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How Mojo is Different from Python?

Mojo is a new programming language designed to tackle several limitations found in Python, most notably Python's low-level performance and the Global Interpreter Lock (GIL) issue, which makes Python effectively single-threaded. While Python excels as a glue layer allowing low-level bindings to C and C++, building these hybrid libraries is complex and requires an understanding of CPython internals.

Here's a comparison between Mojo and Python:

Aspects Python Mojo
Compatibility with Python ecosystem Python is the base language and is inherently compatible with its ecosystem. Mojo aims for full compatibility with Python, leveraging CPython to run all existing Python 3 code without modifications.
Syntax and language structure Python's syntax and language structure are fixed and well-defined. Mojo uses Python as its conceptual foundation but aims to evolve its own syntax and structure as a standalone language.
Code migration Not applicable as Python is the base language. Mojo plans to offer a mechanical migration tool to assist users in converting their Python code to Mojo effectively.
Performance and control Python is known for its ease of use and readability, but it may not always provide optimal performance or low-level control. Mojo seeks to offer predictable low-level performance and control, aiming to handle system-level code that traditionally required C or C++.
Use of static or dynamic typing Python is dynamically typed. Mojo aims to allow the programmer the choice to use static or dynamic typing depending on application requirements.
Interoperability with Python Not applicable as Python is the base language. Mojo is designed to cooperate directly with CPython runtime and to support integration with CPython classes and objects.
Progression Python is a mature language with a large ecosystem. Mojo is in its early stages, but aims to provide a progressive migration approach where incremental migration to Mojo yields incremental benefits.

 

Python's Limitations 

  1. Low-Level Performance: Python is known for having relatively poor low-level performance when compared to languages like C or C++.

  2. Global Interpreter Lock (GIL): The GIL in CPython, the standard Python implementation, makes Python effectively single-threaded, limiting concurrency.

  3. Two-World Problem: While Python excels as a glue layer with low-level bindings to C and C++, building hybrid libraries becomes complex. It requires understanding CPython internals and C/C++ programming, undermining the original simplicity of using Python.

  4. Graph-Based Programming Models: The need for high-performance libraries in fields like Machine Learning has pushed towards graph-based programming models, which offer worse usability than "eager mode" systems.

  5. Limited Debugger Integration: Debuggers generally can't seamlessly step across Python and C code. Those that can aren't widely accepted.

  6. Three-World and N-World Problem: Machine learning developers face even more complexity when they have to work with bespoke programming languages for hardware accelerators, such as CUDA. This is creating a fragmented ecosystem.

  7. Mobile and Server Deployment: Deploying Python code, especially in a way that controls dependencies, hermetically compiles files, and improves multi-threading and performance, is a challenge.


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