Examples

Display the top 10

Display the 10 files allocating the most memory:

import tracemalloc

tracemalloc.start()

# ... run your application ...

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

print("[ Top 10 ]")
for stat in top_stats[:10]:
    print(stat)

Example of output of the Python test suite:

[ Top 10 ]
<frozen importlib._bootstrap>:716: size=4855 KiB, count=39328, average=126 B
<frozen importlib._bootstrap>:284: size=521 KiB, count=3199, average=167 B
/usr/lib/python3.4/collections/__init__.py:368: size=244 KiB, count=2315, average=108 B
/usr/lib/python3.4/unittest/case.py:381: size=185 KiB, count=779, average=243 B
/usr/lib/python3.4/unittest/case.py:402: size=154 KiB, count=378, average=416 B
/usr/lib/python3.4/abc.py:133: size=88.7 KiB, count=347, average=262 B
<frozen importlib._bootstrap>:1446: size=70.4 KiB, count=911, average=79 B
<frozen importlib._bootstrap>:1454: size=52.0 KiB, count=25, average=2131 B
<string>:5: size=49.7 KiB, count=148, average=344 B
/usr/lib/python3.4/sysconfig.py:411: size=48.0 KiB, count=1, average=48.0 KiB

We can see that Python loaded 4.8 MiB data (bytecode and constants) from modules and that the collections module allocated 244 KiB to build namedtuple types.

See Snapshot.statistics() for more options.

Compute differences

Take two snapshots and display the differences:

import tracemalloc
tracemalloc.start()
# ... start your application ...

snapshot1 = tracemalloc.take_snapshot()
# ... call the function leaking memory ...
snapshot2 = tracemalloc.take_snapshot()

top_stats = snapshot2.compare_to(snapshot1, 'lineno')

print("[ Top 10 differences ]")
for stat in top_stats[:10]:
    print(stat)

Example of output before/after running some tests of the Python test suite:

[ Top 10 differences ]
<frozen importlib._bootstrap>:716: size=8173 KiB (+4428 KiB), count=71332 (+39369), average=117 B
/usr/lib/python3.4/linecache.py:127: size=940 KiB (+940 KiB), count=8106 (+8106), average=119 B
/usr/lib/python3.4/unittest/case.py:571: size=298 KiB (+298 KiB), count=589 (+589), average=519 B
<frozen importlib._bootstrap>:284: size=1005 KiB (+166 KiB), count=7423 (+1526), average=139 B
/usr/lib/python3.4/mimetypes.py:217: size=112 KiB (+112 KiB), count=1334 (+1334), average=86 B
/usr/lib/python3.4/http/server.py:848: size=96.0 KiB (+96.0 KiB), count=1 (+1), average=96.0 KiB
/usr/lib/python3.4/inspect.py:1465: size=83.5 KiB (+83.5 KiB), count=109 (+109), average=784 B
/usr/lib/python3.4/unittest/mock.py:491: size=77.7 KiB (+77.7 KiB), count=143 (+143), average=557 B
/usr/lib/python3.4/urllib/parse.py:476: size=71.8 KiB (+71.8 KiB), count=969 (+969), average=76 B
/usr/lib/python3.4/contextlib.py:38: size=67.2 KiB (+67.2 KiB), count=126 (+126), average=546 B

We can see that Python has loaded 8.2 MiB of module data (bytecode and constants), and that this is 4.4 MiB more than had been loaded before the tests, when the previous snapshot was taken. Similarly, the linecache module has cached 940 KiB of Python source code to format tracebacks, all of it since the previous snapshot.

If the system has little free memory, snapshots can be written on disk using the Snapshot.dump() method to analyze the snapshot offline. Then use the Snapshot.load() method reload the snapshot.

Get the traceback of a memory block

Code to display the traceback of the biggest memory block:

import tracemalloc

# Store 25 frames
tracemalloc.start(25)

# ... run your application ...

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('traceback')

# pick the biggest memory block
stat = top_stats[0]
print("%s memory blocks: %.1f KiB" % (stat.count, stat.size / 1024))
for line in stat.traceback.format():
    print(line)

Example of output of the Python test suite (traceback limited to 25 frames):

903 memory blocks: 870.1 KiB
  File "<frozen importlib._bootstrap>", line 716
  File "<frozen importlib._bootstrap>", line 1036
  File "<frozen importlib._bootstrap>", line 934
  File "<frozen importlib._bootstrap>", line 1068
  File "<frozen importlib._bootstrap>", line 619
  File "<frozen importlib._bootstrap>", line 1581
  File "<frozen importlib._bootstrap>", line 1614
  File "/usr/lib/python3.4/doctest.py", line 101
    import pdb
  File "<frozen importlib._bootstrap>", line 284
  File "<frozen importlib._bootstrap>", line 938
  File "<frozen importlib._bootstrap>", line 1068
  File "<frozen importlib._bootstrap>", line 619
  File "<frozen importlib._bootstrap>", line 1581
  File "<frozen importlib._bootstrap>", line 1614
  File "/usr/lib/python3.4/test/support/__init__.py", line 1728
    import doctest
  File "/usr/lib/python3.4/test/test_pickletools.py", line 21
    support.run_doctest(pickletools)
  File "/usr/lib/python3.4/test/regrtest.py", line 1276
    test_runner()
  File "/usr/lib/python3.4/test/regrtest.py", line 976
    display_failure=not verbose)
  File "/usr/lib/python3.4/test/regrtest.py", line 761
    match_tests=ns.match_tests)
  File "/usr/lib/python3.4/test/regrtest.py", line 1563
    main()
  File "/usr/lib/python3.4/test/__main__.py", line 3
    regrtest.main_in_temp_cwd()
  File "/usr/lib/python3.4/runpy.py", line 73
    exec(code, run_globals)
  File "/usr/lib/python3.4/runpy.py", line 160
    "__main__", fname, loader, pkg_name)

We can see that the most memory was allocated in the importlib module to load data (bytecode and constants) from modules: 870 KiB. The traceback is where the importlib loaded data most recently: on the import pdb line of the doctest module. The traceback may change if a new module is loaded.

Pretty top

Code to display the 10 lines allocating the most memory with a pretty output, ignoring <frozen importlib._bootstrap> and <unknown> files:

import os
import tracemalloc

def display_top(snapshot, group_by='lineno', limit=10):
    snapshot = snapshot.filter_traces((
        tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
        tracemalloc.Filter(False, "<unknown>"),
    ))
    top_stats = snapshot.statistics(group_by)

    print("Top %s lines" % limit)
    for index, stat in enumerate(top_stats[:limit], 1):
        frame = stat.traceback[0]
        # replace "/path/to/module/file.py" with "module/file.py"
        filename = os.sep.join(frame.filename.split(os.sep)[-2:])
        print("#%s: %s:%s: %.1f KiB"
              % (index, filename, frame.lineno,
                 stat.size / 1024))

    other = top_stats[limit:]
    if other:
        size = sum(stat.size for stat in other)
        print("%s other: %.1f KiB" % (len(other), size / 1024))
    total = sum(stat.size for stat in top_stats)
    print("Total allocated size: %.1f KiB" % (total / 1024))

tracemalloc.start()

# ... run your application ...

snapshot = tracemalloc.take_snapshot()
display_top(snapshot)

Example of output of the Python test suite:

2013-11-08 14:16:58.149320: Top 10 lines
#1: collections/__init__.py:368: 291.9 KiB
#2: Lib/doctest.py:1291: 200.2 KiB
#3: unittest/case.py:571: 160.3 KiB
#4: Lib/abc.py:133: 99.8 KiB
#5: urllib/parse.py:476: 71.8 KiB
#6: <string>:5: 62.7 KiB
#7: Lib/base64.py:140: 59.8 KiB
#8: Lib/_weakrefset.py:37: 51.8 KiB
#9: collections/__init__.py:362: 50.6 KiB
#10: test/test_site.py:56: 48.0 KiB
7496 other: 4161.9 KiB
Total allocated size: 5258.8 KiB

See Snapshot.statistics() for more options.

Thread to write snapshots into files every minutes

Create a daemon thread writing snapshots every minutes into /tmp/tracemalloc-PPP-CCCC.pickle where PPP is the identifier of the process and CCCC is a counter:

import pickle, gc, os, signal, threading, time, tracemalloc

class TakeSnapshot(threading.Thread):
    daemon = True

    def run(self):
        if hasattr(signal, 'pthread_sigmask'):
            # Available on UNIX with Python 3.3+
            signal.pthread_sigmask(signal.SIG_BLOCK, range(1, signal.NSIG))
        counter = 1
        while True:
            time.sleep(60)
            filename = ("/tmp/tracemalloc-%d-%04d.pickle"
                        % (os.getpid(), counter))
            print("Write snapshot into %s..." % filename)
            gc.collect()
            snapshot = tracemalloc.take_snapshot()
            with open(filename, "wb") as fp:
                # Pickle version 2 can be read by Python 2 and Python 3
                pickle.dump(snapshot, fp, 2)
            snapshot = None
            print("Snapshot written into %s" % filename)
            counter += 1

# save 25 frames
tracemalloc.start(25)
TakeSnapshot().start()