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Python Enhancement Proposals

PEP 690 – Lazy Imports

Author:
Germán Méndez Bravo <german.mb at gmail.com>, Carl Meyer <carl at oddbird.net>
Sponsor:
Barry Warsaw <barry at python.org>
Discussions-To:
Discourse thread
Status:
Draft
Type:
Standards Track
Created:
29-Apr-2022
Python-Version:
3.12
Post-History:
03-May-2022, 03-May-2022

Table of Contents

Abstract

This PEP proposes a feature to transparently defer the 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 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 bitfield 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, which makes all new relevant imports after the call immediately lazy.
  • Enabled in a specific module, via importlib.enable_lazy_imports_in_module().

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 is enabled or not at any moment can be retrieved using importlib.is_lazy_imports_enabled(), which will return True if lazy imports is enabled at the call point or False otherwise.

When 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.

The lazy object is intended to be opaque and self-contained, it has no attributes and it can not be resolved in any way. A repr() of it would be shown as something like: <lazy_object 'fully.qualified.name'>.

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 dictionay 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. Other than the delayed import, the implementation is transparent.

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 by means of using gc.collect() and gc.get_objects(). Lazy objects are not useful but they are also harmless and pose no danger if extracted from the garbage collector in this way.

When a lazy object is added to a dictionary the flag dk_lazy_imports is set and once set, the only case the flag gets cleared is when all lazy import objects get resolved, during one of the “bulk” dictionary lookup operations.

All “bulk” dictionary lookup 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, that 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().

The current status of lazy imports at any given place can be retrieved by using importlib.is_lazy_imports_enabled() and is determined by a combination of the passed -L option flag; an interpreter-wide flag set by importlib.set_lazy_imports() and the container object passed in its excluding keyword argument; and a flag set by importlib.enable_lazy_imports_in_module() in nearest running module frame. All these together are used to cache the current status of lazy imports in the currently running frame. This cache is globally busted whenever any of these API functions is called so that changes take effect immediately for all new import statements.

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 never 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 two optional arguments: a boolean, True by default, for enabling or disabling lazy imports, and an 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 type that will be checked to see whether it contains a module name or not. If the module name is contained in the object, it should be eager. Thus, another example use case for this argument could be:

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() will be to globally set the eager module names/callback. If set_lazy_imports() 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.

set_lazy_imports() may be called more than once, with subsequent calls having only the effect of globally replacing or clearing the excluding list/callback. Generally there should be no reason to do this: the intended use is a single call to set_lazy_imports in the main module, early in the process.

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.

Per-module opt-in

Experience with the reference implementation suggests that the most practical adoption path for lazy imports is for a specific deployed application to opt-in globally, observe whether anything breaks, and opt-out specific modules as needed.

It is less practical to achieve robust and significant startup-time or memory-use wins by piecemeal application of lazy imports. Generally it would require blanket application of the per-module opt-in to most of the codebase, as well as to third-party dependencies (which may be hard or impossible.)

However, under some use cases it may be convenient to have a way to enable lazy imports whether the application/end user requests it or not. This too can be easily achieved:

from importlib import enable_lazy_imports_in_module
enable_lazy_imports_in_module()

After calling enable_lazy_imports_in_module(), every import in the module would be lazy. This could be very helpful for libraries importing subpackages into their main namespace by default, as a mean of exporting them without suffering from the penalties and slowdowns of actually doing the import. This was one of the motivating reasons behind SPEC-1, used by Scientific Python libraries, where exposing symbols for interactive exploration and teaching purposes allows making all the subpackages available there from the start without the additional cost of actually doing the imports.

Testing

The CPython test suite will pass with lazy imports enabled (possibly 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. Caller should use the if (PyErr_Ocurred()) semantics 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 at least 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 arguably also improves the code style) 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 is likely to 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 small performance regressions in a few pyperformance benchmarks, but improvements in others. (TODO update with detailed data from main-branch port of implementation.)

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 on time.
  • Avoid using star imports, as those are always eager.

Reference Implementation

The current reference 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 available in the GitHub Pull Request.

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. The LazyImportError would have source location metadata attached pointing the user to the original import statement, to ease debuggability of errors from lazy imports.

Ensuring that all lazy import errors are raised as LazyImportError would mitigate the potential confusion by reducing 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 would too end up being wrapped in LazyImportError, making such tests fail because true exception types are being magically hidden. The drawbacks here seem to outweigh the hypothetical case where unexpected deferred exceptions are caught by mistake.

Per-module opt-in using future imports

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. Thus, the proposed per-module opt-in uses a function call rather than dedicated syntax. Dedicated syntax would require a new from __optional_features__ import lazy_imports or similar mechanism.

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.enable_lazy_imports_in_module(), 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 (although not simple) 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.

Providing only per-module opt-in with a __future__ import makes it much more difficult for the applications that can benefit from lazy imports to do so immediately, as discussed above.

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.


Source: https://github.com/python/peps/blob/main/pep-0690.rst

Last modified: 2022-10-01 01:19:16 GMT