Goals and Architecture Overview of Python

The main is to provide a compliant, flexible and fast implementation of the Python Language which uses the RPython tool chain to enable new advanced high-level features without having to encode the low-level details. This is called as PyPy.

High Level Goals

Our main motivation to develop the translation framework that provides a full featured, customizable, fast and very compliant Python implementation, working on and interacting with a large variety of platforms and allowing the quick introduction of new advanced language features.

This Python implementation is written in RPython as a relatively simple interpreter, in some respects, it is easier to understand than CPython, that is C reference implementation of Python. Using its high level flexibility to quickly experiment the features or implementation techniques in a traditional approach, require pervasive changes to the source code.

For example, PyPy’s Python interpreter provides a lazily computed object which is a small extension that would require global changes in CPython.

Another example is the garbage collection technique: changing CPython to use a garbage collector not based on reference counting, whereas in PyPy it’s an issue localized in the translation framework which is fully orthogonal to the interpreter source code.

PyPy Python Interpreter

PyPy’s Python Interpreter is written in RPython that implements the full Python language. This interpreter very closely emulates the behavior of CPython and contains the following key components:

Bytecode Compiler - This is responsible for producing the Python code objects from the source code of a user application. The bytecode compiler is the preprocessing phase which produces a compact bytecode format via a chain of flexible passes (tokenizer, lexer, parser, abstract syntax tree builder, bytecode generator).

Bytecode Evaluator – It’s responsible for interpreting the Python code objects. The bytecode evaluator interprets this bytecode. It does most of its work by delegating all actual manipulations of user objects to the object space. The latter can be thought of as the library of built-in types. It defines the implementation of the user objects, like integers and lists, as well as the operations between them, like addition or truth-value-testing.

Standard Object Space – This is responsible for creating and manipulating the Python objects that are seen by the application. The division between bytecode evaluator and object space gives a lot of flexibility. One can plug in different object spaces to get different or enriched behaviour of the Python object.

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