Blog Synopsis: Python has long been plagued by misconceptions, earning the title of the most misunderstood programming language. Persistent myths, such as its purported slowness and limited scalability, have deterred businesses from harnessing the full potential of this programming powerhouse. However, the time has come to dispel these misconceptions. In this blog, we will debunk the prevailing myths surrounding Python for enterprise development, paving the way for a more informed and confident adoption of Python in the corporate realm.
Table of Contents - Common Misconceptions About Python
#2. Python Doesn't Have Concurrency Supports
#4: Python is not for big projects
#5. Python Is Not Suitable For DevOps And Infrastructure
#6. Python Is Not Secure Enough For Enterprise Applications
Regarding enterprise applications, Java and .NET have been industry favorites. Though some of the most popular and widely used websites, such as Google, YouTube, Dropbox, Reddit, Pinterest, Instagram, Quora, Spotify, etc. are been built using Python, the use of this language for enterprise app development is only just starting to accelerate. The reason behind the relatively slower adoption of Python in the enterprise has been ‘reliability’.
Enterprise software needs the flexibility to integrate with legacy systems and authentication systems, customizations to fit the operating models, and numerous stakeholders contribute to the development process. Thus, the development process of enterprise software becomes a lot more complicated than regular software development and hence demand ‘"enterprise grade" programming languages. While in its initial days Python did not meet the infrastructure demands of this enterprise grade scripting language, since the 2000’s, the Python community has worked relentlessly to gore it's open source libraries for web development and data analysis to be enterprise ready. Despite the large scale use of Python, here are 6 myths still making the rounds that impede Enterprise Python adoption:
#1. Python Is Too Slow
Being slow is one of the most widespread myths about Python. And the myth that has stopped many businesses from using it. But let us explain why it's just a myth.
Firstly, Python is a programming language. Programming languages don’t have speed. It’s the runtime that changes the speed of execution. Now, even Python has several runtimes, and the type of runtime you choose will change the speed.
- CPython is one of the most common runtimes used today.
- Jython is used for JVM, aka the machines written with Java
- Microsoft uses Iron Python for the Common Language Runtime, aka. NET.
- PyPy is also the latest addition to Python runtime. It comes with advanced features like garbage collection, JIT Compilation, etc.
Secondly, the perception of Python being slow comes from its interpreted nature. Although Python can’t perform like C++, this does not mean it's slow everywhere. Context is essential, as Python speed will differ due to the type of software used.
Instead of focusing on speed, the focus should be on developers' productivity, clean code, etc. No matter how fast the runtime may be, factors like the application’s architecture network speed will affect the app speed.
However, one of the best things about Python is that it was built for developers. It's easy to use and allows faster code development than JAVA or C++. What may take four weeks with Java will take two or fewer with Python.
Companies like YouTube, Reddit, and PayPal have Python at their core, which should speak about its speed, considering the massive traffic they receive daily.
#2. Python Doesn't Have Concurrency Supports
Firstly, what is concurrency? Concurrency means running two or more tasks at the same time. Concurrency support is essential for enterprise applications running hundreds of tasks at a time.
The rumor that Python does not support concurrency started with its Global Interpreter Lock (GIL) function. GIL can limit the execution of multiple threads in a single process. However, this doesn't mean it doesn't support concurrency.
Some ways to achieve concurrency in Python are through generators. Python even has good frameworks for concurrency, including Eventlet, event, and Twisted.
Now that we have broken the 2nd myth, we hope this encourages you to consider Python for enterprise development. Let us shatter some more myths.
#3. Python Is Not Scalable
Python forms the base of data science models, which process millions of data per second. So, the scale has its definition. YouTube, which receives over a billion monthly visitors, has Python at its core; it's a definition of web apps at scale.
Python has many features that support scalability, which is needed for enterprise apps. Let’s find out a few of them here and debunk the myth.
- It Supports Horizontal and Vertical Scaling
Image explaining horizontal and vertical scaling in development
Horizontal scaling involves adding more machines to a network to distribute the load, while vertical scaling involves increasing the resources of a single machine. Python applications can be designed to scale horizontally by deploying them across multiple servers or containers.
- Web Frameworks
Python has robust web frameworks like Django and Flask that are used to build scalable web applications. These frameworks provide features such as request handling, session management, and caching, contributing to the scalability of web applications.
- Distributed Computing:
Python supports distributed computing through libraries and frameworks like PySpark, Dask, and Celery. These tools enable the distribution of workloads across multiple nodes or machines, enhancing the scalability of data processing and computational tasks.
#4: Python is Not for Big Projects
We discussed Python scalability at the previous point.
But is Python suitable for large projects?
Yes. Python is excellent for large-scale enterprise applications.
A prime example is Instagram and Dropbox, whose entire core was written in Python. However, one of the most essential benefits of Python is that people are hungry. With a small team, you can scale your Python apps to a billion users.
That’s what happened with Dropbox. It has only 70 engineers to develop and maintain the app.
Another example of Python development at scale is Bank of America, which has 5000 developers. The key thing to understand here is that scalability is not limited to programming languages. After one point, all the programming languages will follow the general scaling advice.
It's not the programming language that makes the project scale; the developers, tooling, strong conventions, and code reviews make it big.
#5. Python Is Not Suitable for DevOps and Infrastructure
With DevOps's growing popularity, enterprises prefer languages that support DevOps. And we are confident that no one will add Python to this list. Here’s where everyone is going wrong.
Let’s not forget Python’s inherent capacity lies in automation. No other programming languages can automate things like Python does. And guess what’s the foundation of DevOps? Its automation. So, Python is a perfectly suitable language for DevOps infrastructure. Here’s how Python finds its application in enterprise DevOps.
- Automation Tools: Python is the best language for automation tasks in DevOps toolkits. All the DevOps tools, like Ansible, which is used for configuration management and automation, leverage Python for creating modules and extensions.
- Scripting Capabilities: Python is also a scripting language, which makes it suitable for writing automation scripts for DevOps tasks like provisioning, deployment, and system maintenance. Its readability and ease of use contribute to quickly developing scripts that can automate repetitive tasks in the infrastructure.
- Infrastructure as Code (IaC): Python is also used in writing Infrastructure as Code (IaC) scripts. It allows DevOps teams to define and manage architecture using code. Terraform, a popular IAC platform, uses Python bindings to create and manage infrastructure resources.
- Cloud Orchestration: Python is widely used for cloud orchestration tasks. Libraries like Boto3 provide Python bindings for Amazon Web Services (AWS), enabling developers to interact with AWS services programmatically. Similar libraries exist for other cloud providers, making Python a versatile choice for multi-cloud or hybrid cloud environments.
- Monitoring and Logging: Python is employed to develop scripts and tools for monitoring and logging in DevOps environments. Integrating with monitoring solutions, creating custom dashboards, and parsing log files are everyday tasks where Python excels.
#6. Python Is Not Secure Enough For Enterprise Applications
Last but not least. No language is secure enough.
This is because the security of an application is not solely determined by the programming language used; it depends on how the application is designed, coded, and maintained.
So, if you write non-secure code in Java, it won't make the software magically secure.
Python provides the tools and features necessary to build secure applications like any other programming language. Here’s how Python handles security in Python enterprise development.
- Dependency Scanning: Many times, third-party plugins or integration introduced security vulnerabilities. Fortunately, Python provides tools for dependency scanning, allowing developers to identify and address vulnerabilities in the libraries used by their applications.
- Security Features: Python incorporates security features and mechanisms to help developers build secure applications. For example, Python has standard libraries and modules for cryptographic operations, secure socket communication, and access controls.
- Authentication and Authorization: Python enterprise applications can leverage libraries and frameworks that implement secure authentication and authorization mechanisms. This helps increase the security of apps.
Python enterprise applications are secured when developers follow the best security practices and leverage the language's built-in security features. The key to high security lies in using language capacity along with development practices.
Conclusion,
Python enterprise applications have been here for a long time without many of us even noticing. Many popular apps like YouTube, Dropbox, and Instagram started with Python.
So, don’t let these common myths keep you from using Python for enterprise applications. We hope this blog breaks the myths related to Python enterprise development and pushes you forward to start a new enterprise project. Our team at Clarion Technologies is always here to help you with enterprise Python development.
Good Python programmers are relatively less than PHP or maybe Java. While working on your enterprise application with Python, please make sure that you hire experienced programmers who have good knowledge of the language and an in-depth understanding of the basics of programming.
In every myth surrounding Python, there lies hidden an implied strength. We hope this blog has helped dispel these prevalent Python myths and provided a new perspective on the language.
