Minimalistic StatsD client for Node.js programs

npm install lynx
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A minimalistic node.js client for statsd server. Fork of original work by sivy

lynx features:

  • Minimalistic — there is only a minimum of abstraction between you and statsd
  • Streams — You can stream in and out of a lynx connection
  • Re-usable UDP Connections — Keeps UDP connections open for a certain time
  • Errors — Pluggable error handling, by default errors are ignored

Quick Start

$ npm install lynx
$ node
> var lynx = require('lynx');
// Options in this instantiation include:
//   * `on_error` function to be executed when we have errors
//   * `socket` if you wish to just use a existing udp socket
//   * `scope` to define the a prefix for all stats, e.g. with `scope`
//     'product1' and stat 'somestat' the key would actually be
//     'product1.somestat'
> var metrics = new lynx('localhost', 8125);
{ host: 'localhost', port: 8125 }
> metrics.increment('');
> metrics.decrement('');
> metrics.timing('node_test.some_service.task.time', 500); // time in ms
> metrics.gauge('', 100);
> metrics.set('', 10);

This is the equivalent to:

echo "|c"  | nc -w 0 -u localhost 8125
echo "|c" | nc -w 0 -u localhost 8125
echo "node_test.some_service.task.time:500|ms" | nc -w 0 -u localhost 8125
echo "|g"    | nc -w 0 -u localhost 8125
echo "|s"       | nc -w 0 -u localhost 8125

The protocol is super simple, so feel free to check out the source code to understand how everything works.



If you want to track something that happens really, really frequently, it can overwhelm StatsD with UDP packets. To work around that, use the optional sampling rate for metrics. This will only send packets a certain percentage of time. For very frequent events, this will give you a statistically accurate representation of your data.

Sample rate is an optional parameter to all of the metric API calls. A valid sample rate is 0.0 - 1.0. Values of 0.0 will never send any packets, and values of 1.0 will send every packet.

In these examples we are samping at a rate of 0.1, meaning 1-in-10 calls to send a sample will actually be sent to StatsD.

var metrics = new lynx('localhost', 8125);
metrics.increment('', 0.1);
metrics.decrement('', 0.1);
metrics.timing('node_test.some_service.task.time', 500, 0.1);
metrics.gauge('', 100, 0.1);
metrics.set('', 10, 0.1);
var timer2 = metrics.createTimer('node_test.some_service.task2.time', 0.1);


You can stream to lynx:

  .pipe(new lynx('localhost', port))

Feel free to check the stream-test for more info.


If you wish to measure timing you can use the timer() functionality.

var metrics = new lynx('localhost', 8125)
  , timer   = metrics.createTimer('some.interval')

// Should send something like "some.interval:100|ms"
setTimeout(function () {
}, 100);

Timers use Date.getTime() which is known for being imprecise at the ms level. If this is a problem to you please submit a pull request and I'll take it.


Batching is possible for increment, decrement, and count:

metrics.decrement(['uno', 'two', 'trezentos']);

If you want to mix more than one type of metrics in a single packet you can use send, however you need to construct the values yourself. An example:

// This code is only to exemplify the functionality
// As of the current implementation the sample rate is processed per group
// of stats and not per individual stat, meaning either all would be send
// or none would be sent.
  { "foo" : "-1|c"    // count
  , "bar" : "15|g"    // gauge
  , "baz" : "500|ms"  // timing
  , "boaz": "40|s"    // set
  }, 0.1);            // sample rate at `0.1`

Closing your socket

You can close your open socket when you no longer need it by using metrics.close().


By default errors get logged. If you wish to change this behavior simply specify a on_error function when instantiating the lynx client.

function on_error(err) {

var connection = new lynx('localhost', 1234, {on_error: on_error});

Source code is super minimal, if you want try to get familiar with when errors occur check it out. If you would like to change behavior on how this is handled send a pull request justifying why and including the alterations you would like to propose.


Run the tests with npm.

npm test


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(oo)--',- in caos

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