A fast map-reduce and parallel streaming friendly data-structure for accurate quantile approximation.

This package provides an implementation of Ted Dunning's t-digest data structure in Go.

This project is actively maintained. We are happy to collaborate on features and issues if/when they arrive.

This package uses go modules. Our releases are tagged and signed following the Semantic Versioning scheme.

go get github.com/caio/go-tdigest/v4

package main

import (
	"fmt"
	"math/rand"

	"github.com/caio/go-tdigest/v4"
)

func main() {
	// Analogue to tdigest.New(tdigest.Compression(100))
	t, _ := tdigest.New()

	for i := 0; i < 10000; i++ {
		// Analogue to t.AddWeighted(rand.Float64(), 1)
		t.Add(rand.Float64())
	}

	fmt.Printf("p(.5) = %.6f\n", t.Quantile(0.5))
	fmt.Printf("CDF(Quantile(.5)) = %.6f\n", t.CDF(t.Quantile(0.5)))
}

You can configure your digest upon creation with options documented at options.go. Example:

// Construct a digest with compression=200 and its own
// (thread-unsafe) RNG seeded with 0xCA10:
digest, _ := tdigest.New(
        tdigest.Compression(200),
        tdigest.LocalRandomNumberGenerator(0xCA10),
)

This is a port of the reference implementation with some ideas borrowed from the python version. If you wanna get a quick grasp of how it works and why it's useful, this video and companion article is pretty helpful.