gohistogram - Histograms in Go
This package provides Streaming Approximate Histograms
for efficient quantile approximations.
The histograms in this package are based on the algorithms found in
Ben-Haim & Yom-Tov's A Streaming Parallel Decision Tree Algorithm
(PDF).
Histogram bins do not have a preset size. As values stream into
the histogram, bins are dynamically added and merged.
Another implementation can be found in the Apache Hive project (see
NumericHistogram).
An example:
The accurate method of calculating quantiles (like percentiles) requires
data to be sorted. Streaming histograms make it possible to approximate
quantiles without sorting (or even individually storing) values.
NumericHistogram is the more basic implementation of a streaming
histogram. WeightedHistogram implements bin values as exponentially-weighted
moving averages.
A maximum bin size is passed as an argument to the constructor methods. A
larger bin size yields more accurate approximations at the cost of increased
memory utilization and performance.
A picture of kittens:
Getting started
Using in your own code
$ go get github.com/VividCortex/gohistogram
import "github.com/VividCortex/gohistogram"
Running tests and making modifications
Get the code into your workspace:
$ cd $GOPATH
$ git clone git@github.com:VividCortex/gohistogram.git ./src/github.com/VividCortex/gohistogram
You can run the tests now:
$ cd src/github.com/VividCortex/gohistogram
$ go test .
API Documentation
Full source documentation can be found here.
Contributing
We only accept pull requests for minor fixes or improvements. This includes:
- Small bug fixes
- Typos
- Documentation or comments
Please open issues to discuss new features. Pull requests for new features will be rejected,
so we recommend forking the repository and making changes in your fork for your use case.
License
Copyright (c) 2013 VividCortex
Released under MIT License. Check LICENSE file for details.