Lucene学习总结之七：Lucene搜索过程解析(4)转

2.4、搜索查询对象

 

2.4.1.2、创建Weight对象树

BooleanQuery.createWeight(Searcher) 最终返回return new BooleanWeight(searcher)，BooleanWeight构造函数的具体实现如下：

 

public BooleanWeight(Searcher searcher) {   this.similarity = getSimilarity(searcher);   weights = new ArrayList<Weight>(clauses.size());   //也是一个递归的过程，沿着新的Query对象树一直到叶子节点   for (int i = 0 ; i < clauses.size(); i++) {     weights.add(clauses.get(i).getQuery().createWeight(searcher));   } }

对于TermQuery的叶子节点，其TermQuery.createWeight(Searcher) 返回return new TermWeight(searcher)对象，TermWeight构造函数如下：

 

public TermWeight(Searcher searcher) {   this.similarity = getSimilarity(searcher);   //此处计算了idf   idfExp = similarity.idfExplain(term, searcher);   idf = idfExp.getIdf(); }

//idf的计算完全符合文档中的公式：   public IDFExplanation idfExplain(final Term term, final Searcher searcher) {   final int df = searcher.docFreq(term);   final int max = searcher.maxDoc();   final float idf = idf(df, max);   return new IDFExplanation() {       public float getIdf() {         return idf;       }}; }

public float idf(int docFreq, int numDocs) {   return (float)(Math.log(numDocs/(double)(docFreq+1)) + 1.0); }

而ConstantScoreQuery.createWeight(Searcher) 除了创建ConstantScoreQuery.ConstantWeight(searcher)对象外，没有计算idf。

由此创建的Weight对象树如下：

 

weight    BooleanQuery$BooleanWeight  (id=169)       |   similarity    DefaultSimilarity  (id=177)       |   this$0    BooleanQuery  (id=89)       |   weights    ArrayList<E>  (id=188)       |      elementData    Object[3]  (id=190)       |------[0]    BooleanQuery$BooleanWeight  (id=171)       |          |   similarity    DefaultSimilarity  (id=177)       |          |   this$0    BooleanQuery  (id=105)       |          |   weights    ArrayList<E>  (id=193)       |          |      elementData    Object[2]  (id=199)       |          |------[0]    ConstantScoreQuery$ConstantWeight  (id=183)       |          |               queryNorm    0.0       |          |               queryWeight    0.0       |          |               similarity    DefaultSimilarity  (id=177)       |          |               //ConstantScore(contents:apple*)      |          |               this$0    ConstantScoreQuery  (id=123)       |          |------[1]    TermQuery$TermWeight  (id=175)       |                         idf    2.0986123       |                         idfExp    Similarity$1  (id=241)       |                         queryNorm    0.0       |                         queryWeight    0.0       |                         similarity    DefaultSimilarity  (id=177)       |                         //contents:boy    |                        this$0    TermQuery  (id=124)       |                         value    0.0       |                 modCount    2       |                 size    2       |------[1]    BooleanQuery$BooleanWeight  (id=179)       |          |   similarity    DefaultSimilarity  (id=177)       |          |   this$0    BooleanQuery  (id=110)       |          |   weights    ArrayList<E>  (id=195)       |          |      elementData    Object[2]  (id=204)       |          |------[0]    ConstantScoreQuery$ConstantWeight  (id=206)       |          |               queryNorm    0.0       |          |               queryWeight    0.0       |          |               similarity    DefaultSimilarity  (id=177)       |          |               //ConstantScore(contents:cat*)    |          |               this$0    ConstantScoreQuery  (id=135)       |          |------[1]    TermQuery$TermWeight  (id=207)       |                         idf    1.5389965       |                         idfExp    Similarity$1  (id=210)       |                         queryNorm    0.0       |                         queryWeight    0.0       |                         similarity    DefaultSimilarity  (id=177)    |                         //contents:dog    |                         this$0    TermQuery  (id=136)       |                         value    0.0       |                 modCount    2       |                 size    2       |------[2]    BooleanQuery$BooleanWeight  (id=182)                  |  similarity    DefaultSimilarity  (id=177)                  |  this$0    BooleanQuery  (id=113)                  |  weights    ArrayList<E>  (id=197)                  |     elementData    Object[2]  (id=216)                  |------[0]    BooleanQuery$BooleanWeight  (id=181)                  |          |    similarity    BooleanQuery$1  (id=220)                  |          |    this$0    BooleanQuery  (id=145)                  |          |    weights    ArrayList<E>  (id=221)                  |          |      elementData    Object[2]  (id=224)                  |          |------[0]    TermQuery$TermWeight  (id=226)                  |          |                idf    2.0986123                  |          |                idfExp    Similarity$1  (id=229)                  |          |                queryNorm    0.0                  |          |                queryWeight    0.0                  |          |                similarity    DefaultSimilarity  (id=177)                  |          |                //contents:eat               |          |                this$0    TermQuery  (id=150)                  |          |                value    0.0                  |          |------[1]    TermQuery$TermWeight  (id=227)                  |                          idf    1.1823215                  |                          idfExp    Similarity$1  (id=231)                  |                          queryNorm    0.0                  |                          queryWeight    0.0                  |                          similarity    DefaultSimilarity  (id=177)                  |                          //contents:cat^0.33333325               |                          this$0    TermQuery  (id=151)                  |                          value    0.0                  |                  modCount    2                  |                  size    2                  |------[1]    TermQuery$TermWeight  (id=218)                                idf    2.0986123                                idfExp    Similarity$1  (id=233)                                queryNorm    0.0                                queryWeight    0.0                                similarity    DefaultSimilarity  (id=177)                                //contents:foods                             this$0    TermQuery  (id=154)                                value    0.0                        modCount    2                        size    2            modCount    3            size    3

 

 

 

2.4.1.3、计算Term Weight分数

(1) 首先计算sumOfSquaredWeights

按照公式：

 

代码如下：

float sum = weight.sumOfSquaredWeights();

 

//可以看出，也是一个递归的过程 public float sumOfSquaredWeights() throws IOException {   float sum = 0.0f;   for (int i = 0 ; i < weights.size(); i++) {     float s = weights.get(i).sumOfSquaredWeights();     if (!clauses.get(i).isProhibited())       sum += s;   }   sum *= getBoost() * getBoost();  //乘以query boost   return sum ; }

对于叶子节点TermWeight来讲，其TermQuery$TermWeight.sumOfSquaredWeights()实现如下：

 

public float sumOfSquaredWeights() {   //计算一部分打分，idf*t.getBoost()，将来还会用到。   queryWeight = idf * getBoost();   //计算(idf*t.getBoost())^2   return queryWeight * queryWeight; }

对于叶子节点ConstantWeight来讲，其ConstantScoreQuery$ConstantWeight.sumOfSquaredWeights() 如下：

 

public float sumOfSquaredWeights() {   //除了用户指定的boost以外，其他都不计算在打分内   queryWeight = getBoost();   return queryWeight * queryWeight; }

(2) 计算queryNorm

其公式如下：

 

其代码如下：

 

public float queryNorm(float sumOfSquaredWeights) {   return (float)(1.0 / Math.sqrt(sumOfSquaredWeights)); }

(3) 将queryNorm算入打分

代码为：

weight.normalize(norm);

 

//又是一个递归的过程 public void normalize(float norm) {   norm *= getBoost();   for (Weight w : weights) {     w.normalize(norm);   } }

其叶子节点TermWeight来讲，其TermQuery$TermWeight.normalize(float) 代码如下：

 

public void normalize(float queryNorm) {   this.queryNorm = queryNorm;   //原来queryWeight为idf*t.getBoost()，现在为queryNorm*idf*t.getBoost()。   queryWeight *= queryNorm;   //打分到此计算了queryNorm*idf*t.getBoost()*idf = queryNorm*idf^2*t.getBoost()部分。   value = queryWeight * idf; }

我们知道，Lucene的打分公式整体如下，到此计算了图中，红色的部分：

转：http://forfuture1978.iteye.com/blog/632829