datadog-observability

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Datadog Observability

Datadog可观测性

Overview

概述

Datadog is a SaaS observability platform providing unified monitoring across infrastructure, applications, logs, and user experience. It offers AI-powered anomaly detection, 1000+ integrations, and OpenTelemetry compatibility.

Core Capabilities:

APM: Distributed tracing with automatic instrumentation for 8+ languages
Infrastructure: Host, container, and cloud service monitoring
Logs: Centralized collection with processing pipelines and 15-month retention
Metrics: Custom metrics via DogStatsD with cardinality management
Synthetics: Proactive API and browser testing from 29+ global locations
RUM: Frontend performance with Core Web Vitals and session replay

Datadog是一款SaaS可观测性平台，提供跨基础设施、应用、日志和用户体验的统一监控。它具备AI驱动的异常检测功能、1000+集成以及OpenTelemetry兼容性。

核心功能：

APM：支持8+种语言自动插桩的分布式追踪
基础设施监控：主机、容器和云服务监控
日志管理：集中式收集，附带处理流水线及15个月数据留存
指标管理：通过DogStatsD自定义指标，支持基数管理
合成监控：从全球29+个位置主动进行API和浏览器测试
RUM：包含核心Web指标和会话重放的前端性能监控

When to Use This Skill

何时使用该技能

Activate when:

Setting up production monitoring and observability
Implementing distributed tracing across microservices
Configuring log aggregation and analysis pipelines
Creating custom metrics and dashboards
Setting up alerting and anomaly detection
Optimizing Datadog costs

Do not use when:

Building with open-source stack (use Prometheus/Grafana instead)
Cost is primary concern and budget is limited
Need maximum customization over managed solution

启用场景：

搭建生产环境监控与可观测性体系
在微服务间实施分布式追踪
配置日志聚合与分析流水线
创建自定义指标和仪表盘
配置告警与异常检测
优化Datadog使用成本

禁用场景：

使用开源技术栈（建议使用Prometheus/Grafana替代）
成本为首要考虑因素且预算有限
需要对托管方案进行最大化定制

Quick Start

快速开始

1. Install Datadog Agent

1. 安装Datadog Agent

Docker (simplest):

bash

docker run -d --name dd-agent \
  -e DD_API_KEY=<YOUR_API_KEY> \
  -e DD_SITE="datadoghq.com" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -v /proc/:/host/proc/:ro \
  -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
  gcr.io/datadoghq/agent:7

Kubernetes (Helm):

bash

helm repo add datadog https://helm.datadoghq.com
helm install datadog-agent datadog/datadog \
  --set datadog.apiKey=<YOUR_API_KEY> \
  --set datadog.apm.enabled=true \
  --set datadog.logs.enabled=true

Docker（最简方式）：

bash

docker run -d --name dd-agent \
  -e DD_API_KEY=<YOUR_API_KEY> \
  -e DD_SITE="datadoghq.com" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -v /proc/:/host/proc/:ro \
  -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
  gcr.io/datadoghq/agent:7

Kubernetes（Helm）：

bash

helm repo add datadog https://helm.datadoghq.com
helm install datadog-agent datadog/datadog \
  --set datadog.apiKey=<YOUR_API_KEY> \
  --set datadog.apm.enabled=true \
  --set datadog.logs.enabled=true

2. Instrument Your Application

2. 为应用插桩

Python:

python

from ddtrace import tracer, patch_all

Python：

python

from ddtrace import tracer, patch_all

Automatic instrumentation for common libraries

patch_all()

Manual span for custom operations

with tracer.trace("custom.operation", service="my-service") as span: span.set_tag("user.id", user_id) # your code here


**Node.js:**
```javascript
// Must be first import
const tracer = require('dd-trace').init({
  service: 'my-service',
  env: 'production',
  version: '1.0.0',
});

with tracer.trace("custom.operation", service="my-service") as span: span.set_tag("user.id", user_id) # your code here


**Node.js：**
```javascript
// Must be first import
const tracer = require('dd-trace').init({
  service: 'my-service',
  env: 'production',
  version: '1.0.0',
});

3. Verify in Datadog UI

3. 在Datadog UI中验证

Go to Infrastructure > Host Map to verify agent
Go to APM > Services to see traced services
Go to Logs > Search to verify log collection

进入“基础设施 > 主机地图”验证Agent状态
进入“APM > 服务”查看已追踪的服务
进入“日志 > 搜索”验证日志收集情况

Core Concepts

核心概念

Tagging Strategy

标签策略

Tags enable filtering, aggregation, and cost attribution. Use consistent tags across all telemetry.

Required Tags:

Tag	Purpose	Example
`env`	Environment	`env:production`
`service`	Service name	`service:api-gateway`
`version`	Deployment version	`version:1.2.3`
`team`	Owning team	`team:platform`

Avoid High-Cardinality Tags:

User IDs, request IDs, timestamps
Pod IDs in Kubernetes
Build numbers, commit hashes

标签用于过滤、聚合和成本归因。所有遥测数据应使用统一的标签规范。

必填标签：

标签	用途	示例
`env`	环境标识	`env:production`
`service`	服务名称	`service:api-gateway`
`version`	部署版本	`version:1.2.3`
`team`	负责团队	`team:platform`

避免高基数标签：

用户ID、请求ID、时间戳
Kubernetes中的Pod ID
构建编号、提交哈希

Unified Observability

统一可观测性

Datadog correlates metrics, traces, and logs automatically:

Traces include span tags that link to metrics
Logs inject trace IDs for correlation
Dashboards combine all data sources

Datadog自动关联指标、追踪和日志：

追踪包含关联至指标的 span 标签
日志注入追踪ID以实现关联
仪表盘整合所有数据源

Best Practices

最佳实践

Start Simple

从简入手

Install Agent with basic configuration
Enable automatic instrumentation
Verify data in Datadog UI
Add custom spans/metrics as needed

安装Agent并使用基础配置
启用自动插桩
在Datadog UI中验证数据
根据需要添加自定义span/指标

Progressive Enhancement

渐进式增强

Basic → APM tracing → Custom spans → Custom metrics → Profiling → RUM

基础配置 → APM追踪 → 自定义Span → 自定义指标 → 性能分析 → RUM

Key Instrumentation Points

关键插桩点

HTTP entry/exit points
Database queries
External service calls
Message queue operations
Business-critical flows

HTTP入口/出口点
数据库查询
外部服务调用
消息队列操作
业务关键流程

Common Mistakes

常见误区

High-cardinality tags: Using user IDs or request IDs as tags creates millions of unique metrics
Missing log index quotas: Leads to unexpected bills from log volume spikes
Over-alerting: Creates alert fatigue; alert on symptoms, not causes
Missing service tags: Prevents correlation between metrics, traces, and logs
No sampling for high-volume traces: Ingests everything, causing cost explosion

高基数标签：将用户ID或请求ID作为标签会生成数百万个唯一指标
未设置日志索引配额：日志量突增会导致意外账单
告警过度：引发告警疲劳；应针对症状而非原因告警
缺失服务标签：无法关联指标、追踪和日志
高流量追踪未采样：全量摄入会导致成本激增

Navigation

Agent Installation: Docker, Kubernetes, Linux, Windows, and cloud-specific setup
APM Instrumentation: Python, Node.js, Go, Java instrumentation with code examples
Log Management: Pipelines, Grok parsing, standard attributes, archives
Custom Metrics: DogStatsD patterns, metric types, tagging best practices
Alerting: Monitor types, anomaly detection, alert hygiene
Cost Optimization: Metrics without Limits, sampling, index quotas
Kubernetes: DaemonSet, Cluster Agent, autodiscovery

如需详细实施指南：

Agent安装：Docker、Kubernetes、Linux、Windows及云环境专属配置
APM插桩：Python、Node.js、Go、Java插桩及代码示例
日志管理：流水线、Grok解析、标准属性、归档
自定义指标：DogStatsD模式、指标类型、标签最佳实践
告警配置：监控类型、异常检测、告警治理
成本优化：无限制指标、采样、索引配额
Kubernetes：DaemonSet、集群Agent、自动发现

Complementary Skills

互补技能

When using this skill, consider these related skills (if deployed):

docker: Container instrumentation patterns
kubernetes: K8s-native monitoring patterns
python/nodejs/go: Language-specific APM setup

使用本技能时，可结合以下相关技能（若已部署）：

docker：容器插桩模式
kubernetes：K8s原生监控模式
python/nodejs/go：语言专属APM配置

Resources

资源

Official Documentation:

APM: https://docs.datadoghq.com/tracing/
Logs: https://docs.datadoghq.com/logs/
Metrics: https://docs.datadoghq.com/metrics/
DogStatsD: https://docs.datadoghq.com/developers/dogstatsd/

Cost Management:

Billing: https://docs.datadoghq.com/account_management/billing/
Usage Attribution: https://docs.datadoghq.com/account_management/billing/usage_attribution/

官方文档：

APM：https://docs.datadoghq.com/tracing/
日志：https://docs.datadoghq.com/logs/
指标：https://docs.datadoghq.com/metrics/
DogStatsD：https://docs.datadoghq.com/developers/dogstatsd/

成本管理：

账单：https://docs.datadoghq.com/account_management/billing/
用量归因：https://docs.datadoghq.com/account_management/billing/usage_attribution/

datadog-observability

Original

Translation

Datadog Observability

Datadog可观测性

Overview

概述

When to Use This Skill

何时使用该技能

Quick Start

快速开始

1. Install Datadog Agent

1. 安装Datadog Agent

2. Instrument Your Application

2. 为应用插桩

Automatic instrumentation for common libraries

Automatic instrumentation for common libraries

Manual span for custom operations

Manual span for custom operations

3. Verify in Datadog UI

3. 在Datadog UI中验证

Core Concepts

核心概念

Tagging Strategy

标签策略

Unified Observability

统一可观测性

Best Practices

最佳实践

Start Simple

从简入手

Progressive Enhancement

渐进式增强

Key Instrumentation Points

关键插桩点

Common Mistakes

常见误区

Navigation

导航

Complementary Skills

互补技能

Resources

资源