TECHNICAL SUPPORT
Published 2026-01-19
Imagine you are assembling a complex mechanical system. The servo motors run quietly, the servos adjust their angles precisely, and everything looks perfect. Then, a certain microservice suddenly gets stuck—not a complete crash, just a few milliseconds slower in response. The entire production line began to get slightly out of sync, errors accumulated, and a barely visible scratch appeared on the final product. You may not immediately find the source of the problem, but you know: reliability is not about "not bad", but "not broken at critical moments".
Microservice architecture makes complex systems modular, but fragmentation also brings new vulnerabilities. This is not a technical issue, but a design philosophy issue.
In the past, people used to build the system like a strong castle. But the weakness of the castle is that once the gate is breached, the entire interior may fall. Microservices are like forming a special team, each team member is independent and collaborative. But if there is poor communication between team members, the mission will still fail.
A common situation is: a service slowly slows down because of a memory leak, like a rusty gear. It didn't stop completely, but it dragged down all the links that depended on it. Monitoring systems can take a long time to issue alerts because threshold settings only focus on “survival,” not “health.”
Even more troublesome is that these failures are often random and non-linear. A problem that arises at 3pm on Tuesday may not become apparent again until 10am on Thursday morning. This kind of intermittent failure consumes the most troubleshooting energy.
Reliability is not an accessory added later, but the skeleton from the beginning of the design. It's like when making precision machinery, you consider material fatigue, tolerance fits, and lubrication intervals. You can't wait until the machine wears out before you remember to use better bearings.
How to do this?
Make every service "degradable". Imagine a servo. When it detects that the power supply is unstable, it will automatically switch to a conservative movement mode - sacrificing a little speed to ensure that it does not lose control. The same goes for microservices: when the downstream service it depends on is abnormal, can it provide basic functions? For example, returning cached data, or simplifying the calculation process.
Communication has a timeout and retry mechanism, but try again smartly. Blindly retrying can cause a "thundering herd effect" - like an error signal that continues to amplify within the system. A good pattern is exponential backoff: wait 100 milliseconds for the first failure before trying again, wait 200 milliseconds for the second time, and gradually lengthen the time interval to give the system breathing room to recover.
Also, don’t overlook “graceful termination.” The service needs to know when to stop and complete the task at hand and free up resources before stopping, like an attentive worker tidying up the tool table before leaving get off work.
existkpower, we have a basic principle when looking at reliability: it is observable and testable.
We simulate failures during the development phase. Randomly shut down certain services, randomly inject network delays, and even simulate data center outages. This may sound masochistic, but only by knowing how a system fails can you know how to make it more resilient.
For example, we once designed a positioning control system based on servo motors for a customer. Microservices are responsible for calculating motion trajectories. We intentionally let the trajectory calculation service occasionally return incorrect data to test how the motor control service reacts. It was found that the control service would enter an infinite loop due to invalid instructions. So we added a verification layer - just like adding a physical limiter to the steering gear, even if the command is abnormal, the actuator will not damage itself.
This "chaos engineering" thinking turns unknown faults into known risks.
Reliability also exists in daily habits. for example:
Q: Is complexity an inevitable price?
uncertain. Complexity depends on cutting granularity. Just like mechanical design: dismantle a device into too many parts, and assembly difficulty and failure points will increase; dismantle too few, and the module will be too bulky. Good microservice boundaries usually correspond to the natural boundaries of the business domain. Find that boundary and the complexity decreases.
Question: Monitor so many indicators, which ones should you look at?
Pay attention to the "golden signals": latency, traffic, error count, saturation. These four are like monitoring the current, speed, temperature and vibration of the motor. When they show abnormal trends, they often have more warning value than outright faults.
Q: The test environment can never completely simulate the production environment, what should I do?
Indeed. So our strategy: conduct small-scale real-world testing in a safe corner of production. For example, import 1% of the traffic into the new version of the service, and run the new and old sets of logical comparison results at the same time. It's like testing a new part on a mechanical prototype, confirming it is correct, and then replacing it completely.
To build a reliable system, technology selection is important, but more important is team consensus. Everyone has to understand: a random restart of a service may trigger a butterfly effect.
existkpower, we often say to ourselves: The code you wrote today may be called at three o'clock in the morning. There was no debugging for you in front of the screen. So, please make it considerate enough and independent enough.
Reliability is ultimately about respect—respect for the people who use the system, respect for the businesses that rely on it, and respect for the colleagues working on alerts late at night. This is not a cold technical indicator, but a temperature responsibility.
Good design makes failures rare and harmless. Like a well-tuned machine, even if one gear is slightly worn, the entire system will still run smoothly and continue to complete its mission.
Established in 2005, Kpower has been dedicated to a professional compact motion unit manufacturer, headquartered in Dongguan, Guangdong Province, China. Leveraging innovations in modular drive technology, Kpower integrates 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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