mirror of
https://github.com/hanzoai/volume.git
synced 2026-10-01 00:48:00 +00:00
zeekay
b61441f7d7
A fork of Rancher's local-path-provisioner at v0.0.36. The behaviour is unchanged and deliberately so — a directory on the node a pod lands on is the right thing for a disk. What changes is identity. The provisioner answers to hanzo.ai/volume rather than rancher.io/local-path. That is not branding: two provisioners sharing one name race for the same claims, and a stock k3s carries the upstream one, so a cluster running both would settle our storage by chance. Data lives at /var/lib/hanzo/volume, so reading a Hanzo cluster does not mean learning another vendor's directory. Apache-2.0 as published, unaltered, with Rancher's attribution in NOTICE. The README says what this is not: durability. A volume here dies with its node. That is hanzoai/replicate's question, one layer up, and keeping the two apart is what lets the disk stay this simple.
86 lines
1.8 KiB
Go
86 lines
1.8 KiB
Go
package compress
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import "math"
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// Estimate returns a normalized compressibility estimate of block b.
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// Values close to zero are likely uncompressible.
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// Values above 0.1 are likely to be compressible.
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// Values above 0.5 are very compressible.
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// Very small lengths will return 0.
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func Estimate(b []byte) float64 {
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if len(b) < 16 {
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return 0
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}
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// Correctly predicted order 1
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hits := 0
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lastMatch := false
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var o1 [256]byte
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var hist [256]int
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c1 := byte(0)
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for _, c := range b {
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if c == o1[c1] {
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// We only count a hit if there was two correct predictions in a row.
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if lastMatch {
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hits++
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}
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lastMatch = true
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} else {
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lastMatch = false
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}
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o1[c1] = c
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c1 = c
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hist[c]++
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}
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// Use x^0.6 to give better spread
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prediction := math.Pow(float64(hits)/float64(len(b)), 0.6)
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// Calculate histogram distribution
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variance := float64(0)
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avg := float64(len(b)) / 256
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for _, v := range hist {
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Δ := float64(v) - avg
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variance += Δ * Δ
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}
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stddev := math.Sqrt(float64(variance)) / float64(len(b))
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exp := math.Sqrt(1 / float64(len(b)))
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// Subtract expected stddev
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stddev -= exp
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if stddev < 0 {
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stddev = 0
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}
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stddev *= 1 + exp
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// Use x^0.4 to give better spread
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entropy := math.Pow(stddev, 0.4)
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// 50/50 weight between prediction and histogram distribution
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return math.Pow((prediction+entropy)/2, 0.9)
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}
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// ShannonEntropyBits returns the number of bits minimum required to represent
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// an entropy encoding of the input bytes.
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// https://en.wiktionary.org/wiki/Shannon_entropy
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func ShannonEntropyBits(b []byte) int {
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if len(b) == 0 {
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return 0
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}
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var hist [256]int
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for _, c := range b {
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hist[c]++
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}
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shannon := float64(0)
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invTotal := 1.0 / float64(len(b))
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for _, v := range hist[:] {
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if v > 0 {
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n := float64(v)
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shannon += math.Ceil(-math.Log2(n*invTotal) * n)
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}
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}
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return int(math.Ceil(shannon))
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}
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