path: root/python-pylaprof.spec

%global _empty_manifest_terminate_build 0
Name:		python-pylaprof
Version:	0.4.6
Release:	1
Summary:	A Python sampling profiler for AWS Lambda functions (and not only).
License:	MIT
URL:		https://github.com/glumia/pylaprof
Source0:	https://mirrors.aliyun.com/pypi/web/packages/cd/46/256bc2e69ece84b0b75253a240df54cbce17bffaeea035952e2a620eec1b/pylaprof-0.4.6.tar.gz
BuildArch:	noarch


%description
# pylaprof
pylaprof is a Python library that allows you to profile functions or sections of code.

As a decorator:
```python
from pylaprof import profile

@profile()
def handler(event, context):
  ...
```

As a context manager:
```python
from pylaprof import Profiler

def main():
  ...
  with Profiler():
    # Only code inside this context will be profiled.
    ...
```

It is built around three main abstractions: the *profiler*, the *sampler*, and
the *storer*.

The profiler is the main component of pylaprof, it takes care of taking
snapshots of your program's stack at regular intervals and feeding them
to the *sampler* for processing; at the end of the profiling session, it will
then ask the *sampler* for a report and provide it to the *storer*.

Take a look at the [source](./pylaprof/__init__.py) for more documentation
and some pre-implemented samplers and storers or [here](./examples) for some
usage examples.

## Features
- Accessible: pylaprof's code is thoroughly documented and written to be read and
  understood by other humans.

- Extensible: you can write your own sampler or storer to generate reports in the format
  you like and store them where and how you want.

- Zero external dependencies[^1].

- Close to zero impact on performances (check [benchmark](./benchmark) for
  more details).

- Reliable: pylaprof was built with the context of long-running
  applications or continuously invoked lambda functions in mind.
  It will never break your code or pollute your standard output or error
  with unwanted messages.

- Turn on/off profiling with an environment variable.

- Store the profiling report only if execution takes longer than a threshold.

[^1]: boto3 is optional and required only if you want to use the S3 storer.

### pylaprof-merge
`pylaprof-merge` is a CLI tool to merge multiple stackcollapse reports into a
single one. This might come in handy if you want to get an aggregated overview
of a function or piece of code that is executed frequently for short periods.
It is installed automatically if you get pylaprof with pip.


## Installation
```
pip install pylaprof
```

Or just copy-paste the pylaprof directory where you need it.


## Credits
- This library is heavily inspired to [pprofile](
  https://github.com/vpelletier/pprofile): thanks to its authors for writing such
  accessible and well-documented code.
- Thanks to @jvns for writing and distributing some of her *wizard zines* for free:
  that's what got me into the rabbit hole of profiling in the first place.


%package -n python3-pylaprof
Summary:	A Python sampling profiler for AWS Lambda functions (and not only).
Provides:	python-pylaprof
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-pylaprof
# pylaprof
pylaprof is a Python library that allows you to profile functions or sections of code.

As a decorator:
```python
from pylaprof import profile

@profile()
def handler(event, context):
  ...
```

As a context manager:
```python
from pylaprof import Profiler

def main():
  ...
  with Profiler():
    # Only code inside this context will be profiled.
    ...
```

It is built around three main abstractions: the *profiler*, the *sampler*, and
the *storer*.

The profiler is the main component of pylaprof, it takes care of taking
snapshots of your program's stack at regular intervals and feeding them
to the *sampler* for processing; at the end of the profiling session, it will
then ask the *sampler* for a report and provide it to the *storer*.

Take a look at the [source](./pylaprof/__init__.py) for more documentation
and some pre-implemented samplers and storers or [here](./examples) for some
usage examples.

## Features
- Accessible: pylaprof's code is thoroughly documented and written to be read and
  understood by other humans.

- Extensible: you can write your own sampler or storer to generate reports in the format
  you like and store them where and how you want.

- Zero external dependencies[^1].

- Close to zero impact on performances (check [benchmark](./benchmark) for
  more details).

- Reliable: pylaprof was built with the context of long-running
  applications or continuously invoked lambda functions in mind.
  It will never break your code or pollute your standard output or error
  with unwanted messages.

- Turn on/off profiling with an environment variable.

- Store the profiling report only if execution takes longer than a threshold.

[^1]: boto3 is optional and required only if you want to use the S3 storer.

### pylaprof-merge
`pylaprof-merge` is a CLI tool to merge multiple stackcollapse reports into a
single one. This might come in handy if you want to get an aggregated overview
of a function or piece of code that is executed frequently for short periods.
It is installed automatically if you get pylaprof with pip.


## Installation
```
pip install pylaprof
```

Or just copy-paste the pylaprof directory where you need it.


## Credits
- This library is heavily inspired to [pprofile](
  https://github.com/vpelletier/pprofile): thanks to its authors for writing such
  accessible and well-documented code.
- Thanks to @jvns for writing and distributing some of her *wizard zines* for free:
  that's what got me into the rabbit hole of profiling in the first place.


%package help
Summary:	Development documents and examples for pylaprof
Provides:	python3-pylaprof-doc
%description help
# pylaprof
pylaprof is a Python library that allows you to profile functions or sections of code.

As a decorator:
```python
from pylaprof import profile

@profile()
def handler(event, context):
  ...
```

As a context manager:
```python
from pylaprof import Profiler

def main():
  ...
  with Profiler():
    # Only code inside this context will be profiled.
    ...
```

It is built around three main abstractions: the *profiler*, the *sampler*, and
the *storer*.

The profiler is the main component of pylaprof, it takes care of taking
snapshots of your program's stack at regular intervals and feeding them
to the *sampler* for processing; at the end of the profiling session, it will
then ask the *sampler* for a report and provide it to the *storer*.

Take a look at the [source](./pylaprof/__init__.py) for more documentation
and some pre-implemented samplers and storers or [here](./examples) for some
usage examples.

## Features
- Accessible: pylaprof's code is thoroughly documented and written to be read and
  understood by other humans.

- Extensible: you can write your own sampler or storer to generate reports in the format
  you like and store them where and how you want.

- Zero external dependencies[^1].

- Close to zero impact on performances (check [benchmark](./benchmark) for
  more details).

- Reliable: pylaprof was built with the context of long-running
  applications or continuously invoked lambda functions in mind.
  It will never break your code or pollute your standard output or error
  with unwanted messages.

- Turn on/off profiling with an environment variable.

- Store the profiling report only if execution takes longer than a threshold.

[^1]: boto3 is optional and required only if you want to use the S3 storer.

### pylaprof-merge
`pylaprof-merge` is a CLI tool to merge multiple stackcollapse reports into a
single one. This might come in handy if you want to get an aggregated overview
of a function or piece of code that is executed frequently for short periods.
It is installed automatically if you get pylaprof with pip.


## Installation
```
pip install pylaprof
```

Or just copy-paste the pylaprof directory where you need it.


## Credits
- This library is heavily inspired to [pprofile](
  https://github.com/vpelletier/pprofile): thanks to its authors for writing such
  accessible and well-documented code.
- Thanks to @jvns for writing and distributing some of her *wizard zines* for free:
  that's what got me into the rabbit hole of profiling in the first place.


%prep
%autosetup -n pylaprof-0.4.6

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

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

%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.6-1
- Package Spec generated