Python memory leaks

Question

I have a long-running script which  if let to run long enough  will consume all the memory on my system   Without going into details about the script  I have two questions    Are there any  Best Practices  to follow  which will help prevent leaks from occurring  What techniques are there to debug memory leaks in Python

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Let me recommend mem top tool I created  It helped me to solve a similar issue  It just instantly shows top suspects for memory leaks in a Python program

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Tracemalloc module was integrated as a built-in module starting from Python 3 4  and appearently  it s also available for prior versions of Python as a third-party library  haven t tested it though    This module is able to output the precise files and lines that allocated the most memory  IMHO  this information is infinitly more valuable than the number of allocated instances for each type  which ends up being a lot of tuples 99  of the time  which is a clue  but barely helps in most cases    I recommend you use tracemalloc in combination with pyrasite  9 times out of 10  running the top 10 snippet in a pyrasite-shell will give you enough information and hints to to fix the leak within 10 minutes  Yet  if you re still unable to find the leak cause  pyrasite-shell in combination with the other tools mentioned in this thread will probably give you some more hints too  You should also take a look on all the extra helpers provided by pyrasite  such as the memory viewer

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Not sure about  Best Practices  for memory leaks in python  but python should clear it s own memory by it s garbage collector  So mainly I would start by checking for circular list of some short  since they won t be picked up by the garbage collector

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To detect and locate memory leaks for long running processes  e g  in production environments  you can now use stackimpact  It uses tracemalloc underneath  More info in this post

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I tried out most options mentioned previously but found this small and intuitive package to be the best  pympler  It s quite straight forward to trace objects that were not garbage-collected  check this small example   install package via pip install pympler  from pympler tracker import SummaryTracker tracker   SummaryTracker          some code you want to investigate      tracker print diff     The output shows you all the objects that have been added  plus the memory they consumed   Sample output                                    types       objects     total size                                                                                                       list          1095      160 78 KB                                    str          1093       66 33 KB                                    int           120        2 81 KB                                   dict             3         840 B       frame  codename  create summary              1         560 B           frame  codename  print diff              1         480 B   This package provides a number of more features  Check pympler s documentation  in particular the section Identifying memory leaks

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This is by no means exhaustive advice  But number one thing to keep in mind when writing with the thought of avoiding future memory leaks  loops  is to make sure that anything which accepts a reference to a call-back  should store that call-back as a weak reference

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Have a look at this article  Tracing python memory leaks Also  note that the garbage collection module actually can have debug flags set  Look at the set debug function  Additionally  look at this code by Gnibbler for determining the types of objects that have been created after a call

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You should specially have a look on your global or static data  long living data    When this data grows without restriction  you can also get troubles in Python   The garbage collector can only collect data  that is not referenced any more  But your static data can hookup data elements that should be freed   Another problem can be memory cycles  but at least in theory the Garbage collector should find and eliminate cycles -- at least as long as they are not hooked on some long living data   What kinds of long living data are specially troublesome  Have a good look on any lists and dictionaries -- they can grow without any limit  In dictionaries you might even don t see the trouble coming since when you access dicts  the number of keys in the dictionary might not be of big visibility to you

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As far as best practices  keep an eye for recursive functions  In my case I ran into issues with recursion  where there didn t need to be   A simplified example of what I was doing   def my function          lots of memory intensive operations       like operating on images or huge dictionaries and lists               my flag   True     if my flag     restart the function if a certain flag is true         my function    def main        my function     operating in this recursive manner won t trigger the garbage collection and clear out the remains of the function  so every time through memory usage is growing and growing   My solution was to pull the recursive call out of my function   and have main   handle when to call it again  this way the function ends naturally and cleans up after itself   def my function          lots of memory intensive operations       like operating on images or huge dictionaries and lists               my flag   True               return my flag  def main        result   my function       if result          my function

[python] Python memory leaks

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