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, 03-May-2022 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 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. There are also third-party
packages such as 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
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 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:

  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  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 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 in aggregate
performance on the pyperformance benchmark suite.

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. 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 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.