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
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Low-Level Performance: Python is known for having relatively poor low-level performance when compared to languages like C or C++.
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Global Interpreter Lock (GIL): The GIL in CPython, the standard Python implementation, makes Python effectively single-threaded, limiting concurrency.
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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.
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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.
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Limited Debugger Integration: Debuggers generally can't seamlessly step across Python and C code. Those that can aren't widely accepted.
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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.
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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.