* This implementation was derived by using the 4 rules that are outlined in * Bram Cohen's blog, * and then was further extended to support low-occurrence common elements. *
* The basic idea of the algorithm is to create a histogram of occurrences for * each element of sequence A. Each element of sequence B is then considered in * turn. If the element also exists in sequence A, and has a lower occurrence * count, the positions are considered as a candidate for the longest common * subsequence (LCS). After scanning of B is complete the LCS that has the * lowest number of occurrences is chosen as a split point. The region is split * around the LCS, and the algorithm is recursively applied to the sections * before and after the LCS. *
* By always selecting a LCS position with the lowest occurrence count, this * algorithm behaves exactly like Bram Cohen's patience diff whenever there is a * unique common element available between the two sequences. When no unique * elements exist, the lowest occurrence element is chosen instead. This offers * more readable diffs than simply falling back on the standard Myers' O(ND) * algorithm would produce. *
* To prevent the algorithm from having an O(N^2) running time, an upper limit * on the number of unique elements in a histogram bucket is configured by * {@link #setMaxChainLength(int)}. If sequence A has more than this many * elements that hash into the same hash bucket, the algorithm passes the region * to {@link #setFallbackAlgorithm(DiffAlgorithm)}. If no fallback algorithm is * configured, the region is emitted as a replace edit. *
* During scanning of sequence B, any element of A that occurs more than * {@link #setMaxChainLength(int)} times is never considered for an LCS match * position, even if it is common between the two sequences. This limits the * number of locations in sequence A that must be considered to find the LCS, * and helps maintain a lower running time bound. *
* So long as {@link #setMaxChainLength(int)} is a small constant (such as 64), * the algorithm runs in O(N * D) time, where N is the sum of the input lengths * and D is the number of edits in the resulting EditList. If the supplied * {@link org.eclipse.jgit.diff.SequenceComparator} has a good hash function, * this implementation typically out-performs * {@link org.eclipse.jgit.diff.MyersDiff}, even though its theoretical running * time is the same. *
* This implementation has an internal limitation that prevents it from handling
* sequences with more than 268,435,456 (2^28) elements.
*/
public class HistogramDiff extends LowLevelDiffAlgorithm {
/** Algorithm to use when there are too many element occurrences. */
DiffAlgorithm fallback = MyersDiff.INSTANCE;
/**
* Maximum number of positions to consider for a given element hash.
*
* All elements with the same hash are stored into a single chain. The chain
* size is capped to ensure search is linear time at O(len_A + len_B) rather
* than quadratic at O(len_A * len_B).
*/
int maxChainLength = 64;
/**
* Set the algorithm used when there are too many element occurrences.
*
* @param alg
* the secondary algorithm. If null the region will be denoted as
* a single REPLACE block.
*/
public void setFallbackAlgorithm(DiffAlgorithm alg) {
fallback = alg;
}
/**
* Maximum number of positions to consider for a given element hash.
*
* All elements with the same hash are stored into a single chain. The chain
* size is capped to ensure search is linear time at O(len_A + len_B) rather
* than quadratic at O(len_A * len_B).
*
* @param maxLen
* new maximum length.
*/
public void setMaxChainLength(int maxLen) {
maxChainLength = maxLen;
}
/** {@inheritDoc} */
@Override
public void diffNonCommon(EditList edits,
HashedSequenceComparator cmp, HashedSequence a,
HashedSequence b, Edit region) {
new State<>(edits, cmp, a, b).diffRegion(region);
}
private class State {
private final HashedSequenceComparator cmp;
private final HashedSequence a;
private final HashedSequence b;
private final List cmp,
HashedSequence a, HashedSequence b) {
this.cmp = cmp;
this.a = a;
this.b = b;
this.edits = edits;
}
void diffRegion(Edit r) {
diffReplace(r);
while (!queue.isEmpty())
diff(queue.remove(queue.size() - 1));
}
private void diffReplace(Edit r) {
Edit lcs = new HistogramDiffIndex<>(maxChainLength, cmp, a, b, r)
.findLongestCommonSequence();
if (lcs != null) {
// If we were given an edit, we can prove a result here.
//
if (lcs.isEmpty()) {
// An empty edit indicates there is nothing in common.
// Replace the entire region.
//
edits.add(r);
} else {
queue.add(r.after(lcs));
queue.add(r.before(lcs));
}
} else if (fallback instanceof LowLevelDiffAlgorithm) {
LowLevelDiffAlgorithm fb = (LowLevelDiffAlgorithm) fallback;
fb.diffNonCommon(edits, cmp, a, b, r);
} else if (fallback != null) {
SubsequenceComparator