# 通过 Operator 实现应用程序无侵入增强

> 目前只有 Java、NodeJs、Python、.Net、Golang 支持 Operator 的方式无侵入接入。

## 前提条件

请确保 insight-agent 已经就绪。如若没有，请参考[安装 insight-agent 采集数据](../install/install-agent.md)并确保以下三项就绪：

- 为 insight-agent 开启 trace 功能
- trace 数据的地址以及端口是否填写正确
- deployment/insight-agent-opentelemetry-operator 和
  deployment/insight-agent-opentelemetry-collector 对应的 Pod 已经准备就绪

## 安装 Instrumentation CR

!!! tip

    从 [Insight v0.22.0](../../intro/release-notes.md#v0220) 开始，不再需要手动安装 Instrumentation CR。

在 insight-system 命名空间下安装，不同版本之间有一些细小的差别。

=== "Insight v0.21.x"

    ```bash
    K8S_CLUSTER_UID=$(kubectl get namespace kube-system -o jsonpath='{.metadata.uid}')
    kubectl apply -f - <<EOF
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation
      namespace: insight-system
    spec:
      # https://github.com/open-telemetry/opentelemetry-operator/blob/main/docs/api.md#instrumentationspecresource
      resource:
        addK8sUIDAttributes: true
      env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
      sampler:
        # Enum: always_on, always_off, traceidratio, parentbased_always_on, parentbased_always_off, parentbased_traceidratio, jaeger_remote, xray
        type: always_on
      java:
        image: ghcr.m.daocloud.io/openinsight-proj/autoinstrumentation-java:1.31.0
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
          - name: OTEL_K8S_CLUSTER_UID
            value: $K8S_CLUSTER_UID
      nodejs:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.41.1
      python:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.40b0
      dotnet:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-dotnet:1.0.0
      go:
        # Must set the default value manually for now.
        # See https://github.com/open-telemetry/opentelemetry-operator/issues/1756 for details.
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-go-instrumentation/autoinstrumentation-go:v0.2.2-alpha
    EOF
    ```

=== "Insight v0.20.x"

    ```bash
    kubectl apply -f - <<EOF
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation
      namespace: insight-system
    spec:
      # https://github.com/open-telemetry/opentelemetry-operator/blob/main/docs/api.md#instrumentationspecresource
      resource:
        addK8sUIDAttributes: true
      env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
      sampler:
        # Enum: always_on, always_off, traceidratio, parentbased_always_on, parentbased_always_off, parentbased_traceidratio, jaeger_remote, xray
        type: always_on
      java:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:1.29.0
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
      nodejs:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.41.1
      python:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.40b0
      dotnet:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-dotnet:1.0.0-rc.2
      go:
        # Must set the default value manually for now.
        # See https://github.com/open-telemetry/opentelemetry-operator/issues/1756 for details.
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-go-instrumentation/autoinstrumentation-go:v0.2.2-alpha
    EOF
    ```

=== "Insight v0.18.x"

    ```bash
    kubectl apply -f - <<EOF
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation
      namespace: insight-system
    spec:
      # https://github.com/open-telemetry/opentelemetry-operator/blob/main/docs/api.md#instrumentationspecresource
      resource:
        addK8sUIDAttributes: true
      env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
      sampler:
        # Enum: always_on, always_off, traceidratio, parentbased_always_on, parentbased_always_off, parentbased_traceidratio, jaeger_remote, xray
        type: always_on
      java:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:1.25.0
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
      nodejs:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.37.0
      python:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.38b0
      go:
        # Must set the default value manually for now.
        # See https://github.com/open-telemetry/opentelemetry-operator/issues/1756 for details.
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-go-instrumentation/autoinstrumentation-go:v0.2.1-alpha
    EOF
    ```

=== "Insight v0.17.x"

    ```bash
    kubectl apply -f - <<EOF
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation
      namespace: insight-system
    spec:
      # https://github.com/open-telemetry/opentelemetry-operator/blob/main/docs/api.md#instrumentationspecresource
      resource:
        addK8sUIDAttributes: true
      env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
      sampler:
        # Enum: always_on, always_off, traceidratio, parentbased_always_on, parentbased_always_off, parentbased_traceidratio, jaeger_remote, xray
        type: always_on
      java:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:1.23.0
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
      nodejs:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.34.0
      python:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.33b0
    EOF
    ```

=== "Insight v0.16.x"

    ```bash
    kubectl apply -f - <<EOF
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation
      namespace: insight-system
    spec:
      # https://github.com/open-telemetry/opentelemetry-operator/blob/main/docs/api.md#instrumentationspecresource
      resource:
        addK8sUIDAttributes: true
      env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
      sampler:
        # Enum: always_on, always_off, traceidratio, parentbased_always_on, parentbased_always_off, parentbased_traceidratio, jaeger_remote, xray
        type: always_on
      java:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:1.23.0
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
      nodejs:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.34.0
      python:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.33b0
    EOF
    ```

## 与服务网格链路串联场景

如果您开启了服务网格的链路追踪能力，需要额外增加一个环境变量注入的配置：

### 操作步骤如下

1. 登录 DCE 5.0，进入 __容器管理__ 后选择进入目标集群，
2. 点击左侧导航栏选择 __自定义资源__ ，找到 __instrumentations.opentelemetry.io__ 后进入详情页。
3. 选择 __insight-system__ 命名空间后，编辑 __insight-opentelemetry-autoinstrumentation__ ，在 spec:env: 下添加以下内容：

    ```yaml
        - name: OTEL_SERVICE_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.labels['app'] 
    ```

    ![otel-mesh](https://docs.daocloud.io/daocloud-docs-images/docs/zh/docs/insight/images/otel-mesh.png)

    完整的命令如下（For Insight v0.21.x）：

    ```bash
    K8S_CLUSTER_UID=$(kubectl get namespace kube-system -o jsonpath='{.metadata.uid}')
    kubectl apply -f - <<EOF
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation
      namespace: insight-system
    spec:
      # https://github.com/open-telemetry/opentelemetry-operator/blob/main/docs/api.md#instrumentationspecresource
      resource:
        addK8sUIDAttributes: true
      env:
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
        - name: OTEL_SERVICE_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.labels['app'] 
      sampler:
        # Enum: always_on, always_off, traceidratio, parentbased_always_on, parentbased_always_off, parentbased_traceidratio, jaeger_remote, xray
        type: always_on
      java:
        image: ghcr.m.daocloud.io/openinsight-proj/autoinstrumentation-java:1.31.0
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
          - name: OTEL_K8S_CLUSTER_UID
            value: $K8S_CLUSTER_UID
      nodejs:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.41.1
      python:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.40b0
      dotnet:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-dotnet:1.0.0
      go:
        # Must set the default value manually for now.
        # See https://github.com/open-telemetry/opentelemetry-operator/issues/1756 for details.
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-go-instrumentation/autoinstrumentation-go:v0.2.2-alpha
    EOF
    ```

## 添加注解，自动接入链路

以上就绪之后，您就可以通过注解（Annotation）方式为应用程序接入链路追踪了，OTel 目前支持通过注解的方式接入链路。
根据服务语言，需要添加上不同的 pod annotations。每个服务可添加两类注解之一：

- 只注入环境变量注解

    这类注解只有一个，用于添加 otel 相关的环境变量，比如链路上报地址、容器所在的集群 id、命名空间等（这个注解在应用不支持自动探针语言时十分有用）

    ```yaml
    instrumentation.opentelemetry.io/inject-sdk: "insight-system/insight-opentelemetry-autoinstrumentation"
    ```

    其中 value 被 `/` 分成两部分，第一个值 (insight-system) 是上一步安装的 CR 的命名空间，
    第二个值 (insight-opentelemetry-autoinstrumentation) 是这个 CR 的名字。

- 自动探针注入以及环境变量注入注解

    这类注解目前有 4 个，分别对应 4 种不同的编程语言：java、nodejs、python、dotnet，
    使用它后就会对 spec.pod 下的第一个容器注入自动探针以及 otel 默认环境变量：

    === "Java 应用"

        ```yaml
        instrumentation.opentelemetry.io/inject-java: "insight-system/insight-opentelemetry-autoinstrumentation"
        ```

    === "NodeJs 应用"

        ```yaml
        instrumentation.opentelemetry.io/inject-nodejs: "insight-system/insight-opentelemetry-autoinstrumentation"
        ```

    === "Python 应用"

        ```yaml
        instrumentation.opentelemetry.io/inject-python: "insight-system/insight-opentelemetry-autoinstrumentation"
        ```

    === ".NET 应用"

        ```yaml
        instrumentation.opentelemetry.io/inject-dotnet: "insight-system/insight-opentelemetry-autoinstrumentation"
        ```

    === "Golang 应用"

        由于 Go 自动检测需要设置 [OTEL_GO_AUTO_TARGET_EXE](https://github.com/open-telemetry/opentelemetry-go-instrumentation/blob/main/docs/how-it-works.md)，
        因此您必须通过注解或 Instrumentation 资源提供有效的可执行路径。未设置此值会导致 Go 自动检测注入中止，从而导致接入链路失败。

        ```yaml
        instrumentation.opentelemetry.io/inject-go: "insight-system/insight-opentelemetry-autoinstrumentation"
        instrumentation.opentelemetry.io/otel-go-auto-target-exe: "/path/to/container/executable"
        ```
        
        Go 自动检测也需要提升权限。以下权限是自动设置的并且是必需的。
        
        ```yaml
        securityContext:
          privileged: true
          runAsUser: 0
        ```

!!! tip

    OpenTelemetry Operator 在注入探针时会自动添加一些 OTel 相关环境变量，同时也支持这些环境变量的覆盖。这些环境变量的覆盖优先级：

    ```text
    original container env vars -> language specific env vars -> common env vars -> instrument spec configs' vars
    ```

    但是需要避免手动覆盖 OTEL_RESOURCE_ATTRIBUTES_NODE_NAME，它在 Operator 内部作为一个
    Pod 是否已经注入探针的标识，如果手动添加了，探针可能无法注入。

## 自动注入示例 Demo

注意这个 `annotations` 是加在 spec.annotations 下的。

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
spec:
  selector:
    matchLabels:
      app: my-app
  replicas: 1
  template:
    metadata:
      labels:
        app: my-app
      annotations:
        instrumentation.opentelemetry.io/inject-java: "insight-system/insight-opentelemetry-autoinstrumentation"
    spec:
      containers:
      - name: myapp
        image: ghcr.io/pavolloffay/spring-petclinic:latest
        ports:
          - containerPort: 8080
            protocol: TCP
```

在原本编排基础上最终合并生成的 YAML 内容如下：

```diff
apiVersion: v1
kind: Pod
metadata:
  name: my-deployment-with-sidecar-565bd877dd-nqkk6
  generateName: my-deployment-with-sidecar-565bd877dd-
  namespace: default
  uid: aa89ca0d-620c-4d20-8bc1-37d67bad4ea4
  resourceVersion: '2668986'
  creationTimestamp: '2022-04-08T05:58:48Z'
  labels:
    app: my-app
  annotations:
    instrumentation.opentelemetry.io/inject-java: "insight-system/insight-opentelemetry-autoinstrumentation"
spec:
  volumes:
    - name: kube-api-access-sp2mz
      projected:
        sources:
          - serviceAccountToken:
              expirationSeconds: 3607
              path: token
          - configMap:
              name: kube-root-ca.crt
              items:
                - key: ca.crt
                  path: ca.crt
          - downwardAPI:
              items:
                - path: namespace
                  fieldRef:
                    apiVersion: v1
                    fieldPath: metadata.namespace
        defaultMode: 420
+   - name: opentelemetry-auto-instrumentation-java
+     emptyDir:
+       sizeLimit: 200Mi      
+ initContainers:
+   - name: opentelemetry-auto-instrumentation-java
+     image: ghcr.m.daocloud.io/openinsight-proj/autoinstrumentation-java:1.45.0-eb49d21116a1d8fbf0d9080adddad3a367e68a5e
+     imagePullPolicy: IfNotPresent
+     command:
+       - cp
+       - /javaagent.jar
+       - /otel-auto-instrumentation-java/javaagent.jar
+     resources:
+       limits:
+         cpu: 500m
+         memory: 64Mi
+       requests:
+         cpu: 50m
+         memory: 64Mi
+     terminationMessagePath: /dev/termination-log
+     terminationMessagePolicy: File
+     volumeMounts:
+       - name: opentelemetry-auto-instrumentation-java
+         mountPath: /otel-auto-instrumentation-java
+       - name: kube-api-access-sp2mz
+         readOnly: true
+         mountPath: /var/run/secrets/kubernetes.io/serviceaccount
  containers:
    - name: myapp
      image: ghcr.io/pavolloffay/spring-petclinic:latest
      imagePullPolicy: Always
      ports:
        - containerPort: 8080
          protocol: TCP
+     env:
+       - name: OTEL_JAVAAGENT_DEBUG
+         value: "false"
+       - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
+         value: "true"
+       - name: SPLUNK_PROFILER_ENABLED
+         value: "false"
+       - name: JAVA_TOOL_OPTIONS
+         value: ' -javaagent:/otel-auto-instrumentation-java/javaagent.jar'
+       - name: OTEL_EXPORTER_OTLP_ENDPOINT
+         value: http://insight-agent-opentelemetry-collector.insight-system.svc.cluster.local:4317
+       - name: OTEL_NODE_IP
+         valueFrom:
+           fieldRef:
+             apiVersion: v1
+             fieldPath: status.hostIP
+       - name: OTEL_POD_IP
+         valueFrom:
+           fieldRef:
+             apiVersion: v1
+             fieldPath: status.podIP
+       - name: OTEL_K8S_CLUSTER_UID
+         value: 416d133f-d00a-43e1-b859-f1839a5a93ee
+       - name: OTEL_EXPORTER_OTLP_PROTOCOL
+         value: grpc
+       - name: OTEL_K8S_NAMESPACE_NAME
+         valueFrom:
+           fieldRef:
+             apiVersion: v1
+             fieldPath: metadata.namespace
+       - name: OTEL_LOGS_EXPORTER
+         value: none
+       - name: OTEL_METRICS_EXPORTER
+         value: prometheus
+       - name: OTEL_EXPORTER_PROMETHEUS_PORT
+         value: "9464"
+       - name: OTEL_SERVICE_NAME
+         value: my-app
+       - name: OTEL_RESOURCE_ATTRIBUTES_POD_NAME
+         valueFrom:
+           fieldRef:
+             apiVersion: v1
+             fieldPath: metadata.name
+       - name: OTEL_PROPAGATORS
+         value: tracecontext,baggage,b3,b3multi,jaeger,xray,ottrace
+       - name: OTEL_TRACES_SAMPLER
+         value: always_on
+       - name: OTEL_RESOURCE_ATTRIBUTES_POD_UID
+         valueFrom:
+           fieldRef:
+             apiVersion: v1
+             fieldPath: metadata.uid
+       - name: OTEL_RESOURCE_ATTRIBUTES_NODE_NAME
+         valueFrom:
+           fieldRef:
+             apiVersion: v1
+             fieldPath: spec.nodeName
+       - name: OTEL_RESOURCE_ATTRIBUTES
+         value: k8s.container.name=myapp,k8s.deployment.name=my-app,k8s.deployment.uid=25ce570c-8401-4f07-b8a9-dd64fcf3a1d1,k8s.namespace.name=default,k8s.node.name=$(OTEL_RESOURCE_ATTRIBUTES_NODE_NAME),k8s.pod.name=$(OTEL_RESOURCE_ATTRIBUTES_POD_NAME),k8s.pod.uid=$(OTEL_RESOURCE_ATTRIBUTES_POD_UID),k8s.replicaset.name=my-app-54fc75999c,k8s.replicaset.uid=56358d62-1321-4a62-b2e9-34132988efa0,service.instance.id=default.$(OTEL_RESOURCE_ATTRIBUTES_POD_NAME).myapp,service.version=latest
      resources: {}
      terminationMessagePath: /dev/termination-log
      terminationMessagePolicy: File
      volumeMounts:
        - name: kube-api-access-sp2mz
          readOnly: true
          mountPath: /var/run/secrets/kubernetes.io/serviceaccount
+       - name: opentelemetry-auto-instrumentation-java
+         mountPath: /otel-auto-instrumentation-java
```

🔔 需要注意的是，不同的版本自动注入后生成 YAML 并不完全一致。

## 链路查询

如何查询已经接入的服务，参考[链路查询](../../user-guide/trace/trace.md)。

## 安装多个 Instrumentation CR 满足差异化配置

上面的自动注入 CR 属于 Insight 内置且通用的。实际场景中，为了满足差异化配置需求，可以安装多个 Instrumentation CR，每个 CR 名字不同，再按需引用不同 CR 名字。

典型适用场景如下：

1. 环境隔离：开发/测试/生产环境需独立配置采样率、Exporter 端点等参数。
2. 团队/业务线隔离：不同团队对遥测数据的存储位置、资源标签有独立需求。
3. 服务类型差异化：前端、后端、数据处理服务的采样率、指标收集范围不同。
4. 采样策略精细化：高频服务低采样，核心链路全采样，避免性能开销。
5. 灰度发布与测试：通过新 CR 测试配置变更，验证后逐步替换旧配置。
6. 命名空间隔离：不同命名空间的服务使用独立 CR，避免配置干扰。
7. 合规性要求：欧盟服务需将数据发送至欧盟境内 Collector，符合 GDPR。
8. 资源标签区分：为不同服务群体添加专属标签（如 team: a、env: prod）。

核心逻辑：通过多 CR 实现 “分而治之”，避免单一配置无法满足多维度需求。

例如：

1. 再创建一个 `insight-opentelemetry-autoinstrumentation-debug` Instrumentation CR 用于项目组 B 调试新的 Java Agent 版本：

    ```yaml
    apiVersion: opentelemetry.io/v1alpha1
    kind: Instrumentation
    metadata:
      name: insight-opentelemetry-autoinstrumentation-debug # 👈 用于区分不同 Instrumentation CR
      namespace: insight-system
    spec:
      java:
        image: ghcr.m.daocloud.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:my-debug-xx.xx # 👈 用于测试的版本镜像
        ······
        env:
          - name: OTEL_JAVAAGENT_DEBUG
            value: "false"
          - name: OTEL_INSTRUMENTATION_JDBC_ENABLED
            value: "true"
          - name: SPLUNK_PROFILER_ENABLED
            value: "false"
          - name: OTEL_METRICS_EXPORTER
            value: "prometheus"
          - name: OTEL_METRICS_EXPORTER_PORT
            value: "9464"
        ······  
    ```

2. 更改原本服务使用的注解，使用 `insight-system/insight-opentelemetry-autoinstrumentation-debug` 注解：

    ```yaml
    instrumentation.opentelemetry.io/inject-sdk: "insight-system/insight-opentelemetry-autoinstrumentation-debug"
    ```