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Lightweight in-process concurrent programming
.. This file is included into docs/history.rst
Greenlets are lightweight coroutines for in-process concurrent programming.
The "greenlet" package is a spin-off of Stackless
_, a version of
CPython that supports micro-threads called "tasklets". Tasklets run
pseudo-concurrently (typically in a single or a few OS-level threads)
and are synchronized with data exchanges on "channels".
A "greenlet", on the other hand, is a still more primitive notion of
micro-thread with no implicit scheduling; coroutines, in other words.
This is useful when you want to control exactly when your code runs.
You can build custom scheduled micro-threads on top of greenlet;
however, it seems that greenlets are useful on their own as a way to
make advanced control flow structures. For example, we can recreate
generators; the difference with Python's own generators is that our
generators can call nested functions and the nested functions can
yield values too. (Additionally, you don't need a "yield" keyword. See
the example in test_generator.py
<https://github.com/python-greenlet/greenlet/blob/adca19bf1f287b3395896a8f41f3f4fd1797fdc7/src/greenlet/tests/test_generator.py#L1>
_).
Greenlets are provided as a C extension module for the regular unmodified interpreter.
.. _Stackless
: http://www.stackless.com
There are several libraries that use Greenlet as a more flexible alternative to Python's built in coroutine support:
Concurrence
_Eventlet
_Gevent
_.. _Concurrence: http://opensource.hyves.org/concurrence/ .. _Eventlet: http://eventlet.net/ .. _Gevent: http://www.gevent.org/
The easiest way to get Greenlet is to install it with pip::
pip install greenlet
Source code archives and binary distributions are available on the python package index at https://pypi.org/project/greenlet
The source code repository is hosted on github: https://github.com/python-greenlet/greenlet
Documentation is available on readthedocs.org: https://greenlet.readthedocs.io
This section shows a list of objects the current object is dependent upon in order to be used.
For dependencies that are only relevant as part of the build or runtime of the object, it is best to describe those local to those sections.
This section tracks information useful to describing how to build this object.
greenlet-3.0.3.tar.gz
application/gzip
Python Source for greenlet 3.0.3
https://files.pythonhosted.org/packages/17/14/3bddb1298b9a6786539ac609ba4b7c9c0842e12aa73aaa4d8d73ec8f8185/greenlet-3.0.3.tar.gz
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QmSs2uMDy7SZwbyiWWM9z5Q7LdUfFPdEeh2Pgo1XNtoesA
downloadargparse-1.4.0.tar.gz
application/gzip
Python Source for argparse 1.4.0
https://files.pythonhosted.org/packages/18/dd/e617cfc3f6210ae183374cd9f6a26b20514bbb5a792af97949c5aacddf0f/argparse-1.4.0.tar.gz
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QmUyto3kPC3XY43kf9bh6VoSJnEVvUCX32XX5EPXZCFCNw
download/bin/bash
{{ paths.mount }}/build.sh
greenlet-3.0.3/
3
greenlet
argparse-1.4.0.tar.gz
linux
x86-64
Objects have a set of files that comprise that object, as seen in the Files tab. When an object is built, they have a set of files that are the result of that build. In each case, these sets of files are the ones that are accessible within a virtual machine when running the object.
This section describes how those files are accessible and visible to that machine. When a virtual machine is created, the data is always accessible read-only within a particular mounted directory. However, applications may expect to be located at particular directories. This section allows one to place files in different directories when the virtual machine is launched.
There are two types of access. One is a link where the file is not copied from its original location. Instead, it creates what is called a symbolic link which is a "shortcut" that acts like a normal file but allows the actual data to be in a different directory. The other type is a traditional copy which copies the data explicitly to the provided destination. This takes more time, but the copy can be modified, unlike the read-only link type.
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