在查询如：title:"市场 价格"~5的时候，意思是title包括市场和价格并且市场和价格之前的距离不能大于5.如果查询title:"市场 价格 市场"~5，说明市场后面有价格，价格后面还要有市场，否则没有结果。 主要代码如下：
protected void search(List<AtomicReaderContext> leaves, Weight weight, Collector collector) throws IOException {
// TODO: should we make this threaded...? the Collector could be sync'd?
// always use single thread:
for (AtomicReaderContext ctx : leaves) { // search each subreader
collector.setNextReader(ctx);
Scorer scorer = weight.scorer(ctx, !collector.acceptsDocsOutOfOrder(), true, ctx.reader().getLiveDocs());
if (scorer != null) {
scorer.score(collector);
}
}
}
进入PhraseWeight.score()方法：
public Scorer scorer(AtomicReaderContext context, boolean scoreDocsInOrder,boolean topScorer, Bits acceptDocs) throws IOException {
assert !terms.isEmpty();
final AtomicReader reader = context.reader();
final Bits liveDocs = acceptDocs;
PostingsAndFreq[] postingsFreqs = new PostingsAndFreq[terms.size()];
final Terms fieldTerms = reader.terms(field);
if (fieldTerms == null) {
return null;
}
// Reuse single TermsEnum below:
final TermsEnum te = fieldTerms.iterator(null);
for (int i = 0; i < terms.size(); i++) {
final Term t = terms.get(i);
//这里的state是根据该term是否在索引中存在生成的
final TermState state = states[i].get(context.ord);
//如果该term在索引中不存在，则会return null，说明在该种查询语法中每个term都必须出现，即使AND的关系
if (state == null) { / term doesnt exist in this segment /
assert termNotInReader(reader, t): "no termstate found but term exists in reader";
return null;
}
te.seekExact(t.bytes(), state);//这里是具体的在索引文件里进行搜索的逻辑
DocsAndPositionsEnum postingsEnum = te.docsAndPositions(liveDocs, null, DocsEnum.FLAG_NONE);
// PhraseQuery on a field that did not index
// positions.
if (postingsEnum == null) {
assert te.seekExact(t.bytes(), false) : "termstate found but no term exists in reader";
// term does exist, but has no positions
throw new IllegalStateException("field \"" + t.field() + "\" was indexed without position data; cannot run PhraseQuery (term=" + t.text() + ")");
}
postingsFreqs[i] = new PostingsAndFreq(postingsEnum, te.docFreq(), positions.get(i).intValue(), t);
}
// sort by increasing docFreq order
if (slop == 0) {
ArrayUtil.mergeSort(postingsFreqs);
}
//如果slop为0，即查询语句中的~后面的数字，则返回ExactPhraseScorer，否则返回SloppyPhraseScorer
if (slop == 0) { // optimize exact case
ExactPhraseScorer s = new ExactPhraseScorer(this, postingsFreqs, similarity.exactSimScorer(stats, context));
if (s.noDocs) {
return null;
} else {
return s;
}
} else {
return new SloppyPhraseScorer(this, postingsFreqs, slop, similarity.sloppySimScorer(stats, context));
}
}
接着进入Scorer.score()方法中：
/* Scores and collects all matching documents.
* @param collector The collector to which all matching documents are passed.
/
public void score(Collector collector) throws IOException {
collector.setScorer(this);
int doc;
while ((doc = nextDoc()) != NO_MORE_DOCS) {
collector.collect(doc);
}
}
在这里主要是nextDoc()方法，进入SloppyPhraseScorer.nextDoc()方法：
public int nextDoc() throws IOException {
return advance(max.doc);
}
public int advance(int target) throws IOException {
sloppyFreq = 0.0f;
if (!advanceMin(target)) {
return NO_MORE_DOCS;
}
boolean restart=false;
while (sloppyFreq == 0.0f) {
while (min.doc < max.doc || restart) {
restart = false;
if (!advanceMin(max.doc)) {
return NO_MORE_DOCS;
}
}
// found a doc with all of the terms
sloppyFreq = phraseFreq(); // check for phrase
restart = true;
}
// found a match
return max.doc;
}
private float phraseFreq() throws IOException {
if (!initPhrasePositions()) {
return 0.0f;
}
float freq = 0.0f;
numMatches = 0;
PhrasePositions pp = pq.pop();
System.out.println(pp+"---"+end);
int matchLength = end - pp.position;
int next = pq.top().position;
while (advancePP(pp)) {
if (hasRpts && !advanceRpts(pp)) {
break; // pps exhausted
}
if (pp.position > next) { // done minimizing current match-length
if (matchLength <= slop) {
freq += docScorer.computeSlopFactor(matchLength); // score match
numMatches++;
}
pq.add(pp);
pp = pq.pop();
next = pq.top().position;
matchLength = end - pp.position;
} else {
int matchLength2 = end - pp.position;
if (matchLength2 < matchLength) {
matchLength = matchLength2;
}
}
}
if (matchLength <= slop) {
freq += docScorer.computeSlopFactor(matchLength); // score match
numMatches++;
}
return freq;
}
在这个方法可以看到matchLength和slop进行比较了。pq的heap（父类的属性）里面存放的是每个term所在field中的位置和该field属于哪个文档的信息：如heap[0]=d:0 o:0 p:7 c:0表示offset为0的term在dicid为0的文档中的当前查询field中的第7个位置，在该field中该term剩余count数量为0，即freq的大小,heap[1]=d:0 o:1 p:11 c:0。