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 OTel 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：
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：
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：
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