azure-monitor-opentelemetry-ts
microsoft/skills
利用 Azure Monitor 和 OpenTelemetry,为 Node.js 应用程序自动配置分布式追踪、指标和日志功能。
...展开全部Azure Monitor OpenTelemetry TypeScript SDK
为 Node.js 应用程序自动添加分布式追踪、指标和日志功能。
安装
# 发行版(推荐 - 自动插桩)
npm install @azure/monitor-opentelemetry
# 低级导出器(自定义 OpenTelemetry 配置)
npm install @azure/monitor-opentelemetry-exporter
# 自定义日志采集
npm install @azure/monitor-ingestion
环境变量
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...
AZURE_TOKEN_CREDENTIALS=prod # 仅当在生产环境中使用 DefaultAzureCredential 时才需要
快速入门(自动instrumentation)
重要提示:请在导入其他模块之前调用useAzureMonitor()。
import { useAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor({
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
}
});
// 现在导入您的应用程序
import express from "express";
const app = express();
ESM 支持(Node.js 18.19 及以上版本)
node --import @azure/monitor-opentelemetry/loader ./dist/index.js
package.json:
{
"scripts": {
"start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
}
}
完整配置
import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";
const options: AzureMonitorOpenTelemetryOptions = {
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
storageDirectory: "/path/to/offline/storage",
disableOfflineStorage: false
},
// 采样
samplingRatio: 1.0, // 0-1,追踪的百分比
// 功能
enableLiveMetrics: true,
enableStandardMetrics: true,
enablePerformanceCounters: true,
// instrumentation 库
instrumentationOptions: {
azureSdk: { enabled: true },
http: { enabled: true },
mongoDb: { enabled: true },
mySql: { enabled: true },
postgreSql: { enabled: true },
redis: { enabled: true },
bunyan: { enabled: false },
winston: { enabled: false }
},
// 自定义资源
resource: resourceFromAttributes({ "service.name": "my-service" })
};
useAzureMonitor(options);
自定义追踪
import { trace } from "@opentelemetry/api";
const tracer = trace.getTracer("my-tracer");
const span = tracer.startSpan("doWork");
try {
span.setAttribute("component", "worker");
span.setAttribute("operation.id", "42");
span.addEvent("processing started");
// 此处填写您的代码
} catch (error) {
span.recordException(error as Error);
span.setStatus({ code: 2, message: (error as Error).message });
} finally {
span.end();
}
自定义指标
import { metrics } from "@opentelemetry/api";
const meter = metrics.getMeter("my-meter");
// 计数器
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });
// 直方图
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });
// 可观察仪表
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
result.observe(getActiveConnections(), { pool: "main" });
});
手动配置导出器
跟踪导出器
import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node";
const exporter = new AzureMonitorTraceExporter({
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});
const provider = new NodeTracerProvider({
spanProcessors: [new BatchSpanProcessor(exporter)]
});
provider.register();
指标导出器
import { AzureMonitorMetricExporter } from "@azure/monitor-opentelemetry-exporter";
import { PeriodicExportingMetricReader, MeterProvider } from "@opentelemetry/sdk-metrics";
import { metrics } from "@opentelemetry/api";
const exporter = new AzureMonitorMetricExporter({
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});
const meterProvider = new MeterProvider({
readers: [new PeriodicExportingMetricReader({ exporter })]
});
metrics.setGlobalMeterProvider(meterProvider);
日志导出器
import { AzureMonitorLogExporter } from "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } from "@opentelemetry/sdk-logs";
import { logs } from "@opentelemetry/api-logs";
const exporter = new AzureMonitorLogExporter({
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});
const loggerProvider = new LoggerProvider();
loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter));
logs.setGlobalLoggerProvider(loggerProvider);
自定义日志摄入
import { DefaultAzureCredential, ManagedIdentityCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";
const endpoint = "https://.ingest.monitor.azure.com";
const ruleId = "";
const streamName = "Custom-MyTable_CL";
// 本地开发环境:DefaultAzureCredential。 生产环境:设置 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// 或者在生产环境中直接使用特定凭据:
// 参见 https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();
const client = new LogsIngestionClient(endpoint, credential);
const logs = [
{
Time: new Date().toISOString(),
Computer: "Server1",
Message: "应用程序已启动",
Level: "Information"
}
];
try {
await client.upload(ruleId, streamName, logs);
} catch (error) {
if (isAggregateLogsUploadError(error)) {
for (const uploadError of error.errors) {
console.error("上传失败的日志:", uploadError.failedLogs);
}
}
}
自定义跨度处理器
import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { Span, Context, SpanKind, TraceFlags } from "@opentelemetry/api";
import { useAzureMonitor } from "@azure/monitor-opentelemetry";
class FilteringSpanProcessor implements SpanProcessor {
forceFlush(): Promise { return Promise.resolve(); }
shutdown(): Promise { return Promise.resolve(); }
onStart(span: Span, context: Context): void {}
onEnd(span: ReadableSpan): void {
// 添加自定义属性
span.attributes["CustomDimension"] = "value";
// 过滤掉内部跨度
if (span.kind === SpanKind.INTERNAL) {
span.spanContext().traceFlags = TraceFlags.NONE;
}
}
}
useAzureMonitor({
spanProcessors: [new FilteringSpanProcessor()]
});
采样
import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
// 采样 75% 的跟踪
const sampler = new ApplicationInsightsSampler(0.75);
const provider = new NodeTracerProvider({ sampler });
关闭
import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor();
// 应用程序关闭时
process.on("SIGTERM", async () => {
await shutdownAzureMonitor();
process.exit(0);
});
键类型
import {
useAzureMonitor,
shutdownAzureMonitor,
AzureMonitorOpenTelemetryOptions,
InstrumentationOptions
} from "@azure/monitor-opentelemetry";
import {
AzureMonitorTraceExporter,
AzureMonitorMetricExporter,
AzureMonitorLogExporter,
ApplicationInsightsSampler,
AzureMonitorExporterOptions
} from "@azure/monitor-opentelemetry-exporter";
import {
LogsIngestionClient,
isAggregateLogsUploadError
} from "@azure/monitor-ingestion";
最佳实践
- 请先调用 useAzureMonitor()—— 在导入其他模块之前
- 对于 ESM 项目,请使用 ESM 加载器——
--import @azure/monitor-opentelemetry/loader - 启用离线存储——确保在断开连接的情况下仍能可靠地收集遥测数据
- 设置采样率——适用于高流量应用程序
- 添加自定义维度- 使用跨度处理器进行数据丰富
- 有序关闭- 调用
shutdownAzureMonitor()来刷新遥测数据
---
name: azure-monitor-opentelemetry-ts
description: Auto-instrument Node.js applications with distributed tracing, metrics, and logs using Azure Monitor and OpenTelemetry.
license: MIT
---
# Azure Monitor OpenTelemetry SDK for TypeScript
Auto-instrument Node.js applications with distributed tracing, metrics, and logs.
## Installation
```bash
# Distro (recommended - auto-instrumentation)
npm install @azure/monitor-opentelemetry
# Low-level exporters (custom OpenTelemetry setup)
npm install @azure/monitor-opentelemetry-exporter
# Custom logs ingestion
npm install @azure/monitor-ingestion
```
## Environment Variables
```bash
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```
## Quick Start (Auto-Instrumentation)
**IMPORTANT:** Call `useAzureMonitor()` BEFORE importing other modules.
```typescript
import { useAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor({
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
}
});
// Now import your application
import express from "express";
const app = express();
```
## ESM Support (Node.js 18.19+)
```bash
node --import @azure/monitor-opentelemetry/loader ./dist/index.js
```
**package.json:**
```json
{
"scripts": {
"start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
}
}
```
## Full Configuration
```typescript
import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";
const options: AzureMonitorOpenTelemetryOptions = {
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
storageDirectory: "/path/to/offline/storage",
disableOfflineStorage: false
},
// Sampling
samplingRatio: 1.0, // 0-1, percentage of traces
// Features
enableLiveMetrics: true,
enableStandardMetrics: true,
enablePerformanceCounters: true,
// Instrumentation libraries
instrumentationOptions: {
azureSdk: { enabled: true },
http: { enabled: true },
mongoDb: { enabled: true },
mySql: { enabled: true },
postgreSql: { enabled: true },
redis: { enabled: true },
bunyan: { enabled: false },
winston: { enabled: false }
},
// Custom resource
resource: resourceFromAttributes({ "service.name": "my-service" })
};
useAzureMonitor(options);
```
## Custom Traces
```typescript
import { trace } from "@opentelemetry/api";
const tracer = trace.getTracer("my-tracer");
const span = tracer.startSpan("doWork");
try {
span.setAttribute("component", "worker");
span.setAttribute("operation.id", "42");
span.addEvent("processing started");
// Your work here
} catch (error) {
span.recordException(error as Error);
span.setStatus({ code: 2, message: (error as Error).message });
} finally {
span.end();
}
```
## Custom Metrics
```typescript
import { metrics } from "@opentelemetry/api";
const meter = metrics.getMeter("my-meter");
// Counter
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });
// Histogram
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });
// Observable Gauge
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
result.observe(getActiveConnections(), { pool: "main" });
});
```
## Manual Exporter Setup
### Trace Exporter
```typescript
import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node";
const exporter = new AzureMonitorTraceExporter({
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});
const provider = new NodeTracerProvider({
spanProcessors: [new BatchSpanProcessor(exporter)]
});
provider.register();
```
### Metric Exporter
```typescript
import { AzureMonitorMetricExporter } from "@azure/monitor-opentelemetry-exporter";
import { PeriodicExportingMetricReader, MeterProvider } from "@opentelemetry/sdk-metrics";
import { metrics } from "@opentelemetry/api";
const exporter = new AzureMonitorMetricExporter({
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});
const meterProvider = new MeterProvider({
readers: [new PeriodicExportingMetricReader({ exporter })]
});
metrics.setGlobalMeterProvider(meterProvider);
```
### Log Exporter
```typescript
import { AzureMonitorLogExporter } from "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } from "@opentelemetry/sdk-logs";
import { logs } from "@opentelemetry/api-logs";
const exporter = new AzureMonitorLogExporter({
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});
const loggerProvider = new LoggerProvider();
loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter));
logs.setGlobalLoggerProvider(loggerProvider);
```
## Custom Logs Ingestion
```typescript
import { DefaultAzureCredential, ManagedIdentityCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";
const endpoint = "https://<dce>.ingest.monitor.azure.com";
const ruleId = "<data-collection-rule-id>";
const streamName = "Custom-MyTable_CL";
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();
const client = new LogsIngestionClient(endpoint, credential);
const logs = [
{
Time: new Date().toISOString(),
Computer: "Server1",
Message: "Application started",
Level: "Information"
}
];
try {
await client.upload(ruleId, streamName, logs);
} catch (error) {
if (isAggregateLogsUploadError(error)) {
for (const uploadError of error.errors) {
console.error("Failed logs:", uploadError.failedLogs);
}
}
}
```
## Custom Span Processor
```typescript
import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { Span, Context, SpanKind, TraceFlags } from "@opentelemetry/api";
import { useAzureMonitor } from "@azure/monitor-opentelemetry";
class FilteringSpanProcessor implements SpanProcessor {
forceFlush(): Promise<void> { return Promise.resolve(); }
shutdown(): Promise<void> { return Promise.resolve(); }
onStart(span: Span, context: Context): void {}
onEnd(span: ReadableSpan): void {
// Add custom attributes
span.attributes["CustomDimension"] = "value";
// Filter out internal spans
if (span.kind === SpanKind.INTERNAL) {
span.spanContext().traceFlags = TraceFlags.NONE;
}
}
}
useAzureMonitor({
spanProcessors: [new FilteringSpanProcessor()]
});
```
## Sampling
```typescript
import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
// Sample 75% of traces
const sampler = new ApplicationInsightsSampler(0.75);
const provider = new NodeTracerProvider({ sampler });
```
## Shutdown
```typescript
import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor();
// On application shutdown
process.on("SIGTERM", async () => {
await shutdownAzureMonitor();
process.exit(0);
});
```
## Key Types
```typescript
import {
useAzureMonitor,
shutdownAzureMonitor,
AzureMonitorOpenTelemetryOptions,
InstrumentationOptions
} from "@azure/monitor-opentelemetry";
import {
AzureMonitorTraceExporter,
AzureMonitorMetricExporter,
AzureMonitorLogExporter,
ApplicationInsightsSampler,
AzureMonitorExporterOptions
} from "@azure/monitor-opentelemetry-exporter";
import {
LogsIngestionClient,
isAggregateLogsUploadError
} from "@azure/monitor-ingestion";
```
## Best Practices
1. **Call useAzureMonitor() first** - Before importing other modules
2. **Use ESM loader for ESM projects** - `--import @azure/monitor-opentelemetry/loader`
3. **Enable offline storage** - For reliable telemetry in disconnected scenarios
4. **Set sampling ratio** - For high-traffic applications
5. **Add custom dimensions** - Use span processors for enrichment
6. **Graceful shutdown** - Call `shutdownAzureMonitor()` to flush telemetry
所有文件
0 个文件安装 azure-monitor-opentelemetry-ts
下载技能文件并将其解压到 .claude/skills/ 目录中。
下载ZIP克隆仓库并复制技能文件到您的项目中。
git clone https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-typescript/skills/azure-monitor-opentelemetry-ts # Copy SKILL.md to your .claude/skills/ directory
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