The scalability of your application is what we call its ability to serve the increasing number of requests with no performance decrease. It is important when you expect your product to grow and you want to be prepared for:
- a bigger number of users
- a bigger amount of data to be processed (content-heavy applications)
- a bigger number of features (meaning the bigger number of requests for the app to handle).
Node.js and Python deal with scalability in two different ways: Node with its architecture, and Python with its tools.
In the traditional, synchronous, approach to input/output operations, the app starts the access and then waits for it to complete, blocking the progress of a program while the communication is in progress. The bigger the application gets, the more data it has to process, making the app slower and slower. And how does it work with Node.js?
There are three main reasons that stand behind Node’s scalability:
- It can be easily broken down into microservices.
- It has an event-based model.
- It has a non-blocking I/O.
In an event-based architecture with asynchronous I/O, operations are completed outside the thread and, therefore, they are not blocking it. Although the development of complex applications with a lot of concurrent processes may require some expertise, in general, Node.js is considered to be a good choice when scalability is your focus.
Even though the scalability is achieved in a different way than in Node.js, the solution is rather satisfactory. And by “rather satisfactory” I mean good enough for such big services as Youtube, Pinterest, Reddit, Dropbox, or Quora. Not so bad, right?
When we choose Python (Django), we rely on tools such as Memcached, which uses caches to avoid recomputing data or accessing a slow database, or NGINX. When equipped with such tools, Python applications are able to handle data migrations even with the growing amount of data. Also, it’s worth mentioning that even though Python does not support asynchronous programming by default, it does support coroutines which can suspend their execution before reaching return, and indirectly pass control to another coroutine for some time.
Assuring scalability of any application is quite a challenge and depends more on good practices implemented from the beginning of the development process than on the technology. In this category, there is no true winner – at least when we think about Node.js and Python.
Both Node.js and Python are said to deal well with catching errors that occur during code execution. From the developer’s perspective, it may be worth mentioning that Node.js prints top-bottom and Python prints bottom-top. It doesn’t really matter, however, in terms of business as it affects neither the speed of development nor the app’s quality.
Again, we’ve got a draw.