Documentation
¶
Overview ¶
Package quantile computes approximate quantiles over an unbounded data stream within low memory and CPU bounds.
A small amount of accuracy is traded to achieve the above properties.
Multiple streams can be merged before calling Query to generate a single set of results. This is meaningful when the streams represent the same type of data. See Merge and Samples.
For more detailed information about the algorithm used, see:
Effective Computation of Biased Quantiles over Data Streams ¶
http://www.cs.rutgers.edu/~muthu/bquant.pdf
Example (MergeMultipleStreams) ¶
package main
import (
"fmt"
"github.com/beorn7/perks/quantile"
)
func main() {
// Scenario:
// We have multiple database shards. On each shard, there is a process
// collecting query response times from the database logs and inserting
// them into a Stream (created via NewTargeted(0.90)), much like the
// Simple example. These processes expose a network interface for us to
// ask them to serialize and send us the results of their
// Stream.Samples so we may Merge and Query them.
//
// NOTES:
// * These sample sets are small, allowing us to get them
// across the network much faster than sending the entire list of data
// points.
//
// * For this to work correctly, we must supply the same quantiles
// a priori the process collecting the samples supplied to NewTargeted,
// even if we do not plan to query them all here.
ch := make(chan quantile.Samples)
getDBQuerySamples(ch)
q := quantile.NewTargeted(map[float64]float64{0.90: 0.001})
for samples := range ch {
q.Merge(samples)
}
fmt.Println("perc90:", q.Query(0.90))
}
// This is a stub for the above example. In reality this would hit the remote
// servers via http or something like it.
func getDBQuerySamples(ch chan quantile.Samples) {}
Output:
Example (Simple) ¶
package main
import (
"bufio"
"fmt"
"log"
"os"
"strconv"
"github.com/beorn7/perks/quantile"
)
func main() {
ch := make(chan float64)
go sendFloats(ch)
// Compute the 50th, 90th, and 99th percentile.
q := quantile.NewTargeted(map[float64]float64{
0.50: 0.005,
0.90: 0.001,
0.99: 0.0001,
})
for v := range ch {
q.Insert(v)
}
fmt.Println("perc50:", q.Query(0.50))
fmt.Println("perc90:", q.Query(0.90))
fmt.Println("perc99:", q.Query(0.99))
fmt.Println("count:", q.Count())
}
func sendFloats(ch chan<- float64) {
f, err := os.Open("exampledata.txt")
if err != nil {
log.Fatal(err)
}
sc := bufio.NewScanner(f)
for sc.Scan() {
b := sc.Bytes()
v, err := strconv.ParseFloat(string(b), 64)
if err != nil {
log.Fatal(err)
}
ch <- v
}
if sc.Err() != nil {
log.Fatal(sc.Err())
}
close(ch)
}
Output: perc50: 5 perc90: 16 perc99: 223 count: 2388
Example (Window) ¶
package main
import (
"time"
"github.com/beorn7/perks/quantile"
)
func main() {
// Scenario: We want the 90th, 95th, and 99th percentiles for each
// minute.
ch := make(chan float64)
go sendStreamValues(ch)
tick := time.NewTicker(1 * time.Minute)
q := quantile.NewTargeted(map[float64]float64{
0.90: 0.001,
0.95: 0.0005,
0.99: 0.0001,
})
for {
select {
case t := <-tick.C:
flushToDB(t, q.Samples())
q.Reset()
case v := <-ch:
q.Insert(v)
}
}
}
func sendStreamValues(ch chan float64) {
}
func flushToDB(t time.Time, samples quantile.Samples) {
}
Output:
Index ¶
Examples ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type Sample ¶
type Sample struct {
Value float64 `json:",string"`
Width float64 `json:",string"`
Delta float64 `json:",string"`
}
Sample holds an observed value and meta information for compression. JSON tags have been added for convenience.
type Samples ¶
type Samples []Sample
Samples represents a slice of samples. It implements sort.Interface.
type Stream ¶
type Stream struct {
// contains filtered or unexported fields
}
Stream computes quantiles for a stream of float64s. It is not thread-safe by design. Take care when using across multiple goroutines.
func NewHighBiased ¶
NewHighBiased returns an initialized Stream for high-biased quantiles (e.g. 0.01, 0.1, 0.5) where the needed quantiles are not known a priori, but error guarantees can still be given even for the higher ranks of the data distribution.
The provided epsilon is a relative error, i.e. the true quantile of a value returned by a query is guaranteed to be within 1-(1±Epsilon)*(1-Quantile).
See http://www.cs.rutgers.edu/~muthu/bquant.pdf for time, space, and error properties.
func NewLowBiased ¶
NewLowBiased returns an initialized Stream for low-biased quantiles (e.g. 0.01, 0.1, 0.5) where the needed quantiles are not known a priori, but error guarantees can still be given even for the lower ranks of the data distribution.
The provided epsilon is a relative error, i.e. the true quantile of a value returned by a query is guaranteed to be within (1±Epsilon)*Quantile.
See http://www.cs.rutgers.edu/~muthu/bquant.pdf for time, space, and error properties.
func NewTargeted ¶
NewTargeted returns an initialized Stream concerned with a particular set of quantile values that are supplied a priori. Knowing these a priori reduces space and computation time. The targets map maps the desired quantiles to their absolute errors, i.e. the true quantile of a value returned by a query is guaranteed to be within (Quantile±Epsilon).
See http://www.cs.rutgers.edu/~muthu/bquant.pdf for time, space, and error properties.
func (*Stream) Count ¶
Count returns the total number of samples observed in the stream since initialization.
func (*Stream) Merge ¶
Merge merges samples into the underlying streams samples. This is handy when merging multiple streams from separate threads, database shards, etc.
ATTENTION: This method is broken and does not yield correct results. The underlying algorithm is not capable of merging streams correctly.
func (*Stream) Query ¶
Query returns the computed qth percentiles value. If s was created with NewTargeted, and q is not in the set of quantiles provided a priori, Query will return an unspecified result.
Source Files