/**
* 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.
*/
private void doQueryFirstScoring(Bits acceptDocs, LeafCollector collector, DocsAndCost[] dims)
throws IOException {
// if (DEBUG) {
// System.out.println(" doQueryFirstScoring");
// }
int docID = baseScorer.docID();
nextDoc:
while (docID != PostingsEnum.NO_MORE_DOCS) {
if (acceptDocs != null && acceptDocs.get(docID) == false) {
docID = baseScorer.nextDoc();
continue;
}
LeafCollector failedCollector = null;
for (DocsAndCost dim : dims) {
// TODO: should we sort this 2nd dimension of
// docsEnums from most frequent to least?
if (dim.approximation.docID() < docID) {
dim.approximation.advance(docID);
}
boolean matches = false;
if (dim.approximation.docID() == docID) {
if (dim.twoPhase == null) {
matches = true;
} else {
matches = dim.twoPhase.matches();
}
}
if (matches == false) {
if (failedCollector != null) {
// More than one dim fails on this document, so
// it's neither a hit nor a near-miss; move to
// next doc:
docID = baseScorer.nextDoc();
continue nextDoc;
} else {
failedCollector = dim.sidewaysLeafCollector;
}
}
}
collectDocID = docID;
// TODO: we could score on demand instead since we are
// daat here:
collectScore = baseScorer.score();
if (failedCollector == null) {
// Hit passed all filters, so it's "real":
collectHit(collector, dims);
} else {
// Hit missed exactly one filter:
collectNearMiss(failedCollector);
}
docID = baseScorer.nextDoc();
}
}
public static Map<String, Integer> termFrequencies(
IndexSearcher indexSearcher,
Query documentFilterQuery,
String fieldName,
String propName,
String altName) {
try {
String luceneField = ComplexFieldUtil.propertyField(fieldName, propName, altName);
Weight weight = indexSearcher.createNormalizedWeight(documentFilterQuery, false);
Map<String, Integer> freq = new HashMap<>();
IndexReader indexReader = indexSearcher.getIndexReader();
for (LeafReaderContext arc : indexReader.leaves()) {
if (weight == null) throw new RuntimeException("weight == null");
if (arc == null) throw new RuntimeException("arc == null");
if (arc.reader() == null) throw new RuntimeException("arc.reader() == null");
Scorer scorer = weight.scorer(arc, arc.reader().getLiveDocs());
if (scorer != null) {
while (scorer.nextDoc() != DocIdSetIterator.NO_MORE_DOCS) {
getFrequenciesFromTermVector(
indexReader, scorer.docID() + arc.docBase, luceneField, freq);
}
}
}
return freq;
} catch (IOException e) {
throw ExUtil.wrapRuntimeException(e);
}
}
/*(non-Javadoc) @see org.apache.lucene.search.Scorer#score() */
@Override
public float score() throws IOException {
for (int i = 0; i < valSrcScorers.length; i++) {
vScores[i] = valSrcScorers[i].score();
}
return qWeight
* provider.customScore(subQueryScorer.docID(), subQueryScorer.score(), vScores);
}
@Override
public float score() throws IOException {
float score = qWeight * scorer.score() * vals.floatVal(scorer.docID());
// Current Lucene priority queues can't handle NaN and -Infinity, so
// map to -Float.MAX_VALUE. This conditional handles both -infinity
// and NaN since comparisons with NaN are always false.
return score > Float.NEGATIVE_INFINITY ? score : -Float.MAX_VALUE;
}
/**
* Collect all Spans extracted from a Scorer using a SpanCollector
*
* @param scorer the scorer to extract Spans from
* @param collector the SpanCollector
* @param errorOnNoSpans if true, throw an error if no Spans can be extracted from the Scorer or
* any of its children
* @throws IOException on error
*/
public static void collect(Scorer scorer, SpanCollector collector, boolean errorOnNoSpans)
throws IOException {
List<Spans> allSpans = getSpans(scorer, errorOnNoSpans);
int doc = scorer.docID();
for (Spans spans : allSpans) {
int spanDoc = spans.docID();
// if the Scorer advances lazily, then not all of its subspans may be on
// the correct document
if (spanDoc == doc || (spanDoc < doc && spans.advance(doc) == doc)) {
while (spans.nextStartPosition() != Spans.NO_MORE_POSITIONS) {
spans.collect(collector);
}
}
}
}
@Override
public int docID() {
return subQueryScorer.docID();
}
@Override
public int docID() {
return scorer.docID();
}
@Override
public float score() throws IOException {
return (_func.useInnerScore())
? _func.newScore(_innerScorer.score(), _innerScorer.docID())
: _func.newScore(_innerScorer.docID());
}
@Override
public int docID() {
return _innerScorer.docID();
}
private void doUnionScoring(Bits acceptDocs, LeafCollector collector, DocsAndCost[] dims)
throws IOException {
// if (DEBUG) {
// System.out.println(" doUnionScoring");
// }
final int maxDoc = context.reader().maxDoc();
final int numDims = dims.length;
// TODO: maybe a class like BS, instead of parallel arrays
int[] filledSlots = new int[CHUNK];
int[] docIDs = new int[CHUNK];
float[] scores = new float[CHUNK];
int[] missingDims = new int[CHUNK];
int[] counts = new int[CHUNK];
docIDs[0] = -1;
// NOTE: this is basically a specialized version of
// BooleanScorer, to the minShouldMatch=N-1 case, but
// carefully tracking which dimension failed to match
int nextChunkStart = CHUNK;
while (true) {
// if (DEBUG) {
// System.out.println("\ncycle nextChunkStart=" + nextChunkStart + " docIds[0]=" +
// docIDs[0]);
// }
int filledCount = 0;
int docID = baseScorer.docID();
// if (DEBUG) {
// System.out.println(" base docID=" + docID);
// }
while (docID < nextChunkStart) {
if (acceptDocs == null || acceptDocs.get(docID)) {
int slot = docID & MASK;
// if (DEBUG) {
// System.out.println(" docIDs[slot=" + slot + "]=" + docID + " id=" +
// context.reader().document(docID).get("id"));
// }
// Mark slot as valid:
assert docIDs[slot] != docID : "slot=" + slot + " docID=" + docID;
docIDs[slot] = docID;
scores[slot] = baseScorer.score();
filledSlots[filledCount++] = slot;
missingDims[slot] = 0;
counts[slot] = 1;
}
docID = baseScorer.nextDoc();
}
if (filledCount == 0) {
if (nextChunkStart >= maxDoc) {
break;
}
nextChunkStart += CHUNK;
continue;
}
// First drill-down dim, basically adds SHOULD onto
// the baseQuery:
// if (DEBUG) {
// System.out.println(" dim=0 [" + dims[0].dim + "]");
// }
{
DocsAndCost dc = dims[0];
docID = dc.approximation.docID();
// if (DEBUG) {
// System.out.println(" start docID=" + docID);
// }
while (docID < nextChunkStart) {
int slot = docID & MASK;
if (docIDs[slot] == docID // this also checks that the doc is not deleted
&& (dc.twoPhase == null || dc.twoPhase.matches())) {
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " count=2");
// }
missingDims[slot] = 1;
counts[slot] = 2;
}
docID = dc.approximation.nextDoc();
}
}
for (int dim = 1; dim < numDims; dim++) {
// if (DEBUG) {
// System.out.println(" dim=" + dim + " [" + dims[dim].dim + "]");
// }
DocsAndCost dc = dims[dim];
docID = dc.approximation.docID();
// if (DEBUG) {
// System.out.println(" start docID=" + docID);
// }
while (docID < nextChunkStart) {
int slot = docID & MASK;
if (docIDs[slot] == docID // also means that the doc is not deleted
&& counts[slot] >= dim
&& (dc.twoPhase == null || dc.twoPhase.matches())) {
// This doc is still in the running...
// TODO: single-valued dims will always be true
// below; we could somehow specialize
if (missingDims[slot] >= dim) {
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " count=" + (dim+2));
// }
missingDims[slot] = dim + 1;
counts[slot] = dim + 2;
} else {
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " missing count=" + (dim+1));
// }
counts[slot] = dim + 1;
}
}
docID = dc.approximation.nextDoc();
}
}
// Collect:
// System.out.println(" now collect: " + filledCount + " hits");
for (int i = 0; i < filledCount; i++) {
// NOTE: This is actually in-order collection,
// because we only accept docs originally returned by
// the baseScorer (ie that Scorer is AND'd)
int slot = filledSlots[i];
collectDocID = docIDs[slot];
collectScore = scores[slot];
// if (DEBUG) {
// System.out.println(" docID=" + docIDs[slot] + " count=" + counts[slot]);
// }
// System.out.println(" collect doc=" + collectDocID + " main.freq=" + (counts[slot]-1) + "
// main.doc=" + collectDocID + " exactCount=" + numDims);
if (counts[slot] == 1 + numDims) {
// System.out.println(" hit");
collectHit(collector, dims);
} else if (counts[slot] == numDims) {
// System.out.println(" sw");
collectNearMiss(dims[missingDims[slot]].sidewaysLeafCollector);
}
}
if (nextChunkStart >= maxDoc) {
break;
}
nextChunkStart += CHUNK;
}
}
/** Used when drill downs are highly constraining vs baseQuery. */
private void doDrillDownAdvanceScoring(
Bits acceptDocs, LeafCollector collector, DocsAndCost[] dims) throws IOException {
final int maxDoc = context.reader().maxDoc();
final int numDims = dims.length;
// if (DEBUG) {
// System.out.println(" doDrillDownAdvanceScoring");
// }
// TODO: maybe a class like BS, instead of parallel arrays
int[] filledSlots = new int[CHUNK];
int[] docIDs = new int[CHUNK];
float[] scores = new float[CHUNK];
int[] missingDims = new int[CHUNK];
int[] counts = new int[CHUNK];
docIDs[0] = -1;
int nextChunkStart = CHUNK;
final FixedBitSet seen = new FixedBitSet(CHUNK);
while (true) {
// if (DEBUG) {
// System.out.println("\ncycle nextChunkStart=" + nextChunkStart + " docIds[0]=" +
// docIDs[0]);
// }
// First dim:
// if (DEBUG) {
// System.out.println(" dim0");
// }
DocsAndCost dc = dims[0];
int docID = dc.approximation.docID();
while (docID < nextChunkStart) {
if (acceptDocs == null || acceptDocs.get(docID)) {
int slot = docID & MASK;
if (docIDs[slot] != docID && (dc.twoPhase == null || dc.twoPhase.matches())) {
seen.set(slot);
// Mark slot as valid:
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " id=" +
// context.reader().document(docID).get("id"));
// }
docIDs[slot] = docID;
missingDims[slot] = 1;
counts[slot] = 1;
}
}
docID = dc.approximation.nextDoc();
}
// Second dim:
// if (DEBUG) {
// System.out.println(" dim1");
// }
dc = dims[1];
docID = dc.approximation.docID();
while (docID < nextChunkStart) {
if (acceptDocs == null
|| acceptDocs.get(docID) && (dc.twoPhase == null || dc.twoPhase.matches())) {
int slot = docID & MASK;
if (docIDs[slot] != docID) {
// Mark slot as valid:
seen.set(slot);
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " missingDim=0 id=" +
// context.reader().document(docID).get("id"));
// }
docIDs[slot] = docID;
missingDims[slot] = 0;
counts[slot] = 1;
} else {
// TODO: single-valued dims will always be true
// below; we could somehow specialize
if (missingDims[slot] >= 1) {
missingDims[slot] = 2;
counts[slot] = 2;
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " missingDim=2 id=" +
// context.reader().document(docID).get("id"));
// }
} else {
counts[slot] = 1;
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " missingDim=" + missingDims[slot] +
// " id=" + context.reader().document(docID).get("id"));
// }
}
}
}
docID = dc.approximation.nextDoc();
}
// After this we can "upgrade" to conjunction, because
// any doc not seen by either dim 0 or dim 1 cannot be
// a hit or a near miss:
// if (DEBUG) {
// System.out.println(" baseScorer");
// }
// Fold in baseScorer, using advance:
int filledCount = 0;
int slot0 = 0;
while (slot0 < CHUNK && (slot0 = seen.nextSetBit(slot0)) != DocIdSetIterator.NO_MORE_DOCS) {
int ddDocID = docIDs[slot0];
assert ddDocID != -1;
int baseDocID = baseScorer.docID();
if (baseDocID < ddDocID) {
baseDocID = baseScorer.advance(ddDocID);
}
if (baseDocID == ddDocID) {
// if (DEBUG) {
// System.out.println(" keep docID=" + ddDocID + " id=" +
// context.reader().document(ddDocID).get("id"));
// }
scores[slot0] = baseScorer.score();
filledSlots[filledCount++] = slot0;
counts[slot0]++;
} else {
// if (DEBUG) {
// System.out.println(" no docID=" + ddDocID + " id=" +
// context.reader().document(ddDocID).get("id"));
// }
docIDs[slot0] = -1;
// TODO: we could jump slot0 forward to the
// baseDocID ... but we'd need to set docIDs for
// intervening slots to -1
}
slot0++;
}
seen.clear(0, CHUNK);
if (filledCount == 0) {
if (nextChunkStart >= maxDoc) {
break;
}
nextChunkStart += CHUNK;
continue;
}
// TODO: factor this out & share w/ union scorer,
// except we start from dim=2 instead:
for (int dim = 2; dim < numDims; dim++) {
// if (DEBUG) {
// System.out.println(" dim=" + dim + " [" + dims[dim].dim + "]");
// }
dc = dims[dim];
docID = dc.approximation.docID();
while (docID < nextChunkStart) {
int slot = docID & MASK;
if (docIDs[slot] == docID
&& counts[slot] >= dim
&& (dc.twoPhase == null || dc.twoPhase.matches())) {
// TODO: single-valued dims will always be true
// below; we could somehow specialize
if (missingDims[slot] >= dim) {
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " count=" + (dim+2));
// }
missingDims[slot] = dim + 1;
counts[slot] = dim + 2;
} else {
// if (DEBUG) {
// System.out.println(" set docID=" + docID + " missing count=" + (dim+1));
// }
counts[slot] = dim + 1;
}
}
// TODO: sometimes use advance?
docID = dc.approximation.nextDoc();
}
}
// Collect:
// if (DEBUG) {
// System.out.println(" now collect: " + filledCount + " hits");
// }
for (int i = 0; i < filledCount; i++) {
int slot = filledSlots[i];
collectDocID = docIDs[slot];
collectScore = scores[slot];
// if (DEBUG) {
// System.out.println(" docID=" + docIDs[slot] + " count=" + counts[slot]);
// }
if (counts[slot] == 1 + numDims) {
collectHit(collector, dims);
} else if (counts[slot] == numDims) {
collectNearMiss(dims[missingDims[slot]].sidewaysLeafCollector);
}
}
if (nextChunkStart >= maxDoc) {
break;
}
nextChunkStart += CHUNK;
}
}