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PROGRAMMING, PYTHON
Optimization in Python — Interning
Understand Python’s optimization technique — Interning
There are different Python implementations out there such as CPython, Jython, IronPython, etc. The optimization techniques we are going to discuss in this article are related to CPython which is standard Python implementation.
Interning
Interning is re-using the objects on-demand instead of creating new objects. What does this mean? Let’s try to understand Integer and String interning with examples.
is — this is used to compare the memory location of two python objects.
id — this returns memory location in base-10.
Integer interning
At startup, Python pre-loads/caches a list of integers into the memory. These are in the range -5 to +256. Any time when we try to create an integer object within this range, Python automatically refer to these objects in the memory instead of creating new integer objects.
The reason behind this optimization strategy is simple that integers in the -5 to 256 are used more often. So it makes sense to store them in the main memory. So, Python pre-loads them in the memory at the startup so that speed and memory are optimized.
Example 1:
In this example, both a and b are assigned to value 100. Since it is within the range -5 to +256, Python uses interning so that b will also reference the same memory location instead of creating another integer object with the value 100.
As we can see from the code below, both a and b are referencing the same object in the memory. Python will not create a new object but instead references to a’s memory location. This is all due to integer interning.
Example 2:
In this example, both a and b are assigned with value 1000. Since it is outside the range -5 to +256, Python will create two integer objects. So both a and b will be stored in different locations in the memory.
