Hello I am Cheuk

• Open-Source contributor
  
  
   
• Organisers of community events
  
  
   
• ex EPS board member, PSF fellow
   
• Looking for my next role

Profiling (in software eng.)

• Investigate software behaviour
• When it is being executed
• Dynamic analysis
• the tool that does that is called a profiler

Memory Profiling

• Profiling - investigation of...
• Memory allocation
• Garbage collection
• When the program is executed over time

Why we need a profiler?

• Data science work involves lots of data
• Python apps are not very good at memory management
• Either having memory error, or worse...
• hanging to a reference when it’s not there
• We need to know what's going on

Heap vs Stack

Heap vs Stack

• Store global variables
   
• Allocated anywhere in the memory
   
• Need to be free with reference (GC)
   
• Or free when the process terminated

• Store local variables
   
• Allocated with a defined order
   
• Released in order
  
   
• Free when the function returned

Resident Set vs Virtual Memory

Resident vs Virtual

• Actual memory that is used physically on RAM
   
• Not an accurate measure for total used
  
   
• However, it is a reliable estimate

• Totally memory that the process needs
   
• Not an accurate measure of how much consumed at a time
   
• Estimation of the total amount used

Different OS may be different as well...

• private heap for all Python objects and data structures
   
• Python memory manager talks to the OS manager
   
• pymalloc -> PyMem_RawMalloc -> VMM
   
• object-specific allocators for different object types

• optimized for objects <= 512 bytes
   
• “arenas” with a fixed size of 256 KiB
   
• Block that keeps only one Python object of a fixed size
   
• size from 8 to 512 bytes
   
• if bigger falls back to PyMem_RawMalloc and PyMem_RawRealloc

pymalloc