Spring Boot 3.x接入micrometer

原创

eeaters

发布于 2023-08-02 14:22:31

2K2

发布于 2023-08-02 14:22:31

文章被收录于专栏：阿杰阿杰

服务的可观测性

可观察性可以通过分布式系统下各种组件交互的可见性来帮助识别应用程序中的问题和调试问题。

在Spring Boot 2.x的版本中, 我们可以通过引入Spring Cloud Sleuth来完成对服务信息的收集,然后将信息提交到如zipkin等

在Spring Boot 3.x的版本中, Spring Cloud Sleuth被micrometer替代.

下面以完整的示例演示接入micrometer的流程 .

项目基础依赖

依赖	版本
jdk	20
spring-boot	3.1.2

zipkin-server

选用zipkin进行数据收集和展示. 下载后可以用jdk 1.8版本对jar包进行启动

latest released server

项目依赖

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-tracing</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-tracing-bridge-otel</artifactId>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-zipkin</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-aop</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-aop</artifactId>
</dependency>

spring-boot-starter-actuator : 微服务性能收集, 同时micrometer的自动装配也是在该类下
micrometer-tracing: 链路追踪的核心依赖
micrometer-tracing-bridge-otel: otel是OpenTelemetry的缩写.和brave是不同的实现
opentelemetry-exporter-zipkin: 本地是使用zipkin作为数据收集和展示的.改依赖将服务中的数据提交到zipkin中

yaml配置

management:
  endpoints:
    web:
      exposure:
        include: '*'

logging:
  pattern:
    level: '%5p [${spring.application.name:},%X{traceId:-},%X{spanId:-}]'

这里要注意, Sleuth是将日志的格式自动做了调整, 而micrometer需要指定格式. 不过MDC的key依旧是traceId和spanId

代码

设置需要收集的端点

@FeignClient(
        contextId = "third-part-sf",
        name = "third-part-sf",
        url = "https://openic.sf-express.com",
        configuration = SFConfig.class,
        path = "/open/api/external"
)
@Observed(name = "SFDeliveryClient")
public interface SfDeliveryClient {

    @PostMapping("/precreateorder")
    SfResult<SfPreOrderResp> preCreateOrder(@RequestBody SfPreOrderReq preOrderReq);
}

这里和sleuth使用上有些不同; sleuth是将每次交互(rpc,redis)等发生交互的流程默认都做了收集. micrometer需要添加@Observed来添加一个收集的端点

观察span的创建和销毁

在micrometer中，对于每一次的上下文创建和销毁进行跟踪，

@Slf4j
public class SimpleLoggingHandler implements ObservationHandler<Observation.Context> {
    @Override
    public void onStart(Observation.Context context) {
        log.info("Starting context {} ", context);
    }

    @Override
    public void onStop(Observation.Context context) {
        log.info("Stopping context {} ", context);
    }

    @Override
    public boolean supportsContext(Observation.Context context) {
        return true;
    }
}

----
    
@Configuration
public class ObservedAspectConfiguration {

    @Bean
    public ObservedAspect observedAspect(ObservationRegistry observationRegistry) {
        observationRegistry.observationConfig().observationHandler(new SimpleLoggingHandler());

        return new ObservedAspect(observationRegistry);
    }
}

监控效果展示

端点

http://localhost:8080/actuator/metrics

结果如下：

{
    "names": [
        "SFDeliveryClient",
        "SFDeliveryClient.active",
        "application.ready.time",
        "application.started.time",
        "disk.free",
        "disk.total",
        "executor.active",
        "executor.completed",
        "executor.pool.core",
        "executor.pool.max",
        "executor.pool.size",
        "executor.queue.remaining",
        "executor.queued",
        "hikaricp.connections",
        "hikaricp.connections.acquire",
        "hikaricp.connections.active",
        "hikaricp.connections.creation",
        "hikaricp.connections.idle",
        "hikaricp.connections.max",
        "hikaricp.connections.min",
        "hikaricp.connections.pending",
        "hikaricp.connections.timeout",
        "hikaricp.connections.usage",
        "http.server.requests",
        "http.server.requests.active",
        "jdbc.connections.active",
        "jdbc.connections.idle",
        "jdbc.connections.max",
        "jdbc.connections.min",
        "jvm.buffer.count",
        "jvm.buffer.memory.used",
        "jvm.buffer.total.capacity",
        "jvm.classes.loaded",
        "jvm.classes.unloaded",
        "jvm.compilation.time",
        "jvm.gc.live.data.size",
        "jvm.gc.max.data.size",
        "jvm.gc.memory.allocated",
        "jvm.gc.memory.promoted",
        "jvm.gc.overhead",
        "jvm.gc.pause",
        "jvm.info",
        "jvm.memory.committed",
        "jvm.memory.max",
        "jvm.memory.usage.after.gc",
        "jvm.memory.used",
        "jvm.threads.daemon",
        "jvm.threads.live",
        "jvm.threads.peak",
        "jvm.threads.started",
        "jvm.threads.states",
        "lettuce.command.completion",
        "lettuce.command.firstresponse",
        "logback.events",
        "process.cpu.usage",
        "process.start.time",
        "process.uptime",
        "system.cpu.count",
        "system.cpu.usage",
        "thirdChannelController",
        "thirdChannelController.active",
        "tomcat.sessions.active.current",
        "tomcat.sessions.active.max",
        "tomcat.sessions.alive.max",
        "tomcat.sessions.created",
        "tomcat.sessions.expired",
        "tomcat.sessions.rejected"
    ]
}

http://localhost:8080/actuator/metrics/SFDeliveryClient

{
    "name": "SFDeliveryClient",
    "baseUnit": "seconds",
    "measurements": [
        {
            "statistic": "COUNT",
            "value": 1.0
        },
        {
            "statistic": "TOTAL_TIME",
            "value": 0.229436
        },
        {
            "statistic": "MAX",
            "value": 0.0
        }
    ],
    "availableTags": [
        {
            "tag": "method",
            "values": [
                "preCreateOrder"
            ]
        },
        {
            "tag": "error",
            "values": [
                "none"
            ]
        },
        {
            "tag": "class",
            "values": [
                "io.yujie.fast.delivery.thirdparty.sf.client.SfDeliveryClient"
            ]
        }
    ]
}

日志

日志收集到elk后,可以用traceId或者spanId进行日志检索. 以一条较短的日志打印为例：

2023-08-02T13:53:53.127+08:00  INFO [eeaters-example,d50e2c42f3225b681bf1fef572dfbf0d,c2ad58fc187d2667] 14680 --- [nio-8080-exec-1] i.y.f.d.thirdparty.sf.client.SFConfig    : 签名: ZGQ4MDRhNGNiMWUzYjQ0ZjcwNjhmYTY3ZmViZmJiMGM=

分布式链路追踪

zipkin的页面展示如下:

与sleuth使用不同总结(目前使用总结)

Sleuth是通过TraceEnvironmentPostProcessor自动调整了logger.level格式. micrometer需要手动设置日志的格式
micrometer使用更灵活. 按需添加@Observed注解 . 对服务的资源占用降低. 对服务的掌控感变高，对使用者要求也变高

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

spring boot

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

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