datadog-metrics

Buffered metrics reporting via the DataDog HTTP API.

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

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

Usage

DataDog API key

Make sure the DATADOG_API_KEY environment variable is set to your DataDog API key. You can find the API key under Integrations > APIs. You only need to provide the API key, not the APP key.

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({
    apiKey: 'TESTKEY',
    host: 'myhost',
    prefix: 'myapp.',
    flushIntervalSeconds: 15
});
metricsLogger.gauge('mygauge', 42);

API

Initialization

metrics.init(options)

Where options is an object and can contain the following:

• host: Sets the hostname reported with each metric. (optional)
  • Setting a hostname is useful when you're running the same application on multiple machines and you want to track them separately in DataDog.
• prefix: Sets a default prefix for all metrics. (optional)
  • Use this to namespace your metrics.
• flushIntervalSeconds: How often to send metrics to DataDog. (optional)
  • This defaults to 15 seconds. Set it to 0 to disable auto-flushing which means you must call flush() manually.
• apiKey: Sets the DataDog API key. (optional)
  • It's usually best to keep this in an environment variable. Datadog-metrics looks for the API key in DATADOG_API_KEY by default.

Example:

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

Gauges

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

Record the current value of a metric. They 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.

Example:

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

Counters

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

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.

Example:

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

Histograms

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

Sample a histogram value. Histograms will produce metrics that describe the distribution of the recorded values, namely the minimum, maximum, average, count and the 75th, 85th, 95th and 99th percentiles. Optionally, specify a list of tags to associate with the metric.

Example:

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

Flushing

metrics.flush()

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

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

• 0.2.1
  • Update docs (module code remains unchanged)
• 0.2.0
  • API redesign
  • Remove setDefaultXYZ() and added init()
• 0.1.1
  • Allow increment to be called with a default value of 1
• 0.1.0
  • The first proper release
  • Rename counter to increment
• 0.0.0
  • Work in progress

Meta

This module is heavily inspired by the Python dogapi module.

Daniel Bader – @dbader_org – [email protected]

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

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