您如何在生产 Node 中处理日志记录和监控？

Question

Accepted Answer

**日志记录**记录您的应用程序执行的操作；**监控**跟踪其健康状况和性能。一起来说，它们为您提供**可观测性** — 理解和调试正在运行的生产系统的能力。`console.log` 对此不足以满足；您需要结构化日志、指标和告警。

## 结构化日志记录（不是 console.log）

```js
import pino from "pino";
const logger = pino();

// ✅ structured JSON logs — machine-parseable, searchable, with context
logger.info({ userId: 123, action: "login", ip: req.ip }, "user logged in");
logger.error({ err, requestId }, "failed to process order");
```

使用结构化 logger（pino/winston）输出具有一致字段的 **JSON**。与自由形式的 `console.log` 字符串不同，JSON 日志在日志聚合工具中是可搜索和可过滤的。在每条日志中包含上下文（用户 id、请求 id）。

## 日志级别和记录内容

```text
Levels: error > warn > info > debug (configure threshold per environment)
Log:    requests, errors with stack traces, key business events, slow operations
DON'T log: passwords, tokens, PII, full payloads with secrets (security/privacy)
```

## 关联 ID 在服务之间跟踪请求

```js
// attach a unique id per request, include it in EVERY log for that request
app.use((req, res, next) => {
  req.id = crypto.randomUUID();
  res.setHeader("X-Request-Id", req.id);
  next();
});
// now you can filter all logs for one request — vital in microservices
```

**关联/请求 ID** 让您即使跨多个服务也能重建单个请求的完整故事 — 对于调试分布式系统至关重要。

## 集中化日志

```text
Ship logs to a central platform (ELK/Elasticsearch, Datadog, Grafana Loki, CloudWatch)
→ search/aggregate across all instances; don't grep files on individual servers.
```

## 监控：指标、告警、追踪

```text
Metrics:  request rate, error rate, latency (p50/p95/p99), CPU/memory, event-loop lag
          (often via Prometheus + Grafana, or an APM like Datadog/New Relic)
Health:   /health & /ready endpoints for load balancers and orchestrators
Alerting: notify (PagerDuty/Slack) when error rate spikes or latency degrades
Tracing:  distributed tracing (OpenTelemetry) to follow requests across services
Errors:   error tracking (Sentry) for aggregated exceptions with stack traces & context
```

要监视的关键指标是**