mcpField
D
datadog

datadog

github.com/bagelhole云与 DevOps1.2k314 次安装23 周前更新
代码仓库

关于 datadog

面向基础设施和应用可观测性的 Datadog 使用技能，介绍 Agent、日志、APM、指标、仪表板和告警配置，适用于负责监控的团队。

主要功能

  1. 01安装和配置 Datadog Agent
  2. 02采集文件、容器和 Kubernetes 日志
  3. 03配置 APM 与分布式追踪
  4. 04提交自定义指标
  5. 05创建仪表板和告警监视器

适用场景

  1. 01监控云基础设施和应用
  2. 02追踪跨服务请求
  3. 03汇总应用与基础设施数据
  4. 04为错误率或延迟配置告警

安装

复制下面的命令，在终端中运行。

$npx skills add https://github.com/bagelhole/devops-security-agent-skills --skill datadog

安装后在 Claude Code 中输入/datadog确认已加载。

附带文件references/datadog-integrations.md

详细介绍

Datadog

使用 Datadog 统一可观测性平台监控基础设施和应用。

何时使用此技能

在以下情况下使用此技能：

  • 实施企业级监控
  • 设置 APM 和分布式追踪
  • 为基础设施和应用创建统一仪表板
  • 配置智能告警
  • 监控云基础设施（AWS、Azure、GCP）

前提条件

  • Datadog 账号和 API 密钥
  • Agent 安装权限
  • 用于 APM 的应用代码访问权限

Agent 安装

Linux
# Install agent
DD_API_KEY=<YOUR_API_KEY> DD_SITE="datadoghq.com" bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

# Or via package manager
apt-get update && apt-get install datadog-agent

# Configure API key
echo "api_key: YOUR_API_KEY" >> /etc/datadog-agent/datadog.yaml

# Start agent
systemctl start datadog-agent
systemctl enable datadog-agent
Docker
# docker-compose.yml
version: '3.8'

services:
  datadog-agent:
    image: gcr.io/datadoghq/agent:7
    environment:
      - DD_API_KEY=${DD_API_KEY}
      - DD_SITE=datadoghq.com
      - DD_LOGS_ENABLED=true
      - DD_APM_ENABLED=true
      - DD_PROCESS_AGENT_ENABLED=true
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - /proc/:/host/proc/:ro
      - /sys/fs/cgroup/:/host/sys/fs/cgroup:ro
    ports:
      - "8126:8126"  # APM
      - "8125:8125/udp"  # DogStatsD
Kubernetes
# Using Helm
helm repo add datadog https://helm.datadoghq.com

helm install datadog datadog/datadog \
  --set datadog.apiKey=${DD_API_KEY} \
  --set datadog.site=datadoghq.com \
  --set datadog.logs.enabled=true \
  --set datadog.apm.portEnabled=true \
  --set datadog.processAgent.enabled=true \
  --namespace datadog \
  --create-namespace

Agent 配置

# /etc/datadog-agent/datadog.yaml
api_key: YOUR_API_KEY
site: datadoghq.com

# Hostname
hostname: myserver.example.com

# Tags applied to all metrics
tags:
  - env:production
  - service:myapp
  - team:platform

# Log collection
logs_enabled: true

# APM
apm_config:
  enabled: true
  apm_dd_url: https://trace.agent.datadoghq.com

# Process monitoring
process_config:
  enabled: true

# Container monitoring
container_collect_all: true
docker_labels_as_tags:
  app: service
  environment: env

集成配置

MySQL
# /etc/datadog-agent/conf.d/mysql.d/conf.yaml
init_config:

instances:
  - host: localhost
    port: 3306
    username: datadog
    password: <PASSWORD>
    tags:
      - env:production
    options:
      replication: true
      extra_status_metrics: true
PostgreSQL
# /etc/datadog-agent/conf.d/postgres.d/conf.yaml
init_config:

instances:
  - host: localhost
    port: 5432
    username: datadog
    password: <PASSWORD>
    dbname: mydb
    collect_activity_metrics: true
    collect_database_size_metrics: true
NGINX
# /etc/datadog-agent/conf.d/nginx.d/conf.yaml
init_config:

instances:
  - nginx_status_url: http://localhost:80/nginx_status
    tags:
      - env:production

日志收集

基于文件的日志
# /etc/datadog-agent/conf.d/myapp.d/conf.yaml
logs:
  - type: file
    path: /var/log/myapp/*.log
    service: myapp
    source: python
    sourcecategory: custom
    tags:
      - env:production

  - type: file
    path: /var/log/nginx/access.log
    service: nginx
    source: nginx
    log_processing_rules:
      - type: exclude_at_match
        name: exclude_healthchecks
        pattern: health_check
Docker 日志
# docker-compose.yml
services:
  myapp:
    labels:
      com.datadoghq.ad.logs: '[{"source": "python", "service": "myapp"}]'
Kubernetes 日志
# Pod annotation
apiVersion: v1
kind: Pod
metadata:
  annotations:
    ad.datadoghq.com/myapp.logs: |
      [{
        "source": "python",
        "service": "myapp",
        "log_processing_rules": [{
          "type": "multi_line",
          "name": "python_tracebacks",
          "pattern": "^Traceback"
        }]
      }]

APM 配置

Python
from ddtrace import patch_all, tracer

# Automatic instrumentation
patch_all()

# Configure tracer
tracer.configure(
    hostname='localhost',
    port=8126,
    service='myapp',
    env='production',
    version='1.0.0'
)

# Manual instrumentation
@tracer.wrap(service='myapp', resource='process_order')
def process_order(order_id):
    with tracer.trace('validate_order') as span:
        span.set_tag('order_id', order_id)
        # Validation logic
    
    with tracer.trace('save_order'):
        # Save logic
        pass

# Install library
pip install ddtrace

# Run with auto-instrumentation
ddtrace-run python app.py
Node.js
const tracer = require('dd-trace').init({
  service: 'myapp',
  env: 'production',
  version: '1.0.0',
  logInjection: true
});

// Manual instrumentation
const span = tracer.startSpan('custom_operation');
span.setTag('user_id', userId);
// ... operation
span.finish();

# Install library
npm install dd-trace

# Run with auto-instrumentation
DD_TRACE_ENABLED=true node --require dd-trace/init app.js
Go
import (
    "gopkg.in/DataDog/dd-trace-go.v1/ddtrace/tracer"
)

func main() {
    tracer.Start(
        tracer.WithService("myapp"),
        tracer.WithEnv("production"),
        tracer.WithServiceVersion("1.0.0"),
    )
    defer tracer.Stop()

    // Manual span
    span, ctx := tracer.StartSpanFromContext(ctx, "process_request")
    defer span.Finish()
    span.SetTag("user_id", userID)
}

自定义指标

DogStatsD
from datadog import DogStatsd

statsd = DogStatsd(host='localhost', port=8125)

# Counter
statsd.increment('myapp.orders.count', tags=['env:production'])

# Gauge
statsd.gauge('myapp.queue.size', queue_size, tags=['queue:orders'])

# Histogram
statsd.histogram('myapp.request.duration', response_time)

# Distribution
statsd.distribution('myapp.response_time', duration, tags=['endpoint:/api/orders'])
API 提交
from datadog_api_client import Configuration, ApiClient
from datadog_api_client.v2.api.metrics_api import MetricsApi
from datadog_api_client.v2.model.metric_payload import MetricPayload
from datadog_api_client.v2.model.metric_series import MetricSeries
from datadog_api_client.v2.model.metric_point import MetricPoint

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api = MetricsApi(api_client)
    
    payload = MetricPayload(
        series=[
            MetricSeries(
                metric="custom.metric.name",
                type=MetricSeries.GAUGE,
                points=[MetricPoint(value=42.0, timestamp=int(time.time()))],
                tags=["env:production"]
            )
        ]
    )
    api.submit_metrics(body=payload)

仪表板

仪表板 JSON
{
  "title": "Application Overview",
  "widgets": [
    {
      "definition": {
        "type": "timeseries",
        "title": "Request Rate",
        "requests": [
          {
            "q": "sum:trace.http.request.hits{service:myapp}.as_rate()",
            "display_type": "line"
          }
        ]
      }
    },
    {
      "definition": {
        "type": "query_value",
        "title": "Error Rate",
        "requests": [
          {
            "q": "sum:trace.http.request.errors{service:myapp}.as_rate() / sum:trace.http.request.hits{service:myapp}.as_rate() * 100"
          }
        ],
        "precision": 2
      }
    }
  ]
}

监控器（告警）

指标监控器
{
  "name": "High Error Rate",
  "type": "metric alert",
  "query": "sum(last_5m):sum:trace.http.request.errors{service:myapp}.as_count() / sum:trace.http.request.hits{service:myapp}.as_count() > 0.05",
  "message": "Error rate is {{value}}% for {{service.name}}. @slack-alerts",
  "tags": ["service:myapp", "env:production"],
  "options": {
    "thresholds": {
      "critical": 0.05,
      "warning": 0.02
    },
    "notify_no_data": true,
    "no_data_timeframe": 10
  }
}
APM 监控器
{
  "name": "High Latency Alert",
  "type": "trace-analytics alert",
  "query": "trace-analytics(\"service:myapp @http.status_code:2*\").rollup(\"avg\", \"@duration\").last(\"5m\") > 2000000000",
  "message": "Average latency is above 2 seconds. @pagerduty",
  "options": {
    "thresholds": {
      "critical": 2000000000
    }
  }
}

常见问题

问题：Agent 未上报

问题：Datadog 中没有显示数据
解决方案：检查 API 密钥，使用 datadog-agent status 确认 Agent 状态

问题：缺少追踪数据

问题：没有显示 APM 追踪数据
解决方案：确认已启用 APM，检查追踪器配置，并确认端口 8126

问题：标签基数过高

问题：自定义指标被丢弃
解决方案：减少唯一标签值，并使用分布指标代替直方图

最佳实践

  • 使用一致的服务和环境标签
  • 实施规范的标签命名约定
  • 使用统一服务标记（service、env、version）
  • 设置服务级监控器
  • 为每项服务创建仪表板
  • 实施日志与追踪数据关联
  • 对延迟指标使用分布指标
  • 配置合理的告警升级机制

相关技能

评论

支持 Markdown