Shows top suspects for memory leaks in your Python program.
Project description
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 - 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.
Config defaults:
mem_top(limit=10, width=100, sep='\n', refs_format='{num}\t{type} {obj}', types_format='{num}\t {obj}')
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