You might have heard that Pyston is blowing past python performance. But what Pyston really is? This article discusses the basics of Pyston, its advantages, and its disadvantages. Next, it also discusses why is Pyston blowing past python performance. Finally, we will compare Python and Pyston to analyze which is the best python version to use.

What is Pyston?

Pyston is an open-source Python implementation developed by Dropbox. It was released as open-source in 2015. Pyston aims to provide high performance by using modern just-in-time (JIT) compilation techniques. 

CPython is the standard and most widely used implementation of the Python programming language. It is written in C and is the reference implementation of Python. It means that CPython is the version of Python that most people refer to when they talk about “Python”.

Pyston’s goal is to provide a high-performance alternative to the standard CPython interpreter that is used by most Python developers. To achieve this, Pyston uses LLVM (Low-Level Virtual Machine). This is the same technology that is used by the popular JIT compiler, LLVM, to generate optimized machine code on the fly. This allows Pyston to run Python code much faster than CPython in many cases.

Why Is Pyston Blowing Past Python Performance?

Pyston is much better than Python in performance. This is due to several reasons. Here are five reasons why Pyston is faster than CPython:

1. JIT Compilation: Pyston uses JIT (just-in-time) compilation techniques to compile Python code into machine code dynamically at runtime. This allows Pyston to generate optimized machine code that is specifically tailored to the code being executed. This results in significant performance improvements.
2. Improved Memory Management: Pyston has an optimized memory management system that helps to reduce the overhead associated with allocating and freeing memory. This results in better performance, especially for large applications that perform many memory operations.
3. Optimized Execution Model: Pyston has an optimized execution model that is designed to take advantage of modern hardware, such as multi-core processors and large amounts of memory. This optimized execution model helps Pyston to achieve improved performance compared to traditional Python i.e. CPython.
4. Better Caching: Pyston uses a smarter caching system that helps to reduce the overhead associated with repeatedly executing the same code. This results in improved performance for applications that use loops or perform similar operations repeatedly.
5. Improved Function Calling: Pyston uses a more optimized function call mechanism that reduces the overhead associated with calling functions and methods. This results in improved performance for applications that use many function calls.
6. More Efficient Garbage Collection: Pyston uses a more efficient garbage collection system that helps to reduce the overhead associated with freeing up unused memory. This results in improved performance, especially for applications that create and discard many objects dynamically.
7. Reduced Overhead: Pyston has been designed to minimize overhead and eliminate inefficiencies wherever possible. For example, it has been optimized to minimize the number of memory allocations and reduce the size of the memory allocation metadata.

Overall, these performance improvements help Pyston to be faster than CPython in many cases. However, it is important to note that the performance difference can vary depending on the specific use case and workload.

Advantages of Using Pyston Instead of CPython

Apart from the fact that pyston is blowing past python performance, Pyston also has several other advantages when we compare it to the CPython version of Python. Following are some of the advantages of using Pyston over the standard CPython interpreter:

1. Performance: Pyston aims to provide high performance by using JIT compilation techniques. This means that Pyston can run Python code faster than CPython in many cases. This makes it a good choice for performance-critical applications. And that’s why we say that pyston is blowing past python performance.
2. Compatibility: Pyston is designed to be fully compatible with CPython. It supports the same syntax and almost all the libraries. This makes it easy to switch to Pyston without having to make any changes to your existing code. Pyston retains API compatibility with CPython, although it’s not compatible with the Application Binary Interface (ABI). Therefore, the C extensions will function, but they need to be recompiled.
3. Modern JIT Compilation: Pyston uses modern JIT compilation techniques using DynASM that are not available in CPython. This allows Pyston to generate more optimized machine code, resulting in better performance.
4. Active Development: Pyston is an actively developed project, with a strong community of contributors. This means that it is regularly updated with bug fixes, new features, and performance improvements.
5. Open Source: Just like CPython, Pyston is open-source software, which means that it is free to use and can be modified and distributed as needed. This makes it a good choice for organizations that want to use a high-performance Python implementation without having to pay for a proprietary solution.

Overall, Pyston is a good choice for organizations that need a high-performance Python implementation and are looking for a modern JIT compiler that is fully compatible with CPython.

Disadvantages of Using Pyston

While pyston is blowing past python performance and has several other advantages, there are also some disadvantages to using it over the standard CPython interpreter. Following are some of the disadvantages of Pyston compared to CPython.

1. Limited Support for Libraries: Pyston has limited support for some of the more specialized libraries and packages used in the CPython ecosystem. This may make it difficult to use Pyston for certain types of applications that require specific libraries.
2. Smaller Community: Pyston has a smaller community of users and contributors compared to CPython. This means that it may not have as many available resources or support for troubleshooting and resolving issues.
3. Lack of Stability: Pyston is still a relatively new and experimental project. This means that it may have bugs and stability issues that have not yet been discovered or resolved.
4. Slower Startup Time: Pyston has a slower startup time compared to CPython due to the overhead associated with JIT compilation. This can make it less suitable for applications that need to start up quickly or for short-lived scripts that need to run in a matter of seconds.
5. Increased Memory Usage: Pyston uses more memory than CPython due to its JIT compiler and other performance optimizations. This may make it less suitable for resource-constrained environments or for applications that need to run on machines with limited memory.
6. Installation Overhead: Unlike Python, Pyston doesn’t offer pre-compiled packages, so they will be compiled at the time of installation, which can cause difficulties when installing packages that work with CPython. The same issues occur when trying to recompile those packages for CPython.

Overall, while Pyston has many advantages and offers improved performance over CPython, it also has some limitations and drawbacks that should be considered before deciding to use it for a specific use case.

Pyston or Python, What Should You Use? 

The choice between Pyston and CPython (the standard Python interpreter) will depend on your specific use case and requirements.

• If you are looking for a fast, performant implementation of Python that is well-suited for demanding computational tasks and large-scale applications, then Pyston may be a good choice for you. Its JIT compiler and other performance optimizations can result in significantly improved performance compared to CPython in many cases.
• If you need a more stable and widely used implementation of Python with a large and established community of users and developers, then CPython may be the better choice for you. CPython is widely used and has a large ecosystem of libraries and packages, making it a versatile and flexible option for many different types of applications.

Ultimately, the choice between Pyston and CPython will depend on your specific needs and requirements, as well as your familiarity with the Python ecosystem and the libraries and packages that you need to use. Before making a decision, I would recommend considering your specific use case and to weigh the advantages and disadvantages of both options to determine which one is best for you.

Conclusion

In this article, we have discussed why pyston is blowing past python performance. We have also seen the advantages and disadvantages of Pyston over the CPython implementation of python. To learn more about Python, you can read this article on C# vs Python. You might also like this article on whether should you learn SQL or Python First.

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

Happy Learning!

Disclosure of Material Connection: Some of the links in the post above are “affiliate links.” This means if you click on the link and purchase the item, I will receive an affiliate commission. Regardless, I only recommend products or services I use personally and believe will add value to my readers.