automatic import of python-mprofile

3 files changed, 446 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..58cdd50 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/mprofile-0.0.15.tar.gz
diff --git a/python-mprofile.spec b/python-mprofile.spec
new file mode 100644
index 0000000..d9437d6
--- /dev/null
+++ b/python-mprofile.spec
@@ -0,0 +1,444 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-mprofile
+Version:	0.0.15
+Release:	1
+Summary:	A low-overhead memory profiler.
+License:	MIT
+URL:		http://github.com/timpalpant/mprofile
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f9/06/a2a99bd3caf8daa6ce17154a19a2e05412496379f49cf28f654033072d4c/mprofile-0.0.15.tar.gz
+BuildArch:	noarch
+
+
+%description
+[![PyPI](https://img.shields.io/pypi/v/mprofile)](https://pypi.org/project/mprofile/)
+[![Build Status](https://travis-ci.org/timpalpant/mprofile.svg?branch=master)](https://travis-ci.org/timpalpant/mprofile)
+![PyPI - License](https://img.shields.io/pypi/l/mprofile)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mprofile)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/mprofile.svg)](https://pypistats.org/packages/mprofile)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+# mprofile
+
+A low-overhead sampling memory profiler for Python, derived from [heapprof](https://github.com/humu/heapprof), with an interface similar to [tracemalloc](https://pytracemalloc.readthedocs.io).
+mprofile attempts to give results comparable to tracemalloc, but uses statistical sampling to lower memory and CPU overhead. The sampling algorithm is the one used by [tcmalloc](https://github.com/gperftools/gperftools) and Golang heap profilers.
+
+## Installation & usage
+
+1.  Install the profiler package using PyPI:
+
+    ```shell
+    pip3 install mprofile
+    ```
+
+2.  Enable the profiler in your application, get a snapshot of (sampled) memory usage:
+
+    ```python
+    import mprofile
+
+    mprofile.start(sample_rate=128 * 1024)
+    snap = mprofile.take_snapshot()
+    ```
+
+See the [tracemalloc](https://docs.python.org/3/library/tracemalloc.html) for API documentation. The API and objects returned by mprofile are compatible.
+
+## Compatibility
+
+mprofile is compatible with Python >= 3.4.
+It can also be used with earlier versions of Python, but you must build CPython from source and apply the [pytracemalloc patches](https://pytracemalloc.readthedocs.io/install.html#manual-installation).
+
+## Benchmarks
+
+We are primarily interested in profiling the memory usage of webservers, so used the `tornado_http` benchmark from pyperformance to estimate overhead.
+mprofile has similar performance to tracemalloc when comprehensively tracing all allocations, but when statistical sampling is used, the overhead is significantly reduced.
+In addition, mprofile interns call stacks in a tree data structure that reduces memory overhead of storing the traces.
+
+With the recommended setting of `sample_rate=128kB`, we observe ~5% slow down in the `tornado_http` benchmark.
+
+TODO: Run the full [pyperformance](https://pyperformance.readthedocs.io) suite of benchmarks.
+
+### Baseline
+```
+Python 2.7.16, no profiling:
+tornado_http: Mean +- std dev: 664 ms +- 30 ms
+Maximum resident set size (kbytes): 39176
+```
+
+### tracemalloc
+```
+Python 2.7.16, tracemallocframes=128:
+tornado_http: Mean +- std dev: 1.74 sec +- 0.04 sec
+Maximum resident set size (kbytes): 43752
+
+# Saving only one frame in each stack trace rather than full call stacks.
+Python 2.7.16, tracemallocframes=1:
+tornado_http: Mean +- std dev: 960 ms +- 30 ms
+Maximum resident set size (kbytes): 40000
+```
+
+### mprofile
+```
+Python 2.7.16, mprofileframes=128, mprofilerate=1 (i.e. tracemalloc):
+tornado_http: Mean +- std dev: 1.78 sec +- 0.05 sec
+Maximum resident set size (kbytes): 40588
+
+Python 2.7.16, mprofileframes=128, mprofilerate=1024:
+tornado_http: Mean +- std dev: 888 ms +- 28 ms
+Maximum resident set size (kbytes): 39752
+
+Python 2.7.16, mprofileframes=128, mprofilerate=128 * 1024:
+tornado_http: Mean +- std dev: 700 ms +- 26 ms
+Maximum resident set size (kbytes): 39388
+
+# Saving only one frame in each stack trace rather than full call stacks.
+Python 2.7.16, mprofileframes=1, mprofilerate=1 (i.e. tracemalloc):
+tornado_http: Mean +- std dev: 890 ms +- 19 ms
+Maximum resident set size (kbytes): 40152
+
+Python 2.7.16, mprofileframes=1, mprofilerate=1024:
+tornado_http: Mean +- std dev: 738 ms +- 24 ms
+Maximum resident set size (kbytes): 39568
+
+Python 2.7.16, mprofileframes=1, mprofilerate=128 * 1024:
+tornado_http: Mean +- std dev: 678 ms +- 22 ms
+Maximum resident set size (kbytes): 39328
+```
+
+## Developer notes
+
+Run the unit tests:
+```
+bazel test --test_output=streamed //src:profiler_test
+```
+
+Run the benchmarks:
+```
+bazel test -c opt --test_output=streamed //src:profiler_bench
+```
+
+Run the end-to-end (Python) tests:
+```
+bazel test --config asan --test_output=streamed //test:*
+```
+
+Run tests with ASAN and UBSAN:
+```
+bazel test --config asan --test_output=streamed //src:* //test:*
+```
+
+# Contributing
+
+Pull requests and issues are welcomed!
+
+# License
+
+mprofile is released under the [MIT License](https://opensource.org/licenses/MIT) and incorporates code from [heapprof](https://github.com/humu/heapprof), which is also released under the MIT license.
+
+
+
+
+%package -n python3-mprofile
+Summary:	A low-overhead memory profiler.
+Provides:	python-mprofile
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mprofile
+[![PyPI](https://img.shields.io/pypi/v/mprofile)](https://pypi.org/project/mprofile/)
+[![Build Status](https://travis-ci.org/timpalpant/mprofile.svg?branch=master)](https://travis-ci.org/timpalpant/mprofile)
+![PyPI - License](https://img.shields.io/pypi/l/mprofile)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mprofile)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/mprofile.svg)](https://pypistats.org/packages/mprofile)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+# mprofile
+
+A low-overhead sampling memory profiler for Python, derived from [heapprof](https://github.com/humu/heapprof), with an interface similar to [tracemalloc](https://pytracemalloc.readthedocs.io).
+mprofile attempts to give results comparable to tracemalloc, but uses statistical sampling to lower memory and CPU overhead. The sampling algorithm is the one used by [tcmalloc](https://github.com/gperftools/gperftools) and Golang heap profilers.
+
+## Installation & usage
+
+1.  Install the profiler package using PyPI:
+
+    ```shell
+    pip3 install mprofile
+    ```
+
+2.  Enable the profiler in your application, get a snapshot of (sampled) memory usage:
+
+    ```python
+    import mprofile
+
+    mprofile.start(sample_rate=128 * 1024)
+    snap = mprofile.take_snapshot()
+    ```
+
+See the [tracemalloc](https://docs.python.org/3/library/tracemalloc.html) for API documentation. The API and objects returned by mprofile are compatible.
+
+## Compatibility
+
+mprofile is compatible with Python >= 3.4.
+It can also be used with earlier versions of Python, but you must build CPython from source and apply the [pytracemalloc patches](https://pytracemalloc.readthedocs.io/install.html#manual-installation).
+
+## Benchmarks
+
+We are primarily interested in profiling the memory usage of webservers, so used the `tornado_http` benchmark from pyperformance to estimate overhead.
+mprofile has similar performance to tracemalloc when comprehensively tracing all allocations, but when statistical sampling is used, the overhead is significantly reduced.
+In addition, mprofile interns call stacks in a tree data structure that reduces memory overhead of storing the traces.
+
+With the recommended setting of `sample_rate=128kB`, we observe ~5% slow down in the `tornado_http` benchmark.
+
+TODO: Run the full [pyperformance](https://pyperformance.readthedocs.io) suite of benchmarks.
+
+### Baseline
+```
+Python 2.7.16, no profiling:
+tornado_http: Mean +- std dev: 664 ms +- 30 ms
+Maximum resident set size (kbytes): 39176
+```
+
+### tracemalloc
+```
+Python 2.7.16, tracemallocframes=128:
+tornado_http: Mean +- std dev: 1.74 sec +- 0.04 sec
+Maximum resident set size (kbytes): 43752
+
+# Saving only one frame in each stack trace rather than full call stacks.
+Python 2.7.16, tracemallocframes=1:
+tornado_http: Mean +- std dev: 960 ms +- 30 ms
+Maximum resident set size (kbytes): 40000
+```
+
+### mprofile
+```
+Python 2.7.16, mprofileframes=128, mprofilerate=1 (i.e. tracemalloc):
+tornado_http: Mean +- std dev: 1.78 sec +- 0.05 sec
+Maximum resident set size (kbytes): 40588
+
+Python 2.7.16, mprofileframes=128, mprofilerate=1024:
+tornado_http: Mean +- std dev: 888 ms +- 28 ms
+Maximum resident set size (kbytes): 39752
+
+Python 2.7.16, mprofileframes=128, mprofilerate=128 * 1024:
+tornado_http: Mean +- std dev: 700 ms +- 26 ms
+Maximum resident set size (kbytes): 39388
+
+# Saving only one frame in each stack trace rather than full call stacks.
+Python 2.7.16, mprofileframes=1, mprofilerate=1 (i.e. tracemalloc):
+tornado_http: Mean +- std dev: 890 ms +- 19 ms
+Maximum resident set size (kbytes): 40152
+
+Python 2.7.16, mprofileframes=1, mprofilerate=1024:
+tornado_http: Mean +- std dev: 738 ms +- 24 ms
+Maximum resident set size (kbytes): 39568
+
+Python 2.7.16, mprofileframes=1, mprofilerate=128 * 1024:
+tornado_http: Mean +- std dev: 678 ms +- 22 ms
+Maximum resident set size (kbytes): 39328
+```
+
+## Developer notes
+
+Run the unit tests:
+```
+bazel test --test_output=streamed //src:profiler_test
+```
+
+Run the benchmarks:
+```
+bazel test -c opt --test_output=streamed //src:profiler_bench
+```
+
+Run the end-to-end (Python) tests:
+```
+bazel test --config asan --test_output=streamed //test:*
+```
+
+Run tests with ASAN and UBSAN:
+```
+bazel test --config asan --test_output=streamed //src:* //test:*
+```
+
+# Contributing
+
+Pull requests and issues are welcomed!
+
+# License
+
+mprofile is released under the [MIT License](https://opensource.org/licenses/MIT) and incorporates code from [heapprof](https://github.com/humu/heapprof), which is also released under the MIT license.
+
+
+
+
+%package help
+Summary:	Development documents and examples for mprofile
+Provides:	python3-mprofile-doc
+%description help
+[![PyPI](https://img.shields.io/pypi/v/mprofile)](https://pypi.org/project/mprofile/)
+[![Build Status](https://travis-ci.org/timpalpant/mprofile.svg?branch=master)](https://travis-ci.org/timpalpant/mprofile)
+![PyPI - License](https://img.shields.io/pypi/l/mprofile)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mprofile)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/mprofile.svg)](https://pypistats.org/packages/mprofile)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+# mprofile
+
+A low-overhead sampling memory profiler for Python, derived from [heapprof](https://github.com/humu/heapprof), with an interface similar to [tracemalloc](https://pytracemalloc.readthedocs.io).
+mprofile attempts to give results comparable to tracemalloc, but uses statistical sampling to lower memory and CPU overhead. The sampling algorithm is the one used by [tcmalloc](https://github.com/gperftools/gperftools) and Golang heap profilers.
+
+## Installation & usage
+
+1.  Install the profiler package using PyPI:
+
+    ```shell
+    pip3 install mprofile
+    ```
+
+2.  Enable the profiler in your application, get a snapshot of (sampled) memory usage:
+
+    ```python
+    import mprofile
+
+    mprofile.start(sample_rate=128 * 1024)
+    snap = mprofile.take_snapshot()
+    ```
+
+See the [tracemalloc](https://docs.python.org/3/library/tracemalloc.html) for API documentation. The API and objects returned by mprofile are compatible.
+
+## Compatibility
+
+mprofile is compatible with Python >= 3.4.
+It can also be used with earlier versions of Python, but you must build CPython from source and apply the [pytracemalloc patches](https://pytracemalloc.readthedocs.io/install.html#manual-installation).
+
+## Benchmarks
+
+We are primarily interested in profiling the memory usage of webservers, so used the `tornado_http` benchmark from pyperformance to estimate overhead.
+mprofile has similar performance to tracemalloc when comprehensively tracing all allocations, but when statistical sampling is used, the overhead is significantly reduced.
+In addition, mprofile interns call stacks in a tree data structure that reduces memory overhead of storing the traces.
+
+With the recommended setting of `sample_rate=128kB`, we observe ~5% slow down in the `tornado_http` benchmark.
+
+TODO: Run the full [pyperformance](https://pyperformance.readthedocs.io) suite of benchmarks.
+
+### Baseline
+```
+Python 2.7.16, no profiling:
+tornado_http: Mean +- std dev: 664 ms +- 30 ms
+Maximum resident set size (kbytes): 39176
+```
+
+### tracemalloc
+```
+Python 2.7.16, tracemallocframes=128:
+tornado_http: Mean +- std dev: 1.74 sec +- 0.04 sec
+Maximum resident set size (kbytes): 43752
+
+# Saving only one frame in each stack trace rather than full call stacks.
+Python 2.7.16, tracemallocframes=1:
+tornado_http: Mean +- std dev: 960 ms +- 30 ms
+Maximum resident set size (kbytes): 40000
+```
+
+### mprofile
+```
+Python 2.7.16, mprofileframes=128, mprofilerate=1 (i.e. tracemalloc):
+tornado_http: Mean +- std dev: 1.78 sec +- 0.05 sec
+Maximum resident set size (kbytes): 40588
+
+Python 2.7.16, mprofileframes=128, mprofilerate=1024:
+tornado_http: Mean +- std dev: 888 ms +- 28 ms
+Maximum resident set size (kbytes): 39752
+
+Python 2.7.16, mprofileframes=128, mprofilerate=128 * 1024:
+tornado_http: Mean +- std dev: 700 ms +- 26 ms
+Maximum resident set size (kbytes): 39388
+
+# Saving only one frame in each stack trace rather than full call stacks.
+Python 2.7.16, mprofileframes=1, mprofilerate=1 (i.e. tracemalloc):
+tornado_http: Mean +- std dev: 890 ms +- 19 ms
+Maximum resident set size (kbytes): 40152
+
+Python 2.7.16, mprofileframes=1, mprofilerate=1024:
+tornado_http: Mean +- std dev: 738 ms +- 24 ms
+Maximum resident set size (kbytes): 39568
+
+Python 2.7.16, mprofileframes=1, mprofilerate=128 * 1024:
+tornado_http: Mean +- std dev: 678 ms +- 22 ms
+Maximum resident set size (kbytes): 39328
+```
+
+## Developer notes
+
+Run the unit tests:
+```
+bazel test --test_output=streamed //src:profiler_test
+```
+
+Run the benchmarks:
+```
+bazel test -c opt --test_output=streamed //src:profiler_bench
+```
+
+Run the end-to-end (Python) tests:
+```
+bazel test --config asan --test_output=streamed //test:*
+```
+
+Run tests with ASAN and UBSAN:
+```
+bazel test --config asan --test_output=streamed //src:* //test:*
+```
+
+# Contributing
+
+Pull requests and issues are welcomed!
+
+# License
+
+mprofile is released under the [MIT License](https://opensource.org/licenses/MIT) and incorporates code from [heapprof](https://github.com/humu/heapprof), which is also released under the MIT license.
+
+
+
+
+%prep
+%autosetup -n mprofile-0.0.15
+
+%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-mprofile -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.15-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..10b8c32
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8ccfa9c642d8648adf30aa7fd3d2f9bd  mprofile-0.0.15.tar.gz