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使用 OTel SDK 为应用程序暴露指标

本文仅供希望评估或探索正在开发的 OTLP 指标的用户参考。

OpenTelemetry 项目要求以必须在 OpenTelemetry 协议 (OTLP) 中发出数据的语言提供 API 和 SDK。

针对 Golang 应用程序

Golang 可以通过 sdk 暴露 runtime 指标,具体来说,在应用中添加以下方法开启 metrics 暴露器:

安装相关依赖

切换/进入到应用程序源文件夹后运行以下命令:

go get go.opentelemetry.io/otel \
  go.opentelemetry.io/otel/attribute \
  go.opentelemetry.io/otel/exporters/prometheus \
  go.opentelemetry.io/otel/metric/global \
  go.opentelemetry.io/otel/metric/instrument \
  go.opentelemetry.io/otel/sdk/metric

使用 OpenTelemetry SDK 创建初始化函数

import (
    .....

    "go.opentelemetry.io/otel/attribute"
    otelPrometheus "go.opentelemetry.io/otel/exporters/prometheus"
    "go.opentelemetry.io/otel/metric/global"
    "go.opentelemetry.io/otel/metric/instrument"
    "go.opentelemetry.io/otel/sdk/metric/aggregator/histogram"
    controller "go.opentelemetry.io/otel/sdk/metric/controller/basic"
    "go.opentelemetry.io/otel/sdk/metric/export/aggregation"
    processor "go.opentelemetry.io/otel/sdk/metric/processor/basic"
    selector "go.opentelemetry.io/otel/sdk/metric/selector/simple"
)
func (s *insightServer) initMeter() *otelPrometheus.Exporter {
    s.meter = global.Meter("xxx")

    config := otelPrometheus.Config{
        DefaultHistogramBoundaries: []float64{1, 2, 5, 10, 20, 50},
        Gatherer:                   prometheus.DefaultGatherer,
        Registry:                   prometheus.NewRegistry(),
        Registerer:                 prometheus.DefaultRegisterer,
    }

    c := controller.New(
        processor.NewFactory(
            selector.NewWithHistogramDistribution(
                histogram.WithExplicitBoundaries(config.DefaultHistogramBoundaries),
            ),
            aggregation.CumulativeTemporalitySelector(),
            processor.WithMemory(true),
        ),
    )

    exporter, err := otelPrometheus.New(config, c)
    if err != nil {
        zap.S().Panicf("failed to initialize prometheus exporter %v", err)
    }

    global.SetMeterProvider(exporter.MeterProvider())

    http.HandleFunc("/metrics", exporter.ServeHTTP)

    go func() {
        _ = http.ListenAndServe(fmt.Sprintf(":%d", 8888), nil)
    }()

    zap.S().Info("Prometheus server running on ", fmt.Sprintf(":%d", port))
    return exporter
}

以上方法会为您的应用暴露一个指标接口: http://localhost:8888/metrics

随后,在 main.go 中对其进行初始化:

func main() {
······
    tp := initMeter()
······
}

此外,如果想添加自定义指标,可以参考:

// exposeClusterMetric expose metric like "insight_logging_count{} 1"
func (s *insightServer) exposeLoggingMetric(lserver *log.LogService) {
    s.meter = global.Meter("insight.io/basic")

    var lock sync.Mutex
    logCounter, err := s.meter.AsyncFloat64().Counter("insight_log_total")
    if err != nil {
        zap.S().Panicf("failed to initialize instrument: %v", err)
    }

    _ = s.meter.RegisterCallback([]instrument.Asynchronous{logCounter}, func(ctx context.Context) {
        lock.Lock()
        defer lock.Unlock()
        count, err := lserver.Count(ctx)
        if err == nil || count != -1 {
            logCounter.Observe(ctx, float64(count))
        }
    })
}

随后,在 main.go 调用该方法:

······
s.exposeLoggingMetric(lservice)
······

您可以通过访问 http://localhost:8888/metrics 来检查您的指标是否正常工作。

针对 Java 应用程序

Java 在使用 otel agent 在完成链路的自动接入的基础上,通过添加环境变量:

OTEL_METRICS_EXPORTER=prometheus

就可以直接暴露 JVM 相关指标,您可以通过访问 http://localhost:8888/metrics 来检查您的指标是否正常工作。

随后,再配合 prometheus serviceMonitor 即可完成指标的接入。 如果想暴露自定义指标请参阅 opentelemetry-java-docs/prometheus

主要分以下两步:

  • 创建 meter provider,并指定 prometheus 作为 exporter。

    /*
    * Copyright The OpenTelemetry Authors
    * SPDX-License-Identifier: Apache-2.0
    */
    
    package io.opentelemetry.example.prometheus;
    
    import io.opentelemetry.api.metrics.MeterProvider;
    import io.opentelemetry.exporter.prometheus.PrometheusHttpServer;
    import io.opentelemetry.sdk.metrics.SdkMeterProvider;
    import io.opentelemetry.sdk.metrics.export.MetricReader;
    
    public final class ExampleConfiguration {
    
      /**
      * Initializes the Meter SDK and configures the prometheus collector with all default settings.
      *
      * @param prometheusPort the port to open up for scraping.
      * @return A MeterProvider for use in instrumentation.
      */
      static MeterProvider initializeOpenTelemetry(int prometheusPort) {
        MetricReader prometheusReader = PrometheusHttpServer.builder().setPort(prometheusPort).build();
    
        return SdkMeterProvider.builder().registerMetricReader(prometheusReader).build();
      }
    }
    
  • 自定义 meter 并开启 http server

    package io.opentelemetry.example.prometheus;
    
    import io.opentelemetry.api.common.Attributes;
    import io.opentelemetry.api.metrics.Meter;
    import io.opentelemetry.api.metrics.MeterProvider;
    import java.util.concurrent.ThreadLocalRandom;
    
    /**
    * Example of using the PrometheusHttpServer to convert OTel metrics to Prometheus format and expose
    * these to a Prometheus instance via a HttpServer exporter.
    *
    * <p>A Gauge is used to periodically measure how many incoming messages are awaiting processing.
    * The Gauge callback gets executed every collection interval.
    */
    public final class PrometheusExample {
      private long incomingMessageCount;
    
      public PrometheusExample(MeterProvider meterProvider) {
        Meter meter = meterProvider.get("PrometheusExample");
        meter
            .gaugeBuilder("incoming.messages")
            .setDescription("No of incoming messages awaiting processing")
            .setUnit("message")
            .buildWithCallback(result -> result.record(incomingMessageCount, Attributes.empty()));
      }
    
      void simulate() {
        for (int i = 500; i > 0; i--) {
          try {
            System.out.println(
                i + " Iterations to go, current incomingMessageCount is:  " + incomingMessageCount);
            incomingMessageCount = ThreadLocalRandom.current().nextLong(100);
            Thread.sleep(1000);
          } catch (InterruptedException e) {
            // ignored here
          }
        }
      }
    
      public static void main(String[] args) {
        int prometheusPort = 8888;
    
        // it is important to initialize the OpenTelemetry SDK as early as possible in your process.
        MeterProvider meterProvider = ExampleConfiguration.initializeOpenTelemetry(prometheusPort);
    
        PrometheusExample prometheusExample = new PrometheusExample(meterProvider);
    
        prometheusExample.simulate();
    
        System.out.println("Exiting");
      }
    }
    

随后,待 java 应用程序运行之后,您可以通过访问 http://localhost:8888/metrics 来检查您的指标是否正常工作。

Insight 采集指标

最后重要的是,您已经在应用程序中暴露出了指标,现在需要 Insight 来采集指标。

推荐的指标暴露方式是通过 servicemonitor 或者 podmonitor。

创建 servicemonitor/podmonitor

添加的 servicemonitor/podmonitor 需要打上 label:"operator.insight.io/managed-by": "insight" 才会被 Operator 识别:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: example-app
  labels:
    operator.insight.io/managed-by: insight
spec:
  selector:
    matchLabels:
      app: example-app
  endpoints:
  - port: web
  namespaceSelector:
    any: true

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