Awesome
topk
Sliding-window and regular top-K sketches.
- A fast implementation of the HeavyKeeper top-K sketch inspired by the segmentio implementation and RedisBloom implementation. Significantly faster (~1.5x) than segmentio/topk on small sketches (k <= 1000) and much faster (10x-90x) on large sketches (k >= 10000).
- A sliding-window top-K sketch, also based on HeavyKeeper, as described in "A Sketch Framework for Approximate Data Stream Processing in Sliding Windows"
import (
"github.com/keilerkonzept/topk" // plain sketch
"github.com/keilerkonzept/topk/sliding" // sliding-window sketch
)
Demo application: top K requesting IPs within a sliding time window from a web server access logs dataset
<p> <img src="https://www.keilerkonzept.com/sliding-topk-demo.gif" width="100%" alt="Sliding Top-K Demo Application"> </p>Contents
Examples
Top-K Sketch
package main
import (
"log"
"github.com/keilerkonzept/topk"
)
func main() {
// make a new sketch keeping track of k=3 items using 1024x3 = 3072 buckets.
sketch := topk.New(3, topk.WithWidth(1024), topk.WithDepth(3))
log.Println("the sketch takes up", sketch.SizeBytes(), "bytes in memory")
sketch.Incr("an item") // count "an item" 1 time
sketch.Add("an item", 123) // count "an item" 123 times
sketch.Add("another item", 4) // count "another item" 4 times
sketch.Add("an item", 5) // count "an item" 5 more times
sketch.Add("yet another item", 6) // count "yet another item" 6 times
if sketch.Query("an item") {
// "an item" is in the top K items observed within the last 60 ticks
}
_ = sketch.Count("another item") // return the estimated count for "another item"
// SortedSlice() returns the current top-K entries as a slice of {Fingerprint,Item,Count} structs.
for _, entry := range sketch.SortedSlice() {
log.Println(entry.Item, "has been counted", entry.Count, "times")
}
// Iter is an interator over the (*not* sorted) current top-K entries.
for entry := range sketch.Iter {
log.Println(entry.Item, "has been counted", entry.Count, "times")
}
sketch.Reset() // reset to New() state
}
Sliding-window Top-K Sketch
package main
import (
"log"
"github.com/keilerkonzept/topk/sliding"
)
func main() {
// make a new sketch keeping track of k=3 items over a window of the last 60 ticks
// use width=1024 x depth=3 = 3072 buckets
sketch := sliding.New(3, 60, sliding.WithWidth(1024), sliding.WithDepth(3))
log.Println("the sketch takes up", sketch.SizeBytes(), "bytes in memory")
sketch.Incr("an item") // count "an item" 1 time
sketch.Add("an item", 123) // count "an item" 123 times
sketch.Tick() // advance time by one tick
sketch.Add("another item", 4) // count "another item" 4 times
sketch.Ticks(2) // advance time by two ticks
sketch.Add("an item", 5) // count "an item" 5 more times
sketch.Add("yet another item", 6) // count "yet another item" 6 times
if sketch.Query("an item") {
// "an item" is in the top K items observed within the last 60 ticks
}
_ = sketch.Count("another item") // return the estimated count for "another item"
// SortedSlice() returns the current top-K entries as a slice of {Fingerprint,Item,Count} structs.
for _, entry := range sketch.SortedSlice() {
log.Println(entry.Item, "has been counted", entry.Count, "times")
}
// Iter is an interator over the (*not* sorted) current top-K entries.
for entry := range sketch.Iter {
log.Println(entry.Item, "has been counted", entry.Count, "times")
}
sketch.Reset() // reset to New() state
}
Benchmarks
Top-K Sketch
goos: darwin
goarch: arm64
pkg: github.com/keilerkonzept/topk
cpu: Apple M1 Pro
The Add
benchmark performs random increments in the interval [1,10).
Operation | K | Depth | Width | time | bytes | allocs |
---|---|---|---|---|---|---|
Add | 10 | 3 | 1024 | 358.6 ns/op | 0 B/op | 0 allocs/op |
Add | 10 | 3 | 8192 | 375.0 ns/op | 0 B/op | 0 allocs/op |
Add | 10 | 4 | 1024 | 449.9 ns/op | 0 B/op | 0 allocs/op |
Add | 10 | 4 | 8192 | 436.0 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 3 | 1024 | 371.5 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 3 | 8192 | 387.9 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 4 | 1024 | 452.3 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 4 | 8192 | 471.4 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 3 | 1024 | 257.2 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 3 | 8192 | 232.3 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 4 | 1024 | 249.1 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 4 | 8192 | 251.2 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 3 | 1024 | 264.2 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 3 | 8192 | 227.4 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 4 | 1024 | 267.1 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 4 | 8192 | 261.3 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 3 | 1024 | 216.0 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 3 | 8192 | 215.4 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 4 | 1024 | 220.0 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 4 | 8192 | 269.3 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 3 | 1024 | 235.1 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 3 | 8192 | 277.1 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 4 | 1024 | 278.7 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 4 | 8192 | 302.2 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 3 | 1024 | 129.6 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 3 | 8192 | 98.21 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 4 | 1024 | 129.9 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 4 | 8192 | 114.3 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 3 | 1024 | 141.2 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 3 | 8192 | 140.8 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 4 | 1024 | 131.1 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 4 | 8192 | 109.8 ns/op | 0 B/op | 0 allocs/op |
Sliding-Window Top-K Sketch
goos: darwin
goarch: arm64
pkg: github.com/keilerkonzept/topk/sliding
cpu: Apple M1 Pro
The Add
benchmark performs random increments in the interval [1,10).
Operation | K | Depth | Width | Window size | History size | time | bytes | allocs |
---|---|---|---|---|---|---|---|---|
Add | 10 | 3 | 1024 | 100 | 50 | 696.9 ns/op | 0 B/op | 0 allocs/op |
Add | 10 | 3 | 1024 | 100 | 100 | 1051 ns/op | 0 B/op | 0 allocs/op |
Add | 10 | 3 | 8192 | 100 | 50 | 784.9 ns/op | 0 B/op | 0 allocs/op |
Add | 10 | 3 | 8192 | 100 | 100 | 1146 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 3 | 1024 | 100 | 50 | 712.9 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 3 | 1024 | 100 | 100 | 1054 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 3 | 8192 | 100 | 50 | 763.3 ns/op | 0 B/op | 0 allocs/op |
Add | 100 | 3 | 8192 | 100 | 100 | 1139 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 3 | 1024 | 100 | 50 | 434.9 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 3 | 1024 | 100 | 100 | 560.7 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 3 | 8192 | 100 | 50 | 501.1 ns/op | 0 B/op | 0 allocs/op |
Incr | 10 | 3 | 8192 | 100 | 100 | 728.7 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 3 | 1024 | 100 | 50 | 425.6 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 3 | 1024 | 100 | 100 | 580.0 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 3 | 8192 | 100 | 50 | 497.8 ns/op | 0 B/op | 0 allocs/op |
Incr | 100 | 3 | 8192 | 100 | 100 | 746.2 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 3 | 1024 | 100 | 50 | 228.5 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 3 | 1024 | 100 | 100 | 209.3 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 3 | 8192 | 100 | 50 | 234.5 ns/op | 0 B/op | 0 allocs/op |
Count | 10 | 3 | 8192 | 100 | 100 | 230.7 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 3 | 1024 | 100 | 50 | 237.5 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 3 | 1024 | 100 | 100 | 242.8 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 3 | 8192 | 100 | 50 | 246.5 ns/op | 0 B/op | 0 allocs/op |
Count | 100 | 3 | 8192 | 100 | 100 | 243.4 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 3 | 1024 | 100 | 50 | 101.7 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 3 | 1024 | 100 | 100 | 104.8 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 3 | 8192 | 100 | 50 | 114.0 ns/op | 0 B/op | 0 allocs/op |
Query | 10 | 3 | 8192 | 100 | 100 | 114.5 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 3 | 1024 | 100 | 50 | 135.9 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 3 | 1024 | 100 | 100 | 118.5 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 3 | 8192 | 100 | 50 | 130.1 ns/op | 0 B/op | 0 allocs/op |
Query | 100 | 3 | 8192 | 100 | 100 | 131.5 ns/op | 0 B/op | 0 allocs/op |
Tick | 10 | 3 | 1024 | 100 | 50 | 4191 ns/op | 0 B/op | 0 allocs/op |
Tick | 10 | 3 | 1024 | 100 | 100 | 7010 ns/op | 0 B/op | 0 allocs/op |
Tick | 10 | 3 | 8192 | 100 | 50 | 28699 ns/op | 0 B/op | 0 allocs/op |
Tick | 10 | 3 | 8192 | 100 | 100 | 90979 ns/op | 0 B/op | 0 allocs/op |
Tick | 100 | 3 | 1024 | 100 | 50 | 6539 ns/op | 0 B/op | 0 allocs/op |
Tick | 100 | 3 | 1024 | 100 | 100 | 9343 ns/op | 0 B/op | 0 allocs/op |
Tick | 100 | 3 | 8192 | 100 | 50 | 31349 ns/op | 0 B/op | 0 allocs/op |
Tick | 100 | 3 | 8192 | 100 | 100 | 87488 ns/op | 0 B/op | 0 allocs/op |
Comparison with segmentio/topk
Using benchstat:
$ go test -run='^$' -bench=BenchmarkSketchAddForComparison -count=10 | tee new.txt
$ go test -run='^$' -bench=BenchmarkSegmentioTopkSample -count=10 | tee old.txt
$ benchstat -row /K,/Depth,/Width,/Decay -col .name old.txt new.txt
goos: darwin
goarch: arm64
pkg: github.com/keilerkonzept/topk
cpu: Apple M1 Pro
K | Depth | Width | Decay | segmentio/topk (sec/op) | this package (sec/op) | diff |
---|---|---|---|---|---|---|
10 | 3 | 256 | 0.6 | 641.0n ± 1% | 373.5n ± 3% | -41.73% (p=0.000 n=10) |
10 | 3 | 256 | 0.8 | 602.6n ± 1% | 387.3n ± 2% | -35.73% (p=0.000 n=10) |
10 | 3 | 256 | 0.9 | 550.4n ± 4% | 431.3n ± 2% | -21.63% (p=0.000 n=10) |
100 | 4 | 460 | 0.6 | 763.8n ± 2% | 427.0n ± 1% | -44.09% (p=0.000 n=10) |
100 | 4 | 460 | 0.8 | 720.9n ± 2% | 459.1n ± 4% | -36.30% (p=0.000 n=10) |
100 | 4 | 460 | 0.9 | 660.6n ± 3% | 539.0n ± 22% | -18.41% (p=0.005 n=10) |
1000 | 6 | 6907 | 0.6 | 1107.0n ± 2% | 555.9n ± 8% | -49.79% (p=0.000 n=10) |
1000 | 6 | 6907 | 0.8 | 1040.0n ± 4% | 613.4n ± 2% | -41.02% (p=0.000 n=10) |
1000 | 6 | 6907 | 0.9 | 936.5n ± 1% | 731.5n ± 2% | -21.89% (p=0.000 n=10) |
10000 | 9 | 92103 | 0.6 | 10.693µ ± 2% | 1.058µ ± 2% | -90.11% (p=0.000 n=10) |
10000 | 9 | 92103 | 0.8 | 10.667µ ± 1% | 1.182µ ± 6% | -88.92% (p=0.000 n=10) |
10000 | 9 | 92103 | 0.9 | 10.724µ ± 1% | 1.288µ ± 2% | -87.98% (p=0.000 n=10) |
100000 | 11 | 1151292 | 0.6 | 89.385µ ± 0% | 1.674µ ± 1% | -98.13% (p=0.000 n=10) |
100000 | 11 | 1151292 | 0.8 | 89.349µ ± 1% | 1.708µ ± 1% | -98.09% (p=0.000 n=10) |
100000 | 11 | 1151292 | 0.9 | 89.284µ ± 1% | 1.705µ ± 1% | -98.09% (p=0.000 n=10) |