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.
npm install datadog-metrics --save
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
There's also a longer tutorial that walks you through setting up a monitoring dashboard on DataDog using datadog-metrics.
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. However, you can provide an APP key if you want by setting the DATADOG_APP_KEY
environment variable.
There are three ways to use this module to instrument an application. They differ in the level of control that they provide.
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);
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);
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,
defaultTags: ['env:staging', 'region:us-east-1']
});
metricsLogger.gauge('mygauge', 42);
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.
- This defaults to 15 seconds. Set it to 0 to disable auto-flushing which
means you must call
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.
- It's usually best to keep this in an environment variable.
Datadog-metrics looks for the API key in
appKey
: Sets the DataDog APP key. (optional)- It's usually best to keep this in an environment variable.
Datadog-metrics looks for the APP key in
DATADOG_APP_KEY
by default.
- It's usually best to keep this in an environment variable.
Datadog-metrics looks for the APP key in
defaultTags
: Default tags used for all metric reporting. (optional)- Set tags that are common to all metrics.
Example:
metrics.init({ host: 'myhost', prefix: 'myapp.' });
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);
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);
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);
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.
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
npm test
- 0.4.0
- ADD: Initialize with a default set of tags
- 0.3.0
- FIX: Don't overwrite metrics with the same key but different tags when aggregating them (Thanks @akrylysov and @RavivIsraeli!)
- ADD: Add success/error callbacks to
metrics.flush()
(Thanks @akrylysov!) - ADD: Allow DataDog APP key to be configured (Thanks @gert-fresh!)
- Bump dependencies to latest
- Update docs
- 0.2.1
- Update docs (module code remains unchanged)
- 0.2.0
- API redesign
- Remove
setDefaultXYZ()
and addedinit()
- 0.1.1
- Allow
increment
to be called with a default value of 1
- Allow
- 0.1.0
- The first proper release
- Rename
counter
toincrement
- 0.0.0
- Work in progress
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.