org.apache.lucene.queries.BlendedTermQuery Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of elasticsearch Show documentation
Show all versions of elasticsearch Show documentation
Elasticsearch subproject :server
/*
* Licensed to Elasticsearch under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch licenses this file to you under
* the Apache License, Version 2.0 (the "License"); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package org.apache.lucene.queries;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexReaderContext;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.TermContext;
import org.apache.lucene.index.TermState;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanClause.Occur;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.BoostQuery;
import org.apache.lucene.search.DisjunctionMaxQuery;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.util.ArrayUtil;
import org.apache.lucene.util.InPlaceMergeSorter;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Objects;
/**
* BlendedTermQuery can be used to unify term statistics across
* one or more fields in the index. A common problem with structured
* documents is that a term that is significant in on field might not be
* significant in other fields like in a scenario where documents represent
* users with a "first_name" and a "second_name". When someone searches
* for "simon" it will very likely get "paul simon" first since "simon" is a
* an uncommon last name ie. has a low document frequency. This query
* tries to "lie" about the global statistics like document frequency as well
* total term frequency to rank based on the estimated statistics.
*
* While aggregating the total term frequency is trivial since it
* can be summed up not every {@link org.apache.lucene.search.similarities.Similarity}
* makes use of this statistic. The document frequency which is used in the
* {@link org.apache.lucene.search.similarities.ClassicSimilarity}
* can only be estimated as an lower-bound since it is a document based statistic. For
* the document frequency the maximum frequency across all fields per term is used
* which is the minimum number of documents the terms occurs in.
*
*/
// TODO maybe contribute to Lucene
public abstract class BlendedTermQuery extends Query {
private final Term[] terms;
private final float[] boosts;
public BlendedTermQuery(Term[] terms, float[] boosts) {
if (terms == null || terms.length == 0) {
throw new IllegalArgumentException("terms must not be null or empty");
}
if (boosts != null && boosts.length != terms.length) {
throw new IllegalArgumentException("boosts must have the same size as terms");
}
this.terms = terms;
this.boosts = boosts;
}
@Override
public Query rewrite(IndexReader reader) throws IOException {
Query rewritten = super.rewrite(reader);
if (rewritten != this) {
return rewritten;
}
IndexReaderContext context = reader.getContext();
TermContext[] ctx = new TermContext[terms.length];
int[] docFreqs = new int[ctx.length];
for (int i = 0; i < terms.length; i++) {
ctx[i] = TermContext.build(context, terms[i]);
docFreqs[i] = ctx[i].docFreq();
}
final int maxDoc = reader.maxDoc();
blend(ctx, maxDoc, reader);
return topLevelQuery(terms, ctx, docFreqs, maxDoc);
}
protected abstract Query topLevelQuery(Term[] terms, TermContext[] ctx, int[] docFreqs, int maxDoc);
protected void blend(final TermContext[] contexts, int maxDoc, IndexReader reader) throws IOException {
if (contexts.length <= 1) {
return;
}
int max = 0;
long minSumTTF = Long.MAX_VALUE;
for (int i = 0; i < contexts.length; i++) {
TermContext ctx = contexts[i];
int df = ctx.docFreq();
// we use the max here since it's the only "true" estimation we can make here
// at least max(df) documents have that term. Sum or Averages don't seem
// to have a significant meaning here.
// TODO: Maybe it could also make sense to assume independent distributions of documents and eg. have:
// df = df1 + df2 - (df1 * df2 / maxDoc)?
max = Math.max(df, max);
if (minSumTTF != -1 && ctx.totalTermFreq() != -1) {
// we need to find out the minimum sumTTF to adjust the statistics
// otherwise the statistics don't match
minSumTTF = Math.min(minSumTTF, reader.getSumTotalTermFreq(terms[i].field()));
} else {
minSumTTF = -1;
}
}
if (minSumTTF != -1 && maxDoc > minSumTTF) {
maxDoc = (int)minSumTTF;
}
if (max == 0) {
return; // we are done that term doesn't exist at all
}
long sumTTF = minSumTTF == -1 ? -1 : 0;
final int[] tieBreak = new int[contexts.length];
for (int i = 0; i < tieBreak.length; ++i) {
tieBreak[i] = i;
}
new InPlaceMergeSorter() {
@Override
protected void swap(int i, int j) {
final int tmp = tieBreak[i];
tieBreak[i] = tieBreak[j];
tieBreak[j] = tmp;
}
@Override
protected int compare(int i, int j) {
return Integer.compare(contexts[tieBreak[j]].docFreq(), contexts[tieBreak[i]].docFreq());
}
}.sort(0, tieBreak.length);
int prev = contexts[tieBreak[0]].docFreq();
int actualDf = Math.min(maxDoc, max);
assert actualDf >=0 : "DF must be >= 0";
// here we try to add a little bias towards
// the more popular (more frequent) fields
// that acts as a tie breaker
for (int i : tieBreak) {
TermContext ctx = contexts[i];
if (ctx.docFreq() == 0) {
break;
}
final int current = ctx.docFreq();
if (prev > current) {
actualDf++;
}
contexts[i] = ctx = adjustDF(ctx, Math.min(maxDoc, actualDf));
prev = current;
if (sumTTF >= 0 && ctx.totalTermFreq() >= 0) {
sumTTF += ctx.totalTermFreq();
} else {
sumTTF = -1; // omit once TF is omitted anywhere!
}
}
sumTTF = Math.min(sumTTF, minSumTTF);
for (int i = 0; i < contexts.length; i++) {
int df = contexts[i].docFreq();
if (df == 0) {
continue;
}
// the blended sumTTF can't be greater than the sumTTTF on the field
final long fixedTTF = sumTTF == -1 ? -1 : sumTTF;
contexts[i] = adjustTTF(contexts[i], fixedTTF);
}
}
private TermContext adjustTTF(TermContext termContext, long sumTTF) {
if (sumTTF == -1 && termContext.totalTermFreq() == -1) {
return termContext;
}
TermContext newTermContext = new TermContext(termContext.topReaderContext);
List leaves = termContext.topReaderContext.leaves();
final int len;
if (leaves == null) {
len = 1;
} else {
len = leaves.size();
}
int df = termContext.docFreq();
long ttf = sumTTF;
for (int i = 0; i < len; i++) {
TermState termState = termContext.get(i);
if (termState == null) {
continue;
}
newTermContext.register(termState, i, df, ttf);
df = 0;
ttf = 0;
}
return newTermContext;
}
private static TermContext adjustDF(TermContext ctx, int newDocFreq) {
// Use a value of ttf that is consistent with the doc freq (ie. gte)
long newTTF;
if (ctx.totalTermFreq() < 0) {
newTTF = -1;
} else {
newTTF = Math.max(ctx.totalTermFreq(), newDocFreq);
}
List leaves = ctx.topReaderContext.leaves();
final int len;
if (leaves == null) {
len = 1;
} else {
len = leaves.size();
}
TermContext newCtx = new TermContext(ctx.topReaderContext);
for (int i = 0; i < len; ++i) {
TermState termState = ctx.get(i);
if (termState == null) {
continue;
}
newCtx.register(termState, i, newDocFreq, newTTF);
newDocFreq = 0;
newTTF = 0;
}
return newCtx;
}
public List getTerms() {
return Arrays.asList(terms);
}
@Override
public String toString(String field) {
StringBuilder builder = new StringBuilder("blended(terms:[");
for (int i = 0; i < terms.length; ++i) {
builder.append(terms[i]);
float boost = 1f;
if (boosts != null) {
boost = boosts[i];
}
if (boost != 1f) {
builder.append('^').append(boost);
}
builder.append(", ");
}
if (terms.length > 0) {
builder.setLength(builder.length() - 2);
}
builder.append("])");
return builder.toString();
}
private volatile Term[] equalTerms = null;
private Term[] equalsTerms() {
if (terms.length == 1) {
return terms;
}
if (equalTerms == null) {
// sort the terms to make sure equals and hashCode are consistent
// this should be a very small cost and equivalent to a HashSet but less object creation
final Term[] t = new Term[terms.length];
System.arraycopy(terms, 0, t, 0, terms.length);
ArrayUtil.timSort(t);
equalTerms = t;
}
return equalTerms;
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (!super.equals(o)) return false;
BlendedTermQuery that = (BlendedTermQuery) o;
return Arrays.equals(equalsTerms(), that.equalsTerms());
}
@Override
public int hashCode() {
return Objects.hash(super.hashCode(), Arrays.hashCode(equalsTerms()));
}
public static BlendedTermQuery booleanBlendedQuery(Term[] terms, final boolean disableCoord) {
return booleanBlendedQuery(terms, null, disableCoord);
}
public static BlendedTermQuery booleanBlendedQuery(Term[] terms, final float[] boosts, final boolean disableCoord) {
return new BlendedTermQuery(terms, boosts) {
@Override
protected Query topLevelQuery(Term[] terms, TermContext[] ctx, int[] docFreqs, int maxDoc) {
BooleanQuery.Builder booleanQueryBuilder = new BooleanQuery.Builder();
booleanQueryBuilder.setDisableCoord(disableCoord);
for (int i = 0; i < terms.length; i++) {
Query query = new TermQuery(terms[i], ctx[i]);
if (boosts != null && boosts[i] != 1f) {
query = new BoostQuery(query, boosts[i]);
}
booleanQueryBuilder.add(query, BooleanClause.Occur.SHOULD);
}
return booleanQueryBuilder.build();
}
};
}
public static BlendedTermQuery commonTermsBlendedQuery(Term[] terms, final float[] boosts, final boolean disableCoord, final float maxTermFrequency) {
return new BlendedTermQuery(terms, boosts) {
@Override
protected Query topLevelQuery(Term[] terms, TermContext[] ctx, int[] docFreqs, int maxDoc) {
BooleanQuery.Builder highBuilder = new BooleanQuery.Builder();
highBuilder.setDisableCoord(disableCoord);
BooleanQuery.Builder lowBuilder = new BooleanQuery.Builder();
lowBuilder.setDisableCoord(disableCoord);
for (int i = 0; i < terms.length; i++) {
Query query = new TermQuery(terms[i], ctx[i]);
if (boosts != null && boosts[i] != 1f) {
query = new BoostQuery(query, boosts[i]);
}
if ((maxTermFrequency >= 1f && docFreqs[i] > maxTermFrequency)
|| (docFreqs[i] > (int) Math.ceil(maxTermFrequency
* maxDoc))) {
highBuilder.add(query, BooleanClause.Occur.SHOULD);
} else {
lowBuilder.add(query, BooleanClause.Occur.SHOULD);
}
}
BooleanQuery high = highBuilder.build();
BooleanQuery low = lowBuilder.build();
if (low.clauses().isEmpty()) {
BooleanQuery.Builder queryBuilder = new BooleanQuery.Builder();
queryBuilder.setDisableCoord(disableCoord);
for (BooleanClause booleanClause : high) {
queryBuilder.add(booleanClause.getQuery(), Occur.MUST);
}
return queryBuilder.build();
} else if (high.clauses().isEmpty()) {
return low;
} else {
return new BooleanQuery.Builder()
.setDisableCoord(true)
.add(high, BooleanClause.Occur.SHOULD)
.add(low, BooleanClause.Occur.MUST)
.build();
}
}
};
}
public static BlendedTermQuery dismaxBlendedQuery(Term[] terms, final float tieBreakerMultiplier) {
return dismaxBlendedQuery(terms, null, tieBreakerMultiplier);
}
public static BlendedTermQuery dismaxBlendedQuery(Term[] terms, final float[] boosts, final float tieBreakerMultiplier) {
return new BlendedTermQuery(terms, boosts) {
@Override
protected Query topLevelQuery(Term[] terms, TermContext[] ctx, int[] docFreqs, int maxDoc) {
List queries = new ArrayList<>(ctx.length);
for (int i = 0; i < terms.length; i++) {
Query query = new TermQuery(terms[i], ctx[i]);
if (boosts != null && boosts[i] != 1f) {
query = new BoostQuery(query, boosts[i]);
}
queries.add(query);
}
return new DisjunctionMaxQuery(queries, tieBreakerMultiplier);
}
};
}
}