Building High-Performance Web Applications on Python

When one thinks of programming languages, Python will always be at the forefront. It is a language that has taken the programming sphere by storm, mainly due to its ease of use, flexibility, and overall versatility.

Using Python, competent programmers are able to create a myriad of various programs and applications which are capable of solving a range of problems both big and small, all while handling large amounts of traffic and data.

The aim of this article is to consider the key technologies where Python can be introduced and explore the best practices when undertaking projects using Python. Six topics and techniques will be considered, during which the questions of ‘what’ the given technique implies and ‘how’/‘when’ the given technique should be implemented will be answered.

Framework

Python frameworks are vital for the proper functioning of a web application. Which framework to use should be one of the first decisions for any programmer. There are several popular frameworks, each with its upsides and downsides, but the most popular and widely used are: Django, aiohttp, Flask, and FastAPI.

One of the most important aspects of the choice of a framework doesn’t lie with the technicalities of the frameworks themselves but in the experience of the programmer. One should choose a language with which they are comfortable and have experience. However, certain frameworks are more suited for different tasks.

Django is considered to be a ‘full-featured’ framework. As such, this framework is best used for day-to-day development tasks; however, when compared to other easier-to-learn frameworks such as ‘Flask’, Django can be seen as more complex and challenging.

Although a programmer's experience using a given framework is vital, the choice of a network should principally boil down to the requirements of your project and its complexity, as certain networks are best suited for specific tasks.

Database Management System

A database management system (DBMS) can be described as a computerized data-keeping system. As most web applications are in need of some form of data storage, a DBMS is vital for reliably securing and storing a user's data and allowing for the smooth functioning of a given application.

The choice of a DBMS should coincide with the expected data-size input and overall complexity of the data. The use of a DBMS insufficient in storing large amounts of data and user metrics may result in a system failure.

Even though the reliability and scalability of a DBMS is usually considered the most crucial element, one should also consider the community of users behind it. Choosing a system that has a rich community of active users and contributors will allow for the easy solution of problems as users can work together and discuss possible fixes.

Caching

In short, caching involves the storage of data in a ‘cache’, which is itself a short-term storage component, so that data can be accessed quickly. Its use is vital for the smooth running of programs as the number of database queries and the overall response time of a program can be greatly reduced.

Memcached and Redis are two of many caching systems which are supported by Python; although, the list of such systems is rather large. As is the same for the choice of a DBMS and a Framework, caching systems should be chosen in relation to a project's needs.

However, another factor in a choice of caching system should also come into play; Transparency. Caching should be transparent and done in a way that management of the cache and adjustments to it can be completed with ease.

Asynchronous I/O

Asynchronous I/O, in short, is a technique that allows for the simultaneous execution of multiple I/O operations without blocking the main thread of execution. For I/O-heavy tasks such as network communication and database access, this can greatly enhance both performance and the expansion of web applications.

One example of the various number of I/O libraries supported by Python is Trio. A library that is widely used by programmers both for its powerful performance and great flexibility when the integration of asynchronous I/O into Python-based applications is concerned.

Understanding and implementing asynchronous I/O can be challenging as one must structure code so that tasks can be performed in unison. Ultimately, this may require rewriting code or using different design patterns/libraries.

Despite the various advantages of using asynchronous I/O, leading to enhanced application performance, its disadvantages must also be considered. It is complex in nature and needs cautious resource management.

Load Balancing

Load balancing is the pinnacle in the functioning of web applications. Without it, servers can become overloaded and user requests may be handled haphazardly. Its importance becomes even greater for applications that often experience elevated levels of traffic.

Python is unique in the fact that it supports a number of load-balancing solutions. On its roster are such well-known solutions as NGINX and HAProxy and also a growing number of cloud-based solutions, for example, Amazon Web Services (AWS).

Servers and traffic levels should be monitored frequently, thus, it is important to find a solution that allows for this without difficulty or any hiccups. Doing so will result in a user-friendly application that runs smoothly and efficiently.

Containerization

Containerization can greatly simplify the deployment of web applications so that they function swiftly on all types of OS and environments. It involves the combination of an application with the required files and libraries needed to run on a user's environment. For applications used on multiple OS, containerization is a must.

Thankfully, Python supports a range of such solutions, most notably, Docker and Kubernetes. Applications must be structured in a way that allows for the quick packaging of data into containers, otherwise, systems may become overloaded.

Furthermore, it is not uncommon for issues and problems to arise in containerization solutions, thus, regular monitoring must be upheld and solutions found quickly.

Conclusion

Python is an incredibly powerful language capable of creating a plethora of complex web applications; however, it requires meticulous attention to detail, thus careful planning using the correct solutions is needed for a successful and scalable application. If a programmer takes into account the needs of their program and carefully chooses the correct solutions in all six of the aforementioned categories, they will be able to create a web application that successfully handles large amounts of data and is enjoyable and easy to operate for the end user.