A plugin manager based on setuptools entry points with 10x the speed
Python 3.8 saw the introduction of importlib.metadata into the Python standard library.
It is both fast to import importlib.metadata
and fast to scan for entry points using the entry_points()
function.
The separate importlib-metadata package backports this functionality to python >=3.6, and as of version 4.6.3 (or earlier), scanning is even faster than with the standard library.
Below are timings recorded on a MacBook Air M1 (2020) in a python 3.9.1 conda environment with 248 packages installed (~30 of which register entry points).
pkg_resources
: ~147 msec in total:
$ python -mtimeit -n1 -r1 'import pkg_resources' 1 loop, best of 1: 146 msec per loop $ python -mtimeit -s 'import pkg_resources as pkg' 'pkg.iter_entry_points("aiida.calculations")' 500000 loops, best of 5: 435 nsec per loop
importlib.metadata
: ~39 msec in total:
$ python -mtimeit -n1 -r1 'import importlib.metadata' 1 loop, best of 1: 17.8 msec per loop $ python -mtimeit -s 'import importlib.metadata as im' 'im.entry_points()' 50 loops, best of 5: 21.4 msec per loop
importlib-metadata
package (v4.6.3): ~40 msec in total:
$ python -mtimeit -n1 -r1 'import importlib_metadata' 1 loop, best of 1: 33.8 msec per loop $ python -mtimeit -s 'import importlib_metadata as im' 'im.entry_points()' 50 loops, best of 5: 5.94 msec per loop
reentry
(v1.3.1): 25 msec in total:
$ python -mtimeit -n1 -r1 'import reentry' 1 loop, best of 1: 23.8 msec per loop $ python -mtimeit -s 'from reentry.default_manager import PluginManager as p' 'p().get_entry_map()' 200 loops, best of 5: 1.07 msec per loop
The advent of faster (solid-state) disks, together with the faster importlib implementations have led to a substantial reduction of the speed benefit that reentry
provided (down to ~15 ms or ~40% in the benchmark above).
While there may still be certain edge cases where reentry
is useful, we will stop using it going forward and are thus archiving this repository.
Users interested in continuing the maintenance of reentry
are encouraged to open an issue on the issue tracker.
- finding plugins: reentry keeps a map of entry points in a file
- speed: reentry provides an EntryPoint implementation that trades extra checks for search and load speed
- automatic registering: use
reentry_register: True
in yoursetup.py
to automatically register plugins
Note that reentry_register
creates a build-time
dependency on reentry
. The suggested way to resolve that is using the
method described in PEP518, for
which support has been added in pip 10:
next to setup.py
, put a file pyproject.toml
containing:
[build-system] # Minimum requirements for the build system to execute. requires = ["setuptools", "wheel", "reentry"]
An alternative way for specifying a build dependency is to put:
setup( ... setup_requires=[reentry], ... )
in your setup.py
.
This alternative works with all versions of pip
, but fails on systems,
where python is linked to old SSL
libraries (such as the system python for
some versions of OS X).
- entry points with extra dependencies (
name = module_name:attrs [extras]
) are still supported. Trying to load them, however, will lead to importingpkg_resources
and forego the speedup.
Use the following in your plugins's setup.py
:
setup( ... setup_requires=['reentry'], reentry_register=True, entry_points={ 'my_plugins': ['this_plugin = this_package.subpackage:member'], ... }
And iterate over installed plugin from the host package:
from reentry import manager available_plugins = manager.iter_entry_points(group='my_plugins') for plugin in available_plugins: plugin_object = plugin.load() plugin_object.use()
The syntax is consistent with setuptools
's pkg_resources
, so you may use it as a fallback:
try: from reentry import manager as entry_pt_manager except: import pkg_resources as entry_pt_manager entry_pt_manager.iter_entry_points(...) ...
Reentry supports getting information from a configuration file. The file will be searched at the following paths:
- <HOME>/.reentryrc
- <HOME>/.config/reentry/config
The configuration file has an ini
format and supports the following keys:
[general] datadir=/path/to/data/dir data_filename=name
The datadir
is the folder in which reentry
stores the data file
that contains the information about the registered entry points.
If the config file doesn't exist in one of the above paths, the datadir
is
set to <HOME>/.config/reentry/data
.
data_filename
is the name of the data file, in case you want to pick the
name by your own instead of letting reentry
choose it.
Warning: By default, reentry
creates a separate data file for every python
interpreter in order not to mix entry points between different python
environments on your system. Setting a data_filename
in the configuration
file tells reentry
to always use this data file and may result in
unexpected behavior if you use reentry
in multiple python environments.
You can also set configuration options for reentry
via environment
variables:
datadir
can be defined byREENTRY_DATADIR
.data_filename
can be defined byREENTRY_DATA_FILENAME
.
Environment variables take precedence over the configuration file.
To make entry points usable for plugins in time-critical situations such as command line interfaces!
Setuptool's entry point system is convenient to use for plugin-based python applications. It allows separate python packages to act as plugins to a host package (or to each other), making it easy for the host to find and iterate over the relevant data structures from plugins.
However, the time spent on importing setuptools scales badly with the number of installed distributions and can easily reach 0.5 seconds for moderately complex environments. Finding and loading of plugins can be time-critical, for example in command line tools that need to load subcommands, where 100 ms are a noticeable delay.
Importing setuptools's pkg_resources takes time, because it verifies that
dependencies are installed correctly for all distributions present in the
environment. This allows entry points to have additional dependencies or
"extras" (entry_point = module_name:attrs [extras]
).
Reentry forgoes this dependency check for entry points without 'extras' and thereby manages to be fast and scale better with the number of plugins installed.
Sometimes it might be necessary to update the cached entry points, for example
- after uninstalling a plugin (there are no uninstall hooks by setuptools at the moment)
- after installing a plugin that does not use install hooks
- while developing a plugin / plugin host
for those cases reentry has a commandline interface:
$ reentry --help Usage: reentry [OPTIONS] COMMAND [ARGS]... manage your reentry python entry point cache Options: --help Show this message and exit. Commands: clear Clear entry point map. dev Development related commands. map Print out a map of cached entry points scan Scan for python entry points to cache for faster loading.
$ reentry scan --help Usage: reentry scan [OPTIONS] PATTERN Scan for python entry points to cache for faster loading. Scan only for specific PATTERNs or leave empty to scan all Options: -r, --regex Treat PATTERNs as regular expresions --help Show this message and exit.
$ reentry map --help Usage: reentry map [OPTIONS] Options: --dist TEXT limit map to a distribution --group TEXT limit map to an entry point group --name TEXT limit map to entrypoints that match NAME --help Show this message and exit.
Note: Where needed (e.g. in jupyter notebooks), these operations also be
performed in python using the reentry manager
, e.g.:
from reentry import manager manager.scan()
Reentry provides a drop-in replacement for iter_entry_points:
import click from click_plugins import with_plugins from reentry.manager import iter_entry_points @with_plugins(iter_entry_points('cli_plugins')) @click.group() def cli(): """ command with subcommands loaded from plugin entry points """
For this to work, reentry has to be installed and must have been used to scan for entry points in the 'cli_plugins' group once.
Running the tests:
tox
Creating a release:
tox -e py39-release