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|
%global _empty_manifest_terminate_build 0
Name: python-bounded-pool-executor
Version: 0.0.3
Release: 1
Summary: Bounded Process&Thread Pool Executor
License: MIT
URL: http://github.com/mowshon/bounded_pool_executor
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/23/f1/e34501c1228415e9fbcac8cb9c81098900e78331b30eeee1816176324bab/bounded_pool_executor-0.0.3.tar.gz
BuildArch: noarch
%description
# Bounded Process&Thread Pool Executor
BoundedSemaphore for [ProcessPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor) & [ThreadPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor) from [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html)
## Installation
```bash
pip install bounded-pool-executor
```
# What is the main problem?
If you use the standard module "**concurrent.futures**" and want to simultaneously process several million data, then a queue of workers will take up all the free memory.
If the script is run on a weak VPS, this will lead to a **memory leak**.
## BoundedProcessPoolExecutor VS ProcessPoolExecutor
# BoundedProcessPoolExecutor
**BoundedProcessPoolExecutor** will put a new worker in queue only when another worker has finished his work.
```python
from bounded_pool_executor import BoundedProcessPoolExecutor
from time import sleep
from random import randint
def do_job(num):
sleep_sec = randint(1, 10)
print('value: %d, sleep: %d sec.' % (num, sleep_sec))
sleep(sleep_sec)
with BoundedProcessPoolExecutor(max_workers=5) as worker:
for num in range(10000):
print('#%d Worker initialization' % num)
worker.submit(do_job, num)
```
### Result:
# Classic concurrent.futures.ProcessPoolExecutor
**ProcessPoolExecutor** inserts all workers into the queue and expects tasks to be performed as the new worker is released, depending on the value of `max_workers`.
```python
import concurrent.futures
from time import sleep
from random import randint
def do_job(num):
sleep_sec = randint(1, 3)
print('value: %d, sleep: %d sec.' % (num, sleep_sec))
sleep(sleep_sec)
with concurrent.futures.ProcessPoolExecutor(max_workers=5) as worker:
for num in range(100000):
print('#%d Worker initialization' % num)
worker.submit(do_job, num)
```
### Result:
%package -n python3-bounded-pool-executor
Summary: Bounded Process&Thread Pool Executor
Provides: python-bounded-pool-executor
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-bounded-pool-executor
# Bounded Process&Thread Pool Executor
BoundedSemaphore for [ProcessPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor) & [ThreadPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor) from [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html)
## Installation
```bash
pip install bounded-pool-executor
```
# What is the main problem?
If you use the standard module "**concurrent.futures**" and want to simultaneously process several million data, then a queue of workers will take up all the free memory.
If the script is run on a weak VPS, this will lead to a **memory leak**.
## BoundedProcessPoolExecutor VS ProcessPoolExecutor
# BoundedProcessPoolExecutor
**BoundedProcessPoolExecutor** will put a new worker in queue only when another worker has finished his work.
```python
from bounded_pool_executor import BoundedProcessPoolExecutor
from time import sleep
from random import randint
def do_job(num):
sleep_sec = randint(1, 10)
print('value: %d, sleep: %d sec.' % (num, sleep_sec))
sleep(sleep_sec)
with BoundedProcessPoolExecutor(max_workers=5) as worker:
for num in range(10000):
print('#%d Worker initialization' % num)
worker.submit(do_job, num)
```
### Result:
# Classic concurrent.futures.ProcessPoolExecutor
**ProcessPoolExecutor** inserts all workers into the queue and expects tasks to be performed as the new worker is released, depending on the value of `max_workers`.
```python
import concurrent.futures
from time import sleep
from random import randint
def do_job(num):
sleep_sec = randint(1, 3)
print('value: %d, sleep: %d sec.' % (num, sleep_sec))
sleep(sleep_sec)
with concurrent.futures.ProcessPoolExecutor(max_workers=5) as worker:
for num in range(100000):
print('#%d Worker initialization' % num)
worker.submit(do_job, num)
```
### Result:
%package help
Summary: Development documents and examples for bounded-pool-executor
Provides: python3-bounded-pool-executor-doc
%description help
# Bounded Process&Thread Pool Executor
BoundedSemaphore for [ProcessPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor) & [ThreadPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor) from [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html)
## Installation
```bash
pip install bounded-pool-executor
```
# What is the main problem?
If you use the standard module "**concurrent.futures**" and want to simultaneously process several million data, then a queue of workers will take up all the free memory.
If the script is run on a weak VPS, this will lead to a **memory leak**.
## BoundedProcessPoolExecutor VS ProcessPoolExecutor
# BoundedProcessPoolExecutor
**BoundedProcessPoolExecutor** will put a new worker in queue only when another worker has finished his work.
```python
from bounded_pool_executor import BoundedProcessPoolExecutor
from time import sleep
from random import randint
def do_job(num):
sleep_sec = randint(1, 10)
print('value: %d, sleep: %d sec.' % (num, sleep_sec))
sleep(sleep_sec)
with BoundedProcessPoolExecutor(max_workers=5) as worker:
for num in range(10000):
print('#%d Worker initialization' % num)
worker.submit(do_job, num)
```
### Result:
# Classic concurrent.futures.ProcessPoolExecutor
**ProcessPoolExecutor** inserts all workers into the queue and expects tasks to be performed as the new worker is released, depending on the value of `max_workers`.
```python
import concurrent.futures
from time import sleep
from random import randint
def do_job(num):
sleep_sec = randint(1, 3)
print('value: %d, sleep: %d sec.' % (num, sleep_sec))
sleep(sleep_sec)
with concurrent.futures.ProcessPoolExecutor(max_workers=5) as worker:
for num in range(100000):
print('#%d Worker initialization' % num)
worker.submit(do_job, num)
```
### Result:
%prep
%autosetup -n bounded-pool-executor-0.0.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-bounded-pool-executor -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.3-1
- Package Spec generated
|
