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|
%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
|
