Historically, PyPy has referred to two things:
You’ve already seen the second meaning in action by installing PyPy and running a small script with it. The Python implementation you used was written using a dynamic language framework called RPython, just like CPython was written in C and Jython was written in Java.
But weren’t you told earlier that PyPy was written in Python? Well, that’s a little bit of a simplification. The reason PyPy became known as a Python interpreter written in Python (and not in RPython) is that RPython uses the same syntax as Python.
To clear everything up, here’s how PyPy is produced:
Keep in mind that you don’t need to go through all these steps to use PyPy. The executable is already available for you to install and use.
Also, since it’s very confusing to use the same word for both the framework and the implementation, the team behind PyPy decided to move away from this double usage. Now, PyPy refers only to the Python implementation. The framework is referred to as the RPython translation toolchain.
Next, you’ll learn about the features that make PyPy better and faster than Python in some cases.
Compiled programming languages are more performant but are harder to port to different CPU architectures and operating systems. Interpreted programming languages are more portable, but their performance is much worse than that of compiled languages. These are the two extremes of the spectrum.
Then there are programming languages such as Python that do a mix of both compilation and interpretation. Specifically, Python is first compiled into an intermediate bytecode, which is then interpreted by CPython. This makes the code perform better than code written in a purely interpreted programming language, and it maintains the portability advantage.
However, the performance is still nowhere near that of the compiled version. The reason is that the compiled code can do a lot of optimizations that just aren’t possible with bytecode.
That’s where the just-in-time (JIT) compiler comes in. It tries to get the better parts of the both worlds by doing some real compilation into machine code and some interpretation. In a nutshell, here are the steps JIT compilation takes to provide faster performance:
- Identify the most frequently used components of the code, such as a function in a loop.
- Convert those parts into machine code during runtime.
- Optimize the generated machine code.
- Swap the previous implementation with the optimized machine code version.
Remember the two nested loops at the beginning of the tutorial? PyPy detected that the same operation was being executed over and over again, compiled it into machine code, optimized the machine code, and then swapped the implementations. That’s why you saw such a big improvement in speed.
Whenever you create variables, functions, or any other objects, your computer allocates memory to them. Eventually, some of those objects will no longer be needed. If you don’t clean them up, then your computer may run out of memory and crash your program.
In programming languages such as C and C++, you usually have to deal with this problem manually. Other programming languages such as Python and Java do it for you automatically. This is called automatic garbage collection, and there are several techniques for accomplishing it.
CPython uses a technique called reference counting. Essentially, a Python object’s reference count is incremented whenever the object is referenced, and it’s decremented when the object is dereferenced. When the reference count is zero, CPython automatically calls the memory deallocation function for that object. It’s a straightforward and effective technique, but there’s a catch.
When the reference count of a large tree of objects becomes zero, all the related objects are freed. As a result, you have a potentially long pause during which your program doesn’t progress at all.
Also, there’s a use case in which reference counting simply doesn’t work. Consider the following code:
1class A(object): 2 pass 3 4a = A() 5a.some_property = a 6del a
In the code above, you define new class. Then, you create an instance of the class and assign it to be a property on itself. Finally, you delete the instance.
At this point, the instance is no longer accessible. However, reference counting doesn’t delete the instance from memory because it has a reference to itself, so the reference count is not zero. This problem is called a reference cycle, and it can’t be solved using reference counting.
This is where CPython uses another tool called the cyclic garbage collector. It walks over all objects in memory starting from known roots like the
type object. It then identifies all reachable objects and frees unreachable objects since they aren’t alive anymore. This solves the reference cycle problem. However, it can create even more noticeable pauses when there are a large number of objects in memory.
PyPy, on the other hand, doesn’t use reference counting. Instead, it uses only the second technique, the cycle finder. That is, it periodically walks over alive objects starting from the roots. This gives PyPy some advantage over CPython since it doesn’t bother with reference counting, making the total time spent in memory management less than in CPython.
Also, instead of doing everything in one major undertaking like CPython, PyPy splits the work into a variable number of pieces and runs each piece until none are left. This approach adds just a few milliseconds after each minor collection rather than adding hundreds of milliseconds in one go like CPython.
Garbage collection is complex and has many more details that go beyond the scope of this tutorial. You can find more information about PyPy’s garbage collection in the documentation.