streaming-percentiles

streaming-percentiles-cpp

This is a C++ library with implementations of various percentile algorithms on streams of data. Using the magic of emscripten, this package also cross-compiles a JavaScript version of the streaming percentiles library as well.

For more on streaming percentiles, see Calculating Percentiles on Streaming Data.

Obtaining the Library

You can download pre-built versions of the library from the streaming-percentiles-cpp releases page. Otherwise see CONTRIBUTING.md for instructions on how to compile the library from source.

Usage Example

C++

Here's a simple example on how to use the Greenwald-Khanna streaming percentile algorithm from C++:

#include <stmpct/gk.hpp>

using namespace stmpct;

double epsilon = 0.1;
gk g(epsilon);
for (int i = 0; i < 1000; ++i)
    g.insert(rand());
double p50 = g.quantile(0.5); // Approx. median
double p95 = g.quantile(0.95); // Approx. 95th percentile

JavaScript

Node.JS

Here's how to use the library from Node.JS:

var sp = require('./streamingPercentiles.v1.min.js');

var epsilon = 0.1;
var g = new sp.GK(epsilon);
for (var i = 0; i < 1000; ++i)
    g.insert(Math.random());
var p50 = g.quantile(0.5); // Approx. median
var p95 = g.quantile(0.95); // Approx. 95th percentile

Browser

Here's how to use the library from a browser. Note that the default module name is streamingPercentiles:

<script src="streamingPercentiles.v1.min.js"></script>
<script>
var epsilon = 0.1;
var gk = new streamingPercentiles.GK(epsilon);
for (var i = 0; i < 1000; ++i)
    g.insert(Math.random());
var p50 = g.quantile(0.5);
</script>

API Reference

Coming soon!

Contributing

If you are interested in contributing to the library, please see CONTRIBUTING.md.

Changelog

2.1.0 - 2018-04-14

• Wrapped JS library into a UMD so that it works from Node, require.js, and script src=...
• Added browser-based unit tests usig nightwatch.js
• Changed node unit test framework from unit.js to tape