Pyston vs PyPy: Similarities and Differences

There are different implementations of Python such as CPython, Pyston, PyPy, etc. All these versions are different from each other in various ways. In this article, we will discuss the Pyston and PyPy implementations of Python. Here, we will have a discussion on Pyston vs PyPy to understand the similarities and differences between the two python implementations.

Pyston vs PyPy Summary

If you are looking for a summary of the differences between Pyston and PyPy, the following table contains the differences between Pyston and PyPy.

Pyston	PyPy
Pyston is a fork of CPython and is implemented in C++. It uses LLVM for code generation.	PyPy is implemented in Python and uses a Just-in-time compiler written in RPython.
Pyston focuses on optimizing the performance of Python code at runtime using JIT compilation.	PyPy uses a combination of JIT compilation, meta-tracing stackless features, and advanced garbage collection to improve performance.
Pyston only supports Python 3.	PyPy supports both Python 2 and Python 3
Pyston is released under Apache License 2.0.	PyPy is released under the MIT License.
Pyston is a relatively new project and its community is still growing.	PyPy has a larger and more established community than Pyston.

Now let us discuss Pyston and PyPy in detail to know their similarities and differences. 

What is Pyston?

Pyston is an alternative implementation of the Python programming language. It aims to provide better performance than the standard CPython interpreter. Pyston was developed by a team of engineers at Dropbox.

• Pyston uses a Just-in-time (JIT) compiler to compile Python code to machine code at runtime. This can result in significant performance improvements over the standard CPython interpreter. It also includes a more efficient garbage collector that can reduce memory usage and improve performance.
• One of the main goals of Pyston is to maintain compatibility with existing Python code, so the code written for CPython should also run on Pyston without modification.
• Pyston currently supports Python 3.8 and has been tested on a variety of platforms, including Linux and macOS.

Even after all these benefits, Pyston is still a relatively new project. It is not yet as widely used or supported as some other alternative Python implementations like PyPy. However, it shows a lot of promise and could be a good choice for applications that require high performance and low memory usage.

Suggested reading: Why is Pyston blowing past python performance?

What is PyPy?

Just like Pyston, PyPy is also an alternative implementation of Python that aims to provide significantly better performance than the standard CPython interpreter. It was developed by Armin Rigo between 2002 and 2010.

• PyPy also uses a Just-in-time (JIT) compiler to compile Python code to machine code at runtime. Internally, it uses a technique known as meta-tracing. Meta-tracing transforms an interpreter into a tracing just-in-time compiler. Now, Interpreters are usually easier to write than compilers but run slower. Therefore,  the meta-tracing technique can make it easier to produce efficient implementations of programming languages that run on interpreters.
• PyPy supports both Python 2 and Python 3. It has been tested rigorously on a wide variety of platforms, including Linux, macOS, and Windows. You can run most Python code on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run in PyPy.

PyPy is a well-established project with a large and active community and is widely used in production environments. It has also been used as a platform for running other languages besides Python, including Ruby, JavaScript, and RPython.

Pyston vs PyPy: What Are The Similarities?

Pyston and PyPy are both alternative implementations of the Python programming language. Both implementations aim to improve performance compared to the standard CPython implementation. Following are some of the similarities between the two python implementations.

1. Just-in-time (JIT) compilation: Both Pyston and PyPy use JIT compilation to speed up the execution of Python code. 
2. Compatibility with existing Python code: Both Pyston and PyPy aim to be compatible with existing Python code. They support most of the language features of CPython. This means that code written for CPython should also run on Pyston and PyPy without much overhead. 
3. Open-source software: Both Pyston and PyPy are open-source projects. This means that their source code is available to the public. You can use and redistribute the code under the licensing terms.
4. Support for multiple platforms: Pyston and PyPy are platform-independent and they support multiple operating systems including Windows, macOS, and Linux.
5. Emphasis on performance: Both Pyston and PyPy prioritize performance over other considerations, such as memory usage or startup time. This means that they may not be the best choice for all use cases. However,  Pyston and PyPy are ideal for applications that require high performance.
6. Active development communities: Both Pyston and PyPy have active development communities that continue to improve and maintain the software. This ensures that the implementations remain up-to-date and compatible with the latest versions of Python.

PyPy vs Pyston: What Are The Differences?

As discussed above, Pyston and PyPy have many similarities. However, there are many differences too. Following is a list of differences between Pyston vs PyPy.

1. Implementation approach: Pyston is a fork of CPython and is implemented in C++. It uses LLVM for code generation. PyPy is implemented in Python and uses a Just-in-time compiler written in RPython.
2. Performance optimizations: While both Pyston and PyPy aim to improve the performance of Python code, they use different approaches. Pyston focuses on optimizing the performance of Python code at runtime using JIT compilation. On the other hand, PyPy uses a combination of JIT compilation, meta-tracing stackless features, and advanced garbage collection to improve performance.
3. Python version support: Pyston only supports Python 3 at the moment, while PyPy supports both Python 2 and Python 3.
4. License: Pyston is released under the Apache License 2.0, while PyPy is released under the MIT License.
5. Community size: PyPy has a larger and more established community than Pyston. This means that it has more support resources and is more widely used in production environments. Pyston is a relatively new project and its community is still growing.

Pyston vs PyPy: What Should You Use?

If you are looking to create a performance-focused application, Pyston and PyPy can be suitable for you. However, the choice between Pyston and PyPy depends on your specific use case and requirements. Following are some of the factors that you can consider before deciding on Pyston vs PyPy.

1. Performance requirements: If you need the highest possible performance for your Python code, both Pyston and PyPy can provide significant performance improvements over the standard CPython interpreter. However, depending on the specifics of your application, one may be better than the other.
2. Python version support: If you need to use Python 2, then PyPy is the obvious choice since Pyston only supports Python 3.8 at the moment.
3. Memory usage: If your application has strict memory constraints, Pyston’s efficient garbage collector may be a better choice.
4. License: If the license is an important consideration, Pyston is released under the Apache License 2.0, while PyPy is released under the MIT License.
5. Community support: PyPy has a larger and more established community than Pyston, which means that it has more support resources and is more widely used in production environments. 

Conclusion

In this article, we discussed the Pyston and PyPy implementation of the python programming language. We also had a discussion on Pyston vs PyPy to understand the similarities and differences between Pyston and PyPy. 

To know more about programming, you can read this article on Python vs CPython. YOu might also like this article on python debugging tools.

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy learning!

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