How viable is it to create microservices in Python?
Python offers microservices developers access to advanced scripting, embedded testing and plugin tooling. But is it a good fit for you? And what are the alternatives if it isn't?
Developers have discovered plenty of reasons to create microservices in Python, from its foundation in object-oriented programming, to its ability to handle REST APIs, to its out-of-the-box support for application prototyping. In particular, its proponents praise Python's array of built-in features that help isolate key application processes and integrate dynamic collections of distributed services.
As is the case with any programming language, however, Python also introduces its share of challenges to navigate. For some -- particularly those not well-versed in interpreted languages or have pressing needs for quick compile times -- Python might not be the ideal language for their microservices development efforts.
Let's look at the reasons why developers might want to create microservices in Python, examine the standout features that streamline application build processes, and point out the potential hurdles that developers may encounter. We'll also assess some of the contending languages that offer a slightly different approach to distributed service development projects.
Why language choice matters in microservices development
While designed to operate as independently as possible, microservices must still communicate and share data using a variety of messaging components, network protocols, event triggers, API calls and other integration techniques. Separation of responsibilities between services, including the logic that drives those operations, is a fundamental part of fostering independence within microservices architectures.
Rather than channel through a centralized messaging system, services must communicate between themselves to perform discrete tasks and to scale as needed. This means that the language developers choose for their microservices project should ideally support a number of important communication formats and protocols.
Any language used to create microservices should support REST, which primarily relies on HTTP requests that post, read and delete data. However, protocols such as Remote Procedure Call (RPC), gRPC and GraphQL are also considered by some to be essential for microservices communication.
Developers creating microservices will also rely on containers to spin up one-off, reproducible application environments and enforce bounded contexts between services. As such, the language in use should also provide strong support for container creation, deployment and orchestration.
Advantages of Python for microservices development
Python is an object-oriented language that lets developers treat elements as objects and organize them into reusable entities within a clear modular structure. This allows them to quickly write application code, plug in boilerplate functions and test the programs before converting them to script. Python is also a strongly typed language, meaning it ensures uniform consistency and minimizes errors by enforcing data types. Developers can also reuse the same code within a single application or across several modules, using built-in functions.
Python's advanced scripting capabilities also allow developers to automate systems provisioning and configurations for microservices. Individual code changes are replicated throughout the code base. Developers can build and customize web front ends through server-side scripting, enhanced by Python's backward compatibility with legacy languages such as PHP and the ASP.NET framework. Python's standard library is augmented by thousands of third-party libraries for writing REST services, and there's plenty of support available from the Python community of users.
Finally, Python provides strong support for containers. This support includes built-in container data types such as lists, tuples and sets and those available through the standard library. Developers can package dependencies and run the microservices in isolated environments for testing with these Python features.
Of course, there are a few things to consider before rushing to develop microservices with Python. For instance, speed of execution and the nature of the interpreter environment are often cited as the biggest potential drawbacks when using Python to create microservices.
For one, Python is an interpreted language, which means it generates non-machine code at execution. Then, an interpreter such as CPython, Jython, PyPy or PyCharm transforms it at runtime into bytecode instructions executed in the CPU's virtual environment. This may lead to notably slower execution times than found in Rust and other languages that compile directly into native code.
Python may also prove challenging when it comes to errors and crashes. Because Python is dynamically typed, it's possible that errors ignored during the compilation stage appear suddenly during runtime, either causing applications to delay operations or, at worst, experience total failures.
This is a clear problem for microservices, given that microservice instances may be deployed, retired or changed in a dynamic manner. While there are certain type-safety mechanisms that developers can rely on, it will still require that developers pay careful attention to variable assignments and application testing processes if they choose to create microservices in Python.
Python isn't the only choice when it comes to microservices development, and there are a few options to consider if it doesn't seem like the right fit. Other general-purpose languages, including Go, Java and the .NET languages, offer tangible benefits when building microservices.
Go, also referred to as Golang, doesn't boast a catalog of prebuilt functions to rival the extensive one found in Python, but it does offer developers a safe and straightforward way to manage complex webs of service instances and distributed collections of functional libraries. Go is a statically typed and compiled language, boasting strong memory safety and garbage collection as some of its highlight features. Go also uses lightweight threads called goroutines to promote concurrency -- a big advantage when it comes to microservices.
Java offers stability and consistency for distributed applications. In tandem with the Java framework Spring Boot, developers can use the auto-configuration tool to quickly create REST APIs and link applications. Java developers also have the resources of the Spring Cloud framework for integration and batch and stream processing.
.NET languages such as C#, F# and Visual Basic are designed to produce software within Microsoft's proprietary development language framework. Early versions of the .NET Core framework focused on microservice development and expanding API access. Developers can include dependencies and run multiple instances of .NET Core using the platform's modular architecture. .NET is particularly useful for microservices, as it offers connectors for pre-established external applications along with APIs that facilitate cross-component communications.