Node.js

Get telemetry for your app in less than 5 minutes!

This page will show you how to get started with OpenTelemetry in Node.js.

You will learn how to instrument both traces and metrics and log them to the console.

Note

The logging library for OpenTelemetry for Node.js is still under development hence an example for it is not provided below. Look here for more info about the status of OpenTelemetry in JavaScript.

Prerequisites

Ensure that you have the following installed locally:

Node.js
TypeScript, if you will be using TypeScript.

Example Application

The following example uses a basic Express application. If you are not using Express, that’s OK — you can use OpenTelemetry JavaScript with other web frameworks as well, such as Koa and Nest.JS. For a complete list of libraries for supported frameworks, see the registry.

For more elaborate examples, see examples.

Dependencies

To begin, set up an empty package.json in a new directory:

npm init -y

Next, install Express dependencies.

npm install typescript \
  ts-node \
  @types/node \
  express \
  @types/express

# initialize typescript
npx tsc --init

npm install express

Create and launch an HTTP Server

Create a file named app.ts (or app.js if not using TypeScript) and add the following code to it:

/*app.ts*/
import express, { Express } from 'express';

const PORT: number = parseInt(process.env.PORT || '8080');
const app: Express = express();

function getRandomNumber(min: number, max: number) {
  return Math.floor(Math.random() * (max - min) + min);
}

app.get('/rolldice', (req, res) => {
  res.send(getRandomNumber(1, 6).toString());
});

app.listen(PORT, () => {
  console.log(`Listening for requests on http://localhost:${PORT}`);
});

/*app.js*/
const express = require('express');

const PORT = parseInt(process.env.PORT || '8080');
const app = express();

function getRandomNumber(min, max) {
  return Math.floor(Math.random() * (max - min) + min);
}

app.get('/rolldice', (req, res) => {
  res.send(getRandomNumber(1, 6).toString());
});

app.listen(PORT, () => {
  console.log(`Listening for requests on http://localhost:${PORT}`);
});

Run the application with the following command and open http://localhost:8080/rolldice in your web browser to ensure it is working.

$ npx ts-node app.ts
Listening for requests on http://localhost:8080

$ node app.js
Listening for requests on http://localhost:8080

Instrumentation

The following shows how to install, initialize, and run an application instrumented with OpenTelemetry.

More Dependencies

First, install the Node SDK and autoinstrumentations package.

The Node SDK lets you initialize OpenTelemetry with several configuration defaults that are correct for the majority of use cases.

The auto-instrumentations-node package installs instrumentation libraries that will automatically create spans corresponding to code called in libraries. In this case, it provides instrumentation for Express, letting the example app automatically create spans for each incoming request.

npm install @opentelemetry/sdk-node \
  @opentelemetry/api \
  @opentelemetry/auto-instrumentations-node \
  @opentelemetry/sdk-metrics \
  @opentelemetry/sdk-trace-node

To find all autoinstrumentation modules, you can look at the registry.

Setup

The instrumentation setup and configuration must be run before your application code. One tool commonly used for this task is the –require flag.

Create a file named instrumentation.ts (or instrumentation.js if not using TypeScript) , which will contain your instrumentation setup code.

/*instrumentation.ts*/
import { NodeSDK } from '@opentelemetry/sdk-node';
import { ConsoleSpanExporter } from '@opentelemetry/sdk-trace-node';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
import {
  PeriodicExportingMetricReader,
  ConsoleMetricExporter,
} from '@opentelemetry/sdk-metrics';

const sdk = new NodeSDK({
  traceExporter: new ConsoleSpanExporter(),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new ConsoleMetricExporter(),
  }),
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();

/*instrumentation.js*/
// Require dependencies
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { ConsoleSpanExporter } = require('@opentelemetry/sdk-trace-node');
const {
  getNodeAutoInstrumentations,
} = require('@opentelemetry/auto-instrumentations-node');
const {
  PeriodicExportingMetricReader,
  ConsoleMetricExporter,
} = require('@opentelemetry/sdk-metrics');

const sdk = new NodeSDK({
  traceExporter: new ConsoleSpanExporter(),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new ConsoleMetricExporter(),
  }),
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();

Run the instrumented app

Now you can run your application as you normally would, but you can use the --require flag to load the instrumentation before the application code. Make sure you don’t have other conflicting --require flags such as --require @opentelemetry/auto-instrumentations-node/register on your NODE_OPTIONS environment variable.

$ npx ts-node --require ./instrumentation.ts app.ts
Listening for requests on http://localhost:8080

$ node --require ./instrumentation.js app.js
Listening for requests on http://localhost:8080

Open http://localhost:8080/rolldice in your web browser and reload the page a few times. After a while you should see the spans printed in the console by the ConsoleSpanExporter.

View example output

{
  "traceId": "3f1fe6256ea46d19ec3ca97b3409ad6d",
  "parentId": "f0b7b340dd6e08a7",
  "name": "middleware - query",
  "id": "41a27f331c7bfed3",
  "kind": 0,
  "timestamp": 1624982589722992,
  "duration": 417,
  "attributes": {
    "http.route": "/",
    "express.name": "query",
    "express.type": "middleware"
  },
  "status": { "code": 0 },
  "events": []
}
{
  "traceId": "3f1fe6256ea46d19ec3ca97b3409ad6d",
  "parentId": "f0b7b340dd6e08a7",
  "name": "middleware - expressInit",
  "id": "e0ed537a699f652a",
  "kind": 0,
  "timestamp": 1624982589725778,
  "duration": 673,
  "attributes": {
    "http.route": "/",
    "express.name": "expressInit",
    "express.type": "middleware"
  },
  "status": { code: 0 },
  "events": []
}
{
  "traceId": "3f1fe6256ea46d19ec3ca97b3409ad6d",
  "parentId": "f0b7b340dd6e08a7",
  "name": "request handler - /",
  "id": "8614a81e1847b7ef",
  "kind": 0,
  "timestamp": 1624982589726941,
  "duration": 21,
  "attributes": {
    "http.route": "/",
    "express.name": "/",
    "express.type": "request_handler"
  },
  "status": { code: 0 },
  "events": []
}
{
  "traceId": "3f1fe6256ea46d19ec3ca97b3409ad6d",
  "parentId": undefined,
  "name": "GET /",
  "id": "f0b7b340dd6e08a7",
  "kind": 1,
  "timestamp": 1624982589720260,
  "duration": 11380,
  "attributes": {
    "http.url": "http://localhost:8080/",
    "http.host": "localhost:8080",
    "net.host.name": "localhost",
    "http.method": "GET",
    "http.route": "",
    "http.target": "/",
    "http.user_agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36",
    "http.flavor": "1.1",
    "net.transport": "ip_tcp",
    "net.host.ip": "::1",
    "net.host.port": 8080,
    "net.peer.ip": "::1",
    "net.peer.port": 61520,
    "http.status_code": 304,
    "http.status_text": "NOT MODIFIED"
  },
  "status": { "code": 1 },
  "events": []
}

The generated span tracks the lifetime of a request to the /rolldice route.

Send a few more requests to the endpoint. After a moment, you’ll see metrics in the console output, such as the following:

View example output

{
  descriptor: {
    name: 'http.server.duration',
    type: 'HISTOGRAM',
    description: 'measures the duration of the inbound HTTP requests',
    unit: 'ms',
    valueType: 1
  },
  dataPointType: 0,
  dataPoints: [
    {
      attributes: [Object],
      startTime: [Array],
      endTime: [Array],
      value: [Object]
    }
  ]
}
{
  descriptor: {
    name: 'http.client.duration',
    type: 'HISTOGRAM',
    description: 'measures the duration of the outbound HTTP requests',
    unit: 'ms',
    valueType: 1
  },
  dataPointType: 0,
  dataPoints: []
}
{
  descriptor: {
    name: 'db.client.connections.usage',
    type: 'UP_DOWN_COUNTER',
    description: 'The number of connections that are currently in the state referenced by the attribute "state".',
    unit: '{connections}',
    valueType: 1
  },
  dataPointType: 3,
  dataPoints: []
}
{
  descriptor: {
    name: 'http.server.duration',
    type: 'HISTOGRAM',
    description: 'measures the duration of the inbound HTTP requests',
    unit: 'ms',
    valueType: 1
  },
  dataPointType: 0,
  dataPoints: [
    {
      attributes: [Object],
      startTime: [Array],
      endTime: [Array],
      value: [Object]
    }
  ]
}
{
  descriptor: {
    name: 'http.client.duration',
    type: 'HISTOGRAM',
    description: 'measures the duration of the outbound HTTP requests',
    unit: 'ms',
    valueType: 1
  },
  dataPointType: 0,
  dataPoints: []
}
{
  descriptor: {
    name: 'db.client.connections.usage',
    type: 'UP_DOWN_COUNTER',
    description: 'The number of connections that are currently in the state referenced by the attribute "state".',
    unit: '{connections}',
    valueType: 1
  },
  dataPointType: 3,
  dataPoints: []
}
{
  descriptor: {
    name: 'http.server.duration',
    type: 'HISTOGRAM',
    description: 'measures the duration of the inbound HTTP requests',
    unit: 'ms',
    valueType: 1
  },
  dataPointType: 0,
  dataPoints: [
    {
      attributes: [Object],
      startTime: [Array],
      endTime: [Array],
      value: [Object]
    }
  ]
}
{
  descriptor: {
    name: 'http.client.duration',
    type: 'HISTOGRAM',
    description: 'measures the duration of the outbound HTTP requests',
    unit: 'ms',
    valueType: 1
  },
  dataPointType: 0,
  dataPoints: []
}
{
  descriptor: {
    name: 'db.client.connections.usage',
    type: 'UP_DOWN_COUNTER',
    description: 'The number of connections that are currently in the state referenced by the attribute "state".',
    unit: '{connections}',
    valueType: 1
  },
  dataPointType: 3,
  dataPoints: []
}

Next Steps

Enrich your instrumentation generated automatically with manual instrumentation of your own codebase. This gets you customized observability data.

You’ll also want to configure an appropriate exporter to export your telemetry data to one or more telemetry backends.

If you’d like to explore a more complex example, take a look at the OpenTelemetry Demo, which includes the JavaScript based Payment Service and the TypeScript based Frontend Service.

Troubleshooting

Did something go wrong? You can enable diagnostic logging to validate that OpenTelemetry is initialized correctly:

/*instrumentation.ts*/
import { diag, DiagConsoleLogger, DiagLogLevel } from '@opentelemetry/api';

// For troubleshooting, set the log level to DiagLogLevel.DEBUG
diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.INFO);

// const sdk = new NodeSDK({...

/*instrumentation.js*/
// Require dependencies
const { diag, DiagConsoleLogger, DiagLogLevel } = require('@opentelemetry/api');

// For troubleshooting, set the log level to DiagLogLevel.DEBUG
diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.INFO);

// const sdk = new NodeSDK({...

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Última modificación August 5, 2024: [cleanup] Add autoinstrumentation into the main dictionary (#4964) (2bda479b)