Note
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This package implements the HeavyKeeper algorithm for efficiently finding top-K flows in an unbounded set of flows.
Paper: https://www.usenix.org/system/files/conference/atc18/atc18-gong.pdf
The reference implementation is pretty difficult to follow. I found the RedisBloom implementation to be far eaier to read, if you're interested in seeing a more battle-tested implementation. The RedisBloom implementation also has some optimizations that might be worth looking at (a decay lookup table, for example) and it supports arbitrary increments greater than one, which I've implemented here.
This implementation uses a default width and depth to simplify usage:
width = k * log(k)(minimum of 256)height = log(k)(minimum of 3)
hk := topk.New(100, 0.9)
hk.Add("foo", 1)
hk.Add("bar", 5)
hk.Add("baz", 1)
hk.Add("baz", 1)
for _, fc := range hk.Top() {
fmt.Printf("%s = %d\n", fc.Flow, fc.Count)
}
// bar = 5
// baz = 2
// foo = 1
count, ok := hk.Count("bar")
fmt.Println(count, ok)
// 5 trueThe algorithm itself is rather efficient on its own; I haven't invested any time in further optimizing things (yet).
goos: darwin
goarch: amd64
pkg: github.com/segmentio/topk
cpu: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
BenchmarkAdd/K=10-12 17065675 79.38 ns/op 0 B/op 0 allocs/op
BenchmarkAdd/K=50-12 11193319 106.5 ns/op 0 B/op 0 allocs/op
BenchmarkAdd/K=100-12 9880362 131.8 ns/op 0 B/op 0 allocs/op
BenchmarkAdd/K=500-12 7442464 159.6 ns/op 0 B/op 0 allocs/op
BenchmarkAdd/K=1000-12 7125268 167.8 ns/op 0 B/op 0 allocs/op
BenchmarkAdd/K=5000-12 5797017 206.3 ns/op 0 B/op 0 allocs/op
BenchmarkAdd/K=10000-12 5218218 233.2 ns/op 0 B/op 0 allocs/op