Implementing distributed API service degradation solution based on go-zero

With the continuous development of the Internet, more and more companies will face the problem of high service concurrency. In this case, if there is no corresponding solution to handle the pressure during peak periods, it will bring a very poor experience to users and even cause service crashes, thus affecting the company's reputation and business. Therefore, it is very necessary to implement an effective downgrade scheme.

This article introduces how to implement a distributed API service degradation solution based on Go-Zero. Go-Zero is a microservice framework based on the Go language, including code generator, rpc framework, database framework, cache framework, API gateway and other components. It can help us quickly build high-performance, high-concurrency, distributed applications. program. The following are the specific steps on how to implement an API service degradation solution based on Go-Zero:

Step 1: Define degradation rules

First, we need to define a degradation rule to determine under what circumstances Downgrade logic needs to be triggered. You can judge whether service downgrade is needed based on the following indicators:

• Request speed: If the request response time exceeds a certain threshold, it means that the service can no longer meet the needs of users, and a downgrade can be triggered at this time logic.
• Request volume: If the number of requests received by the service exceeds a certain threshold, it means that the service has exceeded the load limit, and the downgrade logic can be triggered at this time.
• System resources: If the server's CPU, memory, disk and other resource utilization exceeds a certain threshold, it means that the system can no longer bear more load, and the downgrade logic can be triggered at this time.

Based on the above indicators, we can define some rules, such as:

• If the request response time exceeds 1 second, a downgrade will be triggered.
• If the number of requests per minute exceeds 1,000, downgrade is triggered.
• If the server's CPU, memory, disk and other resource utilization exceeds 80%, a downgrade will be triggered.

Step 2: Implement downgrade logic

Once the rules that trigger downgrade are determined, we need to implement the corresponding downgrade logic. In Go-Zero, we can implement downgrade logic through middleware. Middleware is a component that can process requests before and after. It can perform some processing logic before or after the request, such as verifying the request parameters before the request, recording logs after the request, etc.

In this example, we need to implement a downgrade middleware. When the conditions for triggering downgrade are met, the request will be returned to the default value or error message, so that the user can get timely feedback, and The entire system will not crash due to request delays or service exceptions.

Step 3: Integrate Prometheus

To facilitate monitoring and managing our service degradation logic, we can integrate Prometheus into our application. Prometheus is an open source monitoring system that can collect and record different categories of indicator data. Through Prometheus, we can monitor various indicator data, such as request response time, number of requests, server resource utilization, etc., and use these data for diagnosis and debugging.

In Go-Zero, we can use the metrics library provided by Prometheus to collect and record indicators. You can monitor various performance indicators of the application through the metrics library, and display the monitoring results to the Prometheus interface in real time. In this way, we can understand the status of the service in real time, discover and solve problems, and improve the stability and reliability of the system.

Conclusion

Service downgrade is an important measure to ensure system stability, especially in high-concurrency scenarios, which can avoid the system from being overwhelmed. The distributed API service degradation solution based on Go-Zero is a very practical method. It can help us quickly build high-performance, high-reliability distributed applications, and can monitor and control system performance indicators in real time. Analysis allows us to detect and solve problems in a timely manner. I hope this article can provide some help to everyone who encounters API service degradation problems in actual work.

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