When your microservices start to “quarrel”, maybe it’s time to listen to what the message queue has to say

Imagine this: you have designed a beautiful microservice system, and each service is like a precise gear, performing its own duties. At the beginning, they cooperated tacitly and the data flow was smooth. But as your business grows and you provide more and more services, you will find some embarrassing moments - the order service is processed, but the inventory service is not updated in time; the user pays successfully, but the notification service is delayed for a few minutes to respond. It seems that a more reliable communication method is needed between these services.

This is not just a problem of data transmission, but more like the lack of a reliable "microphone" for team collaboration. Services are directly called between each other. Once a certain link gets stuck, the entire chain may be affected. Or, if a service goes down briefly, important data may be lost. At this time, what you need is an intermediary that can buffer, store, and ensure that the message will eventually arrive.

This is why many teams began to introduce message queues. Kafka plays a rather unique role in this regard.

What is Kafka? It's more like a post office that never closes

Simply put, Kafka is a distributed message streaming platform. But it's not the same as an ordinary message queue. You can think of it as a continuously running conveyor belt, or a giant sorting center with multiple levels of shelves. Messages (data) are sent in by producers and stored according to categories, and consumers can retrieve them at their own pace at any time. The key thing is that the message is saved for a period of time and will not be deleted immediately, so that even if the service is temporarily offline, it can continue processing from the last position when it comes back.

Some people may ask: "Our system is running very well now, why do we have to bother with this?" In fact, problems often do not burst out suddenly, but slowly emerge. When the number of your services changes from a few to dozens, and when the amount of data changes from hundreds of items per day to hundreds of items per second, point-to-point direct communication will become insufficient. Delays, losses, duplicate processing... these small troubles can gradually accumulate into big problems.

What can Kafka do in microservices? Three down-to-earth scenes

First, decouple tight bindings between services. In the past, service A directly called service B. If B was busy or hung up, A could only wait or report an error. With Kafka, A only needs to throw the message into the corresponding "topic channel" and continue to do his own work. B. Come and pick it up when it’s convenient. The two parties do not have to wait for each other online in real time, and their independence is suddenly improved.

Second, buffer peak traffic. Orders pour in instantly during a major sale. If each order must immediately trigger a series of services such as inventory, logistics, points, etc., the database and network may be under heavy pressure in an instant. Kafka can receive these requests first and allow downstream services to consume smoothly according to their own processing capabilities to avoid being overwhelmed by sudden traffic.

Third, ensure that data is not lost. Thanks to its persistent storage and distributed design, once a message is successfully written to Kafka, the data will have a copy even if some nodes fail. This is especially important for key messages such as payment results and status changes - you can rest assured that important information will not disappear just because a service is restarted.

Sounds great, but will it be complicated to introduce?

This is a very practical concern. Indeed, building and maintaining a Kafka cluster requires some professional knowledge, such as partition planning, replica settings, monitoring and tuning, etc. But the good news is that there are now many proven practices and tools that make these tasks easier. The key is to be clear about your business scenario from the beginning: How high throughput do you need? How important is the order of messages? How long does the data need to be retained?

When starting out, you don’t have to pursue a large and comprehensive architecture. You can start the pilot with a non-core business flow, such as user behavior log collection or asynchronous notification sending. Familiarize the team with the message production, consumption, and monitoring processes. Once you understand your temperament, you can gradually apply it to the core transaction links.

Don’t forget the supporting monitoring and alarms. What should I do if there is a backlog of messages? How to find out if consumption is delayed? A good observation system allows you to detect problems early rather than waiting until the business side complains.

Selection and adaptation: There is no panacea, only the fit.

There are many choices of message queues on the market. Kafka is characterized by high throughput, persistence and stream processing capabilities. It is particularly suitable if your scenario involves massive logs, real-time data pipelines, or event-driven architecture. But if your message volume is not large and you value extremely low latency and simple deployment, other lightweight solutions may be more suitable.

The important thing is to understand your own needs. The essence of communication between microservices is to make the system more robust and flexible. Tools serve goals, not the other way around.

In the actual implementation process, cultural adaptation is sometimes more difficult than technology. The development team needs to shift from the thinking of "synchronous calls to get results immediately" to the thinking of "asynchronous processing that is ultimately consistent". This requires some time and practice, as well as clear design conventions.

Let the system "speak" for itself

Introducing a message flow platform such as Kafka actually gives your microservice system the ability to communicate autonomously. Services are no longer gears locked by a chain, but agents that can dynamically collaborate through message flows. After the order is generated, the message is sent, and inventory, logistics, and marketing each take what they need and process them in parallel. The system as a whole becomes more resilient and better able to cope with future changes.

You will find that when the "quarrels" between services become orderly "conversations," many of the bottlenecks that were once there will naturally disappear. When data flows, business can flow. This is perhaps the most attractive part of technical architecture - it is not just a cold framework, but an art that makes complex collaboration effortless.

Of course, this road needs to be walked step by step. Start with a small pilot, observe, adjust, and then promote. Good tools ultimately make people's work easier and business operations more stable. When you look back one day and find that the system has been able to calmly handle the traffic peaks that once gave you a headache, that feeling is probably like the moment when you see the precision machinery you designed finally running harmoniously.

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.