Python

Is Python Scalable?

Is Python Really Scalable
Written by Christina Jones

Python language has many limitations in terms of scalability. However, that doesn’t stop it from being used in famous projects such as Pinterest, YouTube, Quora, and Dropbox. All of them have bricks of python code. Every programming language comes with some advantages and drawbacks. You need to choose that suits your projects. 

Scalability should be considered as one of the main factors if you are about to make changes or enlarge the program’s capability in the future. Python is slower in terms of speed and performance but the highlight of this language is development is much easier and faster with python. It is well known that python requires very little code for a purpose. Additionally, the human-readable codes make it an easy language to learn for beginners.

Scalability can be defined as the capability of a programming language to handle the growing amount of data, work, and processes. Also, at the same time, the potential to expand in itself to handle future growth comes in this term. 

Scalability is seen as a whole from two different perspectives: 

  1. The behavior of the system when it’s use increases (Machine Scalability)
  2. The behavior of the system when development potential grows (human scalability)

Machine Scalability

Even though the development team is not available, the machine can handle everything on its own. If something is required, that is the potential of the machine to handle it. Python is not as much scalable as its competitors in terms of speed and performance. If we see languages like Java and C++, they are very scalable because of the reason discussed below.

Speed

The main reason behind python being slower is it’s a scripting language. The majority of scripting language executes slower than a programming language. So does Python. Python requires an interpretation of the code at the runtime. In python, every code requires enough instructions to execute which makes it slow. 

No Multithreading

Multithreading is a method in which we divide the programs to multiple treads to run it on their own so that productivity is increased providing a significant amount of time and speed to the application. In simple, to handle multiple requests at a time for several sub-application, one need multithreading to make it easy and reliable. 

Java, C, C++, and other old languages support powerful multithreading than Python. Python is not able due to GIL (Global Interpreter Lock) which averts python to interpret and execute multiple tasks simultaneously. GIL forces python to run only a single thread within a single process at a time. Python using GIL is not a big thing. There are reasons to support this in particular. However, It impacts the behavior of such an efficient language. Multithreading is a big reason for Java being so much popular in the industry as compared to any other programming language. 

Human Scalability

Human scalability is a situation in context with development is when a program needs big change with the increase in the development team. It is not easy for python to handle this. The reason is simple. It’s a dynamically typed language. When the number of codes grows significantly, it becomes difficult for the system to maintain it at a high scale. It results in the throw of fatal errors sometimes. 

In Java, for the same, we have static handling for adding more lines of codes (more features). It also involves a type-safety system that minimizes the errors at runtime. 

Is scalability the only thing that effects?

Python is slower in performance, but who cares such a minuscule portion of a second when you have a lot of enhanced productivity. Provides flexibility and development in fewer lines of code if we compare it to other vast languages like JAVA, and C, etc. Python is a dynamically typed language that allows the developer to develop programs quickly and efficiently.

How to Maintain Scalability in Python Code?

Scalability is surely a thing to consider when starting with the development of a large application. But, there are several more important criteria should be kept in mind. These are productivity, ecosystem, support, and ease of use, etc.

Here are some tips to maintain scalability in Python.

Make intelligent use of “Collections”

Python supports an unmatched and powerful container for “Collections” such as tuple, list, array, and dict. They are even more valuable if we use it in an efficiently optimized way. It’s easy to spot the overused collection and that can impact the code scalability.

Using generators for scalability in python

The generator function permits you to create an iterator in a very easy way. Now imagine you are building software that takes the text analyzes it and performs some kind of grammar check. Python generators quickly create an iterator that helps in allocating memory for one sentence at a time It also becomes easy to modify the code without putting much effort.

Another benefit of the generator is that it provides encapsulation that provides a useful way to isolate the code dependencies.

Using python implementations

Python implementations like CPython or Jython, which compiles python code into C and Java bytecode can be used to boost the performance of programs. Jython is a well-known python which has been designed to achieve Java’s scalability. Huge thanks to this implementation that we can use Java libraries.

Horizontal and vertical scaling

We can solve this problem of scalability by horizontally and vertically scaling property at the hardware level.  Vertical scrolling is the process of adding more memory to the disk space, and CPU. However, Virtual scrolling has limitations in context with machine’s efficiency or the server’s efficiency. But there is a solution to that which is horizontal scaling through which one can add the entire machine or server. 

Building better architecture

We can build a better architecture which is easily adjustable in the future. A firm should design the architecture such that it has the flexibility to install new features in the future. Developers can do lot of early optimizations such as load balancers, auto-scaling, and proper horizontal and vertical scaling.

Conclusion

I support that python is not as scalable as C or Java. It has limitations but millions developer still uses and loves it by heart. Python is a part of some famous projects such as Pinterest, YouTube, Dropbox, and Quora, etc. Every programming language has some limitations or negative points such as slow development in the case of Java. That doesn’t mean they are not worthy. What language one should choose depends on the overall requirement of the projects. Also, thanks to the methods to overcome the python scalability.

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About the author

Christina Jones

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