一、【Helm】添加OTel Helm repo

helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm repo update

二、【Helm Chart】OTel Collector

收集器可以部署为以下四种模式之一:

• deployment（默认）
• daemonSet
• statefulSet

默认模式为deployment。

helm install otel-collector open-telemetry/opentelemetry-collector \
--set mode=<value>

helm install otel-collector open-telemetry/opentelemetry-collector \
--values <path where you saved the chart>

2.1 daemonset

部署OpenTelemetry Collector的daemonset实例，以收集节点(node)和运行在这些节点上的工作负载(workloads)相关的遥测数据(telemetry)。使用daementset来保证在所有节点上都安装了这个收集器实例。daemenset收集器的每个实例将只从其运行的节点收集数据。

收集器的实例将使用以下组件:

• OTLP Receiver：收集应用程序跟踪、度量和日志。
• Kubernetes Attributes Processor：将Kubernetes元数据添加到传入的应用遥测中。
• Kubeletstats Receiver：从kubelet上的API服务器提取节点、pod和容器指标。
• Filelog Receiver：用于收集写入stdout/stderr的Kubernetes日志和应用程序日志(/var/log/pods/*/*/*.log)。

values-for-daemonset.yaml：

mode: daemonset

presets:
  # enables the k8sattributesprocessor and adds it to the traces, metrics, and logs pipelines
  kubernetesAttributes:
    enabled: true
  # enables the kubeletstatsreceiver and adds it to the metrics pipelines
  kubeletMetrics:
    enabled: true
  # Enables the filelogreceiver and adds it to the logs pipelines
  logsCollection:
    enabled: true
## The chart only includes the loggingexporter by default
## If you want to send your data somewhere you need to
## configure an exporter, such as the otlpexporter
# config:
# exporters:
#   otlp:
#     endpoint: "<SOME BACKEND>"
# service:
#   pipelines:
#     traces:
#       exporters: [ otlp ]
#     metrics:
#       exporters: [ otlp ]
#     logs:
#       exporters: [ otlp ]

2.2 deloyment

部署Collector的deloyment实例，以收集与整个集群相关的遥测数据。只有一个副本的部署确保我们不会产生重复的数据。

收集器的实例将使用以下组件:

• Kubernetes Cluster Receiver：收集集群级指标和实体事件。
• Kubernetes Objects Receiver：从Kubernetes API服务器收集对象，例如events。

values-for-deloyment

mode: deployment

# We only want one of these collectors - any more and we'd produce duplicate data
replicaCount: 1

presets:
  # enables the k8sclusterreceiver and adds it to the metrics pipelines
  clusterMetrics:
    enabled: true
  # enables the k8sobjectsreceiver to collect events only and adds it to the logs pipelines
  kubernetesEvents:
    enabled: true
## The chart only includes the loggingexporter by default
## If you want to send your data somewhere you need to
## configure an exporter, such as the otlpexporter
# config:
# exporters:
#   otlp:
#     endpoint: "<SOME BACKEND>"
# service:
#   pipelines:
#     traces:
#       exporters: [ otlp ]
#     metrics:
#       exporters: [ otlp ]
#     logs:
#       exporters: [ otlp ]

三、【K8S Operator】OTel Operator

OTel K8S整体架构：

3.1 安装OTel Operator

$ helm install \
--set admissionWebhooks.certManager.enabled=false \
--set admissionWebhooks.certManager.autoGenerateCert=true \
opentelemetry-operator open-telemetry/opentelemetry-operator

helm uninstall opentelemetry-operator

收集器可以部署为以下四种模式之一:

• deployment（默认）
• daemonSet
• statefulSet
• sidecar

默认模式为deployment。

3.2 部署OpenTelemetryCollector

3.2.1 Deloyment Mode

独立部署、运维Collector，方便scale、回滚版本。

$ kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: my-collector
spec:
  mode: deployment # This configuration is omittable.
  config: |
    receivers:
      jaeger:
        protocols:
          grpc:
    processors:

    exporters:
      debug:

    service:
      pipelines:
        traces:
          receivers: [jaeger]
          processors: []
          exporters: [debug]
EOF

3.2.2 DeamonSet Mode

作为DaemonSet运行Collector于每个K8s Node之上，收集Node上pod信息。

$ kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: my-collector
spec:
  mode: daemonset
  hostNetwork: true
  config: |
    receivers:
      jaeger:
        protocols:
          grpc:
    processors:

    exporters:
      debug:
        verbosity: detailed

    service:
      pipelines:
        traces:
          receivers: [jaeger]
          processors: []
          exporters: [debug]
EOF

3.2.3 StatefulSetMode

将Collector部署为StatefulSet基本上有三个主要优势:

• Collector实例的name可预测
  如果使用上述两种方法来部署Collector，则Collector实例的pod名称将是唯一的(它的名称加上随机序列)。但是，statfulset中的每个Pod都从statfulset的名称和Pod的序号(my-col-0、my-col-1、my-col-2等)中派生其主机名。
• 当Collector副本失败时，将安排重新调度
  如果Collector pod在StatefulSet中失败，Kubernetes将尝试重新调度具有相同名称的新pod到同一节点。Kubernetes也会尝试将相同的粘性身份(例如volumnes)附加到新的pod上。

$ kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: my-collector
spec:
  mode: statefulset
  replicas: 3
  config: |
    receivers:
      jaeger:
        protocols:
          grpc:
    processors:

    exporters:
      debug:

    service:
      pipelines:
        traces:
          receivers: [jaeger]
          processors: []
          exporters: [debug]
EOF

3.2.4 Sidecar Mode

将Collector作为sidecar注入Pod中，
sidecar模式的最大优点是，它允许人们尽可能快速、可靠地从应用程序中卸载遥测数据。这个Collector实例将在容器级别上工作，不会创建新的pod，这对于保持Kubernetes集群的整洁和易于管理是完美的。此外，当您希望使用不同的收集/导出策略时，还可以使用sidecar模式，这正好适合此应用程序。
一旦Collector Sidecar实例存在于给定的名称空间中，您就可以从该名称空间为deployment注入sidecar（以下2种方式任选1种即可）：

• 为Deployment添加annontation - sidecar.opentelemetry.io/inject: true
• 为Namespace添加annontation - sidecar.opentelemetry.io/inject: true

$ kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: sidecar-for-my-app
spec:
  mode: sidecar
  config: |
    receivers:
      jaeger:
        protocols:
          thrift_compact:
    processors:

    exporters:
      debug:

    service:
      pipelines:
        traces:
          receivers: [jaeger]
          processors: []
          exporters: [debug]
EOF

$ kubectl apply -f - <<EOF
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:
        sidecar.opentelemetry.io/inject: "true" # CORRECT
    spec:
      containers:
      - name: myapp
        image: jaegertracing/vertx-create-span:operator-e2e-tests
        ports:
          - containerPort: 8080
            protocol: TCP
EOF

3.3 部署Instrumentation - 配置应用端自动注入OTel Agent

operator可以注入和配置OpenTelemetry自动注入agent。目前支持：

• Apache HTTPD
• DotNet
• Go
• Java
• Nginx
• NodeJS
• Python

3.3.1 全局配置Instrumentation

kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: my-instrumentation
spec:
  exporter:
    endpoint: http://otel-collector:4317
  propagators:
    - tracecontext
    - baggage
    - b3
  sampler:
    type: parentbased_traceidratio
    argument: "0.25"
  python:
    env:
      # Required if endpoint is set to 4317.
      # Python autoinstrumentation uses http/proto by default
      # so data must be sent to 4318 instead of 4317.
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318
  dotnet:
    env:
      # Required if endpoint is set to 4317.
      # Dotnet autoinstrumentation uses http/proto by default
      # See https://github.com/open-telemetry/opentelemetry-dotnet-instrumentation/blob/888e2cd216c77d12e56b54ee91dafbc4e7452a52/docs/config.md#otlp
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318
  go:
    env:
      # Required if endpoint is set to 4317.
      # Go autoinstrumentation uses http/proto by default
      # so data must be sent to 4318 instead of 4317.
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318
EOF

以上部署成功的CR Instrumentation可以通过如下命令查询：

kubectl get otelinst.

3.3.2 工作负载通过annotation启用自动注入

通过添加annotation启动自动注入：

• 向pod添加annotation以启用注入
• 将annotation添加到namespace中，以便该名称空间中的所有pod都将获得检测
• 将annotation添加到单独的PodSpec对象中，这些对象可以作为Deployment、Statefulset和其他资源的一部分使用

Java:

instrumentation.opentelemetry.io/inject-java: "true"
# 注入到指定的container中（适用于一个Pod中有多个container）
# 如不指定则默认注入到第一个container中，
# 可通过此配置避免向istio-proxy中注入
instrumentation.opentelemetry.io/container-names: "myapp,myapp2"

NodeJS:

instrumentation.opentelemetry.io/inject-nodejs: "true"

Python:

instrumentation.opentelemetry.io/inject-python: "true"

.NET:

.NET auto-instrumentation also honors an annotation that will be used to set the .NET Runtime Identifiers(RIDs). Currently, only two RIDs are supported: linux-x64 and linux-musl-x64. By default linux-x64 is used.

instrumentation.opentelemetry.io/inject-dotnet: "true"
instrumentation.opentelemetry.io/otel-dotnet-auto-runtime: "linux-x64" # for Linux glibc based images, this is default value and can be omitted
instrumentation.opentelemetry.io/otel-dotnet-auto-runtime: "linux-musl-x64"  # for Linux musl based images

Go:

Go auto-instrumentation also honors an annotation that will be used to set the OTEL_GO_AUTO_TARGET_EXE env var. This env var can also be set via the Instrumentation resource, with the annotation taking precedence. Since Go auto-instrumentation requires OTEL_GO_AUTO_TARGET_EXE to be set, you must supply a valid executable path via the annotation or the Instrumentation resource. Failure to set this value causes instrumentation injection to abort, leaving the original pod unchanged.

instrumentation.opentelemetry.io/inject-go: "true"
instrumentation.opentelemetry.io/otel-go-auto-target-exe: "/path/to/container/executable"

Go auto-instrumentation also requires elevated permissions. The below permissions are set automatically and are required.

securityContext:
    privileged: true
    runAsUser: 0

Apache HTTPD:

instrumentation.opentelemetry.io/inject-apache-httpd: "true"

Nginx:

instrumentation.opentelemetry.io/inject-nginx: "true"

OpenTelemetry SDK environment variables only:

instrumentation.opentelemetry.io/inject-sdk: "true"

---

可选值：

• true - inject and Instrumentation resource from the namespace.
• my-instrumentation - name of Instrumentation CR instance in the current namespace.
• my-other-namespace/my-instrumentation - name and namespace of Instrumentation CR instance in another namespace.
• false - do not inject

3.3.3 Pod内多个container注入

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment-with-multi-containers-multi-instrumentations
spec:
  selector:
    matchLabels:
      app: my-pod-with-multi-containers-multi-instrumentations
  replicas: 1
  template:
    metadata:
      labels:
        app: my-pod-with-multi-containers-multi-instrumentations
      annotations:
        instrumentation.opentelemetry.io/inject-java: "true"
        instrumentation.opentelemetry.io/java-container-names: "myapp,myapp2"
        instrumentation.opentelemetry.io/inject-python: "true"
        instrumentation.opentelemetry.io/python-container-names: "myapp3"
    spec:
      containers:
      - name: myapp
        image: myImage1
      - name: myapp2
        image: myImage2
      - name: myapp3
        image: myImage3

3.3.4 Java Pod自动注入

Java Pod被OTel自动注入后，Pod定义被修改如下：

apiVersion: v1
kind: Pod
metadata:
  labels:
    app: app-atom
    version: v1
  name: app-atom-6c97b8dd84-mw222
  namespace: otel-poc
spec:
  containers:
  - env:
    - name: SPRING_OUTPUT_ANSI_ENABLED
      value: NEVER
    - name: LOGGING_CONFIG
      value: /config/logback-spring.xml
    - name: JAVA_TOOL_OPTIONS
      value: ' -javaagent:/otel-auto-instrumentation-java/javaagent.jar'
    - name: OTEL_SERVICE_NAME
      value: app-atom
    - name: OTEL_EXPORTER_OTLP_ENDPOINT
      value: http://otel-collector.opentelemetry-operator-system.svc.cluster.local:4317
    - name: OTEL_RESOURCE_ATTRIBUTES_POD_NAME
      valueFrom:
        fieldRef:
          apiVersion: v1
          fieldPath: metadata.name
    - name: OTEL_RESOURCE_ATTRIBUTES_NODE_NAME
      valueFrom:
        fieldRef:
          apiVersion: v1
          fieldPath: spec.nodeName
    - name: OTEL_PROPAGATORS
      value: tracecontext,baggage,b3
    - name: OTEL_TRACES_SAMPLER
      value: parentbased_traceidratio
    - name: OTEL_TRACES_SAMPLER_ARG
      value: "0.25"
    - name: OTEL_RESOURCE_ATTRIBUTES
      value: k8s.container.name=app-atom,k8s.deployment.name=app-atom,k8s.namespace.name=otel-poc,k8s.node.name=$(OTEL_RESOURCE_ATTRIBUTES_NODE_NAME),k8s.pod.name=$(OTEL_RESOURCE_ATTRIBUTES_POD_NAME),k8s.replicaset.name=app-atom-6c97b8dd84,service.version=latest
    image: otel-poc/app-atom:latest
    imagePullPolicy: IfNotPresent
    livenessProbe:
      failureThreshold: 3
      httpGet:
        path: /actuator/health
        port: 8080
        scheme: HTTP
      initialDelaySeconds: 60
      periodSeconds: 10
      successThreshold: 1
      timeoutSeconds: 5
    name: app-atom
    ports:
    - containerPort: 8080
      name: http
      protocol: TCP
    - containerPort: 9999
      name: http-xxljob
      protocol: TCP
    readinessProbe:
      failureThreshold: 3
      httpGet:
        path: /actuator/health
        port: 8080
        scheme: HTTP
      initialDelaySeconds: 60
      periodSeconds: 10
      successThreshold: 1
      timeoutSeconds: 5
    resources:
      limits:
        cpu: "1"
        memory: 1000Mi
      requests:
        cpu: 10m
        memory: 128Mi
    terminationMessagePath: /dev/termination-log
    terminationMessagePolicy: File
    volumeMounts:
    - mountPath: /config/
      name: app-config
    - mountPath: /var/run/secrets/kubernetes.io/serviceaccount
      name: kube-api-access-vxx27
      readOnly: true
    - mountPath: /otel-auto-instrumentation-java
      name: opentelemetry-auto-instrumentation-java
  dnsPolicy: ClusterFirst
  enableServiceLinks: true
  initContainers:
  - command:
    - cp
    - /javaagent.jar
    - /otel-auto-instrumentation-java/javaagent.jar
    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:231128
    imagePullPolicy: IfNotPresent
    name: opentelemetry-auto-instrumentation-java
    resources:
      limits:
        cpu: 500m
        memory: 64Mi
      requests:
        cpu: 50m
        memory: 64Mi
    terminationMessagePath: /dev/termination-log
    terminationMessagePolicy: File
    volumeMounts:
    - mountPath: /otel-auto-instrumentation-java
      name: opentelemetry-auto-instrumentation-java
    - mountPath: /var/run/secrets/kubernetes.io/serviceaccount
      name: kube-api-access-vxx27
      readOnly: true
  nodeName: k-node1
  preemptionPolicy: PreemptLowerPriority
  priority: 0
  restartPolicy: Always
  schedulerName: default-scheduler
  securityContext: {}
  serviceAccount: default
  serviceAccountName: default
  terminationGracePeriodSeconds: 30
  tolerations:
  - effect: NoExecute
    key: node.kubernetes.io/not-ready
    operator: Exists
    tolerationSeconds: 300
  - effect: NoExecute
    key: node.kubernetes.io/unreachable
    operator: Exists
    tolerationSeconds: 300
  volumes:
  - configMap:
      defaultMode: 420
      name: app-atom
    name: app-config
  - name: kube-api-access-vxx27
    projected:
      defaultMode: 420
      sources:
      - serviceAccountToken:
          expirationSeconds: 3607
          path: token
      - configMap:
          items:
          - key: ca.crt
            path: ca.crt
          name: kube-root-ca.crt
      - downwardAPI:
          items:
          - fieldRef:
              apiVersion: v1
              fieldPath: metadata.namespace
            path: namespace
  - emptyDir:
      sizeLimit: 200Mi
    name: opentelemetry-auto-instrumentation-java

3.3.5 剔除/actuator/health

https://github.com/open-telemetry/opentelemetry-java-instrumentation/issues/1060

---

参考：
https://opentelemetry.io/docs/kubernetes/
https://opentelemetry.io/docs/kubernetes/operator/
https://github.com/open-telemetry/opentelemetry-helm-charts
https://github.com/open-telemetry/opentelemetry-operator
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