PEP 690 – Lazy Imports
- Germán Méndez Bravo <german.mb at gmail.com>, Carl Meyer <carl at oddbird.net>
- Barry Warsaw <barry at python.org>
- Discourse thread
- Standards Track
- 03-May-2022, 03-May-2022
Table of Contents
- Backwards Compatibility
- Security Implications
- Performance Impact
- How to Teach This
- Reference Implementation
- Rejected Ideas
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.
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
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,
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
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
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.
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 (
reversed(dict.keys())). To avoid this performance penalty on the vast
majority of dictionaries, which never contain any lazy objects, we steal a bit
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.
Lazy imports are opt-in, and they can be:
- Globally enabled, either via a new
-Lflag 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
When the flag
-L is passed to the Python interpreter, a new
sys.flags.lazy_imports is set to
True, otherwise it exists as
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
option. Actual current status of whether lazy imports is enabled or not at any
moment can be retrieved using
True if lazy imports is enabled at the call point or
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
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
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).
Say we have a module
# 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.
eggs.py simply references the name
spam after importing it, that
will be enough to trigger the import of
import spam print("imports done") spam
Now if we run
python -L eggs.py, we will see the output
printed first, then a 10 second delay, and then
"spam loaded" printed after
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()
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
foostill 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
foowhich 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
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
APIs will be clear about the intended usage and the risks of adoption without
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
"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:
A new boolean flag in
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
If a module is imported lazily, no entry for it will appear in
at all until it is actually imported on first reference.
If two different modules (
modb) both contain a lazy
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,
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
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
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
dict.copy(), they do not check for or resolve lazy import objects. However, if the source dict has the
dk_lazy_importsflag 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.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
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
PyDict_Next will attempt to resolve all lazy import objects the first time
0 is accessed, and those imports could fail with exceptions. Since
PyDict_Next cannot set an exception,
PyDict_Next will return
immediately in this case, and any exception will be printed to stderr as an
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
this should be checked via
PyErr_Occurred() after the call.
The eagerness of imports within
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
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
-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
Debug logging from
python -v will include logging whenever an import
statement has been encountered but execution of the import will be deferred.
-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
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
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
context manager can just be an alias to a null context manager. The context
manager’s effect is not transitive:
bar will be imported
eagerly, but imports within those modules will still follow the usual laziness
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,
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
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
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
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
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,
excluding argument to
set_lazy_imports, if any, to know whether
a given import will be eager or lazy.
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()
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.
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.
For authors of C extension modules, the proposed public C API is as follows:
|C API||Python API|
void PyDict_ResolveLazyImports(PyObject *dict)resolves all lazy objects in a dictionary, if any. To be used prior calling
PyDict_NextWithError(), works the same way as
PyDict_Next(), with the exception it propagates any errors to the caller by returning
0and setting an exception. Caller should use the
if (PyErr_Ocurred())semantics to check for any errors.
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 and adds subcommands to it via
@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
sub.py. In this case the fix is to ensure the import of
eager, e.g. by using the
importlib.eager_imports() context manager.
There could be issues related to dynamic Python import paths; particularly,
adding (and then removing after the import) paths from
sys.path.insert(0, "/path/to/foo/module") import foo del sys.path 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
sys.path modifications in a context manager. This resolves the
issue, since imports inside a
with block are always eager.
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
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.
Deferred execution of code could produce security concerns if process owner,
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.
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
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
from . import barin
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.baris added as a side effect of
foo.barbeing imported somewhere else. With lazy imports this may not always happen on time.
- Avoid using star imports, as those are always eager.
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.
Wrapping deferred exceptions
To reduce the potential for confusion, exceptions raised in the
course of executing a lazy import could be replaced by a
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
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
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.
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
-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.
-I) can have unexpected and breaking results.
-L is nothing new in this
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
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
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
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.
This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.
Last modified: 2022-10-01 01:19:16 GMT