TECHNICAL SUPPORT
Published 2026-01-19
Remember the excitement you felt when you were new to microservices? Independent deployment, flexible scaling, everything is so beautiful. But soon, reality poured in a bucket of cold water: after the order service has been processed, how does the inventory service immediately know that it needs to be deducted? When user information is updated, how can the analysis service and push service be synchronized in real time? Data bounces around from service to service like a team of out-of-touch team members, leaving a trail of chaos and delays. You scratch your head and wonder, isn't there a reliable "correspondent" who is responsible for delivering messages between these services?
some. It is Apache Kafka. A pretty cool-sounding name. It is not a database or a framework for processing business logic. You can think of it as a super-efficient "central post office" or "data highway" that never rests. In the city of microservices, which is composed of countless small houses (services), Kafka is the main road that runs through the city and is never jammed, carrying all important information flows.
Let's spread the question out. Service A and Service B chat directly (point-to-point calls). Once B is sick (fault) or too busy (high load), A will be stuck and the entire process will stall. What's more troublesome is that if the new service C also wants to listen to the conversation between A and B, it will have to reconnect the line, and the system will become like a mess.
And what Kafka does is smart. It introduces a "publish-subscribe". Service A (producer) doesn't care who needs the information. It just "publishes" the message to a certain "topic" in Kafka (which can be understood as a bulletin board). Any service (consumer) that cares about this message, such as B, C, and D, can "subscribe" to this topic and retrieve the message at any time. A and B/C/D do not need to know each other at all and are completely decoupled.
For example: In the past, the courier (service A) took the package, knocked on the door of the recipients (B, C, D) one by one, and waited for them to sign for it, which was inefficient. Now, the courier simply places the package into a specific slot (theme) of a smart locker (Kafka) and then leaves to deliver the next item. Recipients can go to the locker to pick up their packages at any time through their own passwords (subscription) without disturbing each other. The locker can also temporarily store the package, so that even if the recipient is away on business for a few days, he can still pick it up when he returns.
It's a bit boring to just talk about reason, let's see what it does:
1. Get the activity stream data: Users clicked on your website, searched for a word, added a shopping cart...these tiny actions are as numerous as snowflakes. Every action is an event. Throw these events into Kafka in real time and order, and the downstream analysis services, recommendation engines, and risk control systems can get what they need and start working immediately. Without Kafka, this data would either be lost or blocked at a service door.
2. Act as the "big housekeeper" of system logs: Each microservice is generating logs. The traditional approach is to write files separately, and when checking problems, you have to search everywhere, which is miserable. Let all services send log messages to Kafka in a unified manner, and then centrally import them into specialized log search and analysis tools (such as ELK stack). Now, troubleshooting the problem is like looking for the answer in a search engine, it is clear at a glance.
3. Message decoupling and peak-shaving and valley-filling: This is its core capability. For example, the key event of "order placed successfully". After the order service has processed the core logic, it only needs to send a message to the "order_completed" topic, and it's done. The inventory service, points service, SMS notification service, data warehouse service, etc. all monitor this topic and handle it at their own pace without dragging anyone down. When encountering a major promotion and the order volume skyrockets, Kafka is like a large reservoir. It caches the flood of data first, and downstream services can slowly digest it according to their own capabilities, avoiding the risk of being overwhelmed in an instant.
4. Construct materialized views and event sourcing: The status of some services (such as a dashboard service that requires comprehensive data) can be gradually calculated and reconstructed from a series of events (passed through Kafka). This provides great flexibility and auditability, and you can look back at the state of the system at any point in the past.
There are many message queues on the market. Why do many teams finally choose Kafka? There is indeed something about its design philosophy.
It’s durability and reliability. It persists all messages to disk by default and can be backed up across multiple servers. This means that almost no messages will be lost, and even if the machine is restarted, the data will be safe. This is a lifeline for businesses such as transactions and orders.
That's amazing throughput. Its design goal is to handle massive data flows. Through a series of "black technologies" such as sequential reading and writing of disks and zero-copy technology, it can easily achieve the performance of processing hundreds of thousands or even millions of messages per second on ordinary hardware. When data volume becomes scale, this advantage is decisive.
The other is extremely high availability and scalability. Kafka clusters can be easily expanded by adding nodes to carry greater traffic. Its built-in copy mechanism ensures that even if some servers go down, the entire messaging service will not be interrupted and the data will remain safe.
Of course, it is not without temper. Its concepts require a little learning cost (topics, partitions, replicas, consumer groups...), and for very simple small-scale scenarios, it may seem a bit "killing a chicken with a knife". But when the number of your microservices starts to grow and the data flow becomes complicated, you will find that the initial investment in learning is worth it.
So, when you consider whether to introduce Kafka, you can ask yourself a few simple questions:
If the answer is "yes", then this efficient and reliable "data highway" is worthy of your careful investigation. It may not directly help you write business code, but it can provide you with a solid and orderly data flow foundation, so that each microservice can focus more on doing its own job, instead of being exhausted in chaotic communication.
Ultimately, the value of technology tools lies in solving problems, not adding burdens. Finding a partner who can elegantly solve your core pain points will make the road to building and maintaining complex systems smoother. After all, who doesn’t like a “communications expert” who can make data queue up and arrive on time?
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.
Update Time：2026-01-19
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