You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
106 lines
3.2 KiB
106 lines
3.2 KiB
package merkle
|
|
|
|
import (
|
|
"math/bits"
|
|
)
|
|
|
|
// HashFromByteSlices computes a Merkle tree where the leaves are the byte slice,
|
|
// in the provided order.
|
|
func HashFromByteSlices(items [][]byte) []byte {
|
|
switch len(items) {
|
|
case 0:
|
|
return nil
|
|
case 1:
|
|
return leafHash(items[0])
|
|
default:
|
|
k := getSplitPoint(int64(len(items)))
|
|
left := HashFromByteSlices(items[:k])
|
|
right := HashFromByteSlices(items[k:])
|
|
return innerHash(left, right)
|
|
}
|
|
}
|
|
|
|
// HashFromByteSliceIterative is an iterative alternative to
|
|
// HashFromByteSlice motivated by potential performance improvements.
|
|
// (#2611) had suggested that an iterative version of
|
|
// HashFromByteSlice would be faster, presumably because
|
|
// we can envision some overhead accumulating from stack
|
|
// frames and function calls. Additionally, a recursive algorithm risks
|
|
// hitting the stack limit and causing a stack overflow should the tree
|
|
// be too large.
|
|
//
|
|
// Provided here is an iterative alternative, a test to assert
|
|
// correctness and a benchmark. On the performance side, there appears to
|
|
// be no overall difference:
|
|
//
|
|
// BenchmarkHashAlternatives/recursive-4 20000 77677 ns/op
|
|
// BenchmarkHashAlternatives/iterative-4 20000 76802 ns/op
|
|
//
|
|
// On the surface it might seem that the additional overhead is due to
|
|
// the different allocation patterns of the implementations. The recursive
|
|
// version uses a single [][]byte slices which it then re-slices at each level of the tree.
|
|
// The iterative version reproduces [][]byte once within the function and
|
|
// then rewrites sub-slices of that array at each level of the tree.
|
|
//
|
|
// Experimenting by modifying the code to simply calculate the
|
|
// hash and not store the result show little to no difference in performance.
|
|
//
|
|
// These preliminary results suggest:
|
|
//
|
|
// 1. The performance of the HashFromByteSlice is pretty good
|
|
// 2. Go has low overhead for recursive functions
|
|
// 3. The performance of the HashFromByteSlice routine is dominated
|
|
// by the actual hashing of data
|
|
//
|
|
// Although this work is in no way exhaustive, point #3 suggests that
|
|
// optimization of this routine would need to take an alternative
|
|
// approach to make significant improvements on the current performance.
|
|
//
|
|
// Finally, considering that the recursive implementation is easier to
|
|
// read, it might not be worthwhile to switch to a less intuitive
|
|
// implementation for so little benefit.
|
|
func HashFromByteSlicesIterative(input [][]byte) []byte {
|
|
items := make([][]byte, len(input))
|
|
|
|
for i, leaf := range input {
|
|
items[i] = leafHash(leaf)
|
|
}
|
|
|
|
size := len(items)
|
|
for {
|
|
switch size {
|
|
case 0:
|
|
return nil
|
|
case 1:
|
|
return items[0]
|
|
default:
|
|
rp := 0 // read position
|
|
wp := 0 // write position
|
|
for rp < size {
|
|
if rp+1 < size {
|
|
items[wp] = innerHash(items[rp], items[rp+1])
|
|
rp += 2
|
|
} else {
|
|
items[wp] = items[rp]
|
|
rp++
|
|
}
|
|
wp++
|
|
}
|
|
size = wp
|
|
}
|
|
}
|
|
}
|
|
|
|
// getSplitPoint returns the largest power of 2 less than length
|
|
func getSplitPoint(length int64) int64 {
|
|
if length < 1 {
|
|
panic("Trying to split a tree with size < 1")
|
|
}
|
|
uLength := uint(length)
|
|
bitlen := bits.Len(uLength)
|
|
k := int64(1 << uint(bitlen-1))
|
|
if k == length {
|
|
k >>= 1
|
|
}
|
|
return k
|
|
}
|