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Panopticon is a Node.js utility to aggregate samples generated by a Node.js application. It works in regular, single instance applications but shines in applications that make use of the cluster core module. Samples generated by all processes are sent to the master process and emitted so that the aggregated data may be used.

Panopticon exposes a constructor. Instances live across the cluster, and expose a very simple API for a user to sample data. At regular intervals, the data acquired across the cluster is emitted by the master part of the instance.

In order to minimise parsing load due to interprocess communication, data is sub-aggregated on each worker/master for an interval, and then forwarded to the master for final aggregation, reducing the parsing load to a single object per machine per instance, rather than parsing a small object for every sample taken.

Panopticon has no production dependencies! Find it on npm.


Panopticon is 100% JavaScript with no production dependencies. Simply install with:

npm install panopticon


Require Panopticon like a regular node.js module:

var Panopticon = require('panopticon');

Panopticon itself is a constructor, so when you're ready to start it, make a new object

var panopticon = new Panopticon(options);

options is an object with fields: startTime, name, interval, scaleFactor, persist, transformer

startTime (ms since the unix epoch) is an optional time to start from, interval is the time delay (in ms) between batches of data and scaleFactor scales the reporting from some of the reporter types. If no startTime is provided, then it defaults to 0. Similarly, if no sane interval is provided, it defaults to 10 seconds. By default the scale of reporting is in kilohertz. persist is a boolean, and tells the panopticon if it should be keeping data paths around after each interval.

The startTime, if used, must be the same across your cluster. This is simple to manage using the optional environment argument to cluster.fork. i.e. the master can use startTime =, and pass this value to the forked workers with cluster.fork({ START_TIME: startTime }). If not used (undefined or otherwise falsy) then it defaults to 0, so the first interval will be short, but all workers will have the same starting point without communicating a value. A modulo function is used internally to calculate when the current interval ends, so there is no additional cost associated with starting from 0.

If no value is passed in for scaleFactor, it defaults to 1 (reports in kHz). Panopticon internally calculates the rate of increments, so it needs to be told if this scale is wrong. For example, to change the reporting of incrementers and timed samples to Hz, set this value to 1000. This only affects incrementers and timed samples, since these are concerned with timing. Sets and samples are your responsibility, so if these should be reporting in something other than kHz for those, then you must give the panopticon the data in the scale desired.

By default the PID of each worker and the master are logged, as well as the number of workers (not including the master). Everything else needs to be sent to the panopticon object using one of its acquisition methods. In each case the id is the identifier that should be associated with this piece of data, and path is an array of strings representing subkeys in descending order. The methods are

When your application is shutting down, it should call panopticon.stop() to clear timers.

On the master, halfway between collections from the workers and itself the panopticon object emits aggregated data. This only happens on the master.

panopticon.on('delivery', function (aggregatedData) {
    // Do something with aggregatedData

The delivered data is an object containing all of the data collected over the cluster.


Without persist turned on, a completely fresh batch of data is started by each panopticon every interval. This means that loggers that get fired rarely are only represented in intervals in which they have occurred. Since this is not always desirable, persist tells a panopticon not to start from fresh, but simply to set the loggers to a null state. For incs this is as simple as resetting to 0, and for sets the value from the previous interval is kept. In the case of sample and timedSample, the subfields are set to null since no data recorded should be interpreted as a need for interpolation.


A transformer function can be used to rearrange the aggregated data. For example:

function transformer(data, id) {
    function checkValue(obj) {
        if (typeof obj !== 'object') {

        if (obj.hasOwnProperty('value')) {
            obj.values = {};
            obj.values[id] = obj.value;
            delete obj.value;

        for (var key in obj) {
            if (obj.hasOwnProperty(key)) {


    return data;

The function takes raw data, and looks for occurrences of the 'value' key, associated with panopticon data types. When it finds one, it puts the content into a small object called 'values' against a key which is the worker ID. Panopticon merges objects together on aggregation, so values objects are merged, keeping related data together.


Multiple panoptica may be instantiated. The motivation for this is sampling over different intervals concurrently. Internally Panopticon keeps track of instances with IDs counting up from zero. To ensure consistency panoptica must be instantiated in the same order, meaning that you should avoid instantiating panoptica in separate asynchronous functions with indefinite execution order. Try to keep them in a synchronous group.

If a worker goes down, you may safely restart it. New panoptica instances catch up to the current interval and report to the master as normal.

To differentiate between different panoptica, each aggregated data has an id key, which is the same as the id of the panopticon responsible for it.

Points to note

The node.js implementation of setTimeout is buggy. The resulting timeout can (and does) fire early sometimes, contrary to expectations. This lead to some acrobatics to ensure that when it does fire early, it is reinitialised. This can be seen in Panopticon.prototype.timeUp.

The standard deviation method used by panopticon.sample is single pass. This leaves it more prone than a two pass algorithm to round off errors. A single pass method is used to avoid growing arrays whilst accumulating a batch. The specific algorithm used is the one found in The Art of Computer Programming, Volume 2: Seminumerical Algorithms, section 4.2.2.


Tests for Panopticon are written in nodeunit. To run them, execute the following command in the Panopticon directory:

npm test

If you want to inspect the test coverage, use the following:

npm run-script cover
open coverage/lcov-report/index.html

(the last line assumes that you're on a mac)


Contributions are welcome! Please observe the coding style of Panopticon. If you add functionality, then this must be accompanied by tests. If you break tests, you must have a good reason for doing so and provide updates to existing tests to fix the breakages. Please run JavaScript files changed in your branch through jshint to catch problems. A jshint config file is provided, and jshint is installed as a development dependency.

Panopticon was something of an experiment in using node.js module architecture best practices. Throughout it uses the module-constructor pattern, a form of the substack pattern. Every JavaScript file exposes a constructor function on module.exports. Whatever your thoughts on this, please abide by the choices made for this module.


Class: Panopticon

Instances are event emitters.

Panopticon.registerMethod(name, loggerClass, validator)

This class method allows the extension of Panopticon methods with a new logger class. This is used
internally by Panopticon, but it is also part of the public API. loggerClass receives the

Instances of the loggerClass must have the following methods:

The validator will receive single data points and should return true if they are valid, and false
if not. Invalid points are simply ignored.


Returns a copy of an array which lists registered method names.


Returns the number of panopticon instances that have been started. Useful for testing.


Resets the count of instances. Strictly for testing use only. Do not use this.

Event: 'delivery'

function (data) { }

Master only. A panopticon instance emits this event when it has a dataset object to deliver.

Event: 'sample'

function(data, id) { }

Master and workers. A panopticon instance emits this event when it has data to be
aggregated. This is a private event and should not be acted upon. Use the 'delivery' event.

Event: 'newInterval'

function () { }

Master and workers. A panopticon instance emits this event when a new interval begins. This is
useful for sets, which may be useful to do once per interval.

Event: 'reset'

function () { }

Master and workers. If the panopticon instance is persistent, then this event is emitted at the end
of an interval, immediately before 'newInterval'.

Event: 'stopping'

Master and workers. Emitted when `panopticon.stop()` is called.

panopticon.sample(path, id, n)

The sample method keeps track of the maximum, minimum and standard deviation of n over multiple
calls in an interval. The sample is registered to the aggregated object on the given path with a
key given by id.

panopticon.timedSample(path, id, dt)

Similar to the sample method, but instead of a number n it takes dt, the result of a high
resolution timer
call (a length 2
array of numbers). This method also keeps track of the count and total time over all calls in an
interval., id, n)

Increments by n. If this the first call of inc with this path and id, then the starting
value is assumed to be 0.

panopticon.set(path, id, value)

Set a value on a path with id.