%global _empty_manifest_terminate_build 0
Name:		python-mem-top
Version:	0.2.1
Release:	1
Summary:	Shows top suspects for memory leaks in your Python program.
License:	MIT
URL:		https://github.com/denis-ryzhkov/mem_top
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c3/f0/8b6cc0ff682dee2760bfeb045fb4c0188a6040aee36f6c1e0e4568d4609e/mem_top-0.2.1.tar.gz
BuildArch:	noarch


%description

Usage::

    pip install mem_top
    from mem_top import mem_top

    # From time to time:
    logging.debug(mem_top())
    # print(mem_top())

    # Notice which counters keep increasing over time - they are the suspects.

Counters:

"mem_top" iterates all objects found in memory and calculates:

* refs - number of direct references from this object to other objects, like keys and values of dict

    * E.g. a dict {("some", "complex", "key"): "value"} will have "refs: 2" - 1 ref for key, 1 ref for value
    * Its key ("some", "complex", "key") will have "refs: 3" - 1 ref per item

* bytes - size of this object in bytes
* types - number of objects of this type still kept in memory after garbage collection

Real life example::

    refs:
    144997  <type 'collections.defaultdict'> defaultdict(<type 'collections.deque'>, {<GearmanJobRequest task='...', unique='.
    144996  <type 'dict'> {'.:..............:.......': <GearmanJobRequest task='..................', unique='.................
    18948   <type 'dict'> {...
    1578    <type 'dict'> {...
    968     <type 'dict'> {...
    968     <type 'dict'> {...
    968     <type 'dict'> {...
    767     <type 'list'> [...
    726     <type 'dict'> {...
    608     <type 'dict'> {...

    types:
    292499  <type 'dict'>
    217912  <type 'collections.deque'>
    72702   <class 'gearman.job.GearmanJob'>
    72702   <class 'gearman.job.GearmanJobRequest'>
    12340   <type '...
    3103    <type '...
    1112    <type '...
    855     <type '...
    767     <type '...
    532     <type '...

* Noticed a leak of 6GB RAM and counting.
* Added "mem_top" and let it run for a while.
* When got the result above it became absolutely clear who is leaking here:
  the Python client of Gearman kept increasing its counters over time.
* Found its known bug - https://github.com/Yelp/python-gearman/issues/10
  leaking defaultdict of deques, and a dict of GearmanJobRequest-s,
  just as the "mem_top" showed.
* Replaced "python-gearman" - long story: stale 2.0.2 at PyPI, broken 2.0.X at github, etc.
* "mem_top" confirmed the leak is now completely closed.

Updates:

* Pass e.g. "verbose_types=[dict, list]" to store their values, sorted by "repr" length, in "verbose_file_name".
* Added "bytes" top.

Config defaults::

    mem_top(
        limit=10,                           # limit of top lines per section
        width=100,                          # width of each line in chars
        sep='\n',                           # char to separate lines with
        refs_format='{num}\t{type} {obj}',  # format of line in "refs" section
        bytes_format='{num}\t {obj}',       # format of line in "bytes" section
        types_format='{num}\t {obj}',       # format of line in "types" section
        verbose_types=None,                 # list of types to sort values by `repr` length
        verbose_file_name='/tmp/mem_top',   # name of file to store verbose values in
    )

See also:

* https://docs.python.org/2/library/gc.html#gc.garbage
* https://pypi.python.org/pypi/objgraph





%package -n python3-mem-top
Summary:	Shows top suspects for memory leaks in your Python program.
Provides:	python-mem-top
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-mem-top

Usage::

    pip install mem_top
    from mem_top import mem_top

    # From time to time:
    logging.debug(mem_top())
    # print(mem_top())

    # Notice which counters keep increasing over time - they are the suspects.

Counters:

"mem_top" iterates all objects found in memory and calculates:

* refs - number of direct references from this object to other objects, like keys and values of dict

    * E.g. a dict {("some", "complex", "key"): "value"} will have "refs: 2" - 1 ref for key, 1 ref for value
    * Its key ("some", "complex", "key") will have "refs: 3" - 1 ref per item

* bytes - size of this object in bytes
* types - number of objects of this type still kept in memory after garbage collection

Real life example::

    refs:
    144997  <type 'collections.defaultdict'> defaultdict(<type 'collections.deque'>, {<GearmanJobRequest task='...', unique='.
    144996  <type 'dict'> {'.:..............:.......': <GearmanJobRequest task='..................', unique='.................
    18948   <type 'dict'> {...
    1578    <type 'dict'> {...
    968     <type 'dict'> {...
    968     <type 'dict'> {...
    968     <type 'dict'> {...
    767     <type 'list'> [...
    726     <type 'dict'> {...
    608     <type 'dict'> {...

    types:
    292499  <type 'dict'>
    217912  <type 'collections.deque'>
    72702   <class 'gearman.job.GearmanJob'>
    72702   <class 'gearman.job.GearmanJobRequest'>
    12340   <type '...
    3103    <type '...
    1112    <type '...
    855     <type '...
    767     <type '...
    532     <type '...

* Noticed a leak of 6GB RAM and counting.
* Added "mem_top" and let it run for a while.
* When got the result above it became absolutely clear who is leaking here:
  the Python client of Gearman kept increasing its counters over time.
* Found its known bug - https://github.com/Yelp/python-gearman/issues/10
  leaking defaultdict of deques, and a dict of GearmanJobRequest-s,
  just as the "mem_top" showed.
* Replaced "python-gearman" - long story: stale 2.0.2 at PyPI, broken 2.0.X at github, etc.
* "mem_top" confirmed the leak is now completely closed.

Updates:

* Pass e.g. "verbose_types=[dict, list]" to store their values, sorted by "repr" length, in "verbose_file_name".
* Added "bytes" top.

Config defaults::

    mem_top(
        limit=10,                           # limit of top lines per section
        width=100,                          # width of each line in chars
        sep='\n',                           # char to separate lines with
        refs_format='{num}\t{type} {obj}',  # format of line in "refs" section
        bytes_format='{num}\t {obj}',       # format of line in "bytes" section
        types_format='{num}\t {obj}',       # format of line in "types" section
        verbose_types=None,                 # list of types to sort values by `repr` length
        verbose_file_name='/tmp/mem_top',   # name of file to store verbose values in
    )

See also:

* https://docs.python.org/2/library/gc.html#gc.garbage
* https://pypi.python.org/pypi/objgraph





%package help
Summary:	Development documents and examples for mem-top
Provides:	python3-mem-top-doc
%description help

Usage::

    pip install mem_top
    from mem_top import mem_top

    # From time to time:
    logging.debug(mem_top())
    # print(mem_top())

    # Notice which counters keep increasing over time - they are the suspects.

Counters:

"mem_top" iterates all objects found in memory and calculates:

* refs - number of direct references from this object to other objects, like keys and values of dict

    * E.g. a dict {("some", "complex", "key"): "value"} will have "refs: 2" - 1 ref for key, 1 ref for value
    * Its key ("some", "complex", "key") will have "refs: 3" - 1 ref per item

* bytes - size of this object in bytes
* types - number of objects of this type still kept in memory after garbage collection

Real life example::

    refs:
    144997  <type 'collections.defaultdict'> defaultdict(<type 'collections.deque'>, {<GearmanJobRequest task='...', unique='.
    144996  <type 'dict'> {'.:..............:.......': <GearmanJobRequest task='..................', unique='.................
    18948   <type 'dict'> {...
    1578    <type 'dict'> {...
    968     <type 'dict'> {...
    968     <type 'dict'> {...
    968     <type 'dict'> {...
    767     <type 'list'> [...
    726     <type 'dict'> {...
    608     <type 'dict'> {...

    types:
    292499  <type 'dict'>
    217912  <type 'collections.deque'>
    72702   <class 'gearman.job.GearmanJob'>
    72702   <class 'gearman.job.GearmanJobRequest'>
    12340   <type '...
    3103    <type '...
    1112    <type '...
    855     <type '...
    767     <type '...
    532     <type '...

* Noticed a leak of 6GB RAM and counting.
* Added "mem_top" and let it run for a while.
* When got the result above it became absolutely clear who is leaking here:
  the Python client of Gearman kept increasing its counters over time.
* Found its known bug - https://github.com/Yelp/python-gearman/issues/10
  leaking defaultdict of deques, and a dict of GearmanJobRequest-s,
  just as the "mem_top" showed.
* Replaced "python-gearman" - long story: stale 2.0.2 at PyPI, broken 2.0.X at github, etc.
* "mem_top" confirmed the leak is now completely closed.

Updates:

* Pass e.g. "verbose_types=[dict, list]" to store their values, sorted by "repr" length, in "verbose_file_name".
* Added "bytes" top.

Config defaults::

    mem_top(
        limit=10,                           # limit of top lines per section
        width=100,                          # width of each line in chars
        sep='\n',                           # char to separate lines with
        refs_format='{num}\t{type} {obj}',  # format of line in "refs" section
        bytes_format='{num}\t {obj}',       # format of line in "bytes" section
        types_format='{num}\t {obj}',       # format of line in "types" section
        verbose_types=None,                 # list of types to sort values by `repr` length
        verbose_file_name='/tmp/mem_top',   # name of file to store verbose values in
    )

See also:

* https://docs.python.org/2/library/gc.html#gc.garbage
* https://pypi.python.org/pypi/objgraph





%prep
%autosetup -n mem-top-0.2.1

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

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

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.1-1
- Package Spec generated