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Cause of Memory Leak in Python And How to Fix Them

A  memory leak can not always pop up in the production of a program. There are several reasons for memory leak in python code, such as one might not get sufficient traffic, frequently deployed, no uses of hard memory, and much more.

A flask app we needed to use that had identical features. It might not have a massive wave of business, and it might have many deployments within a week. Although it required a cgroup memory usage purpose, it held a fantastic room to develop. The leak will not appear until one determined to perform a cache warmer that can create vital traffic and where it goes. OOM Killer destroys Uwsgi means!

What is python memory management?

Python prepares memory management at its individual, and this is entirely extracted from the user. It usually does not require understanding how this can be done within, but while operators are going, one must understand it.

When some primitive classes of an article work out of range or remove it with del explicitly, the memory does not deliver back to OS, and it could still be considered for the python method. The presently free things could move to an idea known as freelist and would always remain on the heap. The memory leak in python can be realized only while a garbage collection of the most critical generation occurs.

Here we have allocated the file of ints and removing it explicitly.

import os, psutil, gc, time

l=[i for i in range(100000000)]

print(psutil.Process(os.getpid()).memory_info())

del l

#gc.collect()

print(psutil.Process(os.getpid()).memory_info())

The Output would seems as:

# without GC:

pmem(rss=3268038656L, vms=7838482432L, pfaults=993628, pageins=140)

pmem(rss=2571223040L, vms=6978756608L, pfaults=1018820, pageins=140)

# with GC:

pmem(rss=3268042752L, vms=7844773888L, pfaults=993636, pageins=0)

pmem(rss=138530816L, vms=4552351744L, pfaults=1018828, pageins=0)

Observe it by removing, and we are moving of 3.2G -> 2.5G, but several kinds of stuff (frequently int objects) extending throughout the heap. If one also triggers it with a GC, it works from 3.2G -> 0.13G. Therefore its memory did not return to OS till a GC did trigger. It is just a concept of how can python prepares memory management and how to fix memory leak in python.

Method to confirm whether there is a leak or not

To address a bit more meaning on the leaking memory utilization, this could be a flask app, including a truck frequently on individual API endpoints with various factors.

With the help of a basic understanding of how memory leak in python and how does python memory management work, we used specific GC (garbage collection) with the particular answer that sent back. This will be like this:

@blueprint.route(‘/app_metric/<app>’)

def get_metric(app):

    response, status = get_metrics_for_app(app)

    gc.collect()

    return jsonify(data=response), status

Memory was yet steadily growing with traffic even with the GC collection. Suggesting? THIS IS A LEAK!!

Initiate with heap dump method

So we ought to see this uWSGI operator with large memory allocations. One might not be informed of a memory profiler that can connect to a moving python method and provide real-time object utilizations. That is why a heap dump continued practiced to examine what all is present there. Here is the method for how it could be done:

$> hexdump core.25867 | awk ‘{printf “%s%s%s%s\n%s%s%s%s\n”, $5,$4,$3,$2,$9,$8,$7,$6}’ | sort | uniq -c | sort -nr  | head

123454 00000000000

 212362 ffffffffffffff

 178902 00007f011e72c0

 168871

 144329 00007f004e0c70

 141815 ffffffffffffc

 136763 fffffffffffffa

 132449 00000000000002

 99190 00007f104d86a0

These are the values of symbols and address the mapping of those symbols. To understand what these objects are actually:

$> gdb python core.25867

(gdb) info symbol 0x00007f01104e0c70

PyTuple_Type in section .data of /export/apps/python/3.6.1/lib/libpython3.6m.so.1.0

(gdb) info symbol 0x00007f01104d86a0

PyLong_Type in section .data of /export/apps/python/3.6.1/lib/libpython3.6m.so.1.0

Let’s follow the memory allocation methods

There are no other alternatives that can track memory allocations or memory leak in python. Several python projects are accessible to support the leaners do memory allocation. But one requires to be introduced individually, and as 3.4 python appears bundling tracemalloc. It follows memory allocations and shows to module/line where an object could designate with volume. One can use pictures at irregular duration in the program track and examine the memory distinction among these two spots.

This is a flask app that is assumed to be stateless, and it must not be hard memory allocations within API requests. Therefore how does one get a picture of memory and follow memory allocations within API requests, which is stateless?

The best thing one can do for memory leak in python is to come up with: Transfer a doubt factor in HTTP call that can catch a picture. Transfer various factors that can help to take the different pictures and match them with the original one! 

import tracemalloc

tracemalloc.start()

s1=None

s2=None

@blueprint.route(‘/app_metric/<app>’)

 def get_metric(app):

     global s1,s2

     trace = request.args.get(‘trace’,None)

     response, status = get_metrics_for_app(app)

     if trace == ‘s2’:

         s2=tracemalloc.take_snapshot()

         for i in s2.compare_to(s1,’lineno’)[:10]:

             print(i)

     elif trace == ‘s1’:

         s1=tracemalloc.take_snapshot()

     return jsonify(data=response), status

While trace=s1 is transferred with the call, a memory picture is selected. While trace=s2 is transferred, another image is received, and this will match with the original picture. Here we have printed the distinction, and this will determine who designated how much memory within those two calls and what are the memory leak in python.

Hello, memory leak!

The result of picture difference will seem like:

/<some>/<path>/<here>/foo_module.py:65: size=3326 KiB (+2616 KiB), count=60631 (+30380), average=56 B

/<another>/<path>/<here>/requests-2.18.4-py2.py3-none-any.whl.68063c775939721f06119bc4831f90dd94bb1355/requests-2.18.4-py2.py3-none-any.whl/requests/models.py:823: size=604 KiB (+604 KiB), count=4 (+3), average=151 KiB

/export/apps/python/3.6/lib/python3.6/threading.py:884: size=50.9 KiB (+27.9 KiB), count=62 (+34), average=840 B

/export/apps/python/3.6/lib/python3.6/threading.py:864: size=49.0 KiB (+26.2 KiB), count=59 (+31), average=851 B

/export/apps/python/3.6/lib/python3.6/queue.py:164: size=38.0 KiB (+20.2 KiB), count=64 (+34), average=608 B

/export/apps/python/3.6/lib/python3.6/threading.py:798: size=19.7 KiB (+19.7 KiB), count=35 (+35), average=576 B

/export/apps/python/3.6/lib/python3.6/threading.py:364: size=18.6 KiB (+18.0 KiB), count=36 (+35), average=528 B

/export/apps/python/3.6/lib/python3.6/multiprocessing/pool.py:108: size=27.8 KiB (+15.0 KiB), count=54 (+29), average=528 B

/export/apps/python/3.6/lib/python3.6/threading.py:916: size=27.6 KiB (+14.5 KiB), count=57 (+30), average=496 B

<unknown>:0: size=25.3 KiB (+12.4 KiB), count=53 (+26), average=488 B

It sets out, and one would have a system module that we continued practicing to get downstream requests to obtain information for an answer. This system modules cancel the threadpool module to notice extracted information, such as how much duration it can take to make the downstream request. And for a particular purpose, the issue of profiling could be added to the list, which might be class variable! The initial line can be 2600KB in size, and it can do for each incoming demand. It resembled something like that:

class Profiler(object):

    …

    results = []

    …

    def end():

      timing = get_end_time()

      results.append(timing)

…..

Conclusion

In this article, we have included all the details of memory leak in python that one can face in their program. Using the coding, as mentioned above, programmers can find alternative methods to analyze the memory leaks and fix them with the help of codes. If one can recognize the leaks in the program, they can compare it with the original views. There are different python memory leaks that can trouble the coders in their code, so try to resolve them as soon as possible. 

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