使用结构化日志记录与追踪实现可观测性
了解函数究竟在执行什么。学习结构化 JSON 日志记录、使用 AWS X-Ray 进行分布式追踪，以及用于生产级可观测性的自定义指标。
使用结构化日志记录与追踪实现可观测性 是 CoddyKit 上的免费 Serverless Backend with AWS Lambda & API Gateway 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Serverless Backend with AWS Lambda & API Gateway 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Serverless Backend with AWS Lambda & API Gateway 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
The Three Pillars of Observability
Observability rests on logs, metrics, and traces. Together they answer what happened, how much, and where the time went across your serverless system.
Why Structured Logging?
Plain text logs are hard to search. Structured (JSON) logs let CloudWatch Logs Insights filter and aggregate by field, turning logs into queryable data.
Writing a Structured Log
Emit JSON with consistent fields so you can query by request, level, and custom attributes.
console.log(JSON.stringify({
level: "INFO",
requestId: context.awsRequestId,
action: "createOrder",
orderId: 42
}));Correlation IDs
Pass a correlation ID through every service and log it everywhere. This lets you trace a single user request across multiple functions and queues.
Querying with Logs Insights
CloudWatch Logs Insights runs SQL-like queries over structured logs.
fields @timestamp, action, orderId
| filter level = "ERROR"
| sort @timestamp desc
| limit 20Distributed Tracing with X-Ray
AWS X-Ray records a trace as a request flows through Lambda, API Gateway, DynamoDB, and more, showing a timeline of every segment.
Enabling X-Ray
Turn on active tracing for the function, then instrument the SDK so downstream calls become subsegments.
const AWSXRay = require("aws-xray-sdk-core");
const AWS = AWSXRay.captureAWS(require("aws-sdk"));Custom Subsegments
Wrap your own logic in a subsegment to measure how long a specific block takes inside the trace.
const seg = AWSXRay.getSegment().addNewSubsegment("validate");
validate(input);
seg.close();Custom Metrics with EMF
The Embedded Metric Format lets you emit metrics inside a structured log line. CloudWatch extracts them automatically — no extra API call.
Setting Alarms
Create CloudWatch alarms on error rate, p99 latency, and DLQ depth. Alarms turn passive metrics into proactive alerts before users notice.
Avoiding Log Noise
Log with intent:
- Use levels (DEBUG/INFO/ERROR) and a log-level env var
- Never log secrets or full PII
- Sample high-volume traces to control cost
Quick Check
Test your observability knowledge.
Recap
You learned production observability:
- Logs, metrics, and traces are the three pillars
- Use structured JSON logs and correlation IDs
- X-Ray gives distributed traces across services
- Emit custom metrics with EMF and alarm on them
常见问题解答
「使用结构化日志记录与追踪实现可观测性」课时是免费的吗？
是的 — 「使用结构化日志记录与追踪实现可观测性」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Serverless Backend with AWS Lambda & API Gateway 课程的其余内容，请升级到 CoddyKit PRO。 Serverless Backend with AWS Lambda & API Gateway 课程共包含 4 节课。
「使用结构化日志记录与追踪实现可观测性」这节课中我会学到什么？
了解函数究竟在执行什么。学习结构化 JSON 日志记录、使用 AWS X-Ray 进行分布式追踪，以及用于生产级可观测性的自定义指标。 你通过在浏览器中直接运行的动手代码来练习 Serverless Backend with AWS Lambda & API Gateway，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Serverless Backend with AWS Lambda & API Gateway 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Serverless Backend with AWS Lambda & API Gateway 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「使用结构化日志记录与追踪实现可观测性」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Serverless Backend with AWS Lambda & API Gateway 课中编写并运行代码吗？
能。每节 Serverless Backend with AWS Lambda & API Gateway 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。