why we use kafka in microservices

Have you ever tried to control a large group of servo motors so that they synchronize like a symphony orchestra?

I once worked on an automated production line project, where dozens of robotic arms were running at the same time, and sensor data continued to pour in. At first, I used a regular message queue, but I found that when the amount of data in a certain link suddenly surged, the entire system began to "stuck", just like an intersection during rush hour, where all the cars were jammed, and the robotic arm behind could only wait.

At this time, you will wonder: Is there a way to make the data like the traffic flow on the highway, even if a certain section is congested, other lanes are still clear?

Later, I came into contact with the idea of ​​using Kafka to process microservices. Simply put, it's like a conveyor belt system that never gets clogged. Each of your microservices—such as a program responsible for parsing steering gear position data, or another service that records mechanical vibration frequencies—can independently take the data packets it needs from this conveyor belt and handle its own affairs without interfering with each other.

Give an example. Suppose you have three microservices: A receives sensor signals, B calculates motion trajectories, and C records running logs. In the traditional architecture, B waits for A to finish processing before it can work, and C has to wait for B. But after using Kafka, A puts the original data on the "conveyor belt", and B and C can access it at the same time and work independently. Even if C is temporarily slower, it will not slow down A and B.

Why is this particularly important for mechanical projects? Because mechanical systems are often real-time and highly concurrent. The servo motors of each joint of a six-axis robot feedback data at the millisecond level. If there is a delay in the data processing chain, the entire movement will lose coordination. Kafka's "persistent log" design ensures that even if a service is restarted, the data during the downtime will not be lost - just like a continuous recording of the running log of each device, which can be played back at any time.

Someone asked me: "It sounds good, but will it be complicated?" In fact, the key is not how profound the technology itself is, but whether it matches your scenario. If you only control two or three servos to make simple swings, it may not be useful; but if you are facing an entire workshop automation equipment group with hundreds of data sources, then this architecture can help you avoid the pain of working overtime late at night to troubleshoot problems.

When selecting a model, I usually look at three points: The first is whether the throughput can match your data peak - such as a storm scenario when all sensors report at the same time; the second is whether the delay is within the tolerance range of your mechanical system; the third is the operation and maintenance cost. Kafka has found a good balance point in these dimensions.

Of course, there is no silver bullet. It requires you to have a clear plan for the data flow, such as how to divide topics (Topic) and how to design partitions (Partition). It's like planning a conveyor belt route for a new factory: Which data should go in the fast lane and which can be consolidated for transportation, you need to think about it in advance.

Let’s talk about the actual implementation experience. During initial deployment, the team needs to adapt to this shift in "publish-subscribe" thinking and no longer insist on direct calls between services. But once it starts running, the system's flexibility is significantly enhanced. When a service is upgraded, other services will run as usual; when a new data analysis module is added, you only need to let it subscribe to the relevant data stream without changing the original link.

The digital world of mechanical systems is often invisible, but it determines whether the movements of physical equipment are precise and smooth. Choosing an appropriate data architecture is like equipping a precise mechanical mechanism with a matching nervous system - it may not be directly visible, but it makes the rotation of each gear more certain.

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.kpowerhas 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.