Lucene学习总结之七：Lucene搜索过程解析(5)
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2.4、搜索查询对象
2.4.2、创建Scorer及SumScorer对象树
当创建完Weight对象树的时候，调用IndexSearcher.search(Weight, Filter, int)，代码如下：
//(a)创建文档号收集器
TopScoreDocCollector collector = TopScoreDocCollector.create(nDocs, !weight.scoresDocsOutOfOrder());
search(weight, filter, collector);
//(b)返回搜索结果
return collector.topDocs();
public void search(Weight weight, Filter filter, Collector collector)
throws IOException {
if (filter == null) {
for (int i = 0; i < subReaders.length; i++) {
collector.setNextReader(subReaders[i], docStarts[i]);
//(c)创建Scorer对象树，以及SumScorer树用来合并倒排表
Scorer scorer = weight.scorer(subReaders[i], !collector.acceptsDocsOutOfOrder(), true);
if (scorer != null) {
//(d)合并倒排表，(e)收集文档号
scorer.score(collector);
}
}
} else {
for (int i = 0; i < subReaders.length; i++) {
collector.setNextReader(subReaders[i], docStarts[i]);
searchWithFilter(subReaders[i], weight, filter, collector);
}
}
}
在本节中，重点分析(c)创建Scorer对象树，以及SumScorer树用来合并倒排表，在2.4.3节中，分析 (d)合并倒排表，在2.4.4节中，分析文档结果收集器的创建(a)，结果文档的收集(e)，以及文档的返回(b)。
BooleanQuery$BooleanWeight.scorer(IndexReader, boolean, boolean) 代码如下：
public Scorer scorer(IndexReader reader, boolean scoreDocsInOrder, boolean topScorer){
//存放对应于MUST语句的Scorer
List<Scorer> required = new ArrayList<Scorer>();
//存放对应于MUST_NOT语句的Scorer
List<Scorer> prohibited = new ArrayList<Scorer>();
//存放对应于SHOULD语句的Scorer
List<Scorer> optional = new ArrayList<Scorer>();
//遍历每一个子语句，生成子Scorer对象，并加入相应的集合，这是一个递归的过程。
Iterator<BooleanClause> cIter = clauses.iterator();
for (Weight w : weights) {
BooleanClause c = cIter.next();
Scorer subScorer = w.scorer(reader, true, false);
if (subScorer == null) {
if (c.isRequired()) {
return null;
}
} else if (c.isRequired()) {
required.add(subScorer);
} else if (c.isProhibited()) {
prohibited.add(subScorer);
} else {
optional.add(subScorer);
}
}
//此处在有关BooleanScorer及scoreDocsInOrder一节会详细描述
if (!scoreDocsInOrder && topScorer && required.size() == 0 && prohibited.size() < 32) {
return new BooleanScorer(similarity, minNrShouldMatch, optional, prohibited);
}
//生成Scorer对象树，同时生成SumScorer对象树
return new BooleanScorer2(similarity, minNrShouldMatch, required, prohibited, optional);
}
对其叶子节点TermWeight来说，TermQuery$TermWeight.scorer(IndexReader, boolean, boolean) 代码如下：
public Scorer scorer(IndexReader reader, boolean scoreDocsInOrder, boolean topScorer) throws IOException {
//此Term的倒排表
TermDocs termDocs = reader.termDocs(term);
if (termDocs == null)
return null;
return new TermScorer(this, termDocs, similarity, reader.norms(term.field()));
}
TermScorer(Weight weight, TermDocs td, Similarity similarity, byte[] norms) {
super(similarity);
this.weight = weight;
this.termDocs = td;
//得到标准化因子
this.norms = norms;
//得到原来计算得的打分：queryNorm*idf^2*t.getBoost()
this.weightValue = weight.getValue();
for (int i = 0; i < SCORE_CACHE_SIZE; i++)
scoreCache[i] = getSimilarity().tf(i) * weightValue;
}
对其叶子节点ConstantWeight来说，ConstantScoreQuery$ConstantWeight.scorer(IndexReader, boolean, boolean) 代码如下：
public ConstantScorer(Similarity similarity, IndexReader reader, Weight w) {
super(similarity);
theScore = w.getValue();
//得到所有的文档号，形成统一的倒排表，参与倒排表合并。
DocIdSet docIdSet = filter.getDocIdSet(reader);
DocIdSetIterator docIdSetIterator = docIdSet.iterator();
}
对于BooleanWeight，最后要产生的是BooleanScorer2，其构造函数代码如下：
public BooleanScorer2(Similarity similarity, int minNrShouldMatch,
List<Scorer> required, List<Scorer> prohibited, List<Scorer> optional) {
super(similarity);
//为了计算打分公式中的coord项做统计
coordinator = new Coordinator();
this.minNrShouldMatch = minNrShouldMatch;
//SHOULD的部分
optionalScorers = optional;
coordinator.maxCoord += optional.size();
//MUST的部分
requiredScorers = required;
coordinator.maxCoord += required.size();
//MUST_NOT的部分
prohibitedScorers = prohibited;
//事先计算好各种情况的coord值
coordinator.init();
//创建SumScorer为倒排表合并做准备
countingSumScorer = makeCountingSumScorer();
}
Coordinator.init() {
coordFactors = new float[maxCoord + 1];
Similarity sim = getSimilarity();
for (int i = 0; i <= maxCoord; i++) {
//计算总的子语句的个数和一个文档满足的子语句的个数之间的关系，自然是一篇文档满足的子语句个个数越多，打分越高。
coordFactors[i] = sim.coord(i, maxCoord);
}
}
在生成Scorer对象树之外，还会生成SumScorer对象树，来表示各个语句之间的关系，为合并倒排表做准备。
在解析BooleanScorer2.makeCountingSumScorer() 之前，我们先来看不同的语句之间都存在什么样的关系，又将如何影响倒排表合并呢？
语句主要分三类：MUST，SHOULD，MUST_NOT
语句之间的组合主要有以下几种情况：
- 多个MUST，如"(+apple +boy +dog)"，则会生成ConjunctionScorer(Conjunction 交集)，也即倒排表取交集
- MUST和SHOULD，如"(+apple boy)"，则会生成ReqOptSumScorer(required optional)，也即MUST的倒排表返回，如果文档包括SHOULD的部分，则增加打分。
- MUST和MUST_NOT，如"(+apple –boy)"，则会生成ReqExclScorer(required exclusive)，也即返回MUST的倒排表，但扣除MUST_NOT的倒排表中的文档。
- 多个SHOULD，如"(apple boy dog)"，则会生成DisjunctionSumScorer(Disjunction 并集)，也即倒排表去并集
- SHOULD和MUST_NOT，如"(apple –boy)"，则SHOULD被认为成MUST，会生成ReqExclScorer
- MUST，SHOULD，MUST_NOT同时出现，则MUST首先和MUST_NOT组合成ReqExclScorer，SHOULD单独成为SingleMatchScorer，然后两者组合成ReqOptSumScorer。
下面分析生成SumScorer的过程：
BooleanScorer2.makeCountingSumScorer() 分两种情况：
- 当有MUST的语句的时候，则调用makeCountingSumScorerSomeReq()
- 当没有MUST的语句的时候，则调用makeCountingSumScorerNoReq()
首先来看makeCountingSumScorerSomeReq代码如下：
private Scorer makeCountingSumScorerSomeReq() {
if (optionalScorers.size() == minNrShouldMatch) {
//如果optional的语句个数恰好等于最少需满足的optional的个数，则所有的optional都变成required。于是首先所有的optional生成ConjunctionScorer(交集)，然后再通过addProhibitedScorers将prohibited加入，生成ReqExclScorer(required exclusive)
ArrayList<Scorer> allReq = new ArrayList<Scorer>(requiredScorers);
allReq.addAll(optionalScorers);
return addProhibitedScorers(countingConjunctionSumScorer(allReq));
} else {
//首先所有的required的语句生成ConjunctionScorer(交集)
Scorer requiredCountingSumScorer =
requiredScorers.size() == 1
? new SingleMatchScorer(requiredScorers.get(0))
: countingConjunctionSumScorer(requiredScorers);
if (minNrShouldMatch > 0) {
//如果最少需满足的optional的个数有一定的限制，则意味着optional中有一部分要相当于required，会影响倒排表的合并。因而required生成的ConjunctionScorer(交集)和optional生成的DisjunctionSumScorer(并集)共同组合成一个ConjunctionScorer(交集)，然后再加入prohibited，生成ReqExclScorer
return addProhibitedScorers(
dualConjunctionSumScorer(
requiredCountingSumScorer,
countingDisjunctionSumScorer(
optionalScorers,
minNrShouldMatch)));
} else { // minNrShouldMatch == 0
//如果最少需满足的optional的个数没有一定的限制，则optional并不影响倒排表的合并，仅仅在文档包含optional部分的时候增加打分。所以required和prohibited首先生成ReqExclScorer，然后再加入optional，生成ReqOptSumScorer(required optional)
return new ReqOptSumScorer(
addProhibitedScorers(requiredCountingSumScorer),
optionalScorers.size() == 1
? new SingleMatchScorer(optionalScorers.get(0))
: countingDisjunctionSumScorer(optionalScorers, 1));
}
}
}
然后我们来看makeCountingSumScorerNoReq代码如下：
private Scorer makeCountingSumScorerNoReq() {
// minNrShouldMatch optional scorers are required, but at least 1
int nrOptRequired = (minNrShouldMatch < 1) ? 1 : minNrShouldMatch;
Scorer requiredCountingSumScorer;
if (optionalScorers.size() > nrOptRequired)
//如果optional的语句个数多于最少需满足的optional的个数，则optional中一部分相当required，影响倒排表的合并，所以生成DisjunctionSumScorer
requiredCountingSumScorer = countingDisjunctionSumScorer(optionalScorers, nrOptRequired);
else if (optionalScorers.size() == 1)
//如果optional的语句只有一个，则返回SingleMatchScorer，不存在倒排表合并的问题。
requiredCountingSumScorer = new SingleMatchScorer(optionalScorers.get(0));
else
//如果optional的语句个数少于等于最少需满足的optional的个数，则所有的optional都算required，所以生成ConjunctionScorer
requiredCountingSumScorer = countingConjunctionSumScorer(optionalScorers);
//将prohibited加入，生成ReqExclScorer
return addProhibitedScorers(requiredCountingSumScorer);
}
经过此步骤，生成的Scorer对象树如下：
scorer BooleanScorer2 (id=50)
| coordinator BooleanScorer2$Coordinator (id=53)
| countingSumScorer ReqOptSumScorer (id=54)
| minNrShouldMatch 0
|---optionalScorers ArrayList<E> (id=55)
| | elementData Object[10] (id=69)
| |---[0] BooleanScorer2 (id=73)
| | coordinator BooleanScorer2$Coordinator (id=74)
| | countingSumScorer BooleanScorer2$1 (id=75)
| | minNrShouldMatch 0
| |---optionalScorers ArrayList<E> (id=76)
| | | elementData Object[10] (id=83)
| | |---[0] ConstantScoreQuery$ConstantScorer (id=86)
| | | docIdSetIterator OpenBitSetIterator (id=88)
| | | similarity DefaultSimilarity (id=64)
| | | theScore 0.47844642
| | | //ConstantScore(contents:cat*)
| | | this$0 ConstantScoreQuery (id=90)
| | |---[1] TermScorer (id=87)
| | doc -1
| | doc 0
| | docs int[32] (id=93)
| | freqs int[32] (id=95)
| | norms byte[4] (id=96)
| | pointer 0
| | pointerMax 2
| | scoreCache float[32] (id=98)
| | similarity DefaultSimilarity (id=64)
| | termDocs SegmentTermDocs (id=103)
| | //weight(contents:dog)
| | weight TermQuery$TermWeight (id=106)
| | weightValue 1.1332052
| | modCount 2
| | size 2
| |---prohibitedScorers ArrayList<E> (id=77)
| | elementData Object[10] (id=84)
| | size 0
| |---requiredScorers ArrayList<E> (id=78)
| elementData Object[10] (id=85)
| size 0
| similarity DefaultSimilarity (id=64)
| size 1
|---prohibitedScorers ArrayList<E> (id=60)
| | elementData Object[10] (id=71)
| |---[0] BooleanScorer2 (id=81)
| | coordinator BooleanScorer2$Coordinator (id=114)
| | countingSumScorer BooleanScorer2$1 (id=115)
| | minNrShouldMatch 0
| |---optionalScorers ArrayList<E> (id=116)
| | | elementData Object[10] (id=119)
| | |---[0] BooleanScorer2 (id=122)
| | | | coordinator BooleanScorer2$Coordinator (id=124)
| | | | countingSumScorer BooleanScorer2$1 (id=125)
| | | | minNrShouldMatch 0
| | | |---optionalScorers ArrayList<E> (id=126)
| | | | | elementData Object[10] (id=138)
| | | | |---[0] TermScorer (id=156)
| | | | | docs int[32] (id=162)
| | | | | freqs int[32] (id=163)
| | | | | norms byte[4] (id=96)
| | | | | pointer 0
| | | | | pointerMax 1
| | | | | scoreCache float[32] (id=164)
| | | | | similarity DefaultSimilarity (id=64)
| | | | | termDocs SegmentTermDocs (id=165)
| | | | | //weight(contents:eat)
| | | | | weight TermQuery$TermWeight (id=166)
| | | | | weightValue 2.107161
| | | | |---[1] TermScorer (id=157)
| | | | doc -1
| | | | doc 1
| | | | docs int[32] (id=171)
| | | | freqs int[32] (id=172)
| | | | norms byte[4] (id=96)
| | | | pointer 1
| | | | pointerMax 3
| | | | scoreCache float[32] (id=173)
| | | | similarity DefaultSimilarity (id=64)
| | | | termDocs SegmentTermDocs (id=180)
| | | | //weight(contents:cat^0.33333325)
| | | | weight TermQuery$TermWeight (id=181)
| | | | weightValue 0.22293752
| | | | size 2
| | | |---prohibitedScorers ArrayList<E> (id=127)
| | | | elementData Object[10] (id=140)
| | | | modCount 0
| | | | size 0
| | | |---requiredScorers ArrayList<E> (id=128)
| | | elementData Object[10] (id=142)
| | | modCount 0
| | | size 0
| | | similarity BooleanQuery$1 (id=129)
| | |---[1] TermScorer (id=123)
| | doc -1
| | doc 3
| | docs int[32] (id=131)
| | freqs int[32] (id=132)
| | norms byte[4] (id=96)
| | pointer 0
| | pointerMax 1
| | scoreCache float[32] (id=133)
| | similarity DefaultSimilarity (id=64)
| | termDocs SegmentTermDocs (id=134)
| | //weight(contents:foods)
| | weight TermQuery$TermWeight (id=135)
| | weightValue 2.107161
| | size 2
| |---prohibitedScorers ArrayList<E> (id=117)
| | elementData Object[10] (id=120)
| | size 0
| |---requiredScorers ArrayList<E> (id=118)
| elementData Object[10] (id=121)
| size 0
| similarity DefaultSimilarity (id=64)
| size 1
|---requiredScorers ArrayList<E> (id=63)
| elementData Object[10] (id=72)
|---[0] BooleanScorer2 (id=82)
| coordinator BooleanScorer2$Coordinator (id=183)
| countingSumScorer ReqExclScorer (id=184)
| minNrShouldMatch 0
|---optionalScorers ArrayList<E> (id=185)
| elementData Object[10] (id=189)
| size 0
|---prohibitedScorers ArrayList<E> (id=186)
| | elementData Object[10] (id=191)
| |---[0] TermScorer (id=195)
| docs int[32] (id=197)
| freqs int[32] (id=198)
| norms byte[4] (id=96)
| pointer 0
| pointerMax 0
| scoreCache float[32] (id=199)
| similarity DefaultSimilarity (id=64)
| termDocs SegmentTermDocs (id=200)
| //weight(contents:boy)
| weight TermQuery$TermWeight (id=201)
| weightValue 2.107161
| size 1
|---requiredScorers ArrayList<E> (id=187)
| elementData Object[10] (id=193)
|---[0] ConstantScoreQuery$ConstantScorer (id=203)
docIdSetIterator OpenBitSetIterator (id=206)
similarity DefaultSimilarity (id=64)
theScore 0.47844642
//ConstantScore(contents:apple*)
this$0 ConstantScoreQuery (id=207)
size 1
similarity DefaultSimilarity (id=64)
size 1
similarity DefaultSimilarity (id=64)
生成的SumScorer对象树如下：
scorer BooleanScorer2 (id=50)
| coordinator BooleanScorer2$Coordinator (id=53)
|---countingSumScorer ReqOptSumScorer (id=54)
|---optScorer BooleanScorer2$SingleMatchScorer (id=79)
| | lastDocScore NaN
| | lastScoredDoc -1
| |---scorer BooleanScorer2 (id=73)
| | coordinator BooleanScorer2$Coordinator (id=74)
| |---countingSumScorer BooleanScorer2$1(DisjunctionSumScorer) (id=75)
| | currentDoc -1
| | currentScore NaN
| | doc -1
| | lastDocScore NaN
| | lastScoredDoc -1
| | minimumNrMatchers 1
| | nrMatchers -1
| | nrScorers 2
| | scorerDocQueue ScorerDocQueue (id=243)
| | similarity null
| |---subScorers ArrayList<E> (id=76)
| | elementData Object[10] (id=83)
| |---[0] ConstantScoreQuery$ConstantScorer (id=86)
| | doc -1
| | doc -1
| | docIdSetIterator OpenBitSetIterator (id=88)
| | similarity DefaultSimilarity (id=64)
| | theScore 0.47844642
| | //ConstantScore(contents:cat*)
| | this$0 ConstantScoreQuery (id=90)
| |---[1] TermScorer (id=87)
| doc -1
| doc 0
| docs int[32] (id=93)
| freqs int[32] (id=95)
| norms byte[4] (id=96)
| pointer 0
| pointerMax 2
| scoreCache float[32] (id=98)
| similarity DefaultSimilarity (id=64)
| termDocs SegmentTermDocs (id=103)
| //weight(contents:dog)
| weight TermQuery$TermWeight (id=106)
| weightValue 1.1332052
| size 2
| this$0 BooleanScorer2 (id=73)
| minNrShouldMatch 0
| optionalScorers ArrayList<E> (id=76)
| prohibitedScorers ArrayList<E> (id=77)
| requiredScorers ArrayList<E> (id=78)
| similarity DefaultSimilarity (id=64)
| similarity DefaultSimilarity (id=64)
| this$0 BooleanScorer2 (id=50)
|---reqScorer ReqExclScorer (id=80)
|---exclDisi BooleanScorer2 (id=81)
| | coordinator BooleanScorer2$Coordinator (id=114)
| |---countingSumScorer BooleanScorer2$1(DisjunctionSumScorer) (id=115)
| | currentDoc -1
| | currentScore NaN
| | doc -1
| | lastDocScore NaN
| | lastScoredDoc -1
| | minimumNrMatchers 1
| | nrMatchers -1
| | nrScorers 2
| | scorerDocQueue ScorerDocQueue (id=260)
| | similarity null
| |---subScorers ArrayList<E> (id=116)
| | elementData Object[10] (id=119)
| |---[0] BooleanScorer2 (id=122)
| | | coordinator BooleanScorer2$Coordinator (id=124)
| | |---countingSumScorer BooleanScorer2$1(DisjunctionSumScorer) (id=125)
| | | currentDoc 0
| | | currentScore 0.11146876
| | | doc -1
| | | lastDocScore NaN
| | | lastScoredDoc -1
| | | minimumNrMatchers 1
| | | nrMatchers 1
| | | nrScorers 2
| | | scorerDocQueue ScorerDocQueue (id=270)
| | | similarity null
| | |---subScorers ArrayList<E> (id=126)
| | | elementData Object[10] (id=138)
| | |---[0] TermScorer (id=156)
| | | doc -1
| | | doc 2
| | | docs int[32] (id=162)
| | | freqs int[32] (id=163)
| | | norms byte[4] (id=96)
| | | pointer 0
| | | pointerMax 1
| | | scoreCache float[32] (id=164)
| | | similarity DefaultSimilarity (id=64)
| | | termDocs SegmentTermDocs (id=165)
| | | //weight(contents:eat)
| | | weight TermQuery$TermWeight (id=166)
| | | weightValue 2.107161
| | |---[1] TermScorer (id=157)
| | doc -1
| | doc 1
| | docs int[32] (id=171)
| | freqs int[32] (id=172)
| | norms byte[4] (id=96)
| | pointer 1
| | pointerMax 3
| | scoreCache float[32] (id=173)
| | similarity DefaultSimilarity (id=64)
| | termDocs SegmentTermDocs (id=180)
| | //weight(contents:cat^0.33333325)
| | weight TermQuery$TermWeight (id=181)
| | weightValue 0.22293752
| | size 2
| | this$0 BooleanScorer2 (id=122)
| | doc -1
| | doc 0
| | minNrShouldMatch 0
| | optionalScorers ArrayList<E> (id=126)
| | prohibitedScorers ArrayList<E> (id=127)
| | requiredScorers ArrayList<E> (id=128)
| | similarity BooleanQuery$1 (id=129)
| |---[1] TermScorer (id=123)
| doc -1
| doc 3
| docs int[32] (id=131)
| freqs int[32] (id=132)
| norms byte[4] (id=96)
| pointer 0
| pointerMax 1
| scoreCache float[32] (id=133)
| similarity DefaultSimilarity (id=64)
| termDocs SegmentTermDocs (id=134)
| //weight(contents:foods)
| weight TermQuery$TermWeight (id=135)
| weightValue 2.107161
| size 2
| this$0 BooleanScorer2 (id=81)
| doc -1
| doc -1
| minNrShouldMatch 0
| optionalScorers ArrayList<E> (id=116)
| prohibitedScorers ArrayList<E> (id=117)
| requiredScorers ArrayList<E> (id=118)
| similarity DefaultSimilarity (id=64)
|---reqScorer BooleanScorer2$SingleMatchScorer (id=237)
| doc -1
| lastDocScore NaN
| lastScoredDoc -1
|---scorer BooleanScorer2 (id=82)
| coordinator BooleanScorer2$Coordinator (id=183)
|---countingSumScorer ReqExclScorer (id=184)
|---exclDisi TermScorer (id=195)
| doc -1
| doc -1
| docs int[32] (id=197)
| freqs int[32] (id=198)
| norms byte[4] (id=96)
| pointer 0
| pointerMax 0
| scoreCache float[32] (id=199)
| similarity DefaultSimilarity (id=64)
| termDocs SegmentTermDocs (id=200)
| //weight(contents:boy)
| weight TermQuery$TermWeight (id=201)
| weightValue 2.107161
|---reqScorer BooleanScorer2$2(ConjunctionScorer) (id=281)
| coord 1.0
| doc -1
| lastDoc -1
| lastDocScore NaN
| lastScoredDoc -1
|---scorers Scorer[1] (id=283)
|---[0] ConstantScoreQuery$ConstantScorer (id=203)
doc -1
doc -1
docIdSetIterator OpenBitSetIterator (id=206)
similarity DefaultSimilarity (id=64)
theScore 0.47844642
//ConstantScore(contents:apple*)
this$0 ConstantScoreQuery (id=207)
similarity DefaultSimilarity (id=64)
this$0 BooleanScorer2 (id=82)
val$requiredNrMatchers 1
similarity null
minNrShouldMatch 0
optionalScorers ArrayList<E> (id=185)
prohibitedScorers ArrayList<E> (id=186)
requiredScorers ArrayList<E> (id=187)
similarity DefaultSimilarity (id=64)
similarity DefaultSimilarity (id=64)
this$0 BooleanScorer2 (id=50)
similarity null
similarity null
minNrShouldMatch 0
optionalScorers ArrayList<E> (id=55)
prohibitedScorers ArrayList<E> (id=60)
requiredScorers ArrayList<E> (id=63)
similarity DefaultSimilarity (id=64)
- Lucene学习总结之七：Lucene搜索过程解析(5)
- Lucene学习总结之七：Lucene搜索过程解析(5)
- Lucene学习总结之七：Lucene搜索过程解析(5)
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