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Azure Monitor OpenTelemetry for JavaScript

npm version

Getting started

Install the package

npm install @azure/monitor-opentelemetry

Currently supported environments

Warning: This SDK only works for Node.js environments. Use the Application Insights JavaScript SDK for web and browser scenarios.

See our support policy for more details.

Prerequisites

Enable Azure Monitor OpenTelemetry Client

Important: useAzureMonitor must be called before you import anything else. There may be resulting telemetry loss if other libraries are imported first.

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";

const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    connectionString:
      process.env["APPLICATIONINSIGHTS_CONNECTION_STRING"] || "<your connection string>",
  },
};
useAzureMonitor(options);
  • Connection String could be set using the environment variable APPLICATIONINSIGHTS_CONNECTION_STRING

Configuration

import { AzureMonitorOpenTelemetryOptions, useAzureMonitor } from "@azure/monitor-opentelemetry";
import { Resource } from "@opentelemetry/resources";

const resource = new Resource({ testAttribute: "testValue" });
const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    // Offline storage
    storageDirectory: "c://azureMonitor",
    // Automatic retries
    disableOfflineStorage: false,
    // Application Insights Connection String
    connectionString:
      process.env["APPLICATIONINSIGHTS_CONNECTION_STRING"] || "<your connection string>",
  },
  samplingRatio: 1,
  instrumentationOptions: {
    // Instrumentations generating traces
    azureSdk: { enabled: true },
    http: { enabled: true },
    mongoDb: { enabled: true },
    mySql: { enabled: true },
    postgreSql: { enabled: true },
    redis: { enabled: true },
    redis4: { enabled: true },
    // Instrumentations generating logs
    bunyan: { enabled: true },
    winston: { enabled: true },
  },
  enableLiveMetrics: true,
  enableStandardMetrics: true,
  browserSdkLoaderOptions: {
    enabled: false,
    connectionString: "",
  },
  resource: resource,
  logRecordProcessors: [],
  spanProcessors: [],
};

useAzureMonitor(options);

| Property | Description | Default | | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- | --- | | azureMonitorExporterOptions | Azure Monitor OpenTelemetry Exporter Configuration. More info here | | | | | samplingRatio | Sampling ratio must take a value in the range [0,1], 1 meaning all data will sampled and 0 all Tracing data will be sampled out. | 1 | | instrumentationOptions | Allow configuration of OpenTelemetry Instrumentations. | {"http": { enabled: true },"azureSdk": { enabled: false },"mongoDb": { enabled: false },"mySql": { enabled: false },"postgreSql": { enabled: false },"redis": { enabled: false },"bunyan": { enabled: false }, "winston": { enabled: false } } | | browserSdkLoaderOptions | Allow configuration of Web Instrumentations. | { enabled: false, connectionString: "" } | | resource | Opentelemetry Resource. More info here | | | samplingRatio | Sampling ratio must take a value in the range [0,1], 1 meaning all data will sampled and 0 all Tracing data will be sampled out. | 1 | | enableLiveMetrics | Enable/Disable Live Metrics. | true | | enableStandardMetrics | Enable/Disable Standard Metrics. | true | | logRecordProcessors | Array of log record processors to register to the global logger provider. | | | spanProcessors | Array of span processors to register to the global tracer provider. | | | enableTraceBasedSamplingForLogs | Enable log sampling based on trace. | false |

Options could be set using configuration file applicationinsights.json located under root folder of @azure/monitor-opentelemetry package installation folder, Ex: node_modules/@azure/monitor-opentelemetry. These configuration values will be applied to all AzureMonitorOpenTelemetryClient instances.

{
    "samplingRatio": 0.8,
    "enableStandardMetrics": true,
    "enableLiveMetrics": true,
    "instrumentationOptions":{
        "azureSdk": {
            "enabled": false
        }
    },
    ...
}

Custom JSON file could be provided using APPLICATIONINSIGHTS_CONFIGURATION_FILE environment variable.

process.env.APPLICATIONINSIGHTS_CONFIGURATION_FILE =
  "C:/applicationinsights/config/customConfig.json";

// Application Insights SDK setup....

Instrumentation libraries

The following OpenTelemetry Instrumentation libraries are included as part of Azure Monitor OpenTelemetry.

Warning: Instrumentation libraries are based on experimental OpenTelemetry specifications. Microsoft's preview support commitment is to ensure that the following libraries emit data to Azure Monitor Application Insights, but it's possible that breaking changes or experimental mapping will block some data elements.

Distributed Tracing

Metrics

Logs

Other OpenTelemetry Instrumentations are available here and could be added using TracerProvider in AzureMonitorOpenTelemetryClient.

import { useAzureMonitor } from "@azure/monitor-opentelemetry";
import { metrics, trace } from "@opentelemetry/api";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import { ExpressInstrumentation } from "@opentelemetry/instrumentation-express";

useAzureMonitor();
const instrumentations = [new ExpressInstrumentation()];
registerInstrumentations({
  tracerProvider: trace.getTracerProvider(),
  meterProvider: metrics.getMeterProvider(),
  instrumentations: instrumentations,
});

Application Insights Browser SDK Loader

Application Insights Browser SDK Loader allows you to inject the web SDK into node server responses when the following conditions are true:

  • Response has status code 200.
  • Response method is GET.
  • Server response has the Conent-Type html header.
  • Server resonse contains both and tags.
  • Response does not contain current /backup web Instrumentation CDN endpoints. (current and backup Web Instrumentation CDN endpoints here)

Further information on usage of the browser SDK loader can be found here.

Set the Cloud Role Name and the Cloud Role Instance

You might set the Cloud Role Name and the Cloud Role Instance via OpenTelemetry Resource attributes.

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { Resource } from "@opentelemetry/resources";
import { SemanticResourceAttributes } from "@opentelemetry/semantic-conventions";

// ----------------------------------------
// Setting role name and role instance
// ----------------------------------------
const customResource = Resource.EMPTY;
customResource.attributes[SemanticResourceAttributes.SERVICE_NAME] = "my-helloworld-service";
customResource.attributes[SemanticResourceAttributes.SERVICE_NAMESPACE] = "my-namespace";
customResource.attributes[SemanticResourceAttributes.SERVICE_INSTANCE_ID] = "my-instance";

const options: AzureMonitorOpenTelemetryOptions = { resource: customResource };
useAzureMonitor(options);

For information on standard attributes for resources, see Resource Semantic Conventions.

Modify telemetry

This section explains how to modify telemetry.

Add span attributes

To add span attributes, use either of the following two ways:

These attributes might include adding a custom property to your telemetry.

Tip: The advantage of using options provided by instrumentation libraries, when they're available, is that the entire context is available. As a result, users can select to add or filter more attributes. For example, the enrich option in the HttpClient instrumentation library gives users access to the httpRequestMessage itself. They can select anything from it and store it as an attribute.

Add a custom property to a Trace

Any attributes you add to spans are exported as custom properties.

Use a custom processor:

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { ReadableSpan, Span, SpanProcessor } from "@opentelemetry/sdk-trace-base";
import { SemanticAttributes } from "@opentelemetry/semantic-conventions";

class SpanEnrichingProcessor implements SpanProcessor {
  forceFlush(): Promise<void> {
    return Promise.resolve();
  }
  shutdown(): Promise<void> {
    return Promise.resolve();
  }
  onStart(_span: Span): void {}
  onEnd(span: ReadableSpan) {
    span.attributes["CustomDimension1"] = "value1";
    span.attributes["CustomDimension2"] = "value2";
    span.attributes[SemanticAttributes.HTTP_CLIENT_IP] = "<IP Address>";
  }
}

// Enable Azure Monitor integration.
const options: AzureMonitorOpenTelemetryOptions = {
  // Add the SpanEnrichingProcessor
  spanProcessors: [new SpanEnrichingProcessor()],
};
useAzureMonitor(options);

Add Operation Name to Traces and Logs

Use a custom span processor and log record processor in order to attach and correlate operation name from requests to dependencies and logs.

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { ReadableSpan, Span, SpanProcessor } from "@opentelemetry/sdk-trace-base";
import { LogRecordProcessor } from "@opentelemetry/sdk-logs";
import { SemanticAttributes } from "@opentelemetry/semantic-conventions";
import { AI_OPERATION_NAME } from "@azure/monitor-opentelemetry-exporter";
import { Context, trace } from "@opentelemetry/api";

class SpanEnrichingProcessor implements SpanProcessor {
  forceFlush(): Promise<void> {
    return Promise.resolve();
  }
  shutdown(): Promise<void> {
    return Promise.resolve();
  }
  onStart(_span: Span, _context: Context): void {
    const parentSpan = trace.getSpan(_context);
    if (parentSpan && "name" in parentSpan) {
      // If the parent span has a name we can assume it is a ReadableSpan and cast it.
      _span.attributes[AI_OPERATION_NAME] = (parentSpan as unknown as ReadableSpan).name;
    }
  }
  onEnd(span: ReadableSpan) {}
}

class LogRecordEnrichingProcessor implements LogRecordProcessor {
  forceFlush(): Promise<void> {
    return Promise.resolve();
  }
  shutdown(): Promise<void> {
    return Promise.resolve();
  }
  onEmit(_logRecord, _context): void {
    const parentSpan = trace.getSpan(_context);
    if (parentSpan && "name" in parentSpan) {
      // If the parent span has a name we can assume it is a ReadableSpan and cast it.
      _logRecord.attributes[AI_OPERATION_NAME] = (parentSpan as unknown as ReadableSpan).name;
    }
  }
}

// Enable Azure Monitor integration.
const options: AzureMonitorOpenTelemetryOptions = {
  // Add the SpanEnrichingProcessor
  spanProcessors: [new SpanEnrichingProcessor()],
  logRecordProcessors: [new LogRecordEnrichingProcessor()],
};
useAzureMonitor(options);

Filter telemetry

You might use the following ways to filter out telemetry before it leaves your application.

  1. Exclude the URL option provided by many HTTP instrumentation libraries.

    The following example shows how to exclude a certain URL from being tracked by using the HTTP/HTTPS instrumentation library:

    import {
  useAzureMonitor,
  AzureMonitorOpenTelemetryOptions,
} from "@azure/monitor-opentelemetry";
import { IncomingMessage } from "http";
import { RequestOptions } from "https";
import { HttpInstrumentationConfig } from "@opentelemetry/instrumentation-http";

const httpInstrumentationConfig: HttpInstrumentationConfig = {
  enabled: true,
  ignoreIncomingRequestHook: (request: IncomingMessage) => {
    // Ignore OPTIONS incoming requests
    if (request.method === "OPTIONS") {
      return true;
    }
    return false;
  },
  ignoreOutgoingRequestHook: (options: RequestOptions) => {
    // Ignore outgoing requests with /test path
    if (options.path === "/test") {
      return true;
    }
    return false;
  },
};
const options: AzureMonitorOpenTelemetryOptions = {
  instrumentationOptions: {
    http: httpInstrumentationConfig,
  },
};
useAzureMonitor(options);

  2. Use a custom processor. You can use a custom span processor to exclude certain spans from being exported. To mark spans to not be exported, set TraceFlag to DEFAULT. Use the add custom property example, but replace the following lines of code:

    ```typescript
...
import { SpanKind, TraceFlags } from "@opentelemetry/api";
import { ReadableSpan, SpanProcessor } from "@opentelemetry/sdk-trace-base";

class SpanEnrichingProcessor implements SpanProcessor {
    ...

    onEnd(span: ReadableSpan) {
        if(span.kind == SpanKind.INTERNAL){
            span.spanContext().traceFlags = TraceFlags.NONE;
        }
    }
}
```

Custom telemetry

This section explains how to collect custom telemetry from your application.

Add Custom Metrics

You may want to collect metrics beyond what is collected by instrumentation libraries.

The OpenTelemetry API offers six metric "instruments" to cover a variety of metric scenarios and you'll need to pick the correct "Aggregation Type" when visualizing metrics in Metrics Explorer. This requirement is true when using the OpenTelemetry Metric API to send metrics and when using an instrumentation library.

The following table shows the recommended aggregation types] for each of the OpenTelemetry Metric Instruments.

OpenTelemetry Instrument Azure Monitor Aggregation Type
Counter Sum
Asynchronous Counter Sum
Histogram Average, Sum, Count (Max, Min for Python and Node.js only)
Asynchronous Gauge Average
UpDownCounter (Python and Node.js only) Sum
Asynchronous UpDownCounter (Python and Node.js only) Sum

Caution: Aggregation types beyond what's shown in the table typically aren't meaningful.

The OpenTelemetry Specification describes the instruments and provides examples of when you might use each one.

import { useAzureMonitor } from "@azure/monitor-opentelemetry";
import { ObservableResult, metrics } from "@opentelemetry/api";

useAzureMonitor();
const meter = metrics.getMeter("testMeter");

let histogram = meter.createHistogram("histogram");
let counter = meter.createCounter("counter");
let gauge = meter.createObservableGauge("gauge");
gauge.addCallback((observableResult: ObservableResult) => {
  let randomNumber = Math.floor(Math.random() * 100);
  observableResult.observe(randomNumber, { testKey: "testValue" });
});

histogram.record(1, { testKey: "testValue" });
histogram.record(30, { testKey: "testValue2" });
histogram.record(100, { testKey2: "testValue" });

counter.add(1, { testKey: "testValue" });
counter.add(5, { testKey2: "testValue" });
counter.add(3, { testKey: "testValue2" });

Add Custom Exceptions

Select instrumentation libraries automatically support exceptions to Application Insights. However, you may want to manually report exceptions beyond what instrumention libraries report. For instance, exceptions caught by your code are not ordinarily not reported, and you may wish to report them and thus draw attention to them in relevant experiences including the failures blade and end-to-end transaction view.

import { useAzureMonitor } from "@azure/monitor-opentelemetry";
import { trace, Exception } from "@opentelemetry/api";

useAzureMonitor();
const tracer = trace.getTracer("testMeter");

let span = tracer.startSpan("hello");
try {
  throw new Error("Test Error");
} catch (error) {
  span.recordException(error as Exception);
}

Troubleshooting

Self-diagnostics

Azure Monitor OpenTelemetry uses the OpenTelemetry API Logger for internal logs. To enable it, use the following code:

import { useAzureMonitor } from "@azure/monitor-opentelemetry";
import { DiagLogLevel } from "@opentelemetry/api";

process.env.APPLICATIONINSIGHTS_INSTRUMENTATION_LOGGING_LEVEL = "VERBOSE";
process.env.APPLICATIONINSIGHTS_LOG_DESTINATION = "file";
process.env.APPLICATIONINSIGHTS_LOGDIR = "C:/applicationinsights/logs";

useAzureMonitor();

APPLICATIONINSIGHTS_INSTRUMENTATION_LOGGING_LEVEL environment variable could be used to set desired log level, supporting the following values: NONE, ERROR, WARN, INFO, DEBUG, VERBOSE and ALL.

Logs could be put into local file using APPLICATIONINSIGHTS_LOG_DESTINATION environment variable, supported values are file and file+console, a file named applicationinsights.log will be generated on tmp folder by default, including all logs, /tmp for *nix and USERDIR/AppData/Local/Temp for Windows. Log directory could be configured using APPLICATIONINSIGHTS_LOGDIR environment variable.

Examples

For complete samples of a few champion scenarios, see the samples/ folder.

Key concepts

For more information on the OpenTelemetry project, please review the OpenTelemetry Specifications.

Plugin Registry

To see if a plugin has already been made for a library you are using, please check out the OpenTelemetry Registry.

If you cannot your library in the registry, feel free to suggest a new plugin request at opentelemetry-js-contrib.

Contributing

If you'd like to contribute to this library, please read the contributing guide to learn more about how to build and test the code.

Impressions