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Send Data with the OpenTelemetry JavaScript SDK

Table of Contents

Use the OpenTelemetry JavaScript SDK to instrument Node.js applications in a standard, vendor-agnostic, and future-proof way and send telemetry data to Honeycomb.

In this guide, we will walk you through instrumenting with OpenTelemetry for JavaScript, which will include adding automatic instrumentation to your application.

Before You Begin 

Before you can set up automatic instrumentation for your Node.js application, you will need to do a few things.

Prepare Your Development Environment 

To complete the required steps, you will need:

• A working Node.js environment
• An application written in Node.js

Get Your Honeycomb API Key 

To send data to Honeycomb, you’ll need to sign up for a free Honeycomb account and create a Honeycomb Ingest API Key. To get started, you can create a key that you expect to swap out when you deploy to production. Name it something helpful, perhaps noting that it’s a getting started key. Make note of your API key; for security reasons, you will not be able to see the key again, and you will need it later!

If you want to use an API key you previously stored in a secure location, you can also look up details for Honeycomb API Keys any time in your Environment Settings, and use them to retrieve keys from your storage location.

Add Automatic Instrumentation 

Automatic instrumentation is enabled by adding instrumentation packages. Add custom, or manual, instrumentation using the OpenTelemetry API.

Acquire Dependencies 

npm install --save @opentelemetry/auto-instrumentations-node

yarn add @opentelemetry/auto-instrumentations-node

yarn add --dev ts-node

Initialize 

// Example filename: tracing.ts
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';

const sdk: NodeSDK = new NodeSDK({
  traceExporter: new OTLPTraceExporter(),
  instrumentations: [    
    getNodeAutoInstrumentations({
      // We recommend disabling fs automatic instrumentation because 
      // it can be noisy and expensive during startup
      '@opentelemetry/instrumentation-fs': {
        enabled: false,
      },
    }),
  ],
});

sdk.start();

// Example filename: tracing.js
'use strict';

const opentelemetry = require('@opentelemetry/sdk-node');
const { OTLPTraceExporter } =  require('@opentelemetry/exporter-trace-otlp-http');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');

const sdk = new opentelemetry.NodeSDK({
    traceExporter: new OTLPTraceExporter(),
    instrumentations: [
        getNodeAutoInstrumentations({
            // we recommend disabling fs autoinstrumentation since it can be noisy
            // and expensive during startup
            '@opentelemetry/instrumentation-fs': {
                enabled: false,
            },
        }),
    ],
});

sdk.start();

Configure 

Use environment variables to configure the OpenTelemetry SDK:

export OTEL_SERVICE_NAME="your-service-name"
export OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf"
export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.honeycomb.io:443" # US instance
#export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.eu1.honeycomb.io:443" # EU instance
export OTEL_EXPORTER_OTLP_HEADERS="x-honeycomb-team=your-api-key"

export OTEL_EXPORTER_OTLP_HEADERS="x-honeycomb-team=your-api-key,x-honeycomb-dataset=your-dataset"

Run 

ts-node -r ./tracing.ts YOUR_APPLICATION_NAME.ts

node -r ./tracing.js YOUR_APPLICATION_NAME.js

Add Custom Instrumentation 

Automatic instrumentation is the easiest way to get started with instrumenting your code. To get additional insight into your system, you should also add custom, or manual, instrumentation where appropriate.

Follow the instructions below to add custom instrumentation to your code.

To learn more about custom, or manual, instrumentation, visit the comprehensive set of topics covered by Manual Instrumentation for JavaScript in OpenTelemetry’s documentation.

Acquire Dependencies 

To start adding custom instrumentation, ensure that the OpenTelemetry API package exists as a direct dependency in your project. This package provides access to the high-level instrumentation APIs, which gives the ability to retrieve the current span to enrich with additional attributes, to create new spans, and to generate metrics.

npm install --save @opentelemetry/api

yarn add @opentelemetry/api

Add Attributes to Spans 

Adding attributes to a currently executing span in a trace can be useful. For example, you may have an application or service that handles users, and you want to associate the user with the span when querying your service in Honeycomb. To do this, get the current span from the context and set an attribute with the user ID:

import opentelemetry from '@opentelemetry/api';

function handleUser(user: User) {
  let activeSpan = opentelemetry.trace.getActiveSpan();
  activeSpan.setAttribute("user.id", user.getId());
}

const opentelemetry = require("@opentelemetry/api");

function handleUser(user) {
  let activeSpan = opentelemetry.trace.getActiveSpan();
  activeSpan.setAttribute("user.id", user.getId());
}

This will add a user.id field to the current span, so you can use the field in WHERE, GROUP BY or ORDER clauses in the Honeycomb query builder.

Initialize a Tracer 

To create spans, you need to initialize a Tracer.

import opentelemetry from '@opentelemetry/api';

const tracer = opentelemetry.trace.getTracer("tracer.name.here");

const opentelemetry = require("@opentelemetry/api");

const tracer = opentelemetry.trace.getTracer("tracer.name.here");

When you create a Tracer, OpenTelemetry requires you to give it a name as a string. This string is the only required parameter.

When traces are sent to Honeycomb, the name of the Tracer is turned into the library.name field, which can be used to show all spans created from a particular tracer.

In general, pick a name that matches the appropriate scope for your traces. If you have one tracer for each service, then use the service name. If you have multiple tracers that live in different “layers” of your application, then use the name that corresponds to that “layer”.

The library.name field is also used with traces created from instrumentation libraries.

You can then use this tracer to create custom spans.

Create New Spans 

Automatic instrumentation can show the shape of requests to your system, but only you know the truly important parts. To get the full picture of what is happening, you must add custom, or manual, instrumentation and create some custom spans. To do this, grab the tracer from the OpenTelemetry API:

import opentelemetry from '@opentelemetry/api';

const tracer = opentelemetry.trace.getTracer("my-service-tracer");

function runQuery() {
  tracer.startActiveSpan("expensive-query", (span) => {
    // ... do cool stuff
    span.end();
  });
}

const opentelemetry = require("@opentelemetry/api");

const tracer = opentelemetry.trace.getTracer("my-service-tracer");

function runQuery() {
  tracer.startActiveSpan("expensive-query", (span) => {
    // ... do cool stuff
    span.end();
  });
}

Add Multi-Span Attributes 

Sometimes you want to add the same attribute to many spans within the same trace. This attribute may include variables calculated during your program, or other useful values for correlation or debugging purposes.

To add this attribute to multiple spans, leverage the OpenTelemetry concept of baggage. Baggage allows you to add a key with a value as an attribute to every subsequent child span within the current application context.

First, install the baggage span processor package using your terminal:

npm install --save @opentelemetry/baggage-span-processor

Then, when configuring the OpenTelemetry SDK tracer provider, add the BaggageSpanProcessor:

import { BaggageSpanProcessor } from "@opentelemetry/baggage-span-processor";

const sdk = new NodeSDK({
  // ...
  processors: [ new BaggageSpanProcessor() ]
});

Finally, add a baggage entry for the current trace and replace key and value with your desired key-value pair:

import {
  Context,
  context,
  propagation,
} from '@opentelemetry/api';

tracer.startActiveSpan('main', (span) => {
  span.setAttribute('key', 'value'); // add to current span

  // new context based on current, with key/values added to baggage
  const ctx: Context = propagation.setBaggage(
    context.active(),
    propagation.createBaggage({ 'key': { value: 'value' } })
  );

  // within the new context, do some work and baggage will be
  // applied as attributes on child spans
  context.with(ctx, () => {
    tracer.startActiveSpan('childSpan', (childSpan) => {
      doTheWork();
      childSpan.end();
    });
  });

  span.end();
});

tracer.startActiveSpan('main', (span) => {
  span.setAttribute('key', 'value'); // add to current span

  // new context based on current, with key/values added to baggage
  const ctx = propagation.setBaggage(
    context.active(),
    propagation.createBaggage({ 'key': { value: 'value' } })
  );

  // within the new context, do some work and baggage will be
  // applied as attributes on child spans
  context.with(ctx, () => {
    tracer.startActiveSpan('childSpan', (childSpan) => {
      doTheWork();
      childSpan.end();
    });
  });

  span.end();
});

Note: Any Baggage attributes that you set in your application will be attached to outgoing network requests as a header. If your service communicates to a third party API, do NOT put sensitive information in the Baggage attributes.

Sampling 

You can configure the OpenTelemetry SDK to sample the data it generates. Honeycomb weights sampled data based on sample rate, so you must set a resource attribute containing the sample rate.

Use a TraceIdRatioBased sampler, with a ratio expressed as 1/N. Then, also create a resource attribute called SampleRate with the value of N. This allows Honeycomb to reweigh scalar values, like counts, so that they are accurate even with sampled data.

In the example below, our goal is to keep approximately half (1/2) of the data volume. The resource attribute contains the denominator (2), while the OpenTelemetry sampler argument contains the decimal value (0.5).

To get access to a sampler, install the core OpenTelemetry package:

npm install --save @opentelemetry/core

Import the TraceIdRatioBasedSampler, and add as a sampler to the NodeSDK along with the SampleRate in the Resource.

// tracing.js
const { TraceIdRatioBasedSampler } = require("@opentelemetry/sdk-trace-node");

const sdk = new NodeSDK({
  resource: new Resource({
    [SemanticResourceAttributes.SERVICE_NAME]: "<YOUR_SERVICE_NAME>",
    SampleRate: 2,
  }),
  traceExporter,
  instrumentations: [getNodeAutoInstrumentations()],
  sampler: new TraceIdRatioBasedSampler(0.5),
});

Choosing between gRPC and HTTP 

Most OpenTelemetry SDKs have an option to export telemetry as OTLP either over gRPC or HTTP/protobuf, with some also offering HTTP/JSON. If you are trying to choose between gRPC and HTTP, keep in mind:

• Some SDKs default to using gRPC, and it may be easiest to start with the default option.
• Some firewall policies are not set up to handle gRPC and require using HTTP.
• gRPC may improve performance, but its long-lived connections may cause problems with load balancing, especially when using Refinery.

gRPC default export uses port 4317, whereas HTTP default export uses port 4318.

Endpoint URLs for OTLP/HTTP 

When using the OTEL_EXPORTER_OTLP_ENDPOINT environment variable with an SDK and an HTTP exporter, the final path of the endpoint is modified by the SDK to represent the specific signal being sent.

For example, when exporting trace data, the endpoint is updated to append v1/traces. When exporting metrics data, the endpoint is updated to append v1/metrics. So, if you were to set the OTEL_EXPORTER_OTLP_ENDPOINT to https://api.honeycomb.io, traces would be sent to https://api.honeycomb.io/v1/traces and metrics would be sent to https://api.honeycomb.io/v1/metrics.

The same modification is not necessary for gRPC.

export OTEL_EXPORTER_OTLP_ENDPOINT=https://api.honeycomb.io # US instance
#export OTEL_EXPORTER_OTLP_ENDPOINT=https://api.eu1.honeycomb.io # EU instance

If the desired outcome is to send data to a different endpoint depending on the signal, use OTEL_EXPORTER_OTLP_<SIGNAL>_ENDPOINT instead of the more generic OTEL_EXPORTER_OTLP_ENDPOINT.

When using a signal-specific environment variable, these paths must be appended manually. Set OTEL_EXPORTER_OTLP_TRACES_ENDPOINT for traces, appending the endpoint with v1/traces, and OTEL_EXPORTER_OTLP_METRICS_ENDPOINT for metrics, appending the endpoint with v1/metrics.

Send both traces and metrics to Honeycomb using this method by setting the following variables:

export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.honeycomb.io/v1/traces # US instance
#export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.eu1.honeycomb.io/v1/traces # EU instance

export OTEL_EXPORTER_OTLP_METRICS_ENDPOINT=https://api.honeycomb.io/v1/metrics # US instance
#export OTEL_EXPORTER_OTLP_METRICS_ENDPOINT=https://api.eu1.honeycomb.io/v1/metrics # EU instance

More details about endpoints and signals can be found in the OpenTelemetry Specification.

To configure the endpoint URL and API key in code instead of with environment variables, specify /v1/traces like so:

// Configure OTLPTraceExporter
const traceExporter =
  new OTLPTraceExporter({
    url: "https://api.honeycomb.io/v1/traces", // US instance
    //url: "https://api.eu1.honeycomb.io/v1/traces", // EU instance
    headers: {
      "x-honeycomb-team": 'your-api-key',
    },
  });

Troubleshooting 

To explore common issues when sending data, visit Common Issues with Sending Data in Honeycomb.

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