
imports of subsystems from the main module are made explicitly lazy, but then
imports within the subsystems are all eager. This is extremely fragile, though
-- it only takes one mis-placed import to undo the carefully constructed
shallow laziness. Globally enabling lazy imports, on the other hand, provides
in-depth robust laziness where you always pay only for the imports you use.

There may be use cases (e.g. for static typing) where individually-marked lazy
imports are desirable to avoid forward references, but the perf/memory benefits
of globally lazy imports are not needed. Since this is a different set of
motivating use cases and requires new syntax, we prefer not to include it in
this PEP. Another PEP could build on top of this implementation and propose the
additional syntax.


Environment variable to enable lazy imports
-------------------------------------------

Providing an environment variable opt-in lends itself too easily to abuse of the
feature. It may seem tempting for a Python user to, for instance, globally set
the environment variable in their shell in the hopes of speeding up all the
Python programs they run. This usage with untested programs is likely to lead to
spurious bug reports and maintenance burden for the authors of those tools. To
avoid this, we choose not to provide an environment variable opt-in at all.


Removing the ``-L`` flag
------------------------

We do provide the ``-L`` CLI flag, which could in theory be abused in a similar
way by an end user running an individual Python program that is run with
``python somescript.py`` or ``python -m somescript`` (rather than distributed
via Python packaging tools). But the potential scope for misuse is much less
with ``-L`` than an environment variable, and ``-L`` is valuable for some
applications to maximize startup time benefits by ensuring that all imports from
the start of a process will be lazy, so we choose to keep it.

It is already the case that running arbitrary Python programs with command line
flags they weren't intended to be used with (e.g. ``-s``, ``-S``, ``-E``, or
``-I``) can have unexpected and breaking results. ``-L`` is nothing new in this
regard.


Half-lazy imports
-----------------

It would be possible to eagerly run the import loader to the point of finding
the module source, but then defer the actual execution of the module and
creation of the module object. The advantage of this would be that certain
classes of import errors (e.g. a simple typo in the module name) would be
caught eagerly instead of being deferred to the use of an imported name.

The disadvantage would be that the startup time benefits of lazy imports would
be significantly reduced, since unused imports would still require a filesystem
``stat()`` call, at least. It would also introduce a possibly non-obvious split
between *which* import errors are raised eagerly and which are delayed, when
lazy imports are enabled.

This idea is rejected for now on the basis that in practice, confusion about
import typos has not been an observed problem with the reference
implementation. Generally delayed imports are not delayed forever, and errors
show up soon enough to be caught and fixed (unless the import is truly unused.)

Another possible motivation for half-lazy imports would be to allow modules
themselves to control via some flag whether they are imported lazily or eagerly.
This is rejected both on the basis that it requires half-lazy imports, giving up
some of the performance benefits of import laziness, and because in general
modules do not decide how or when they are imported, the module importing them
decides that. There isn't clear rationale for this PEP to invert that control;
instead it just provides more options for the importing code to make the
decision.


Lazy dynamic imports
--------------------

It would be possible to add a ``lazy=True`` or similar option to
``__import__()`` and/or ``importlib.import_module()``, to enable them to
perform lazy imports. That idea is rejected in this PEP for lack of a clear
use case. Dynamic imports are already far outside the :pep:`8` code style
recommendations for imports, and can easily be made precisely as lazy as
desired by placing them at the desired point in the code flow. These aren't
commonly used at module top level, which is where lazy imports applies.


Deep eager-imports override
---------------------------

The proposed ``importlib.eager_imports()`` context manager and excluded modules
in the ``importlib.set_lazy_imports(excluding=...)`` override all have shallow
effects: they only force eagerness for the location they are applied to, not
transitively. It would be possible to provide a deep/transitive version of one
or both. That idea is rejected in this PEP because the implementation would be
complex (taking into account threads and async code), experience with the
reference implementation has not shown it to be necessary, and because it
prevents local reasoning about laziness of imports.

A deep override can lead to confusing behavior because the
transitively-imported modules may be imported from multiple locations, some of
which use the "deep eager override" and some of which don't. Thus those modules
may still be imported lazily initially, if they are first imported from a
location that doesn't have the override.

With deep overrides it is not possible to locally reason about whether a given
import will be lazy or eager. With the behavior specified in this PEP, such
local reasoning is possible.


Making lazy imports the default behavior
----------------------------------------

Making lazy imports the default/sole behavior of Python imports, instead of
opt-in, would have some long-term benefits, in that library authors would
(eventually) no longer need to consider the possibility of both semantics.

However, the backwards-incompatibilies are such that this could only be
considered over a long time frame, with a ``__future__`` import. It is not at
all clear that lazy imports should become the default import semantics for
Python.

This PEP takes the position that the Python community needs more experience with
lazy imports before considering making it the default behavior, so that is
entirely left to a possible future PEP.


Copyright
=========

This document is placed in the public domain or under the
CC0-1.0-Universal license, whichever is more permissive.
