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deadpool-executor-2022.9.6
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Deadpool
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deadpool-executor-2022.9.6 |
| نام | deadpool-executor |
| نسخه کتابخانه | 2022.9.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Caleb Hattingh <caleb.hattingh@gmail.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/deadpool-executor/ |
| مجوز | - |
.. image:: https://github.com/cjrh/deadpool/workflows/Python%20application/badge.svg
:target: https://github.com/cjrh/deadpool/actions
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:target: https://coveralls.io/github/cjrh/deadpool?branch=main
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:target: https://pypi.python.org/pypi/deadpool-executor
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:target: https://img.shields.io/github/tag/cjrh/deadpool.svg
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:target: https://img.shields.io/badge/install-pip%20install%20deadpool--executor-ff69b4.svg
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:target: https://pypi.org/project/deadpool-executor/
.. image:: https://img.shields.io/badge/calver-YYYY.MM.MINOR-22bfda.svg
:alt: This project uses calendar-based versioning scheme
:target: http://calver.org/
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:alt: Downloads
:target: https://pepy.tech/project/deadpool-executor
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:alt: This project uses the "black" style formatter for Python code
:target: https://github.com/python/black
.. image:: https://api.securityscorecards.dev/projects/github.com/cjrh/deadpool/badge
:alt: OpenSSF Scorecard
:target: https://api.securityscorecards.dev/projects/github.com/cjrh/deadpool
deadpool
========
``Deadpool`` is a process pool that is really hard to kill.
``Deadpool`` is an implementation of the ``Executor`` interface
in the ``concurrent.futures`` standard library. ``Deadpool`` is
a process pool executor, quite similar to the stdlib's
`ProcessPoolExecutor`_.
This document assumes that you are familiar with the stdlib
`ProcessPoolExecutor`_. If you are not, it is important
to understand that ``Deadpool`` makes very specific tradeoffs that
can result in quite different behaviour to the stdlib
implementation.
.. contents::
:local:
:backlinks: entry
Installation
------------
The python package name is *deadpool-executor*, so to install
you must type ``$ pip install deadpool-executor``. The import
name is *deadpool*, so in your Python code you must type
``import deadpool`` to use it.
Why would I want to use this?
-----------------------------
I created ``Deadpool`` because I became frustrated with the
stdlib `ProcessPoolExecutor`_, and various other community
implementations of process pools. In particular, I had a use-case
that required a high server uptime, but also had variable and
unpredictable memory requirements such that certain tasks could
trigger the `OOM killer`_, often resulting in a "broken" process
pool. I also needed task-specific timeouts that could kill a "hung"
task, which the stdlib executor doesn't provide.
You might wonder, isn't it bad to just kill a task like that?
In my use-case, we had extensive logging and monitoring to alert
us if any tasks failed; but it was paramount that our services
continue to operate even when tasks got killed in OOM scenarios,
or specific tasks took too long. This is the primary trade-off
that ``Deadpool`` offers.
I also tried using the `Pebble <https://github.com/noxdafox/pebble>`_
community process pool. This is a cool project, featuring several
of the properties I've been looking for such as timeouts, and
more resilient operation. However, during testing I found several
occurrences of a mysterious `RuntimeError`_ that caused the Pebble
pool to become broken and no longer accept new tasks.
My goal with ``Deadpool`` is to make a process pool executor that
is impossible to break. The tradeoffs are that I care less about:
- being cross-platform
- optimizing per-task latency
What differs from `ProcessPoolExecutor`_?
-----------------------------------------
``Deadpool`` is generally similar to `ProcessPoolExecutor`_ since it executes
tasks in subprocesses, and implements the standard ``Executor`` abstract
interface. However, it differs in the following ways:
- ``Deadpool`` makes a new subprocess for every task submitted to
the pool (up to the ``max_workers`` limit). It is like having
``max_tasks_per_child == 1`` (a new feature in
Python 3.11, although it was available in `multiprocessing.Pool`_
since Python 3.2). I have ideas about making this configurable, but
for now this is a much less important than overall resilience of
the pool. This also means that ``Deadpool`` doesn't suffer from
long-lived subprocesses being affected by memory leaks, usually
created by native extensions.
- ``Deadpool`` defaults to the `forkserver <https://docs.python.org/3.11/library/multiprocessing.html#contexts-and-start-methods>`_ multiprocessing
context, unlike the stdlib pool which defaults to ``fork`` on
Linux. It's just a setting though, you can change it in the same way as
with the stdlib pool.
- ``Deadpool`` does not keep a pool of processes around indefinitely.
There will only be as many concurrent processes running as there
is work to be done, up to the limit set by the ``max_workers``
parameter; but if there are fewer tasks to be executed, there will
be fewer active subprocesses. When there are no pending or active
tasks, there will be *no subprocesses present*. They are created
on demand as necessary and disappear when not required.
- ``Deadpool`` tasks can have timeouts. When a task hits the timeout,
the underlying subprocess in the pool is killed with ``SIGKILL``.
The entire process tree of that subprocess is killed.
- ``Deadpool`` tasks can have priorities. The priority is set in the
``submit()`` call. See the examples later in this document for further
discussion on priorities.
- The shutdown parameters ``wait`` and ``cancel_futures`` can behave
differently to how they work in the _ProcessPoolExecutor. This is
discussed in more detail later in this document.
- If a ``Deadpool`` subprocess in the pool is killed by some
external actor, for example, the OS runs out of memory and the
`OOM killer`_ kills a pool subprocess that is using too much memory,
``Deadpool`` does not care and further operation is unaffected.
``Deadpool`` will not, and indeed cannot raise
`BrokenProcessPool <https://docs.python.org/3/library/concurrent.futures.html?highlight=broken%20process%20pool#concurrent.futures.process.BrokenProcessPool>`_ or
`BrokenExecutor <https://docs.python.org/3/library/concurrent.futures.html?highlight=broken%20process%20pool#concurrent.futures.BrokenExecutor>`_.
- ``Deadpool`` also allows a ``finalizer``, with corresponding
``finalargs``, that will be called after a task is executed on
a subprocess, but before the subprocess terminates. It is
analogous to the ``initializer`` and ``initargs`` parameters.
Just like the ``initializer`` callable, the ``finalizer``
callable is executed inside the subprocess. It is not guaranteed that
the finalizer will always run. If a process is killed, e.g. due to a
timeout or any other reason, the finalizer will not run. The finalizer
could be used for things like flushing pending monitoring messages,
such as traces and so on.
- ``Deadpool`` currently only works on Linux. There isn't any specific
reason it can't work on other platforms.
Show me some code
-----------------
Simple case
^^^^^^^^^^^
The simple case works exactly the same as with `ProcessPoolExecutor`_:
.. code-block:: python
from deadpool import Deadpool
def f():
return 123
with deadpool.Deadpool() as exe:
fut = exe.submit(f)
result = fut.result()
assert result == 123
It is intended that all the basic behaviour should "just work" in the
same way, and ``Deadpool`` should be a drop-in replacement for
`ProcessPoolExecutor`_; but there are some subtle differences so you
should read all of this document to see if any of those will affect you.
Timeouts
^^^^^^^^
If a timeout is reached on a task, the subprocess running that task will be
killed, as in ``SIGKILL``. ``Deadpool`` doesn't mind, but your own
application should: if you use timeouts it is likely important that your tasks
be `idempotent <https://en.wikipedia.org/wiki/Idempotence>`_, especially if
your application will restart tasks, or restart them after application deployment,
and other similar scenarios.
.. code-block:: python
import time
import deadpool
def f():
time.sleep(10.0)
with deadpool.Deadpool() as exe:
fut = exe.submit(f, deadpool_timeout=1.0)
with pytest.raises(deadpool.TimeoutError)
fut.result()
Handling OOM killed situations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
import time
import deadpool
def f():
x = list(range(10**100))
with deadpool.Deadpool() as exe:
fut = exe.submit(f, timeout=1.0)
try:
result = fut.result()
except deadpool.ProcessError:
print("Oh no someone killed my task!")
As long as the OOM killer terminates the subprocess (and not the main process),
which is likely because it'll be your subprocess that is using too much
memory, this will not hurt the pool, and it will be able to receive and
process more tasks.
Design Details
--------------
Typical Example - with timeouts
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Here's a typical example of how code using Deadpool might look. The
output of this code should be similar to the following:
.. code-block:: bash
$ python examples/entrypoint.py
...................xxxxxxxxxxx.xxxxxxx.x.xxxxxxx.x
$
Each ``.`` is a successfully completed task, and each ``x`` is a task
that timed out. Below is the code for this example.
.. code-block:: python
import random, time
import deadpool
def work():
time.sleep(random.random() * 4.0)
print(".", end="", flush=True)
return 1
def main():
with deadpool.Deadpool() as exe:
futs = (exe.submit(work, timeout=2.0) for _ in range(50))
for fut in deadpool.as_completed(futs):
try:
assert fut.result() == 1
except deadpool.TimeoutError:
print("x", end="", flush=True)
if __name__ == "__main__":
main()
print()
- The work function will be busy for a random time period between 0 and
4 seconds.
- There is a ``deadpool_timeout`` kwarg given to the ``submit`` method.
This kwarg is special and will be consumed by Deadpool. You cannot
use this kwarg name for your own task functions.
- When a task completes, it prints out ``.`` internally. But when a task
raises a ``deadpool.TimeoutError``, a ``x`` will be printed out instead.
- When a task times out, keep in mind that the underlying process that
is executing that task is killed, literally with the ``SIGKILL`` signal.
Deadpool tasks have priority
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The example below is similar to the previous one for timeouts. In fact
this example retains the timeouts to show how the different features
compose together. In this example we create tasks with different
priorities, and we change the printed character of each task to show
that higher priority items are executed first.
The code example will print something similar to the following:
.. code-block:: bash
$ python examples/priorities.py
!!!!!xxxxxxxxxxx!x..!...x.xxxxxxxx.xxxx.x...xxxxxx
You can see how the ``!`` characters, used for indicating higher priority
tasks, appear towards the front indicating that they were executed sooner.
Below is the code.
.. code-block:: python
import random, time
import deadpool
def work(symbol):
time.sleep(random.random() * 4.0)
print(symbol, end="", flush=True)
return 1
def main():
with deadpool.Deadpool(max_backlog=100) as exe:
futs = []
for _ in range(25):
fut = exe.submit(work, ".",deadpool_timeout=2.0, deadpool_priority=10)
futs.append(fut)
fut = exe.submit(work, "!",deadpool_timeout=2.0, deadpool_priority=0)
futs.append(fut)
for fut in deadpool.as_completed(futs):
try:
assert fut.result() == 1
except deadpool.TimeoutError:
print("x", end="", flush=True)
if __name__ == "__main__":
main()
print()
- When the tasks are submitted, they are given a priority. The default
value for the ``deadpool_priority`` parameter is 0, but here we'll
write them out explicity. Half of the tasks will have priority 10 and
half will have priority 0.
- A lower value for the ``deadpool_priority`` parameters means a **higher**
priority. The highest priority allowed is indicated by 0. Negative
priority values are not allowed.
- I also specified the ``max_backlog`` parameter when creating the
Deadpool instance. This is discussed in more detail next, but quickly:
task priority can only be enforced on what is in the submitted backlog
of tasks, and the ``max_backlog`` parameter controls the depth of that
queue. If ``max_backlog`` is too low, then the window of prioritization
will not include tasks submitted later which might have higher priorities
than earlier-submitted tasks. The ``submit`` call will in fact block
if the ``max_backlog`` depth has been reached.
Controlling the backlog of submitted tasks
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
By default, the ``max_backlog`` parameter is set to 5. This parameter is
used to create the "submit queue" size. The submit queue is the place
where submitted tasks are held before they are executed in background
processes.
If the submit queue is large (``max_backlog``), it will mean
that a large number of tasks can be added to the system with the
``submit`` method, even before any tasks have finished exiting. Conversely,
a low ``max_backlog`` parameter means that the submit queue will fill up
faster. If the submit queue is full, it means that the next call to
``submit`` will block.
This kind of blocking is fine, and typically desired. It means that
backpressure from blocking is controlling the amount of work in flight.
By using a smaller ``max_backlog``, it means that you'll also be
limiting the amount of memory in use during the execution of all the tasks.
However, if you nevertheless still accumulate received futures as my
example code above is doing, that accumulation, i.e., the list of futures,
will contribute to memory growth. If you have a large amount of work, it
will be better to set a *callback* function on each of the futures rather
than processing them by iterating over ``as_completed``.
The example below illustrates this technique for keeping memory
consumption down:
.. code-block:: python
import random, time
import deadpool
def work():
time.sleep(random.random() * 4.0)
print(".", end="", flush=True)
return 1
def cb(fut):
try:
assert fut.result() == 1
except deadpool.TimeoutError:
print("x", end="", flush=True)
def main():
with deadpool.Deadpool() as exe:
for _ in range(50):
exe.submit(work, deadpool_timeout=2.0).add_done_callback(cb)
if __name__ == "__main__":
main()
print()
With this callback-based design, we no longer have an accumulation of futures
in a list. We get the same kind of output as in the "typical example" from
earlier:
.. code-block:: bash
$ python examples/callbacks.py
.....xxx.xxxxxxxxx.........x..xxxxx.x....x.xxxxxxx
Speaking of callbacks, the customized ``Future`` class used by Deadpool
lets you set a callback for when the task begins executing on a real
system process. That can be configured like so:
.. code-block:: python
with deadpool.Deadpool() as exe:
f = exe.submit(work)
def cb(fut: deadpool.Future):
print(f"My task is running on process {fut.pid}")
f.add_pid_callback(cb)
More about shutdown
^^^^^^^^^^^^^^^^^^^
In the documentation for ProcessPoolExecutor_, the following function
signature is given for the shutdown_ method of the executor interface:
.. code-block:: python
shutdown(wait=True, *, cancel_futures=False)
I want to honor this, but it presents some difficulties because the
semantics of the ``wait`` and ``cancel_futures`` parameters need to be
somewhat different for Deadpool.
In Deadpool, this is what the combinations of those flags mean:
.. csv-table:: Shutdown flags
:header: ``wait``, ``cancel_futures``, ``effect``
:widths: 10, 10, 80
:align: left
``True``, ``True``, "Wait for already-running tasks to complete; the
``shutdown()`` call will unblock (return) when they're done. Cancel
all pending tasks that are in the submit queue, but have not yet started
running. The ``fut.cancelled()`` method will return ``True`` for such
cancelled tasks."
``True``, ``False``, "Wait for already-running tasks to complete.
Pending tasks in the
submit queue that have not yet started running will *not* be cancelled, and
will all continue to execute. The ``shutdown()`` call will return only
after all submitted tasks have completed. "
``False``, ``True``, "Already-running tasks **will be cancelled** and this
means the underlying subprocesses executing these tasks will receive
SIGKILL. Pending tasks on the submit queue that have not yet started
running will also be cancelled."
``False``, ``False``, "This is a strange one. What to do if the caller
doesn't want to wait, but also doesn't want to cancel things? In this
case, already-running tasks will be allowed to complete, but pending
tasks on the submit queue will be cancelled. This is the same outcome as
as ``wait==True`` and ``cancel_futures==True``. An alternative design
might have been to allow all tasks, both running and pending, to just
keep going in the background even after the ``shutdown()`` call
returns. Does anyone have a use-case for this?"
If you're using ``Deadpool`` as a context manager, you might be wondering
how exactly to set these parameters in the ``shutdown`` call, since that
call is made for you automatically when the context manager exits.
For this, Deadpool provides additional parameters that can be provided
when creating the instance:
.. code-block:: python
# This is pseudocode
import deadpool
with deadpool.DeadPool(
shutdown_wait=True,
shutdown_cancel_futures=True
):
fut = exe.submit(...)
.. _shutdown: https://docs.python.org/3/library/concurrent.futures.html?highlight=brokenprocesspool#concurrent.futures.Executor.shutdown
.. _ProcessPoolExecutor: https://docs.python.org/3/library/concurrent.futures.html?highlight=broken%20process%20pool#processpoolexecutor
.. _RuntimeError: https://github.com/noxdafox/pebble/issues/42#issuecomment-551245730
.. _OOM killer: https://en.wikipedia.org/wiki/Out_of_memory#Out_of_memory_management
.. _multiprocessing.Pool: https://docs.python.org/3.11/library/multiprocessing.html#multiprocessing.pool.Pool
نیازمندی
| مقدار | نام |
|---|---|
| - | psutil |
| - | pytest |
| - | pytest-cov |
| - | flake8 |
| - | coverage[toml] |
نحوه نصب
نصب پکیج whl deadpool-executor-2022.9.6:
pip install deadpool-executor-2022.9.6.whl
نصب پکیج tar.gz deadpool-executor-2022.9.6:
pip install deadpool-executor-2022.9.6.tar.gz