Earlier in the chapter, I talked about the limitations of the GIL or the Global Interpreter Lock that is present within Python, but what does this actually mean?
First, I think it's important to know exactly what the GIL does for us. The GIL is essentially a mutual exclusion lock which prevents multiple threads from executing Python code in parallel. It is a lock that can only be held by one thread at any one time, and if you wanted a thread to execute its own code, then it would first have to acquire the lock before it could proceed to execute its own code. The advantage that this gives us is that while it is locked, nothing else can run at the same time:
In the preceding diagram, we see an example of how multiple threads are hampered by this GIL. Each thread has to wait and acquire the GIL before it can progress further, and then release the GIL, typically before it has had a chance to complete its work. It follows a random round-robin approach, and you have no guarantees as to which thread will acquire the lock first.
Why is this necessary, you might ask? Well, the GIL has been a long-disputed part of Python, and over the years has triggered many a debate over its usefulness. But it was implemented with good intentions and to combat the non-thread safe Python memory management. It prevents us from taking advantage of multiprocessor systems in certain scenarios.
Guido Van Rossum, the creator of Python, posted an update on the removal of the GIL and its benefits in a post here: http://www.artima.com/weblogs/viewpost.jsp?thread=214235. He states that he wouldn't be against someone creating a branch of Python that is GIL-less, and he would accept a merge of this code if, and only if, it didn't negatively impact the performance of a single-threaded application.
There have been prior attempts at getting rid of the GIL, but it was found that the addition of all the extra locks to ensure thread-safety actually slowed down an application by a factor of more then two. In other words, you would have been able to get more work done with a single CPU than you would have with just over two CPUs. There are, however, libraries such as NumPy that can do everything they need to without having to interact with the GIL, and working purely outside of the GIL is something I'm going to be exploring in greater depth in the future chapters of this book.
It must also be noted that there are other implementations of Python, such as Jython and IronPython, that don't feature any form of Global Interpreter Lock, and as such can fully exploit multiprocessor systems. Jython and IronPython both run on different virtual machines, so, they can take advantage of their respective runtime environments.
Jython is an implementation of Python that works directly with the Java platform. It can be used in a complementary fashion with Java as a scripting language, and has been shown to outperform CPython, which is the standard implementation of Python, when working with some large datasets. For the majority of stuff though, CPython's single-core execution typically outperforms Jython and its multicore approach.
The advantage to using Jython is that you can do some pretty cool things with it when working in Java, such as import existing Java libraries and frameworks, and use them as though they were part of your Python code.
IronPython is the .NET equivalent of Jython and works on top of Microsoft's .NET framework. Again, you'll be able to use it in a complementary fashion with .NET applications. This is somewhat beneficial for .NET developers, as they are able to use Python as a fast and expressive scripting language within their .NET applications.
If Python has such obvious, known limitations when it comes to writing performant, concurrent applications, then why do we continue to use it? The short answer is that it's a fantastic language to get work done in, and by work, I'm not necessarily talking about crunching through a computationally expensive task. It's an intuitive language, which is easy to pick up and understand for those who don't necessarily have a lot of programming experience.
The language has seen a huge adoption rate amongst data scientists and mathematicians working in incredibly interesting fields such as machine learning and quantitative analysis, who find it to be an incredibly useful tool in their arsenal.
In both the Python 2 and 3 ecosystems, you'll find a huge number of libraries that are designed specifically for these use cases, and by knowing about Python's limitations, we can effectively mitigate them, and produce software that is efficient and capable of doing exactly what is required of it.
So now that we understand what threads and processes are, as well as some of the limitations of Python, it's time to have a look at just how we can utilize multi-threading within our application in order to improve the speed of our programs.