zhaih commented on code in PR #11881:
URL: https://github.com/apache/lucene/pull/11881#discussion_r1014686625
##########
lucene/facet/src/java/org/apache/lucene/facet/DrillSidewaysScorer.java:
##########
@@ -166,89 +159,158 @@ public int score(LeafCollector collector, Bits
acceptDocs, int min, int maxDoc)
return Integer.MAX_VALUE;
}
+ /**
+ * Query-first scoring specialization when there is only one drill-sideways
dimension, which is
+ * likely a common scenario.
+ */
+ private void doQueryFirstScoringSingleDim(
+ Bits acceptDocs, LeafCollector collector, DocsAndCost dim) throws
IOException {
+ int docID = baseApproximation.docID();
+ while (docID != DocIdSetIterator.NO_MORE_DOCS) {
+ assert docID == baseApproximation.docID();
+
+ if (acceptDocs != null && acceptDocs.get(docID) == false) {
+ docID = baseApproximation.nextDoc();
+ continue;
+ }
+
+ if (baseTwoPhase != null && baseTwoPhase.matches() == false) {
+ docID = baseApproximation.nextDoc();
+ continue;
+ }
+
+ // We have either a near-miss or full match. Check the sideways dim to
see which it is:
+ collectDocID = docID;
+ if (advanceIfBehind(docID, dim.approximation) != docID
+ || (dim.twoPhase != null && dim.twoPhase.matches() == false)) {
+ // The sideways dim missed, so we have a "near miss":
+ collectNearMiss(dim.sidewaysLeafCollector);
+ } else {
+ // Hit passed all filters, so it's "real":
+ collectHit(collector, dim);
+ }
+
+ docID = baseApproximation.nextDoc();
+ }
+ }
+
/**
* Used when base query is highly constraining vs the drilldowns, or when
the docs must be scored
- * at once (i.e., like BooleanScorer2, not BooleanScorer). In this case we
just .next() on base
- * and .advance() on the dim filters.
+ * at once (i.e., like BooleanScorer2, not BooleanScorer).
*/
private void doQueryFirstScoring(Bits acceptDocs, LeafCollector collector,
DocsAndCost[] dims)
throws IOException {
setScorer(collector, ScoreCachingWrappingScorer.wrap(baseScorer));
- List<DocsAndCost> allDims = Arrays.asList(dims);
- CollectionUtil.timSort(allDims, APPROXIMATION_COMPARATOR);
+ // Specialize the single-dim use-case as we have a more efficient
implementation for that:
+ if (dims.length == 1) {
+ doQueryFirstScoringSingleDim(acceptDocs, collector, dims[0]);
+ return;
+ }
+
+ // Sort our sideways dims by approximation cost so we can advance the
lower cost ones first:
+ List<DocsAndCost> sidewaysDims = new ArrayList<>(dims.length);
+ sidewaysDims.addAll(List.of(dims));
+ CollectionUtil.timSort(sidewaysDims, APPROXIMATION_COMPARATOR);
- List<DocsAndCost> twoPhaseDims = null;
+ // Maintain (optional) subset of sideways dims that support two-phase
iteration, sorted by
+ // matchCost:
+ List<DocsAndCost> sidewaysTwoPhaseDims = null;
for (DocsAndCost dim : dims) {
if (dim.twoPhase != null) {
- if (twoPhaseDims == null) {
- twoPhaseDims = new ArrayList<>(dims.length);
+ if (sidewaysTwoPhaseDims == null) {
+ sidewaysTwoPhaseDims = new ArrayList<>();
}
- twoPhaseDims.add(dim);
+ sidewaysTwoPhaseDims.add(dim);
}
}
- if (twoPhaseDims != null) {
- CollectionUtil.timSort(twoPhaseDims, TWO_PHASE_COMPARATOR);
+ if (sidewaysTwoPhaseDims != null) {
+ CollectionUtil.timSort(sidewaysTwoPhaseDims, TWO_PHASE_COMPARATOR);
}
+ // We keep track of a "runaway" dimension, which is a previously "near
missed" dimension that
+ // has advanced beyond the docID the rest of the dimensions are positioned
on. This functions
+ // a bit like the "head" queue in WANDScorer's "min should match"
implementation. We use a
+ // single-valued PQ ordered by docID to easily determine the "closest"
runaway dim we'll use
Review Comment:
If we'll go with this version, maybe change this comment. (Since not PQ)
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