automatic import of python-diskcache

3 files changed, 161 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/diskcache-5.4.0.tar.gz
diff --git a/python-diskcache.spec b/python-diskcache.spec
new file mode 100644
index 0000000..1b837a7
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+++ b/python-diskcache.spec
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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
diff --git a/sources b/sources
new file mode 100644
index 0000000..db53fce
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+2fb9349bd1cf5f214b5f6eee273e7b41  diskcache-5.4.0.tar.gz