Is Python Effective for Microservices Architecture?
When it comes to choosing a language for Microservices, Python might see the perfect one. Let’s see if that’s true in this analysis of Python efficiency.
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Python is one of the options when choosing between programming languages suitable for microservices architecture. It has perks like an active community, better prototyping, and popularity among developers. It has some limitations, so other languages can be used to avoid them.
Quick Development Architectural Style Review and Statistics
Two main development architectural styles are monolithic architecture and microservices architecture. Monolithic has an all-in-one principle and functions as an integral structure, which works best for small development projects or start-ups. When a platform grows and a business needs complex applications, it'll be reasonable to split it into a microservices architecture.
Some are better for monolithic architecture. In fact, many businesses choose to build monolithic applications as the monolithic approach was more widespread until some decades ago. As we see, microservices in Python is not the first choice but is quite often picked by developers, so let's discuss it in detail.
Microservices in Python
A dynamic language without a static type system would be suited for microservices architecture. The combination of the programming language and this architecture provides an opportunity to create unique solutions and complex applications in software.
Technically, every language that can establish communication between microservices will work. Creating microservices in Python allows using certain features/libraries/tools that might not be available elsewhere. Moreover, in case you need to iterate fast and the team can do your best with Python, it will be the right choice. Seek functionality in Python as it's an object-oriented language, so it is beneficial to add specific functions.
Main Types of Microservices Architecture in Python
To understand the nature of microservices in Python, let's look at two different types of them: all-to-all service and event-driven type.
All-to-all service looks similar to the usual development process. The relationship between microservices in Python is partly vertical, so they constitute a chain and are activated one by one. The communication between microservices here is more strict. The all-to-all service is similar to a monolithic approach but divided into separate blocks.
Communication is faster and more complicated in the event-driven type of microservices architecture. The principle here is that the microservice gets triggered after certain conditions. In other words, when the user and application interact (a request), the software executes certain actions (events).
Communication Between Microservices
Microservices-based applications are not as straightforward as monolithic applications, so communication between them is more chaotic. Here the processes don't activate each other but rather react to events or requests.
Separate services need specific software called message brokers to create connections. They have different functionality and specifics. Amazon Web Services offer ready solutions, but development teams can choose message brokers themselves. The most popular are RabbitMQ, ZeroMQ, Apache ActiveMQ, Apache Kafka, IBM MQ, etc.
Advantages of Python in Microservices Architecture
With the right API formatting, prototyping is easier and quicker than in other languages. Prototyping serves good for correcting design problems and identifying improvements.
Python allows developers to automate systems provisioning and configurations for microservices. Individual code changes get replicated throughout the code base.
It's compatible with legacy languages like ASP and PHP that help create web service front-ends to host Microservices.
The active community of Python developers provides great support and resources when you look for certain info. It doesn't mean other languages don't have that, but Python is generally quite popular.
Disadvantages of Python in Microservices Architecture
Speed of execution might suffer. Python is an interpreted language, so it generates non-machine code at execution. After that, interpreters like CPython, Jython, PyPy, or PyCharm transform it at runtime into bytecode instructions executed in the CPU's virtual environment. Some languages compile into native code, which is faster to execute (C and C++).
Dynamic typing might lead to many errors and crashes. Some errors that were ignored during compilation could appear during runtime. That's risky and might lead to delays or crashes. Developers need to be attentive and test application processes continuously. It might seem obvious, but that adds extra work.
My Insights on Microservices in Python
We've had a fair share of Python-based apps on microservices architecture. The two most popular frameworks for projects were Flask and Django. The reasons we've worked with them were:
Django has various tools to be used in projects. It is faster too, but some choices are made for you, so if you don't agree or want unique application requirements, use other frameworks like Flask or Fast API.
Python is good for teams of any size and experience level. Why? Because the language is popular among developers, even though it scales well in expressiveness and freedom, it might be structured and controlled too.
If you have the app idea and want to develop using microservices architecture, investigate the most popular frameworks and languages and choose one depending on the functionality you need, the team's expertise, and the resources you have. Business logic and business capabilities are essential here, so think about microservices applications when you have complex systems, want continuous delivery, and need some functionality like asynchronous communication. In other cases, microservices implementation using Python might be pricey and not reasonable.
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