applicationinsights

What is applicationinsights?

The applicationinsights npm package is a powerful tool for monitoring and diagnosing performance issues in your applications. It provides a range of features for collecting telemetry data, tracking custom events, and monitoring dependencies, among other capabilities.

What are applicationinsights's main functionalities?

Telemetry Client

The Telemetry Client allows you to track custom events and metrics. This example demonstrates how to set up the client and track a custom event.

const appInsights = require('applicationinsights');
appInsights.setup('YOUR_INSTRUMENTATION_KEY').start();
const client = appInsights.defaultClient;
client.trackEvent({ name: 'my custom event', properties: { customProperty: 'custom value' } });

Request Tracking

Request Tracking automatically collects and sends telemetry data for incoming HTTP requests. This example shows how to set up a basic HTTP server with request tracking enabled.

const appInsights = require('applicationinsights');
appInsights.setup('YOUR_INSTRUMENTATION_KEY').start();
const http = require('http');
http.createServer((req, res) => {
  res.writeHead(200, { 'Content-Type': 'text/plain' });
  res.end('Hello World\n');
}).listen(8080);

Dependency Tracking

Dependency Tracking allows you to monitor the performance and success of external dependencies. This example demonstrates how to manually track a dependency.

const appInsights = require('applicationinsights');
appInsights.setup('YOUR_INSTRUMENTATION_KEY').start();
const client = appInsights.defaultClient;
client.trackDependency({ target: 'http://example.com', name: 'GET /', data: 'http://example.com', duration: 100, resultCode: 200, success: true });

Exception Tracking

Exception Tracking captures and sends telemetry data for exceptions. This example shows how to track an exception.

const appInsights = require('applicationinsights');
appInsights.setup('YOUR_INSTRUMENTATION_KEY').start();
const client = appInsights.defaultClient;
try {
  throw new Error('Test error');
} catch (e) {
  client.trackException({ exception: e });
}

Other packages similar to applicationinsights

winston

Winston is a versatile logging library for Node.js. While it focuses more on logging rather than telemetry, it can be used to log events, errors, and other information. It does not provide the same level of built-in telemetry and performance monitoring as applicationinsights.

log4js

Log4js is another logging library for Node.js, inspired by the Java log4j library. It provides a variety of appenders for logging to different destinations. Like winston, it focuses on logging rather than telemetry and performance monitoring.

newrelic

New Relic is a comprehensive application performance monitoring tool that provides similar functionalities to applicationinsights, including telemetry, error tracking, and performance monitoring. It is a commercial product with a broader set of features and integrations.

datadog

Datadog is a monitoring and analytics platform for developers, IT operations teams, and business users. It offers extensive features for monitoring applications, including telemetry, performance metrics, and log management. It is a commercial product with a wide range of integrations and capabilities.

README

Azure Monitor Application Insights Distro for Node.js (Preview)

Azure Monitor Application Insights Distro SDK monitors your backend services and components after you deploy them to help you discover and rapidly diagnose performance and other issues. Add this SDK to your Node.js services to include deep info about Node.js processes and their external dependencies such as database and cache services. You can use this SDK for your Node.js services hosted anywhere: your datacenter, Azure VMs and Web Apps, and even other public clouds. This solution is based on OpenTelemetry, to learn more about OpenTelemetry concepts, see the OpenTelemetry overview or OpenTelemetry FAQ.

Important: The Azure Monitor OpenTelemetry-based Offerings for Node.js applications are currently in preview. See the Supplemental Terms of Use for Microsoft Azure Previews for legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability.

Limitations of current preview release

Consider whether this preview is right for you. It enables distributed tracing, metrics, logs and excludes:

• Live Metrics
• Autopopulation of Cloud Role Name and Cloud Role Instance in Azure environments
• Autopopulation of User ID and Authenticated User ID when you use the Application Insights JavaScript SDK
• Autopopulation of User IP (to determine location attributes)
• Ability to override Operation Name
• Ability to manually set User ID or Authenticated User ID
• Propagating Operation Name to Dependency Telemetry

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

Get started

Follow the steps in this section to instrument your application with OpenTelemetry.

Prerequisites

• Azure subscription: Create an Azure subscription for free

• Application Insights resource: Create an Application Insights resource

• Application using an officially supported version of Node.js runtime:

  • OpenTelemetry supported runtimes
  • Azure Monitor OpenTelemetry Exporter supported runtimes

Install the library

npm install applicationinsights@beta

The following packages are also used for some specific scenarios described later in this article:

npm install @opentelemetry/api
npm install @opentelemetry/sdk-trace-base
npm install @opentelemetry/semantic-conventions
npm install @opentelemetry/instrumentation-http

Enable Azure Monitor Application Insights

Important: ApplicationInsightsClient must be setup and started before you import anything else. There may be resulting telemetry loss if other libraries are imported first.

const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");

const config = new ApplicationInsightsConfig();
config.connectionString = "<YOUR_CONNECTION_STRING>";
const appInsights = new ApplicationInsightsClient(config);
appInsights.start();

• If the Connection String is set in the environment variable APPLICATIONINSIGHTS_CONNECTION_STRING, ApplicationInsightsConfig constructor can be called with no arguments. This makes it easy to use different connection strings for different environments.

Configuration

The ApplicationInsightsConfig object provides a number of options to setup SDK behavior.

const config = new ApplicationInsightsConfig();
config.connectionString = "<YOUR_CONNECTION_STRING>";
config.samplingRatio = 1;
config.enableAutoCollectExtendedMetrics = false;
config.instrumentations =  {
  "http": { enabled: true },
  "azureSdk": { enabled: false },
  "mongoDb": { enabled: false },
  "mySql": { enabled: false },
  "postgreSql": { enabled: false },
  "redis": { enabled: false }
};
const appInsights = new ApplicationInsightsClient(config);
appInsights.start();

Property	Description	Default
connectionString	Application Insights Resource Connection String
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
enableAutoCollectExceptions	Sets the state of exception tracking. If true uncaught exceptions will be sent to Application Insights	true
enableAutoCollectPerformance	Sets the state of performance tracking. If true performance counters will be collected every second and sent to Application Insights	true
enableAutoCollectStandardMetrics	Sets the state of Standard Metrics tracking. If true Standard Metrics will be collected every minute and sent to Application Insights	true
enableAutoCollectHeartbeat	Sets the state of request tracking. If true HeartBeat metric data will be collected every 15 minutes and sent to Application Insights	true
storageDirectory	Directory to store retriable telemetry when it fails to export	Windows %TEMP%\Microsoft\AzureMonitor Non-Windows %TMPDIR%/Microsoft/AzureMonitor
disableOfflineStorage	Disable offline storage when telemetry cannot be exported	false
aadTokenCredential	Azure Credential instance to be used to authenticate the App. AAD Identity Credential Classes
instrumentations	Allow configuration of OpenTelemetry Instrumentations.	{"http": { enabled: true },"azureSdk": { enabled: false },"mongoDb": { enabled: false },"mySql": { enabled: false },"postgreSql": { enabled: false },"redis": { enabled: false }}
logInstrumentations	Allow configuration of Log Instrumentations.	{"console": { enabled: false },"bunyan": { enabled: false },"winston": { enabled: false }}
extendedMetrics	Enable/Disable specific extended Metrics(gc, heap and loop).	{"gc":false,"heap":false,"loop":false}
resource	Specify custom Opentelemetry Resource.

All these properties except aadTokenCredential and resource could be configured using configuration file applicationinsights.json located under root folder of applicationinsights package installation folder, Ex: node_modules/applicationinsights. These configuration values will be applied to all ApplicationInsightsClients created in the SDK.

{
    "connectionString": "<YOUR_CONNECTION_STRING>",
    "samplingRatio": 0.8,
    "enableAutoCollectExceptions": true,
    "enableAutoCollectHeartbeat": true,
    "instrumentations":{
        "azureSdk": {
            "enabled": false
        }
    },
    "logInstrumentations":{
        "console": {
            "enabled": true
        }
    }
    ...
}
  

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 Application Insights Distro.

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

• HTTP/HTTPS
• MongoDB
• MySQL
• Postgres
• Redis
• Redis-4
• Azure SDK

Metrics

• HTTP/HTTPS

Other OpenTelemetry Instrumentations are available here and could be added using TraceHandler in ApplicationInsightsClient.

   const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");
   const { ExpressInstrumentation } = require('@opentelemetry/instrumentation-express');

   const appInsights = new ApplicationInsightsClient(new ApplicationInsightsConfig());
   const traceHandler = appInsights.getTraceHandler();
   traceHandler.addInstrumentation(new ExpressInstrumentation());
   appInsights.start();
   

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. This step updates Cloud Role Name and Cloud Role Instance from their default values to something that makes sense to your team. They'll appear on the Application Map as the name underneath a node. Cloud Role Name uses service.namespace and service.name attributes, although it falls back to service.name if service.namespace isn't set. Cloud Role Instance uses the service.instance.id attribute value.

const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");
const { Resource } = require("@opentelemetry/resources");
const { SemanticResourceAttributes } = require("@opentelemetry/semantic-conventions");

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

let config = new ApplicationInsightsConfig();
config.resource = customResource;
const appInsights = new ApplicationInsightsClient(config);
appInsights.start();

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

Enable Sampling

You may want to enable sampling to reduce your data ingestion volume which reduces your cost. Azure Monitor provides a custom fixed-rate sampler that populates events with a "sampling ratio", which Application Insights converts to "ItemCount". This ensures accurate experiences and event counts. The sampler is designed to preserve your traces across services. The sampler expects a sample rate of between 0 and 1 inclusive. A rate of 0.1 means approximately 10% of your telemetry will be sent.

const config = new ApplicationInsightsConfig();
config.connectionString = "<YOUR_CONNECTION_STRING>";
config.samplingRatio = 0.1;
const appInsights = new ApplicationInsightsClient(config);
appInsights.start();

---

Tip: If you're not sure where to set the sampling rate, start at 5% (i.e., 0.05 sampling ratio) and adjust the rate based on the accuracy of the operations shown in the failures and performance blades. A higher rate generally results in higher accuracy.

Modify telemetry

This section explains how to modify telemetry.

Add span attributes

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

• Use options provided by instrumentation libraries.
• Add a custom span processor.

These attributes might include adding a custom property to your telemetry. You might also use attributes to set optional fields in the Application Insights schema, like Client IP.

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. They populate the customDimensions field in the requests or the dependencies tables in Application Insights.

Use a custom processor:

const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");
const { ReadableSpan, Span, SpanProcessor } = require("@opentelemetry/sdk-trace-base");
const { SemanticAttributes } = require("@opentelemetry/semantic-conventions");

const appInsights = new ApplicationInsightsClient(new ApplicationInsightsConfig());

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>";
    }
}

appInsights.getTraceHandler().addSpanProcessor(new SpanEnrichingProcessor());
appInsights.start();

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:

   const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");
   const { IncomingMessage } = require("http");
   const { RequestOptions } = require("https");
   const { HttpInstrumentationConfig }= require("@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 config = new ApplicationInsightsConfig();
   config.instrumentations.http = httpInstrumentationConfig;
   const appInsights = new ApplicationInsightsClient(config);
   appInsights.start();
   
   

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:

   ...
   import { SpanKind, TraceFlags } from "@opentelemetry/api";
   
   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.

    const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");

    const appInsights = new ApplicationInsightsClient(new ApplicationInsightsConfig());
    
    const customMetricsHandler = appInsights.getMetricHandler().getCustomMetricsHandler();
    const meter =  customMetricsHandler.getMeter();\

    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"});
    });

    appInsights.start();
    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.

const { ApplicationInsightsClient, ApplicationInsightsConfig } = require("applicationinsights");

const appInsights = new ApplicationInsightsClient(new ApplicationInsightsConfig());
const tracer = appInsights.getTraceHandler().getTracer();
let span = tracer.startSpan("hello");
try{
    throw new Error("Test Error");
}
catch(error){
    span.recordException(error);
}

Troubleshooting

Self-diagnostics

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

import { ApplicationInsightsClient, ApplicationInsightsConfig } from "applicationinsights";
import { DiagLogLevel } from "@opentelemetry/api";

const appInsights = new ApplicationInsightsClient(new ApplicationInsightsConfig());
const logger = appInsights.getLogger();
logger.updateLogLevel(DiagLogLevel.DEBUG);

APPLICATIONINSIGHTS_INSTRUMENTATION_LOGGING_LEVEL environment varialbe 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.

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

// Application Insights SDK setup....

Support

For help and questions about using this project, please create a Support request issue on https://github.com/microsoft/ApplicationInsights-node.js/issues.

For OpenTelemetry issues, contact the OpenTelemetry JavaScript community directly. Support Policy

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Data Collection

As this SDK is designed to enable applications to perform data collection which is sent to the Microsoft collection endpoints the following is required to identify our privacy statement.

The software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. You may turn off the telemetry as described in the repository. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft’s privacy statement. Our privacy statement is located at https://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

License

MIT