TECHNICAL SUPPORT
Published 2026-01-19
Have you ever encountered this situation? Dozens of servo motors on the production line are running at the same time, and data is pouring in like a tide. The servo angle data here has not yet been saved, and the real-time torque information of the robotic arm has squeezed in again. The database can't breathe, and the system occasionally freezes for a few milliseconds - but just these few milliseconds can really cause a headache sometimes.
Isn't this the situation we often encounter when doing automation projects? Devices are getting smarter and smarter, and the amount of data is getting larger and larger. The traditional single database architecture is beginning to seem inadequate.
So here comes the question: How should the database be designed under the microservice architecture?
A voice said: "How simple it is for all services to share a database!" But in actual operation, you will find that there are a lot of troubles hidden behind this simplicity. Changes to the table structure of a certain service may accidentally affect other functions; the data coupling is too tight, and it is difficult to expand a specific module.
Another way of thinking is becoming popular: each microservice manages its own database. Sounds reasonable, right? Let the module responsible for steering gear control manage its own position data, and let the module that monitors the motor status store its own running log. Each performs his or her duties without interfering with each other.
But a new question arises: after the data is dispersed, what should we do with data queries that require cross-services? For example, if you want to analyze the overall efficiency of a certain mechanical unit, you need to look at the response speed of the servo motor, the positioning accuracy of the relevant steering gear, and the load data of the mechanical structure. The data is scattered in different places, and piecing it together is time-consuming and laborious.
We have been thinking about this for some days. existkpowerIn actual project experience, I found that the key is to find a suitable "database schema" - not a rigid template, but a flexible data organization method.
For example, the "separate database for each service" model is particularly useful in motor control systems. The data of the real-time control module is stored independently to ensure response speed; the historical analysis module uses another set of storage strategies to facilitate long-term trend analysis. The data boundaries are clear, and services exchange necessary information through clear interfaces instead of directly looking through other people's databases.
How to ensure data consistency? We often adopt an event-driven approach. When a servo completes the specified action, it will publish an "action completed" event. Other services that care about this status - such as the motion trajectory recording service - receive and process this event and update their own data. This approach avoids direct database dependencies between services.
Last year we assisted in an automated production line renovation project, which used these ideas. There are twelve servo motors working together on the production line, and each of them must record operating parameters, fault codes, and maintenance history. The servos of the three sets of robotic arms need to exchange position data in real time.
If all data is crammed into one database, real-time control data and analysis data will occupy resources. We adopted a divide-and-conquer strategy: configure a high-performance time series database for real-time control services to store real-time values of motor speed and torque; configure a relational database for equipment management services to store equipment parameters and maintenance records; and the analysis service obtains data from different sources according to needs and performs comprehensive processing.
The result? The real-time control response delay is reduced by 40%, and the data analysis query speed is increased. Because each database can be targeted at its own main tasks, there is no need to juggle all types of data operations.
When choosing a database schema, we usually ask several questions:
What is the data access model for this service? Is it frequent real-time reading and writing of small amounts of data, or occasional large-scale analysis queries?
What is the expected data growth? Some sensor data generate multiple records per second, which can accumulate to an astonishing amount in a month.
How strong are the data dependencies between services? Do you really need real-time synchronization, or is a short delay acceptable?
After answering these questions, the mode choice becomes much clearer. Control data with high real-time requirements may be suitable for in-memory databases or specific time series databases; for configuration data that requires complex query relationships, traditional relational databases may be more suitable; for large amounts of historical log data, columnar storage engines sometimes perform better.
A good database schema shouldn't be like a straitjacket, but like a well-fitting work suit that allows you to move freely while providing the necessary support. existkpowerIn our project experience, we have found that the most successful implementations are often those teams that know how to be flexible.
They choose a primary database schema for a core service, paired with secondary storage solutions for specific functions. Just like a precise mechanical system, each component has the most suitable material and structure, so that it can achieve maximum effectiveness when combined.
Sometimes looking at these data flowing smoothly between different services, you will think of those well-designed transmission systems-power is transmitted between gears, every link is just right, and finally converted into precise movement.
Database design under microservices is ultimately about finding a balance: the balance between service independence and data integrity, the balance between real-time performance and query flexibility, and the balance between short-term development efficiency and long-term maintenance costs.
There is no one-size-fits-all answer, only appropriate choices for specific scenarios. Every project is a new exploration, from motor control to mechanical coordination, data architecture is always the cornerstone that silently supports everything.
When night falls and the equipment on the production line enters maintenance period, those silently running databases are still sorting out the day's data and preparing for efficient operation tomorrow - just like a set of precise mechanical devices, each part is in the correct position and fulfilling 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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