Understanding Hybrid Compilers: How Python Executes Code

 

In the previous lecture, we discussed the difference between a compiler and an interpreter. Now, let's explore the hybrid type of compilers, which function as both a compiler and an interpreter. Python is an example of a hybrid compiler. When we learn about hybrid compilers, we also understand how a Python program runs.

How Python's Compiler Works

When you write a Python program, you save it with the .py extension. For example, if you create a program file called program.py, this is your source code.

To run this program, you use the command:

 python program.py

 

This command calls Python's hybrid compiler, which processes the source code. However, unlike traditional compilers that convert source code directly into machine code, Python’s compiler takes a different approach.

The Two-Step Process in Python Execution

1. Compilation to Bytecode:

   • The Python compiler converts the source code (.py file) into an intermediate representation called bytecode.

   • This bytecode is stored in a hidden .pyc (Python compiled) file. Although this file is not always visible, it exists in memory.

   • The compiler also checks for syntax errors. If there are no errors, the bytecode is created.

2. Interpretation by the Python Virtual Machine (PVM):

   • The interpreter takes this bytecode and translates it into machine code.

   • The machine code is then executed line by line.

   • The interpreter does not check for errors again, as the compilation step has already ensured an error-free bytecode.

This two-step process makes Python a hybrid language, as it uses both compilation and interpretation.

The Role of Python Virtual Machine (PVM)

The interpreter that executes Python’s bytecode runs within an environment called the Python Virtual Machine (PVM). This runtime environment ensures the program executes correctly, regardless of the underlying hardware.

Benefits of Bytecode Compilation

• Error Checking: Since errors are checked during compilation, the interpreter does not need to recheck them, making execution more efficient.

• Platform Independence: Bytecode can be executed on any machine with a Python interpreter, without requiring recompilation.

• Faster Execution: Since compilation is done once and stored as bytecode, running a program multiple times is faster than interpreting it from scratch each time.

Just-In-Time (JIT) Compilation

Modern Python interpreters implement Just-In-Time (JIT) compilation to improve performance. Instead of translating each line separately, JIT compiles bytecode into machine code in larger chunks, reducing execution time.

Comparison with Java and C#

Java and C# follow a similar execution model:

• Java uses Java Virtual Machine (JVM) to interpret bytecode.

• C# uses Common Language Runtime (CLR) for execution.

• The principle remains the same: compile source code to bytecode, then interpret and execute it.

Conclusion

Python’s hybrid approach offers a balance between performance and flexibility. By using both compilation and interpretation, Python ensures efficient execution while maintaining ease of use. In the next lecture, we will discuss the advantages of hybrid compilation and why Python is widely preferred for development.

Stay tuned for more insights into Python’s execution process!