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@@ -81,7 +81,7 @@ func (tab *Table) chooseBucketRefreshTarget() common.Hash {
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if printTable {
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fmt.Println()
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}
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- for i, b := range tab.buckets {
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+ for i, b := range &tab.buckets {
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entries += len(b.entries)
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if printTable {
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for _, e := range b.entries {
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@@ -93,7 +93,7 @@ func (tab *Table) chooseBucketRefreshTarget() common.Hash {
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prefix := binary.BigEndian.Uint64(tab.self.sha[0:8])
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dist := ^uint64(0)
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entry := int(randUint(uint32(entries + 1)))
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- for _, b := range tab.buckets {
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+ for _, b := range &tab.buckets {
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if entry < len(b.entries) {
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n := b.entries[entry]
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dist = binary.BigEndian.Uint64(n.sha[0:8]) ^ prefix
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@@ -121,7 +121,7 @@ func (tab *Table) readRandomNodes(buf []*Node) (n int) {
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// TODO: tree-based buckets would help here
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// Find all non-empty buckets and get a fresh slice of their entries.
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var buckets [][]*Node
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- for _, b := range tab.buckets {
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+ for _, b := range &tab.buckets {
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if len(b.entries) > 0 {
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buckets = append(buckets, b.entries[:])
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}
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@@ -175,7 +175,7 @@ func (tab *Table) closest(target common.Hash, nresults int) *nodesByDistance {
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// obviously correct. I believe that tree-based buckets would make
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// this easier to implement efficiently.
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close := &nodesByDistance{target: target}
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- for _, b := range tab.buckets {
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+ for _, b := range &tab.buckets {
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for _, n := range b.entries {
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close.push(n, nresults)
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}
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