kafka communication between microservices

That night, the assembly line alarm sounded again.

You stand in the workshop, listening to the uncoordinated clicking sounds of the robotic arm. The data of several servo units are like kites with broken strings - the instructions have been sent, but a certain joint is half a beat slow. The temperature is not abnormal and the load is normal, but the entire movement trajectory is slightly deviated, resulting in a batch of parts having to be reworked.

After checking again and again, I found that the problem was not in the motor itself, but in the communication between several control modules - the data was lost for a few milliseconds, and the actions could not be connected.

Control deviations like this caused by "unreliable communication between microservices" are actually quite common in mechanical and automation systems. Especially when multiple servo units, sensors, and PLCs need to work together, if the data flow is delayed, lost, or out of sequence, the entire system may experience visible or invisible jitter.

At this time, you may need a more robust communication architecture to send real-time data stably one after another like parts on a conveyor belt, so that no one is "left behind" halfway.

And Kafka can help.

It is not magic, just a design idea: let data be collected, distributed and retained in a continuous flow, and each microservice can access it on demand without blocking each other. You can think of it as a high-speed and orderly conveyor belt - all servo motor position data, torque feedback, and temperature information are put into "data packages" one by one. Any module that needs this data can take the package it needs from the conveyor belt at any time without affecting the reading of other modules.

In this way, even if a service is temporarily busy or restarted, the data flow will not be interrupted, it will just wait quietly on the conveyor belt until it is picked up.

What are the benefits? It's obvious.

For example, you can expand the system more flexibly - add a monitoring unit without rewiring or interrupting existing communications, just let it start reading information from the same data stream. Or when a certain control module needs historical data, it can directly trace back the previous data packets without having to stop the entire system.

Of course, implementation requires some design considerations. For example, you need to plan the division of data topics - should each motor be a separate topic, or should it be divided by data type? The data retention policy must be set according to the real-time requirements of the actual scenario. There is also the configuration of consumer groups to ensure that key control services can get the latest data first, while analysis services can slowly digest existing data.

This sounds a bit abstract, but if you put it into a real scenario, it will be easier to understand.

Suppose you have an assembly line, and six servo axes need to work together to complete a precise movement. If traditional point-to-point communication is used, once the network jitters between the main control PLC and a certain driver, the entire action chain may be out of balance. If Kafka is used as the data center, and each axis publishes its own status data in real time, the main control PLC only needs to subscribe to these data streams to calculate the next instruction in real time. Even if the data of one axis is a few milliseconds late, the other axes will not be stuck - they will continue to fine-tune according to the latest existing data, and then quickly integrate it when the lagging data arrives, and the coherence of the overall action will be maintained.

Some people also ask, will this increase the complexity of the system?

Yes, you need to spend more time designing the data flow and deployment architecture at the beginning. But in the long run, it changes the reliability of communication from "relying on the transient state of the network" to "relying on persistent data flow", which in turn reduces the risk of sporadic failures. It's like you use a conveyor belt instead of workers carrying parts hand-to-hand - the track needs to be laid initially, but once it's running, it's not easy to interrupt production because someone's hand slips.

How to start using it?

There is no standard answer, but you can usually pilot it from a subsystem. For example, first let the temperature monitoring and servo status report go through the Kafka stream, and keep the core control instructions still going through the original real-time channel. After you become familiar with the characteristics of data flows and monitoring tools, you can gradually migrate more data modules with moderate real-time requirements. The key is to reserve enough logs and monitoring so that you can clearly see whether there are "blocks" or "packet leaks" in the data flow.

Speaking of this, you may think - this sounds more like IT architecture work, what does it have to do with mechanical control?

In fact, the relationship is getting closer and closer. Today's servo system is no longer a simple "instruction-execution" cycle. It needs to integrate more real-time feedback, environmental data, and predictive maintenance indicators. If these data are still synchronized one by one through traditional communication methods, efficiency and reliability will encounter bottlenecks. Streaming data platforms like Kafka provide an idea: treat data as a continuous flow of water instead of scattered water droplets. Each part of the system can be accessed on demand, which can not only decouple but also maintain overall synchronization.

Of course, all this is inseparable from a stable and reliable hardware foundation. The performance of servo motors and drives is still the prerequisite for all this - just like a conveyor belt running smoothly, the parts on it must themselves be precise and strong.

Therefore, if you are also troubled by data delays and losses in multi-device collaboration, you may want to think beyond the perspective of simply looking at hardware or networks and think about whether you should design a more robust "transmission system" for the data flow itself.

After all, what makes the robot move smoothly is not only how fast the motor rotates, but also how steadily the data runs.

kpowerFocusing on servo drive and automation, we build a more reliable motion control foundation for you from hardware to data flow.

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.