path: root/python-pypeln.spec

%global _empty_manifest_terminate_build 0
Name:		python-pypeln
Version:	0.4.9
Release:	1
Summary:	please add a summary manually as the author left a blank one
License:	MIT
URL:		https://cgarciae.github.io/pypeln
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/da/87/7e4929696a4cf29fede0756d38c5cc08395d91bd7feac8d6072edf0a1ecf/pypeln-0.4.9.tar.gz
BuildArch:	noarch

Requires:	python3-stopit
Requires:	python3-typing_extensions
Requires:	python3-dataclasses

%description
_Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines._
#### Main Features
* **Simple**: Pypeln was designed to solve _medium_ data tasks that require parallelism and concurrency where using frameworks like Spark or Dask feels exaggerated or unnatural.
* **Easy-to-use**: Pypeln exposes a familiar functional API compatible with regular Python code.
* **Flexible**: Pypeln enables you to build pipelines using Processes, Threads and asyncio.Tasks via the exact same API.
* **Fine-grained Control**: Pypeln allows you to have control over the memory and cpu resources used at each stage of your pipelines.
For more information take a look at the [Documentation](https://cgarciae.github.io/pypeln).
![diagram](https://github.com/cgarciae/pypeln/blob/master/docs/images/diagram.png?raw=true)
## Installation
Install Pypeln using pip:
```bash
pip install pypeln
```
## Basic Usage
With Pypeln you can easily create multi-stage data pipelines using 3 type of workers:
### Processes
You can create a pipeline based on [multiprocessing.Process](https://docs.python.org/3.4/library/multiprocessing.html#multiprocessing.Process) workers by using the `process` module:
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1
def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.process.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.process.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
At each stage the you can specify the numbers of `workers`. The `maxsize` parameter limits the maximum amount of elements that the stage can hold simultaneously.
### Threads
You can create a pipeline based on [threading.Thread](https://docs.python.org/3/library/threading.html#threading.Thread) workers by using the `thread` module:
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1
def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.thread.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.thread.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
Here we have the exact same situation as in the previous case except that the worker are Threads.
### Tasks
You can create a pipeline based on [asyncio.Task](https://docs.python.org/3.4/library/asyncio-task.html#asyncio.Task) workers by using the `task` module:
```python
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1
async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.task.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
Conceptually similar but everything is running in a single thread and Task workers are created dynamically. If the code is running inside an async task can use `await` on the stage instead to avoid blocking:
```python
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1
async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3
def main():
    data = range(10) # [0, 1, 2, ..., 9] 
    stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
    stage = pl.task.filter(slow_gt3, stage, workers=2)
    data = await stage # e.g. [5, 6, 9, 4, 8, 10, 7]
asyncio.run(main())
```
### Sync
The `sync` module implements all operations using synchronous generators. This module is useful for debugging or when you don't need to perform heavy CPU or IO tasks but still want to retain element order information that certain functions like `pl.*.ordered` rely on.
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    return x + 1
def slow_gt3(x):
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.sync.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.sync.filter(slow_gt3, stage, workers=2)
data = list(stage) # [4, 5, 6, 7, 8, 9, 10]
```
Common arguments such as `workers` and `maxsize` are accepted by this module's functions for API compatibility purposes but are ignored.
## Mixed Pipelines
You can create pipelines using different worker types such that each type is the best for its given task so you can get the maximum performance out of your code:
```python
data = get_iterable()
data = pl.task.map(f1, data, workers=100)
data = pl.thread.flat_map(f2, data, workers=10)
data = filter(f3, data)
data = pl.process.map(f4, data, workers=5, maxsize=200)
```
Notice that here we even used a regular python `filter`, since stages are iterables Pypeln integrates smoothly with any python code, just be aware of how each stage behaves.
## Pipe Operator
In the spirit of being a true pipeline library, Pypeln also lets you create your pipelines using the pipe `|` operator:
```python
data = (
    range(10)
    | pl.process.map(slow_add1, workers=3, maxsize=4)
    | pl.process.filter(slow_gt3, workers=2)
    | list
)
```
## Run Tests
A sample script is provided to run the tests in a container (either Docker or Podman is supported), to run tests:
```bash
$ bash scripts/run-tests.sh
```
This script can also receive a python version to check test against, i.e
```bash
$ bash scripts/run-tests.sh 3.7
```
## Related Stuff
* [Making an Unlimited Number of Requests with Python aiohttp + pypeln](https://medium.com/@cgarciae/making-an-infinite-number-of-requests-with-python-aiohttp-pypeln-3a552b97dc95)
* [Process Pools](https://docs.python.org/3.4/library/multiprocessing.html?highlight=process#module-multiprocessing.pool)
* [Making 100 million requests with Python aiohttp](https://www.artificialworlds.net/blog/2017/06/12/making-100-million-requests-with-python-aiohttp/)
* [Python multiprocessing Queue memory management](https://stackoverflow.com/questions/52286527/python-multiprocessing-queue-memory-management/52286686#52286686)
* [joblib](https://joblib.readthedocs.io/en/latest/)
* [mpipe](https://vmlaker.github.io/mpipe/)
## Contributors
* [cgarciae](https://github.com/cgarciae)
* [Davidnet](https://github.com/Davidnet)
## License
MIT

%package -n python3-pypeln
Summary:	please add a summary manually as the author left a blank one
Provides:	python-pypeln
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-pypeln
_Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines._
#### Main Features
* **Simple**: Pypeln was designed to solve _medium_ data tasks that require parallelism and concurrency where using frameworks like Spark or Dask feels exaggerated or unnatural.
* **Easy-to-use**: Pypeln exposes a familiar functional API compatible with regular Python code.
* **Flexible**: Pypeln enables you to build pipelines using Processes, Threads and asyncio.Tasks via the exact same API.
* **Fine-grained Control**: Pypeln allows you to have control over the memory and cpu resources used at each stage of your pipelines.
For more information take a look at the [Documentation](https://cgarciae.github.io/pypeln).
![diagram](https://github.com/cgarciae/pypeln/blob/master/docs/images/diagram.png?raw=true)
## Installation
Install Pypeln using pip:
```bash
pip install pypeln
```
## Basic Usage
With Pypeln you can easily create multi-stage data pipelines using 3 type of workers:
### Processes
You can create a pipeline based on [multiprocessing.Process](https://docs.python.org/3.4/library/multiprocessing.html#multiprocessing.Process) workers by using the `process` module:
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1
def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.process.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.process.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
At each stage the you can specify the numbers of `workers`. The `maxsize` parameter limits the maximum amount of elements that the stage can hold simultaneously.
### Threads
You can create a pipeline based on [threading.Thread](https://docs.python.org/3/library/threading.html#threading.Thread) workers by using the `thread` module:
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1
def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.thread.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.thread.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
Here we have the exact same situation as in the previous case except that the worker are Threads.
### Tasks
You can create a pipeline based on [asyncio.Task](https://docs.python.org/3.4/library/asyncio-task.html#asyncio.Task) workers by using the `task` module:
```python
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1
async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.task.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
Conceptually similar but everything is running in a single thread and Task workers are created dynamically. If the code is running inside an async task can use `await` on the stage instead to avoid blocking:
```python
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1
async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3
def main():
    data = range(10) # [0, 1, 2, ..., 9] 
    stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
    stage = pl.task.filter(slow_gt3, stage, workers=2)
    data = await stage # e.g. [5, 6, 9, 4, 8, 10, 7]
asyncio.run(main())
```
### Sync
The `sync` module implements all operations using synchronous generators. This module is useful for debugging or when you don't need to perform heavy CPU or IO tasks but still want to retain element order information that certain functions like `pl.*.ordered` rely on.
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    return x + 1
def slow_gt3(x):
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.sync.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.sync.filter(slow_gt3, stage, workers=2)
data = list(stage) # [4, 5, 6, 7, 8, 9, 10]
```
Common arguments such as `workers` and `maxsize` are accepted by this module's functions for API compatibility purposes but are ignored.
## Mixed Pipelines
You can create pipelines using different worker types such that each type is the best for its given task so you can get the maximum performance out of your code:
```python
data = get_iterable()
data = pl.task.map(f1, data, workers=100)
data = pl.thread.flat_map(f2, data, workers=10)
data = filter(f3, data)
data = pl.process.map(f4, data, workers=5, maxsize=200)
```
Notice that here we even used a regular python `filter`, since stages are iterables Pypeln integrates smoothly with any python code, just be aware of how each stage behaves.
## Pipe Operator
In the spirit of being a true pipeline library, Pypeln also lets you create your pipelines using the pipe `|` operator:
```python
data = (
    range(10)
    | pl.process.map(slow_add1, workers=3, maxsize=4)
    | pl.process.filter(slow_gt3, workers=2)
    | list
)
```
## Run Tests
A sample script is provided to run the tests in a container (either Docker or Podman is supported), to run tests:
```bash
$ bash scripts/run-tests.sh
```
This script can also receive a python version to check test against, i.e
```bash
$ bash scripts/run-tests.sh 3.7
```
## Related Stuff
* [Making an Unlimited Number of Requests with Python aiohttp + pypeln](https://medium.com/@cgarciae/making-an-infinite-number-of-requests-with-python-aiohttp-pypeln-3a552b97dc95)
* [Process Pools](https://docs.python.org/3.4/library/multiprocessing.html?highlight=process#module-multiprocessing.pool)
* [Making 100 million requests with Python aiohttp](https://www.artificialworlds.net/blog/2017/06/12/making-100-million-requests-with-python-aiohttp/)
* [Python multiprocessing Queue memory management](https://stackoverflow.com/questions/52286527/python-multiprocessing-queue-memory-management/52286686#52286686)
* [joblib](https://joblib.readthedocs.io/en/latest/)
* [mpipe](https://vmlaker.github.io/mpipe/)
## Contributors
* [cgarciae](https://github.com/cgarciae)
* [Davidnet](https://github.com/Davidnet)
## License
MIT

%package help
Summary:	Development documents and examples for pypeln
Provides:	python3-pypeln-doc
%description help
_Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines._
#### Main Features
* **Simple**: Pypeln was designed to solve _medium_ data tasks that require parallelism and concurrency where using frameworks like Spark or Dask feels exaggerated or unnatural.
* **Easy-to-use**: Pypeln exposes a familiar functional API compatible with regular Python code.
* **Flexible**: Pypeln enables you to build pipelines using Processes, Threads and asyncio.Tasks via the exact same API.
* **Fine-grained Control**: Pypeln allows you to have control over the memory and cpu resources used at each stage of your pipelines.
For more information take a look at the [Documentation](https://cgarciae.github.io/pypeln).
![diagram](https://github.com/cgarciae/pypeln/blob/master/docs/images/diagram.png?raw=true)
## Installation
Install Pypeln using pip:
```bash
pip install pypeln
```
## Basic Usage
With Pypeln you can easily create multi-stage data pipelines using 3 type of workers:
### Processes
You can create a pipeline based on [multiprocessing.Process](https://docs.python.org/3.4/library/multiprocessing.html#multiprocessing.Process) workers by using the `process` module:
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1
def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.process.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.process.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
At each stage the you can specify the numbers of `workers`. The `maxsize` parameter limits the maximum amount of elements that the stage can hold simultaneously.
### Threads
You can create a pipeline based on [threading.Thread](https://docs.python.org/3/library/threading.html#threading.Thread) workers by using the `thread` module:
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1
def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.thread.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.thread.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
Here we have the exact same situation as in the previous case except that the worker are Threads.
### Tasks
You can create a pipeline based on [asyncio.Task](https://docs.python.org/3.4/library/asyncio-task.html#asyncio.Task) workers by using the `task` module:
```python
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1
async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.task.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
```
Conceptually similar but everything is running in a single thread and Task workers are created dynamically. If the code is running inside an async task can use `await` on the stage instead to avoid blocking:
```python
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1
async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3
def main():
    data = range(10) # [0, 1, 2, ..., 9] 
    stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
    stage = pl.task.filter(slow_gt3, stage, workers=2)
    data = await stage # e.g. [5, 6, 9, 4, 8, 10, 7]
asyncio.run(main())
```
### Sync
The `sync` module implements all operations using synchronous generators. This module is useful for debugging or when you don't need to perform heavy CPU or IO tasks but still want to retain element order information that certain functions like `pl.*.ordered` rely on.
```python
import pypeln as pl
import time
from random import random
def slow_add1(x):
    return x + 1
def slow_gt3(x):
    return x > 3
data = range(10) # [0, 1, 2, ..., 9] 
stage = pl.sync.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.sync.filter(slow_gt3, stage, workers=2)
data = list(stage) # [4, 5, 6, 7, 8, 9, 10]
```
Common arguments such as `workers` and `maxsize` are accepted by this module's functions for API compatibility purposes but are ignored.
## Mixed Pipelines
You can create pipelines using different worker types such that each type is the best for its given task so you can get the maximum performance out of your code:
```python
data = get_iterable()
data = pl.task.map(f1, data, workers=100)
data = pl.thread.flat_map(f2, data, workers=10)
data = filter(f3, data)
data = pl.process.map(f4, data, workers=5, maxsize=200)
```
Notice that here we even used a regular python `filter`, since stages are iterables Pypeln integrates smoothly with any python code, just be aware of how each stage behaves.
## Pipe Operator
In the spirit of being a true pipeline library, Pypeln also lets you create your pipelines using the pipe `|` operator:
```python
data = (
    range(10)
    | pl.process.map(slow_add1, workers=3, maxsize=4)
    | pl.process.filter(slow_gt3, workers=2)
    | list
)
```
## Run Tests
A sample script is provided to run the tests in a container (either Docker or Podman is supported), to run tests:
```bash
$ bash scripts/run-tests.sh
```
This script can also receive a python version to check test against, i.e
```bash
$ bash scripts/run-tests.sh 3.7
```
## Related Stuff
* [Making an Unlimited Number of Requests with Python aiohttp + pypeln](https://medium.com/@cgarciae/making-an-infinite-number-of-requests-with-python-aiohttp-pypeln-3a552b97dc95)
* [Process Pools](https://docs.python.org/3.4/library/multiprocessing.html?highlight=process#module-multiprocessing.pool)
* [Making 100 million requests with Python aiohttp](https://www.artificialworlds.net/blog/2017/06/12/making-100-million-requests-with-python-aiohttp/)
* [Python multiprocessing Queue memory management](https://stackoverflow.com/questions/52286527/python-multiprocessing-queue-memory-management/52286686#52286686)
* [joblib](https://joblib.readthedocs.io/en/latest/)
* [mpipe](https://vmlaker.github.io/mpipe/)
## Contributors
* [cgarciae](https://github.com/cgarciae)
* [Davidnet](https://github.com/Davidnet)
## License
MIT

%prep
%autosetup -n pypeln-0.4.9

%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-pypeln -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.9-1
- Package Spec generated