Python Memory Management 101.Deeping in Garbage collector

In this talk I will try explain the memory internals of Python and discover how it handles memory management and object creation.
The idea is explain how objects are created and deleted in Python and how garbage collector(gc) functions to automatically release memory when the object taking the space is no longer in use.
I will review the main mechanisms for memory allocation and how the garbage collector works in conjunction with the memory manager for reference counting of the python objects.
Finally, I will comment the best practices for memory management such as writing efficient code in python scripts.

Transcript

1. José Manuel Ortega
   @jmortegac
   Python Memory
   Management 101
   Deeping in Garbage collector
   

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2. About me
   ● @jmortegac
   ● http://jmortega.github.io
   ● https://www.linkedin.com/in/jmortega1/
   

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3. Agenda
   ● Introduction to memory management
   ● Garbage collector and reference counting
   with python
   ● Review the gc module for configuring the
   python garbage collector
   ● Best practices for memory managment
   

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4. Introduction to memory management
   ● Memory management is the process of
   efficiently allocating, de-allocating, and
   coordinating memory so that all the
   different processes run smoothly and can
   optimally access different system
   resources.
   

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5. Python Objects in Memory
   

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6. Python Memory Manager
   

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7. Heap allocation
   

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8. Heap allocation
   def main():
   x=300
   print(id(x))
   w=fun(x)
   print(id(w))
   def sqr(x):
   print (id(x))
   z=x*x
   print(id(z))
   return z
   if __name__ == "__main__":
   main()
   

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9. Python Objects in Memory
   ● Each variable in Python acts as an object
   ● Python is a dynamically typed language
   which means that we do not need to
   declare types for variables.
   

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10. Python Objects in Memory
    

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11. Python Objects in Memory
    

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12. Python Objects in Memory
    

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13. Python Objects in Memory
    

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14. id() method
    

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15. id() function
    

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16. id() function
    

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17. Is Operator
    

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18. Reference counting
    ● Python manages objects by using reference
    counting
    ● Reference counting works by counting the
    number of times an object is referenced by
    other objects in the application.
    ● When references to an object are removed,
    the reference count for an object is
    decremented.
    

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19. Reference counting
    ● A reference is a container object pointing at
    another object.
    ● Reference counting is a simple technique
    in which objects are allocated when there is
    reference to them in a program
    

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20. Reference counting
    ● when reference count increases?
    ○ x=1
    ○ def(x):
    ○ list.append(x)
    

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21. Reference counting
    

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22. Reference counting
    

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23. Reference counting
    

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24. Reference counting
    ● Easy to implement
    ● Objects are immediately deleted when
    reference counter is 0
    ✗ Not thread-safe
    ✗ Doesn’t detect cyclic references
    ✗ space overhead - reference count is
    stored for every object
    

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25. Python Garbage collector
    ● The Python Garbage Collector (GC) runs
    during the program execution and is
    triggered if the reference count reduces to
    zero.
    

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26. Garbage collector(GC) module
    

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27. Python Garbage collector
    ● Reference Counting + Generational GC
    ● RefCount reaches zero, immediate
    deletion
    ● Deleted objects with cyclic references
    are deleted with Tracing GC
    

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28. Garbage collector(GC) reference cycle
    

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29. Garbage collector(GC) reference cycle
    >>> def ref_cycle():
    ... list = [1, 2, 3, 4]
    ... list.append(list)
    ... return list
    

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30. Garbage collector(GC) reference cycle
    import gc
    for i in range(8):
    ref_cycle()
    n = gc.collect()
    print("Number of unreachable objects collected by GC:", n)
    print("Uncollectable garbage:", gc.garbage)
    print("Number of unreachable objects collected by GC:",
    gc.collect())
    

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31. Garbage collector(GC) reference cycle
    

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32. Python Object Graphs
    https://mg.pov.lt/objgraph/
    >>> x = []
    >>> list = [x, [x], dict(x=x)]
    >>> import objgraph
    >>> objgraph.show_refs([y],
    filename='sample-graph.png')
    

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33. Best practices for memory managment
    ● Using gc.collect() carefully
    print("Collecting...")
    n = gc.collect()
    print("Number of unreachable objects collected:", n)
    print("Uncollectable garbage:", gc.garbage)
    

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34. Best practices for memory managment
    ● Using with context manager for working
    with files
    with open('data.txt', 'r') as file:
    data = ','.join(line.strip() for line in file)
    

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35. Best practices for memory managment
    ● Avoid List Slicing with [:]
    list= [1,2,3,4]
    list[1:3]
    list[slice(1,3)]
    

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36. Best practices for memory managment
    ● String Concatenation
    string= “hello”
    string+= “world”
    wordList = ("hello", "world")
    string = " ".join(wordList)
    

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37. Best practices for memory managment
    ● Use Iterators and Generators
    

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38. References
    ● https://stackabuse.com/basics-of-memory-management-in-p
    ython/
    ● https://realpython.com/python-memory-management
    ● https://rushter.com/blog/python-garbage-collector
    ● https://pythonchb.github.io/PythonTopics/weak_references.
    html
    

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39. https://www.youtube.com/c/JoseManuelOrtegadev
    

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40. https://speakerdeck.com/jmortega
    

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