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