building microservices with python

Is it reliable to build microservices with Python?

You must have had this experience: several modules in the device start to do their own thing, data is passed around like a maze, and occasionally "lost contact". Want to add a new feature? Pulling the whole body is like changing the tires of a high-speed car. Components that are independent but must work together - such as precision servo motors - perform their own duties, but if the control system behind it is bloated and "big", it will be too laborious to adjust.

Doesn't this feel a bit like using a complex mechanical arm to pick up a small screw? It’s not that it can’t be done, it’s just that you can’t put in the effort, and it’s easy to go wrong. As a result, many people began to think about "microservices". But when it comes to using Python to do this, many people will ask: Python? It says it's very convenient, but the speed...is it feasible?

Dismantle the "big guy" and make each part come alive

Imagine you designed a clever mechanical device. The core controller, motion module, and sensor feedback are each packaged into small independent units with clear functions. They talk to each other through a clear and simple interface. This is what the microservices architecture wants to do: break a large and complex application into a series of small services that work together. Each service only focuses on doing one thing and can be independently developed, deployed, and even upgraded.

What role does Python play in this scenario? It's like a useful, all-purpose toolbox. You want to quickly design a service interface and define what it receives and outputs. The Python framework allows you to achieve it almost "blurting out". Its syntax is concise and reads like plain sentences, allowing developers to focus more on business instead of getting entangled in complex code structures. For project stages that require rapid iteration and verification of ideas, this advantage is real.

Q: It sounds beautiful, but what about the “pits” in reality? For example, will communication between services be very slow? Indeed, if not designed properly, frequent network calls between services can become a bottleneck. But this is not a Python problem, it is an architectural design problem. Reasonable service division, coupled with efficient communication protocols (such as gRPC, which can also be combined well with Python), can make data transmission brisk. The Python community has a large number of mature libraries to support these, which is like equipping each service unit with a high-speed, reliable dedicated data pipeline.

Q: What about performance? Python is not known for its speed after all. This is a good question. For extremely computationally intensive core logic (such as some real-time motion control), you can write the core module in C/C++ and then call and integrate it in Python. Python has become an excellent "glue" and "scheduler" here, responsible for overall command and handing over the most important special tasks to faster "experts" for execution. Many successful industrial applications do exactly this.

Make reliability the norm

In the field of machinery and automation, stability and reliability are above all else. A system that is hit or miss is unusable. How does microservice architecture, combined with Python, bring reliability?

Isolation. A problem with one service will not bring down the entire system like dominoes. It only affects the functional area for which it is responsible. You can quickly locate the "faulty unit" and repair or restart it while the rest continues to function as usual. This is like an automated production line. If a certain screw-driving servo motor module needs to be adjusted, you can pause it individually for maintenance without having to stop the entire production line.

Observability. There are powerful tools in the Python ecosystem that allow you to easily install a "dashboard" for each service—monitoring its health, performance indicators, and logging. You can clearly see where the data flow is slow and which service is under heavy pressure, so you can give early warning instead of waiting for problems to occur before putting out the fire.

kpowerI have deep feelings when assisting customers to implement this type of architecture. We have seen that after refactoring traditional monolithic control software into a Python microservice cluster, the mean time between failures of the system was significantly improved. Because updates have become smoother: you can upgrade the motion trajectory planning service today and upgrade the status monitoring service next week without interfering with each other. This flexibility is a huge boon for complex equipment that needs to endure and adapt to new needs.

From idea to reality: Here’s how to start

If you think this idea is worth a try, you might as well start with a specific, relatively independent function point. Don't think about rebuilding the entire system in one fell swoop. For example, first split the device status reporting and data collection functions into an independent microservice. Using Python's FastAPI or Flask framework, you may be able to build a robust service that provides a clear API with just a few hundred lines of code.

Then, let it run and talk to your main system via HTTP or message queue. Feel the convenience brought by this decoupling - now you can store or store this data service at will without touching the main code brick by brick.

In this process, you will slowly accumulate experience: How to design service boundaries reasonably? How to determine the most common data format? How to manage these growing numbers of services? These are sweet annoyances, questions that arise naturally as systems evolve. Python's rich ecosystem can provide you with feasible tool options for your questions at every stage.

In the final analysis, there is no absolute advantage or disadvantage in technology, only whether it is in sync with the other. Building microservices with Python is not a pursuit of ultimate performance, but a pragmatic choice for development efficiency, system maintainability and smooth team collaboration. It makes building complex control systems more modular, clearer, and more flexible. When every part can operate lightly and attentively, the power and reliability of the entire system will come naturally.

Established in 2005,kpowerhas been dedicated to a professional compact motion unit manufacturer, headquartered in Dongguan, Guangdong Province, China. Leveraging innovations in modular drive technology,kpowerintegrates high-performance motors, precision reducers, and multi-protocol control systems to provide efficient and customized smart drive system solutions. Kpower has delivered professional drive system solutions to over 500 enterprise clients globally with products covering various fields such as Smart Home Systems, Automatic Electronics, Robotics, Precision Agriculture, Drones, and Industrial Automation.