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%global _empty_manifest_terminate_build 0
Name: python-diskcache
Version: 5.4.0
Release: 1
Summary: Disk Cache -- Disk and file backed persistent cache.
License: Apache 2.0
URL: http://www.grantjenks.com/docs/diskcache/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c7/34/d23a9bc5b2a84917879b977f00fdb97a7700b186a32bf7b0cf5f29f4c2d9/diskcache-5.4.0.tar.gz
BuildArch: noarch
%description
`DiskCache`_ is an Apache2 licensed disk and file backed cache library, written
in pure-Python, and compatible with Django.
The cloud-based computing of 2021 puts a premium on memory. Gigabytes of empty
space is left on disks as processes vie for memory. Among these processes is
Memcached (and sometimes Redis) which is used as a cache. Wouldn't it be nice
to leverage empty disk space for caching?
Django is Python's most popular web framework and ships with several caching
backends. Unfortunately the file-based cache in Django is essentially
broken. The culling method is random and large caches repeatedly scan a cache
directory which slows linearly with growth. Can you really allow it to take
sixty milliseconds to store a key in a cache with a thousand items?
In Python, we can do better. And we can do it in pure-Python!
In [1]: import pylibmc
In [2]: client = pylibmc.Client(['127.0.0.1'], binary=True)
In [3]: client[b'key'] = b'value'
In [4]: %timeit client[b'key']
10000 loops, best of 3: 25.4 µs per loop
In [5]: import diskcache as dc
In [6]: cache = dc.Cache('tmp')
In [7]: cache[b'key'] = b'value'
In [8]: %timeit cache[b'key']
100000 loops, best of 3: 11.8 µs per loop
**Note:** Micro-benchmarks have their place but are not a substitute for real
measurements. DiskCache offers cache benchmarks to defend its performance
claims. Micro-optimizations are avoided but your mileage may vary.
DiskCache efficiently makes gigabytes of storage space available for
caching. By leveraging rock-solid database libraries and memory-mapped files,
cache performance can match and exceed industry-standard solutions. There's no
need for a C compiler or running another process. Performance is a feature and
testing has 100% coverage with unit tests and hours of stress.
%package -n python3-diskcache
Summary: Disk Cache -- Disk and file backed persistent cache.
Provides: python-diskcache
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-diskcache
`DiskCache`_ is an Apache2 licensed disk and file backed cache library, written
in pure-Python, and compatible with Django.
The cloud-based computing of 2021 puts a premium on memory. Gigabytes of empty
space is left on disks as processes vie for memory. Among these processes is
Memcached (and sometimes Redis) which is used as a cache. Wouldn't it be nice
to leverage empty disk space for caching?
Django is Python's most popular web framework and ships with several caching
backends. Unfortunately the file-based cache in Django is essentially
broken. The culling method is random and large caches repeatedly scan a cache
directory which slows linearly with growth. Can you really allow it to take
sixty milliseconds to store a key in a cache with a thousand items?
In Python, we can do better. And we can do it in pure-Python!
In [1]: import pylibmc
In [2]: client = pylibmc.Client(['127.0.0.1'], binary=True)
In [3]: client[b'key'] = b'value'
In [4]: %timeit client[b'key']
10000 loops, best of 3: 25.4 µs per loop
In [5]: import diskcache as dc
In [6]: cache = dc.Cache('tmp')
In [7]: cache[b'key'] = b'value'
In [8]: %timeit cache[b'key']
100000 loops, best of 3: 11.8 µs per loop
**Note:** Micro-benchmarks have their place but are not a substitute for real
measurements. DiskCache offers cache benchmarks to defend its performance
claims. Micro-optimizations are avoided but your mileage may vary.
DiskCache efficiently makes gigabytes of storage space available for
caching. By leveraging rock-solid database libraries and memory-mapped files,
cache performance can match and exceed industry-standard solutions. There's no
need for a C compiler or running another process. Performance is a feature and
testing has 100% coverage with unit tests and hours of stress.
%package help
Summary: Development documents and examples for diskcache
Provides: python3-diskcache-doc
%description help
`DiskCache`_ is an Apache2 licensed disk and file backed cache library, written
in pure-Python, and compatible with Django.
The cloud-based computing of 2021 puts a premium on memory. Gigabytes of empty
space is left on disks as processes vie for memory. Among these processes is
Memcached (and sometimes Redis) which is used as a cache. Wouldn't it be nice
to leverage empty disk space for caching?
Django is Python's most popular web framework and ships with several caching
backends. Unfortunately the file-based cache in Django is essentially
broken. The culling method is random and large caches repeatedly scan a cache
directory which slows linearly with growth. Can you really allow it to take
sixty milliseconds to store a key in a cache with a thousand items?
In Python, we can do better. And we can do it in pure-Python!
In [1]: import pylibmc
In [2]: client = pylibmc.Client(['127.0.0.1'], binary=True)
In [3]: client[b'key'] = b'value'
In [4]: %timeit client[b'key']
10000 loops, best of 3: 25.4 µs per loop
In [5]: import diskcache as dc
In [6]: cache = dc.Cache('tmp')
In [7]: cache[b'key'] = b'value'
In [8]: %timeit cache[b'key']
100000 loops, best of 3: 11.8 µs per loop
**Note:** Micro-benchmarks have their place but are not a substitute for real
measurements. DiskCache offers cache benchmarks to defend its performance
claims. Micro-optimizations are avoided but your mileage may vary.
DiskCache efficiently makes gigabytes of storage space available for
caching. By leveraging rock-solid database libraries and memory-mapped files,
cache performance can match and exceed industry-standard solutions. There's no
need for a C compiler or running another process. Performance is a feature and
testing has 100% coverage with unit tests and hours of stress.
%prep
%autosetup -n diskcache-5.4.0
%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-diskcache -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 5.4.0-1
- Package Spec generated
|
