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dbader / node-datadog-metrics

Buffered metrics reporting via the DataDog HTTP API
https://dbader.org/blog/monitoring-your-nodejs-app-with-datadog
MIT License
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datadog-metrics

Buffered metrics reporting via the Datadog HTTP API.

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Datadog-metrics lets you collect application metrics through Datadog's HTTP API. Using the HTTP API has the benefit that you don't need to install the Datadog Agent (StatsD). Just get an API key, install the module and you're ready to go.

The downside of using the HTTP API is that it can negatively affect your app's performance. Datadog-metrics solves this issue by buffering metrics locally and periodically flushing them to Datadog.

Installation

Datadog-metrics is compatible with Node.js v12 and later. You can install it with NPM:

npm install datadog-metrics --save

Example

Save the following into a file named example_app.js:

var metrics = require('datadog-metrics');
metrics.init({ host: 'myhost', prefix: 'myapp.' });

function collectMemoryStats() {
    var memUsage = process.memoryUsage();
    metrics.gauge('memory.rss', memUsage.rss);
    metrics.gauge('memory.heapTotal', memUsage.heapTotal);
    metrics.gauge('memory.heapUsed', memUsage.heapUsed);
};

setInterval(collectMemoryStats, 5000);

Run it:

DATADOG_API_KEY=YOUR_KEY DEBUG=metrics node example_app.js

Tutorial

There's also a longer tutorial that walks you through setting up a monitoring dashboard on Datadog using datadog-metrics.

Usage

Datadog API key

Make sure the DATADOG_API_KEY environment variable is set to your Datadog API key (you can also set it via the apiKey option in code). You can find the API key under Integrations > APIs. Please note the API key is different from an application key. For more details, see Datadog’s “API and Application Keys” docs.

Module setup

There are three ways to use this module to instrument an application. They differ in the level of control that they provide.

Use case #1: Just let me track some metrics already!

Just require datadog-metrics and you're ready to go. After that you can call gauge, increment and histogram to start reporting metrics.

var metrics = require('datadog-metrics');
metrics.gauge('mygauge', 42);

Use case #2: I want some control over this thing!

If you want more control you can configure the module with a call to init. Make sure you call this before you use the gauge, increment and histogram functions. See the documentation for init below to learn more.

var metrics = require('datadog-metrics');
metrics.init({ host: 'myhost', prefix: 'myapp.' });
metrics.gauge('mygauge', 42);

Use case #3: Must. Control. Everything.

If you need even more control you can create one or more BufferedMetricsLogger instances and manage them yourself:

var metrics = require('datadog-metrics');
var metricsLogger = new metrics.BufferedMetricsLogger({
    site: 'datadoghq.eu',
    apiKey: 'TESTKEY',
    host: 'myhost',
    prefix: 'myapp.',
    flushIntervalSeconds: 15,
    defaultTags: ['env:staging', 'region:us-east-1'],
    onError (error) {
        console.error('There was an error auto-flushing metrics:', error);
    }
});
metricsLogger.gauge('mygauge', 42);

API

Initialization

metrics.init(options)

Where options is an object and can contain the following:

Example:

metrics.init({ host: 'myhost', prefix: 'myapp.' });

Disabling metrics using NullReporter:

metrics.init({ host: 'myhost', reporter: metrics.NullReporter() });

Send metrics to a totally different service instead of Datadog:

metrics.init({
  reporter: {
    report(series, onSuccess, onError) {
      // `series` is an array of metrics objects, formatted basically how the
      // Datadog v1 metrics API and v1 distributions API want them.
      fetch('https://my-datadog-like-api.com/series', {
          method: 'POST',
          body: JSON.stringify({ series })
        })
          .then(response => response.json())
          .then(() => onSuccess())
          .catch(onError);
    }
  }
});

Gauges

metrics.gauge(key, value[, tags[, timestamp]])

Record the current value of a metric. The most recent value in a given flush interval will be recorded. Optionally, specify a set of tags to associate with the metric. This should be used for sum values such as total hard disk space, process uptime, total number of active users, or number of rows in a database table. The optional timestamp is in milliseconds since 1 Jan 1970 00:00:00 UTC, e.g. from Date.now().

Example:

metrics.gauge('test.mem_free', 23);

Counters

metrics.increment(key[, value[, tags[, timestamp]]])

Increment the counter by the given value (or 1 by default). Optionally, specify a list of tags to associate with the metric. This is useful for counting things such as incrementing a counter each time a page is requested. The optional timestamp is in milliseconds since 1 Jan 1970 00:00:00 UTC, e.g. from Date.now().

Example:

metrics.increment('test.requests_served');
metrics.increment('test.awesomeness_factor', 10);

Histograms

metrics.histogram(key, value[, tags[, timestamp[, options]]])

Sample a histogram value. Histograms will produce metrics that describe the distribution of the recorded values, namely the minimum, maximum, average, median, count and the 75th, 85th, 95th and 99th percentiles. Optionally, specify a list of tags to associate with the metric. The optional timestamp is in milliseconds since 1 Jan 1970 00:00:00 UTC, e.g. from Date.now().

Example:

metrics.histogram('test.service_time', 0.248);

You can also specify an options object to adjust which aggregations and percentiles should be calculated. For example, to only calculate an average, count, and 99th percentile:

metrics.histogram('test.service_time', 0.248, ['tag:value'], Date.now(), {
    // Aggregates can include 'max', 'min', 'sum', 'avg', 'median', or 'count'.
    aggregates: ['avg', 'count'],
    // Percentiles can include any decimal between 0 and 1.
    percentiles: [0.99]
});

Distributions

metrics.distribution(key, value[, tags[, timestamp]])

Send a distribution value. Distributions are similar to histograms (they create several metrics for count, average, percentiles, etc.), but they are calculated server-side on Datadog’s systems. This is much higher-overhead than histograms, and the individual calculations made from it have to be configured on the Datadog website instead of in the options for this package.

You should use this in environments where you have many instances of your application running in parallel, or instances constantly starting and stopping with different hostnames or identifiers and tagging each one separately is not feasible. AWS Lambda or serverless functions are a great example of this. In such environments, you also might want to use a distribution instead of increment or gauge (if you have two instances of your app sending those metrics at the same second, and they are not tagged differently or have different host names, one will overwrite the other — distributions will not).

Example:

metrics.distribution('test.service_time', 0.248);

Flushing

metrics.flush([onSuccess[, onError]])

Calling flush sends any buffered metrics to Datadog. Unless you set flushIntervalSeconds to 0 it won't be necessary to call this function.

It can be useful to trigger a manual flush by calling if you want to make sure pending metrics have been sent before you quit the application process, for example.

Logging

Datadog-metrics uses the debug library for logging at runtime. You can enable debug logging by setting the DEBUG environment variable when you run your app.

Example:

DEBUG=metrics node app.js

Tests

npm test

Release History

Meta

This module is heavily inspired by the Python dogapi module.

Daniel Bader – @dbader_org – mail@dbader.org

Distributed under the MIT license. See LICENSE for more information.

https://github.com/dbader/node-datadog-metrics