Monolithic solutions have become almost archaic with the emergence of service-oriented architectures. Nowadays, developers can build more advanced, scalable, & easy-to-maintain architectures using microservices. Among the top three best languages for microservices lies Python. The other two are Java and JavaScript.
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This blog post explores whether it is viable to create Python microservices, the pros, and cons, and why language choice matters in microservices development.
Most developers side with Python to build microservices for a reason. Some cite its object-oriented programming feature and advanced approach to application prototyping, while others quote superior REST APIs management. Also, its broad range of features through which developers can isolate application processes and integrate distribution services seamlessly helps.
However, Python also brings with it a set of challenges for developers. Firstly, it is an interpreted language and may create issues for those unfamiliar. Moreover, Python takes longer to compile code and often struggles with the early identification of crashes or errors. That said, let’s explore why you should create microservices with Python.
Python’s object-oriented structure and ability to transform elements into objects for better efficiency often win over many developers as it helps them write code faster, integrate boilerplate functions better, and test programs before script conversion.
Furthermore, being a strongly typed language, Python enables developers to rock consistency and drop error counts by designating data types. Plus, you can reuse its code using built-in functions across several modules for more efficient development.
Many developers build microservices with Python for its cutting-edge scripting capabilities through which they automate configurations and due to its ability to replicate and implement an individual code change throughout the code base.
Other reasons why developing microservices with Python is a more logical option is backward compatibility with ASP.NET and PHP frameworks and extensive standard library. The library entails thousands of third-party entries for REST services development and helps developers find apt solutions for development issues.
In addition, Python’s built-in container support for data types like tuples, lists, and sets simplifies development further. Thanks to features like these, developers can run Python microservices in isolated spaces by packaging dependencies, leading to efficient testing.
Python indeed packs a punch, but before you rush to develop microservices with Python, it is best to know common limitations. The top two are slow execution speed and complex interpreter environment.
Remember, Python is an interpreted language and initially creates a non-machine code. Next, the code transforms into bytecode instructions via interpreters like Jython, CPython, PyCharm, and PyPy. The transformation takes place in the virtual environment within the CPU. Undoubtedly, the process is a necessity for Python as it works with an interpreted code, but not so for languages that process data in native code.
Further, Python is not as efficient as other options in the early identification of crashes or errors. Here being a dynamically typed language works against it as it lets some errors slip through in compilation. At such times, the errors reappear during runtime suddenly and cause an operational delay, or worst, a total failure.
However, there is a cure to this curse, like using type-safety mechanisms to limit errors. Still, it is a problem worth knowing if you plan to build Python microservices.
Microservices mainly function independently, yet they still need to communicate data using a mix of network protocols, messaging components, API calls, and event triggers, and for it, they need language integration.
Moreover, services don’t use a centralized messaging system and rely upon inter-function communication to perform tasks. Here’s where language comes in, as the microservice’s ability to communicate between themselves depends on the number of protocols and formats supported by the language.
Using Python to develop microservices is a viable alternative, as its pros outweigh the limitations, especially for those familiar with it. However, Golang or Java could prove a better option for those well-versed, as the former provides better concurrency, while Java offers high stability.
Lastly, you must pick a language that supports REST, and Python does tick the box. In addition, some developers also check for GraphQL, RPC, and gRPC compatibility, features offered by Python. No wonder Python is among the top choices for developers building microservices.
Also Read: Guide on How Xperti Helps You Find Python Jobs in the U.S.
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