How to use the Hyperf framework for data monitoring

How to use the Hyperf framework for data monitoring

Introduction:
Data monitoring is one of the important links to ensure the stable operation of the system. This article will introduce how to use the Hyperf framework for data monitoring and give specific code examples.

1. Introduction to Hyperf framework
Hyperf is a high-performance PHP coroutine framework based on Swoole extension, with powerful dependency injection function and complete microservice component support. The design concept of the Hyperf framework is high performance, flexible configuration, and high development efficiency.

2. The Importance of Data Monitoring
Data monitoring can obtain the operating status of the system in real time and effectively, and discover and solve potential problems in a timely manner to ensure the stable operation of the system. At the same time, data monitoring can also provide important reference information for system optimization and help developers better understand the operating status of the system.

3. Steps to use Hyperf framework for data monitoring

1. Install Hyperf framework
   Install Hyperf framework through Composer:

   composer create-project hyperf/hyperf

2. Add data monitoring component
   Add data monitoring component in the config/autoload/dependencies.php file:

   return [
    'dependencies' => [
        HyperfMetricListenerPrometheusExporterListener::class => [
            // ...
            PromeExporter::class,
        ],
        // ...
    ],
   ];

3. Configure data monitoring information
   Configure data monitoring information in the config/autoload/prometheus.php file:

   return [
    'default' => [
        'namespace' => 'app',
        'adapter' => HyperfMetricAdapterPrometheusRedisAdapterFactory::class,
        'config' => [
            'host' => env('PROMETHEUS_REDIS_HOST', '127.0.0.1'),
            'port' => env('PROMETHEUS_REDIS_PORT', 6379),
            'password' => env('PROMETHEUS_REDIS_PASSWORD', ''),
            'db' => env('PROMETHEUS_REDIS_DB', 0),
            'namespace' => env('PROMETHEUS_REDIS_NAMESPACE', 'prometheus:'),
        ],
    ],
   ];

4. Write data monitoring code
   Add data monitoring where monitoring is required Code:

   use HyperfMetricAnnotationCounter;
   use HyperfMetricAnnotationHistogram;
   use HyperfMetricAnnotationMetric;
   use HyperfMetricAnnotationTimers;
   use HyperfMetricListenerPrometheusExporterListener;
   use HyperfMetricTimerTimerAveragePeriodTask;
   
   class DemoController extends AbstractController
   {
    /**
     * @Counter(name="demo_api_total", description="Total requests of demo API", labels={"module", "controller", "action"})
     * @Histogram(name="demo_api_duration_seconds", description="Duration seconds of demo API", labels={"module", "controller", "action"})
     * @Timers(name="demo_api_timer")
     */
    #[Metric("demo_api_total", description: "Total requests of demo API", labels: ["module", "controller", "action"])]
    #[Metric("demo_api_duration_seconds", description: "Duration seconds of demo API", labels: ["module", "controller", "action"])]
    #[Metric("demo_api_timer")]
    public function demoApi()
    {
        // 业务代码
    }
   }

4. Data monitoring example
The following is an example showing how to use the Hyperf framework for data monitoring. For example, we want to monitor the number of requests and request duration for a user registration function.

1. Add monitoring annotation

   use HyperfMetricAnnotationCounter;
   use HyperfMetricAnnotationHistogram;
   use HyperfMetricAnnotationMetric;
   
   class UserController extends AbstractController
   {
    /**
     * @Counter(name="user_register_total", description="Total requests of user register")
     * @Histogram(name="user_register_duration_seconds", description="Duration seconds of user register")
     */
    #[Metric("user_register_total", description: "Total requests of user register")]
    #[Metric("user_register_duration_seconds", description: "Duration seconds of user register")]
    public function register()
    {
        // 业务代码
    }
   }

2. Add monitoring middleware

   use HyperfMetricAdapterPrometheusCounter;
   use HyperfMetricAdapterPrometheusHistogram;
   
   class PrometheusExporterMiddleware extends AbstractMiddleware
   {
    public function process(ServerRequestInterface $request, RequestHandlerInterface $handler): ResponseInterface
    {
        // 注册监控指标
        $counter = new Counter('user_register_total');
        $histogram = new Histogram('user_register_duration_seconds');
   
        // 开始监控
        $counter->inc();
        $timer = $histogram->startTimer();
   
        // 执行下一个中间件
        $response = $handler->handle($request);
   
        // 结束监控
        $timer->observe();
   
        return $response;
    }
   }

3. Register middleware
   Register middleware in the config/autoload/middlewares.php file:

   return [
    'http' => [
        // ...
        AppMiddlewarePrometheusExporterMiddleware::class
    ],
   ];

5. Summary
Through the introduction of this article, we can see that Hyperf The framework provides powerful data monitoring functions, can easily monitor the system in real time, and has good scalability and flexibility. Using the Hyperf framework for data monitoring helps ensure the stable operation of the system and optimize system performance.

The above are the steps and specific code examples on how to use the Hyperf framework for data monitoring. I hope it will be helpful for readers to understand and apply the Hyperf framework for data monitoring. I wish you success in your project development!

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