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Good coding practices for backend developers

Posted on November 3, 2022November 7, 2022 by harsha satyakam

Backend development is often not appreciated as much as the front end for apparent reasons. When the technology empowering those immersive UI/UX designs is hiding from the end-user, it is easy to lose sight of its importance. But the industry understands the complexity involved in writing backend code. There is a lot to handle, from APIs and server optimization to caching and cookies. Amidst all these coding responsibilities, it is easy for backend developers to commit mistakes.

This article lists some good coding practices that backend developers can use to write code efficiently without worrying about bugs or errors.

1. Validate inputs

Once your application is live, a lot will happen. Hackers may penetrate the system, or users can trigger an unintended use case. To avoid these issues, you must implement rules to validate inputs. This will ensure that only valid inputs are going to the downstream systems.

There are two best coding practices to validate the inputs. First, API gateways can be used for validation. The validation of inputs at the API gateway level is a more generic type that uses policies like schema and format to validate the information. Second, microservice level validation can be implemented to check the entries. There are many open-source libraries available that you can import into your project for microservice-level validations. Joi validator is one package you can use to ease input validation.

2. Handle errors carefully

No error must be left unattended as it further complicates debugging. After validating the inputs, the discovered errors must be appropriately handled. The microservices/mesh architecture errors must be handled correctly because many services are interconnected here. If one service crashed due to a mistake, it could send ripple effects across, affecting the whole system. It will be a whole lot of trouble to handle errors at a system level.

Many backend developers use a combination of methods to handle errors that can cause a system-level impact. For instance, Circuit Breaker is one way to prevent repeated invocation of services that are most likely to fail and cause a ripple effect. Here is an example of how you can implement Circuit Breaker in NodeJS and Spring Cloud. Another way is to use error codes to handle exceptions. This approach will ensure that the code returns a response code without crashing the service. Most enterprise-level services handle API errors with HTTP codes to facilitate troubleshooting.

3. Separation of concerns

Separation of concerns is one of the essential elements of software architecture. Enterprises use this approach to correctly separate the security concerns and improve the maintainability of the code. Backend developers can make their code modular with this approach. It is a coding practice that will make your code readable to other developers making it easier for other developers to collaborate with you, as they do not have to spend unnecessary time getting up to speed.

There are several ways to structure your code; for instance, the MVC framework is primarily implemented to separate the software’s model, view, and controller layers. The idea is to establish a standard practice for the team to follow to separate concerns. Here are some excellent references on the separation of concerns. You can choose the one that best fits your application.

Separation of concerns is a practice that significantly reduces barriers to entry when it comes to coding and makes it easier to read and learn from. This directly translates into the overall speed and efficiency of product development.

4. Write test cases and documentation

Backend development is complicated, and writing test cases before development helps visualize the end product. This is important because it minimizes subsequent changes that can prolong product development. When you write test cases, you are putting up barriers that detect any downstream impact of the changes you made to the code base before things spiral out of control.

Test-driven backend development is one of the best practices that advocates writing test cases before you start the development. It might take more time, but it’s a recommended approach for critical services because the bottom-top of software development is more efficient than the top-down approach.

Another best practice for backend developers is to document everything they write. When working on a product, it can be enticing to keep writing code for productivity and deploying the project faster without any documentation. However, as the product code grows, in the absence of documentation, developers will have to reach out to each other more frequently to clarify codes. This will eventually slow down the project development and hinder productivity. Backend developers are often tasked with the API documentation. Fortunately, you can use the Postman documentation tool to document your API development.

Conclusion

The best practices for coding that are listed here might not appear to be effective if you are working on the project single-handedly. However, as more developers join your team, you will understand the importance of these practices rather than implementing these approaches later on in the software development life-cycle.

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Essential backend developer skills to learn for 2023

Posted on October 31, 2022November 8, 2022 by harsha satyakam

Each development project has two components – frontend and backend. Mostly, end users interact with the front end, but backend infrastructure and components go unappreciated. However, in the software development domain, backend developers are generally better paid. It is because of the complex solutions they build. To be successful as a backend developer, you need to acquire the essential backend development skills that are more relevant today like native-cloud development, and low-code application architecture.

This article will list the essential backend developer skills that will be valuable in 2023.

Let’s get started.

1. Pick the correct backend programming language

The web development domain is highly dynamic, with new technology introduced frequently. But, instead of getting distracted by the noise, focus on the programming languages with a solid presence. Java, Python, and PHP are three programming languages preferred for backend development.

Java is most widely used for building robust systems. It is a strictly typed programming language that’s scalable and general-purpose. A backend developer must be skilled in Java to build a solid infrastructure. As for PHP, it is the most commonly used server-side scripting language. As an open-source language, it is one of the most cost-effective technologies that most companies want to use for their projects. Python is another essential programming language for backend developers. It is a premier programming language that is simple to learn but robust enough for any complex project.

2. Knowledge of databases

As proficient as you have to be with the programming languages for backend development, you also need strong database skills. A database is critical for storing data and content in any web application. Your ability to recover, arrange, alter and store information efficiently defines the performance of your application. Any backend developer should have extensive knowledge of various DBMS technologies.

In 2022, the two most commonly used databases are SQL and NoSQL. The SQL database is one in which all data is mapped on a table, and each entry in the table is linked to another entry through recursive methods. While SQL databases are suitable for storing queries and producing results, speed becomes a problem when creating highly scalable applications. The NoSQL database offers the solution as there is no need to structure the data beforehand. Such databases work on JSON (JavaScript Object Notation) and XML (Extensible Markup Language) and are extremely fast.

3. Knowledge of APIs

API stands for Application Programming Interface. It is a medium through which software can communicate and share data. In modern web applications, API plays a crucial role in driving mass adoption as the application can be used for multiple purposes.

Simply put, API is a set of rules and definitions that software, clients, and services follow to communicate with each other over the Internet. The server and client are the two receiving ends, while the API connects them both. For a backend developer, it is essential to understand the HTTP methods of API development. The most commonly used API commands are:

GET: to retrieve a resource
DELETE: to delete a resource
PUT: to replace an existing resource
POST: to create a new resource

Most employers expect backend developers to have extensive knowledge of APIs. To connect your backend with other services, you should know how to create an API in various programming languages such as Python, NodeJS, PHP, etc. JSON and XML are the two most recognized formats of API information generation.

4. Server infrastructure knowledge

Server infrastructure is changing day by day. Today, cloud-native servers are a standard, and businesses no longer want to store and operate their applications through a central location. Since backend developers are working behind the scenes, they must understand various web servers and their technologies. The most commonly used servers are Apache, Nginx, IIS servers, and Microsoft IIS.

Some of the essential backend skills expected from a developer include the ability to manage a hosting environment. As a backend developer, you must be able to scale the applications according to changing demands and configure servers to handle the increased traffic. Accessibility and security compliance related to servers is also essential information. You should know version control among other technologies, such as Git and GitHub.

Another essential skill here is to familiarize yourself with cloud server infrastructures such as AWS, Google Cloud Platform, and Heroku. These leaders in the cloud server market are often used to deploy container-based services such as IaaS (Infrastructure as a Service), PaaS, and SaaS (Software as a Service).

Conclusion

Backend developers are in hot demand. To land your dream job in 2022, you must master the above-listed backend skills. Apart from these, you should also have strong computer science skills, such as data structures, algorithms, and programming patterns.

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5 essential metrics to judge backend performance

Posted on September 28, 2022 by

Performance is a crucial aspect of any application. With billions of web applications on the Internet, the overall user experience, performance, and reliability make an application stand out. The web application you create must operate seamlessly to build trust among users. Backend developers are at the helm of building high-performance systems. But how do they determine that the backend infrastructure they are building will have high performance under peak conditions?

This is where performance metrics are used to understand the vital elements of a software system. Once an application is deployed in the production environment, it becomes critically important to measure its performance.

Here we explore the essential metrics associated with backend performance that must be optimized.

1. Latency

Latency is the time a data packet travels from one point on a network to another. It is an important performance metric that measures the responsiveness of a system. The ideal latency time for most web applications and websites must be less than 100ms. However, for large-scale multi-node software systems like maps and navigation services, it can be between 2 to 5 seconds.

Usually, performance testing is done to ensure that a system has the least latency possible, but optimizations can be done beforehand.

One of the best practices is reducing the number of server requests per page. Using cached files, optimizing HTTPS headers, and minimizing the use of JavaScript files can help. HTML5 WebSockets can be used to bypass HTTP calls. This will significantly reduce the latency due to too many HTTP requests.

2. Throughput

It measures the volume of traffic a web application can handle without breaking down. There is a direct correlation between latency and throughput. This is simply because when the throughput rises, the traffic increases, putting the server under high pressure, resulting in slower load times.

In performance testing, latency and throughput are concurrently measured to ensure that the system is optimized to perform without reaching the crisis point. The crisis point of a software system is determined by hardware configurations, network conditions, and software architecture.

By reducing the latency, the throughput is automatically optimized. Backend developers can optimize hardware configurations like RAM, Cache, and I/O per second to make the system faster.

3. CPU usage

It is a backend performance metric that is easiest to measure and optimize. CPU usage is simply the time service uses the CPU. Usually, it is calculated as a percentage and indicates the time service uses the CPU to complete its tasks.

There can be several reasons why a system has high CPU usage. If the backend developer uses too many dependencies in the code, several processes can run in the background even when not required. This hogs the CPU usage. Poorly coded functions can have high CPU requirements, such as erroneous for loops. Systems under malware attacks can witness high CPU usage as well.

Backend developers must adequately plan the background processes when designing a system. Giving priority to only critical processes running in the background saves CPU cycles. Installing malware protection is also essential. Restarting the server in emergencies like system failure can help kill most unnecessary procedures.

4. Server uptime

Server uptime is the duration of time the server is up and running, providing the desired service to the end-user. In performance testing, the server uptime is calculated as:

Server uptime = (Amount of time the server is running enabling service in real-time / The total amount of time uptime expected under ideal conditions) x 100

While primarily the server uptime depends on the hardware and the operating system, the software also contributes. While deploying a system, back-end engineers must ensure that the server has high-quality hardware that will not fail under high-pressure conditions. A poorly designed system architecture can have a devastating effect on server uptime. A well-known incident was when NASA was hacked due to system architecture issues, and the servers had to be shut down for 21 days to limit the risk and evaluate the extent of the attack.

Backend performance is not just about optimizing for speed but also ensuring security. Some steps back-end developers can take are regular software updates, building a robust architecture, and limiting the use of untested third-party open source libraries and plugins in the application.

5. Memory

Memory allocation is another essential metric that affects backend performance. Different languages allocate memory differently. Before you deploy a system in the production environment, you must evaluate its memory requirements.

Backend developers must know how the language used for system design allocates and cleans up memory. This is essential for designing systems to scale from a technical and a financial perspective. High memory servers are expensive, and it is necessary to plan memory allocation for a viable application.

For instance, Ruby hangs on to the allocated memory even when it does not require it at a given moment; this can be a significant issue for applications with high traffic. For such applications, Java or Python is a better-suited programming language.

Conclusion

The adage “what can be measured, can be improved” is essential in backend performance. We hope you will consider these five critical metrics when performance testing your application before deploying it in the production environment.

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4 effective tips and tricks to write better Python code

Posted on September 28, 2022 by

Python is one of the most discussed programming languages among tech professionals at present. Not only for software development but also for machine learning and data science, Python is the preferred choice. Due to its fairly easy learning curve and mature and supportive community, new learners can get started easily. Also, hundreds of open-source libraries and frameworks, and the versatility and efficiency of Python make it a valuable programming language to add to your skills.

This article shares some tips and tricks for beginners and experienced programmers to write better Python code. The tips will help write more readable and faster code quickly.

1. Start using list comprehension instead of for loops

For loops in Python code can cause unnecessary nesting that results in hard-to-read and maintain code. You can make your Python code much simpler with a list comprehension. It is a more straightforward and elegant way to create lists from existing lists. Using brackets, you can repeatedly execute each iteration with a single line of code. You can make your code time and space efficient by replacing loops and iterative statements with a list comprehension.

Here’s the usual syntax for list comprehension:

newList = [ expression(element) for element in oldList if condition ]

In practical applications, it is used like this:

# Loop iteration using list comprehension

List = [character for character in ‘HackerNoon’]

# Displaying list

print(List)

# Output

# [‘H’, ‘a’, ‘c’, ‘k’, ‘e’, ‘r’, ‘N’, ‘o’, ‘o’, ‘n’]”

2. Using NotImplementedError for offensive programming

Python comes with an in-built NotImplementedError exception, a valuable feature for offensive programming. In offensive programming, developers avoid defensive principles when dealing with software bugs.

One example is when we control the input to align it with the format accepted by our code. It is not easy to sanitize every input, and it can be an inconvenience for future development.

Have a look at the code below in which we define a registering decorator and some functions:

import math

REGISTRY = {}

def register(name):

def _decorator(fn):

REGISTRY[name] = fn

return fn

return _decorator

@register(“relu”)

def rectified(x):

return x if x > 0 else 0

@register(“sigmoid”)

def sigmoid(x):

return 1/(1 + math.exp(-x))

def activate(x, funcname):

if funcname not in REGISTRY:

raise NotImplementedError(f”Function {funcname} is not implemented”)

else:

func = REGISTRY[funcname]

return func(x)

print(activate(1.23, “relu”))

print(activate(1.23, “sigmoid”))

print(activate(1.23, “tanh”))

Here we used the NotImplementedError with a custom error message for the function activate(). The output of the code will be:

1.23

0.7738185742694538

Traceback (most recent call last):

File “/Users/MLM/offensive.py”, line 28, in <module>

print(activate(1.23, “tanh”))

File “/Users/MLM/offensive.py”, line 21, in activate

raise NotImplementedError(f”Function {funcname} is not implemented”)

NotImplementedError: Function tanh is not implemented

The result of the first two functions is printed, but the third function fails as we haven’t defined the tanh function yet.

This is an example of how you can use the NotImplementedError: in places in your code that still don’t meet the validity of the conditions but later on will have validation as the project proceeds.

3. Use generators to save memory

In Python, the basic function of a generator is to evaluate the elements of a code on demand. As the syntax for list comprehension, here you use parentheses instead of square brackets.

Here’s a code to generate the square of all even numbers in a list using generators:

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print(“The given list is:”, myList)

mygen = (element ** 2 for element in myList if element % 2 == 0)

print(“Elements obtained from the generator are:”)

for ele in mygen:

print(ele)

The output:

The given list is: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Elements obtained from the generator are:

100

Beginners might find it hard to decide how the list comprehension differs from generators, given that their syntax is almost identical.

Unlike list or set comprehension, generator comprehension does not initialize any objects. This is how using a generator comprehension lowers the memory requirement for a program.

4. Define default values in Dictionaries with .get() and .setdefault()

Beginners often struggle with providing a default value for the Dictionaries function in their Python code. You can forget about assignment default value with the use of .setdefault() and .get().

With the .setdefault() method, you can set dict[key]=default if there is no key for the dict function already. You set the .setdefault() like this:

dict.setdefault(key, default=None)

Example code:

a_dictionary = {“a”: 1, “b”: 2, “d”: 4}

a_dictionary.setdefault(“c”, 3)

print(a_dictionary)

The output of the above code would look like this:

{‘a’: 1, ‘b’: 2, ‘d’: 4, ‘c’: 3}

The same functionality can be achieved using the .get() method, but you pass a default value for the key here. Here’s how it works:

a_dictionary = {“a”: 1, “b”: 2, “d”: 4}

print(a_dictionary.get(“c”, 3))

print(a_dictionary)

The output of the above code:

{‘a’: 1, ‘b’: 2, ‘d’: 4}

Conclusion

Python has scope in multiple fields, and as you learn the scope of the language, you will know more tricks to optimize your code.

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What makes Python a poor choice for large-scale full-stack development?

Posted on September 19, 2022 by

Millions of developers prefer Python as a programming language; thus, it is one of the most loved open-source programming languages with a strong developer community. Still, some developers view Python as one of the anomalies of software development. They believe Python is strictly a “glue” scripting language more suitable for routine system automation or connecting two applications. That’s not true; Python as a programming language is powerful enough for industrial-strength software development.

When we look under the hood, Python has every quality that makes it suitable for several software development projects. It follows an object-oriented approach and is open-source. Also, hundreds of thousands of modules and libraries further extend the scope of what can be achieved with Python.

However, Python still fails to penetrate one computing segment, and that’s enterprise development. Python is still viewed as just a scripting language for enterprise-level software development.

This article assesses the shortcomings of Python that make it a poor choice for large-scale full-stack development.

1. Absence of GUI and teamwork tools

The primary reason Python may not be fit for full-stack development at an enterprise level is that it’s unnecessarily complex to create graphical user interfaces in Python. For GUI, Python is shipped with Tkinter, which is most often used by Python developers to create interfaces but is not capable enough to meet the requirements of large-scale projects fully. Modern software development is highly graphic, and lacking a proper GUI tool in Python puts it at a disadvantage.

Another reason why Python is not usually preferred for large-scale full-stack development is the lack of tools for teams to collaborate. In enterprise software development, this shortcoming is unacceptable and viewed as a near-fatal flaw. Programming languages like Java and C++ are preferred for enterprise-level full-stack development because they have dedicated tools to help teams collaborate while working on large-scale projects. It can be risky when working with multiple programmers and using a language with no such support tool. While Python addresses this issue using modules and namespace resolution to ensure no conflict within a multi-programmer environment, it is still not convincing enough for teams to include it in large-scale projects.

2. Database access layer limitations

Large-scale software development projects have substantial data requirements. Modern applications produce or consume data by terabytes, and such data management requires established technologies such as ODBC and JDBC. Unfortunately, Python’s database access layer is less robust than these technologies. It might not be understated to categorize Python’s DB layer as primitive and underdeveloped. This is again a significant flaw that makes Python unfit for any full-stack developer to use for enterprise-level applications.

Modern software development is highly agile, and organizations look for programming languages that can interact immediately and smoothly with complex legacy data or adopt robust and rapid SQL database access. Python doesn’t comply with these requirements, and as there are no words on it in the near term, it is not suitable for large-scale development stacks.

3. Python is slow at runtime

Another primary reason to avoid Python in your stack is slower runtime compared to other programming languages like PHP, JavaScript, C++, or Java. While it might not be a problem when you have a few hundred lines of code, it becomes a primary concern when a full-stack developer has to write giant programs with hundreds of thousands of lines of code.

Python is not very sensitive to hardware resources as a high-level programming language. Python code requires an interpreter instead of a compiler. As an interpreter compiles the code line by line, it is not difficult to assume how it can be a problem when you’re working with a giant code base. Another reason why Python is slow at runtime is that it’s a dynamically typed language. In dynamically typed languages, variable data types are not specified when we use any variable. The data type is assigned at the runtime. As a result, the interpreter checks the data type every time a variable is read, written, or referenced to allocate memory accordingly. It slows down Python code at runtime.

4. Inadequate documentation

Suppose you have any experience working on large-scale projects. In that case, you understand how important it is to have comprehensive documentation available on technology before you decide to include it in your development stack. As compared to its rival programming languages, such as Java, Perl, and PHP, Python severely suffers from the lack of extensive documentation. Furthermore, there are not as many books available on Python. For comparison, PHP has twice as many titles as compared to Python on the market. While the online Python documentation is largely well organized and a good starting point, it only acts as sparse reference material. If your team does not have experienced coders, you can be stuck for a long time without enough learning resources for Python developers to help you fix issues.

Conclusion

While a powerful high-level programming language, Python has its limitations with large-scale full-stack development. Although the highly active Python community and dedicated development team across the globe are working on rectifying the limitations of Python to make it suitable for enterprise-level development, at present, it is better suited for small-scale software development projects.

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4 reasons why Python’s popularity is still soaring

Posted on September 19, 2022September 19, 2022 by

Python is one of the fastest-growing programming languages that continue to soar in popularity. According to Tiobe, a software quality assurance company that indexes the popularity of programming languages every month, the popularity of Python is at an all-time high.

The programming language yet again topped in its August 2022 rankings. When so many new technologies are emerging every day in the software development industry, one might wonder why the popularity of Python is still soaring.

According to the Tiobe index report, Python seems unstoppable because it’s hard to find a field of programming in which the programming language is not used extensively nowadays. From web development to data science, it has applications in every domain.

First developed by Guido van Rossum in the 1980s, Python started seeing a significant uptick in its adoption when Python 2.0 was released in 2000. It had many important new features such as cycle-detecting garbage collector, memory management, and support for Unicode. The most recent Python 3.0 was released as a backward-incompatible update in 2008. The release corrected some fundamental design flaws in the language and made it much more robust and fit for modern software development.

In this post, we look closely at why Python continues to grow in popularity.

1. Simplicity

One distinctive feature of Python from other programming languages is its simplicity. The ease of learning, understanding, and use make it easy to adopt. People with little or no software development experience can use Python to build applications. It is one of the most studied programming languages in the world, as we discovered by JetBrains’ State of Developer Ecosystem research. The simplicity of adoption is a major driving force behind the increasing popularity of Python. Universities, online schools, and companies are using Python as the first programming language to teach the fundamental coding principles to students and employees. Anything can be achieved using Python, whether it’s web development, writing scripts to automate tasks, or analyzing vast amounts of data. Given its scope of application, Python keeps attracting newcomers.

Here are some quality Python learning resources.

2. Versatility

If you are an experienced programmer, you will appreciate not only Python’s fully featured object-oriented approach but also its versatility and cross-platform compatibility. Not many languages can combine OOPS and cross-platform compatibility like Python. Most experienced programmers use Python to write admin scripts or “duct tape” code that can bind applications together in a toolchain. This feature is important as it allows freedom to test code without building an environment from scratch to run the code. Unsurprisingly, Python has many applications in DevOps and testing automation.

Not only on-premise environments but cloud platforms also support Python, given its incredible applications. You can deploy Python on any operating system, including Windows, Linux, Mac, or any cloud platform. Even some of the popular cloud platforms offer Python as a cloud-based “function as a service,” for instance, AWS Lambda, Azure Functions, and Google Cloud Functions.

3. Open-source and extensible

Python is an open-source programming language. It implies it has zero start-up costs and no barriers to entry. Anyone can download the interpreter and development tools to start building Python applications. It is another reason why Python continues to grow in popularity even when premium alternatives are available. Another benefit that works in its favor is how extensible Python is. Hundreds of thousands of freely available open-source packages further extend Python’s core functionality for application development. With a simple “pip install,” you can import and use any Python library or package. Due to its vast community support and growing popularity, Python has dethroned JavaScript as the most discussed programming language on the popular developer community platform, Stack Overflow.

4. Data Science applications

Modern websites and applications produce a humongous amount of data. This is why data has become the single most valuable commodity today. But raw data is not helpful, and Python offers a wide array of options to collect, analyze, and represent data in meaningful information.

There are several Python libraries dedicated to data science and numeric computation. NumPy, TensorFlow, Keras, PyTorch, Pandas, Scikit-learn, and PySpark are the Python libraries used for data analysis, machine learning, significant data transformations, and neural networks. The JetBrains survey we mentioned above found that 49% of the respondents use Python for data analytics and 42% for machine learning applications. Due to its scope of use in modern AI and machine learning applications, Python developers are in high demand and will continue to be so for the foreseeable future.

Conclusion

These are some of the significant reasons why Python is growing at such a fast pace. Its incredible applications in modern software development and AI and machine learning further boost its popularity among beginners and experienced developers. As a Python developer, you can explore almost any domain of software development.

Talent500 is where Python developers can find truly challenging and career-redefining opportunities with fast-growing start-ups and Fortune 500 companies. To know more sign up here.

5 Reasons why PHP is still so important for web development

Posted on September 19, 2022September 19, 2022 by

For over 25 years, PHP has been one of the most popular server-side languages predominantly used for web development. Interestingly, whenever a new development language in the web domain emerges, chatter starts about whether PHP is relevant or not anymore.

While it is true that PHP has seen a drop in popularity over the recent years (dropping from the 5th position in 2017 to 8th in 2020 in Stack Overflow annual developer survey), it’s far from ‘dead.’ Even today, nearly 80% of all websites are created using PHP in its vanilla form or through frameworks like Laravel, CodeIgniter, and WordPress.

There are many reasons to use PHP for web development, which we will explore in this article.

1. PHP is free and open-source

PHP makes web development extremely economical as a server-side language because it’s an open-source language available for free. As a web developer, you must provide clients with as many options as possible on the economic front. If you are proficient in PHP, you can offer your services for a website, e-commerce store, or any web application development at a lower cost. As an open-source language that has been around for a quarter of a century, PHP has a vast community that ensures that timely updates are made to the programming language such that it is in sync with the latest requirements of web development. The availability of a highly active community of PHP developers ensures that you always have assistance and help available whenever you are stuck with the project.

2. Compatible with all OSes

PHP also enjoys compatibility with all the available operating systems and web development technologies. You can use PHP on Windows, Linux, UNIX, or Mac. While modern web development technologies like Swift for Mac or .Net for Windows have advantages, they are not as robust as PHP. Compatibility with all operating systems implies you can deploy your PHP application or website on any web server. Furthermore, the programming language integrates perfectly with MySQL and Apache servers. As modern web development is about cross-platform compatibility, PHP is an ideal language. If you are building an application that will run on multiple platforms, choose PHP, and you don’t have to worry about what operating system is running on the server anymore.

3. Dynamic, flexible, and secure

Modern web development projects must be secured in the wake of more sophisticated cyber-attacks. One advantage of using PHP for your web applications is its fool-proof encryption. Its encryption capability and scalability make PHP an extraordinarily dynamic and flexible web development technology for any project. PHP scripts can auto-load web apps and websites without any manual intervention. Using PHP, you can script your environment to install security updates automatically. Due to its open-source nature, several encryption libraries are available, like PrivateBin, php-encryption, and Libsodium, which you can directly import your project.

4. Robust PHP frameworks

Modern websites and applications are hardly built from scratch. The availability of frameworks accelerates the development process. PHP has one of the largest collections of frameworks used for web development. That is why almost 80% of websites are built using PHP. You can choose any of the available PHP frameworks to create and launch web apps quickly and easily. These modern PHP frameworks support the MVC (Model View Controller) architecture, which expedites coding and enables developers to create efficient and top-notch solutions. Laravel, CodeIgniter, Cake PHP, Symfony, Zend Framework, and Yii are all PHP frameworks.

To give you an idea of the popularity of these frameworks, some of the most popular websites in the world use PHP frameworks. This includes Facebook, WordPress, Tumblr, Wikipedia, Slack, and Canva.

A notable mention is Canva, an Australian graphic design platform introduced in 2013. Today, it is one of the most popular online platforms for graphics design. It chose PHP as its server-side language, which indicates that even modern enterprise-level websites use PHP.

5. Database flexibility

Web applications today consume and produce data in terabytes; that’s a lot. To empower such websites and applications, you need scalable databases that support modern technological infrastructure. MySQL, MongoDB, PostgreSQL, Big Data, etc., are some of the most popular current databases, and PHP is compatible with every single one. It is a highly flexible language in database connectivity, allowing you to use any database in your web application. Another advantage of this flexibility is that you can migrate your web application to another database without worrying about compatibility issues.

Conclusion

PHP is still one of the leading programming languages for web development. It offers the most cost-friendly, flexible, secure, and robust development options, whether it’s a website, e-commerce store, or any other web application. There are many reasons to use this feature-rich programming language, but we hope the above reasons will encourage you to learn PHP.

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