Yes, memory profiling support is quite poor for Python. I used some code
like the following in the past to track down a nasty memory leak in the ndb
module (https://issuetracker.google.com/u/1/issues/35901184):
@contextlib.contextmanager
def memoryTracker():
if constants.UNITTEST:
yield
return
if not constants.LOCAL:
memory_usage_before = runtime.memory_usage().current()
common_types_before = dict(objgraph.most_common_types(shortnames=False))
yield
gc.collect()
if gc.garbage:
loggin.warn(
'Leaking objects with __del__ methods? (len %d, types %s...)'
% (len(gc.garbage), [g.__class__ for g in gc.garbage[:10]])
)
if not constants.LOCAL:
memory_usage_after = runtime.memory_usage().current()
logging.info(
'Memory usage after: %d (diff: %d)'
% (memory_usage_after, memory_usage_after - memory_usage_before)
)
common_types_after = dict(objgraph.most_common_types(shortnames=False))
common_types_diff = {
name: common_types_after.get(name, 0) -
common_types_before.get(name, 0)
for name in frozenset(common_types_before.keys() +
common_types_after.keys())
}
logging.info(
'Most common types:\n'
+ '\n'.join(
'%s\t%d (diff: %+d)' % (name, common_types_after.get(name, 0),
diff)
for name, diff in sorted(common_types_diff.items(), key=lambda
e: e[1], reverse=True)
)
)
I hereby place the code in the public domain. Feel free to use it in any
way you like. I use it something like:
with memoryTracker():
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