96 lines
2.8 KiB
YAML
96 lines
2.8 KiB
YAML
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apiVersion: opentelemetry.io/v1beta1
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kind: OpenTelemetryCollector
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metadata:
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name: otel-gateway
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namespace: opentelemetry-operator-system
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spec:
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mode: deployment
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replicas: 1
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serviceAccount: otel-gateway-collector # Operator 会自动创建并绑定权限
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config:
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receivers:
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otlp:
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protocols:
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grpc:
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endpoint: 0.0.0.0:4317
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http:
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endpoint: 0.0.0.0:4318
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# --- 核心：采集 K8s 集群状态指标 ---
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# 采集 Deployment, DaemonSet, StatefulSet, HPA, Node 等资源的状态
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k8s_cluster:
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collection_interval: 30s
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node_conditions_to_report: [Ready, MemoryPressure, DiskPressure, PIDPressure]
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allocatable_types_to_report: [cpu, memory]
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processors:
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batch:
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send_batch_size: 1000
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timeout: 10s
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memory_limiter:
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check_interval: 1s
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limit_percentage: 70
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spike_limit_percentage: 30
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# 增加 K8s 元数据标签 (这也是 Gateway 的重要作用)
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k8sattributes:
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extract:
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metadata:
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- k8s.namespace.name
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- k8s.pod.name
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- k8s.deployment.name
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- k8s.statefulset.name
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- k8s.daemonset.name
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- k8s.cronjob.name
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- k8s.job.name
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- k8s.node.name
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pod_association:
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- sources:
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- from: resource_attribute
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name: k8s.pod.ip
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- sources:
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- from: resource_attribute
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name: k8s.pod.uid
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- sources:
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- from: connection
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exporters:
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# 1. 导出 Metrics 到外部 Prometheus (使用 Remote Write)
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prometheusremotewrite:
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endpoint: "http://10.0.0.38:9090/api/v1/write"
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# 如果有 Basic Auth，在此配置
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# external_labels:
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# cluster: "test-k8s-cluster"
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# 2. 导出 Traces 到外部 Tempo (使用 OTLP gRPC)
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# otlp/tempo:
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# endpoint: "<你的TEMPO_IP>:4317"
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# tls:
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# insecure: true
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# 3. 导出 Logs 到外部 Elasticsearch (可选)
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# elasticsearch:
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# endpoints: ["http://<你的ES_IP>:9200"]
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# logs_index: "k8s-logs"
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debug:
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verbosity: basic
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service:
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pipelines:
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metrics:
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receivers: [otlp, k8s_cluster]
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processors: [memory_limiter, batch]
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# 确保 k8sattributes 在 batch 之前或之后取决于架构，Gateway通常主要做转发
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# 这里 k8s_cluster 产生的数据自带标签，otlp 来的数据应在 Agent 端打好标签
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exporters: [prometheusremotewrite]
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traces:
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receivers: [otlp]
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processors: [memory_limiter, batch]
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exporters: [otlp/tempo]
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# logs:
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# receivers: [otlp]
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# processors: [memory_limiter, batch]
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# exporters: [elasticsearch]
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