correlate-observability-signals

pjt222

更新于 6 days ago

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关于

This skill unifies metrics, logs, and traces for cohesive debugging, enabling rapid root cause analysis across observability signals. It helps implement log-to-trace linking via exemplars and build unified dashboards using RED/USE methods. Use it when investigating complex, multi-system incidents or moving from siloed tools to a unified observability platform.

快速安装

Claude Code

技能文档

關聯可觀測信號

合指、誌、跡於一，為統除錯通三柱。

用

跨系複事查
減 MTTR
築統可觀儀板
施分跡
孤具轉統可觀

入

必：Prometheus（指）
必：誌聚系（Loki、Elasticsearch、CloudWatch）
必：分跡後端（Tempo、Jaeger、Zipkin）
可：Grafana 統視
可：OpenTelemetry 儀器

行

見 Extended Examples 備檔與模。

一：施跡脈傳

以 OpenTelemetry 加跡 ID 於諸誌與指：

// Go example: Propagate trace context to logs
package main

import (
    "context"
    "log"

    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/trace"
)

func handleRequest(ctx context.Context, userID string) {
    // Extract trace context
    span := trace.SpanFromContext(ctx)
    traceID := span.SpanContext().TraceID().String()

    // Include trace ID in structured logs
    log.Printf("trace_id=%s user_id=%s action=process_request", traceID, userID)

    // Business logic here
    processData(ctx, userID)
}

func processData(ctx context.Context, userID string) {
    tracer := otel.Tracer("my-service")
    ctx, span := tracer.Start(ctx, "processData")
    defer span.End()

    traceID := span.SpanContext().TraceID().String()
    log.Printf("trace_id=%s user_id=%s action=process_data", traceID, userID)

    // More work
}

Python 例：

# Python: Flask with OpenTelemetry
from flask import Flask, request
from opentelemetry import trace
from opentelemetry.instrumentation.flask import FlaskInstrumentor
import logging

app = Flask(__name__)
FlaskInstrumentor().instrument_app(app)

logging.basicConfig(
    format='%(asctime)s trace_id=%(otelTraceID)s span_id=%(otelSpanID)s %(message)s',
    level=logging.INFO
)

@app.route('/api/users/<user_id>')
def get_user(user_id):
    span = trace.get_current_span()
    trace_id = format(span.get_span_context().trace_id, '032x')

    logging.info(f"Fetching user {user_id}", extra={
        'otelTraceID': trace_id,
        'otelSpanID': format(span.get_span_context().span_id, '016x')
    })

    # Business logic
    return {"user_id": user_id}

得：諸誌含 trace_id，誌跡可聯。

敗：跡 ID 缺→察 OpenTelemetry SDK 初與脈傳。

二：設 Prometheus 中 exemplars

Exemplars 聯指至跡：

# prometheus.yml
global:
  scrape_interval: 15s
  # Enable exemplar storage
  exemplars:
    max_exemplars: 100000  # Per TSDB block

scrape_configs:
  - job_name: 'api-service'
    static_configs:
      - targets: ['api-service:8080']
    # Scrape exemplars
    metric_relabel_configs:
      - source_labels: [__name__]
        regex: 'http_request_duration_seconds.*'
        action: keep

儀應以發 exemplars：

// Go: Emit exemplars with Prometheus histogram
package main

import (
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promauto"
    "go.opentelemetry.io/otel/trace"
)

var httpDuration = promauto.NewHistogramVec(
    prometheus.HistogramOpts{
        Name:    "http_request_duration_seconds",
        Help:    "HTTP request duration",
        Buckets: prometheus.DefBuckets,
    },
    []string{"method", "endpoint", "status"},
)

func recordRequest(ctx context.Context, method, endpoint, status string, duration float64) {
    // Get trace ID for exemplar
    span := trace.SpanFromContext(ctx)
    traceID := span.SpanContext().TraceID().String()

    // Record metric with exemplar
    observer := httpDuration.WithLabelValues(method, endpoint, status)
    observer.(prometheus.ExemplarObserver).ObserveWithExemplar(
        duration,
        prometheus.Labels{"trace_id": traceID},
    )
}

於 Prometheus 中查 exemplars：

# Histogram with exemplars
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

於 Grafana，exemplars 現為直方圖上之點，可聯跡。

得： Grafana 指圖顯 exemplars，擊之開對應跡。

敗：驗 Prometheus 版 ≥2.26（exemplar 支），察 Grafana 資源設啟 exemplars。

三：以 RED 法築統儀板

RED 法：Rate、Errors、Duration（服之度）

{
  "dashboard": {
    "title": "API Service - RED Dashboard",
    "panels": [
      {
        "title": "Request Rate (req/s)",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{job=\"api-service\"}[5m])) by (endpoint)",
            "legendFormat": "{{ endpoint }}"
          }
        ],
        "exemplars": true
      },
      {
        "title": "Error Rate (%)",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{job=\"api-service\", status=~\"5..\"}[5m])) / sum(rate(http_requests_total{job=\"api-service\"}[5m])) * 100",
            "legendFormat": "Error %"
          }
        ],
        "exemplars": true
      },
      {
        "title": "Request Duration (p50, p95, p99)",
        "type": "graph",
        "targets": [
          {
            "expr": "histogram_quantile(0.50, rate(http_request_duration_seconds_bucket{job=\"api-service\"}[5m]))",
            "legendFormat": "p50"
          },
          {
            "expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket{job=\"api-service\"}[5m]))",
            "legendFormat": "p95"
          },
          {
            "expr": "histogram_quantile(0.99, rate(http_request_duration_seconds_bucket{job=\"api-service\"}[5m]))",
            "legendFormat": "p99"
          }
        ],
        "exemplars": true
      },
      {
        "title": "Correlated Logs",
        "type": "logs",
        "datasource": "Loki",
        "targets": [
          {
            "expr": "{job=\"api-service\"} |= \"error\""
          }
        ],
        "options": {
          "showTime": true,
          "enableLogDetails": true
        }
      }
    ]
  }
}

得：單儀板顯率、誤、時延+關聯誌。

敗：板顯「No Data」→驗指名合儀器。

四：施資 USE 法

USE 法：Utilization、Saturation、Errors（如 CPU、存、碟之資）

{
  "dashboard": {
    "title": "Node Resources - USE Dashboard",
    "panels": [
      {
        "title": "CPU Utilization (%)",
        "type": "graph",
        "targets": [
          {
            "expr": "100 - (avg(rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
            "legendFormat": "CPU Usage %"
          }
        ]
      },
      {
        "title": "CPU Saturation (Load Average)",
        "type": "graph",
        "targets": [
          {
            "expr": "node_load1",
            "legendFormat": "1min load"
          },
          {
            "expr": "node_load5",
            "legendFormat": "5min load"
          },
          {
            "expr": "count(node_cpu_seconds_total{mode=\"idle\"})",
            "legendFormat": "CPU cores (threshold)"
          }
        ]
      },
      {
        "title": "Memory Utilization (%)",
        "type": "graph",
        "targets": [
          {
            "expr": "(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100",
            "legendFormat": "Memory Usage %"
          }
        ]
      },
      {
        "title": "Memory Saturation (Page Faults)",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(node_vmstat_pgmajfault[5m])",
            "legendFormat": "Major page faults/s"
          }
        ]
      },
      {
        "title": "Disk Utilization (%)",
        "type": "graph",
        "targets": [
          {
            "expr": "(node_filesystem_size_bytes - node_filesystem_free_bytes) / node_filesystem_size_bytes * 100",
            "legendFormat": "{{ device }}"
          }
        ]
      },
      {
        "title": "Disk Saturation (IO Wait %)",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(node_cpu_seconds_total{mode=\"iowait\"}[5m]) * 100",
            "legendFormat": "IO Wait %"
          }
        ]
      }
    ]
  }
}

得：儀板顯諸 USE 維之資康。

敗：保 node_exporter 行且採系指。

五：於 Loki 中聯誌至跡

設 Loki 取跡 ID：

# loki-config.yml
schema_config:
  configs:
    - from: 2024-01-01
      store: boltdb-shipper
      object_store: s3
      schema: v11
      index:
        prefix: index_
        period: 24h

# Derived fields for trace linking
query_config:
  derived_fields:
    - name: TraceID
      source: trace_id
      url: 'https://tempo.company.com/trace/${__value.raw}'
      urlDisplayLabel: 'View Trace'

於 Grafana，設 Loki 資源：

{
  "name": "Loki",
  "type": "loki",
  "url": "http://loki:3100",
  "jsonData": {
    "derivedFields": [
      {
        "datasourceUid": "tempo-uid",
        "matcherRegex": "trace_id=(\\w+)",
        "name": "TraceID",
        "url": "$${__value.raw}"
      }
    ]
  }
}

得： Loki 誌中擊跡 ID→於 Tempo 中開對應跡。

敗：驗 regex 合誌式，察 Tempo 資源 UID。

六：建統事視

築板合諸信號：

{
  "dashboard": {
    "title": "Incident Investigation",
    "templating": {
      "list": [
        {
# ... (see EXAMPLES.md for complete configuration)

事中流：

高誤率警發
值班者開 Grafana 板
識定時之誤率尖
擊時延直方圖之 exemplar 點→開跡
跡顯慢庫查
擊跨段「View Logs」→開該跡之誌
誌揭致超時之具 SQL
根因識於 <2 分

得：單板除錯，躍於指/誌/跡間。

敗：聯敗→察資源設與跡 ID 傳。

驗

諸應誌含跡 ID
Prometheus 採 exemplars
Grafana 板直方圖顯 exemplar 點
擊 exemplar 於 Tempo/Jaeger 開對應跡
Loki 誌含可用之「View Trace」聯
關鍵服已建 RED 板
基建已建 USE 板
統事板於 GameDay 中測

忌

跡 ID 式不一：OpenTelemetry 用 32 字符 hex，Jaeger 用 16。擇一
缺脈傳：跡 ID 不跨服→分跡破。用 OpenTelemetry 自動儀器
Exemplar 過負：過多 exemplars（>100k）緩 Prometheus。採樣高量指
鐘偏：跡跨多服。設 NTP；鐘漂生跡序問
留策不合：跡逾而指存→聯破。齊留策

參

setup-prometheus-monitoring - 聯之指基
configure-log-aggregation - 聯之誌基
instrument-distributed-tracing - 聯之跡基
build-grafana-dashboards - 統視層

GitHub 仓库

pjt222/agent-almanac

路径: i18n/wenyan-ultra/skills/correlate-observability-signals

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