Shows top suspects for memory leaks in your Python program.
Usage:
pip install mem_top
from mem_top import mem_top
# From time to time:
logging.debug(mem_top()) # Or just print().
Result:
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 '...
Explaining result:
- 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.
- Found its known bug - Yelp/python-gearman#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.
Config defaults:
mem_top(limit=10, width=100, sep='\n', refs_format='{num}\t{type} {obj}', types_format='{num}\t {obj}')
mem_top version 0.1.1
Copyright (C) 2014-2015 by Denis Ryzhkov denisr@denisr.com
MIT License, see http://opensource.org/licenses/MIT