结合源码分析Solr&Lucene查询打分的工作流程
基于solr4.4solr中的搜索打分是在QueryComponent中进行的。
在prepare中根据查询的参数，QueryParser对查询语句进行分词，并生成Query对象树。 QParser parser = QParser.getParser(rb.getQueryString(), defType, req); Query q = parser.getQuery(); if (q == null) { // normalize a null query to a query that matches nothing q = new BooleanQuery(); } 在process方法中，进行搜索打分的过程
调用SolrIndexSearcher进行查询，
SolrIndexSearcher searcher = req.getSearcher(); // normal search result searcher.search(result,cmd);search(Query query, Filter filter, Collector results) SolrIndexSearcher集成lucene的IndexSearcher类，
最终调用IndexSearcher的search(Query query, Filter filter, Collector results)public void search(Query query, Filter filter, Collector results) throws IOException {//在这个方法中，会先创建Weight树，计算TermWeight search(leafContexts, createNormalizedWeight(wrapFilter(query, filter)), results);} protected void search(List<AtomicReaderContext> leaves, Weight weight, Collector collector) throws IOException { .........//根据weight树，构造Score对象树，以及SumScore对象树，为合并倒排表做准备// Scorer scorer = weight.scorer(ctx, !collector.acceptsDocsOutOfOrder(), true, ctx.reader().getLiveDocs()); if (scorer != null) { try {//根据SumScorer对象树，进行文档的合并，收集文档结果结合，并进行打分排名 scorer.score(collector); } catch (CollectionTerminatedException e) { // collection was terminated prematurely // continue with the following leaf } } }} 1、先看一下Weight对象树的生成，
这一部分包括query的打分计算，参见红色部分
IndexSearcher.createNormalizedWeight(Query query)//重写Query对象树 query = rewrite(query);//创建weight对象树，递归计算idf Weight weight = query.createWeight(this);计算Weight分数， float v = weight.getValueForNormalization();//计算queryNorm float norm = getSimilarity().queryNorm(v); if (Float.isInfinite(norm) || Float.isNaN(norm)) { norm = 1.0f; }//将queryNorm的计算打分，递归调用weight weight.normalize(norm, 1.0f); 根据Query对象树，递归的调用query对象节点的createWeight方法比如BooleanQuery对应的是BooleanWeight对象，每个BooleanWeight包含weight对象数组最终叶子节点为TermWeight对象public TermWeight(IndexSearcher searcher, TermContext termStates) throws IOException { assert termStates != null : "TermContext must not be null"; this.termStates = termStates; this.similarity = searcher.getSimilarity();//计算idf this.stats = similarity.computeWeight( getBoost(), searcher.collectionStatistics(term.field()), searcher.termStatistics(term, termStates)); }public final SimWeight computeWeight(float queryBoost, CollectionStatistics collectionStats, TermStatistics... termStats) { final Explanation idf = termStats.length == 1 ? idfExplain(collectionStats, termStats) : idfExplain(collectionStats, termStats); return new IDFStats(collectionStats.field(), idf, queryBoost);}public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats) { final long df = termStats.docFreq(); final long max = collectionStats.maxDoc(); final float idf = idf(df, max); return new Explanation(idf, "idf(docFreq=" + df + ", maxDocs=" + max + ")");} 计算Weight分数 public float getValueForNormalization() throws IOException { float sum = 0.0f; for (int i = 0 ; i < weights.size(); i++) { // call sumOfSquaredWeights for all clauses in case of side effects float s = weights.get(i).getValueForNormalization(); // sum sub weights if (!clauses.get(i).isProhibited()) // only add to sum for non-prohibited clauses sum += s; } sum *= getBoost() * getBoost(); // boost each sub-weight return sum ; } 2、根据weight树，构造Score对象树，以及SumScore对象树，为合并倒排表做准备
Scorer scorer = weight.scorer(ctx, !collector.acceptsDocsOutOfOrder(), true, ctx.reader().getLiveDocs());BooleanWeight递归调用节点weight.score创建score对象 public Scorer scorer(AtomicReaderContext context, boolean scoreDocsInOrder, boolean topScorer, Bits acceptDocs) throws IOException { List<Scorer> required = new ArrayList<Scorer>(); List<Scorer> prohibited = new ArrayList<Scorer>(); List<Scorer> optional = new ArrayList<Scorer>(); Iterator<BooleanClause> cIter = clauses.iterator(); for (Weight w: weights) { BooleanClause c =cIter.next(); Scorer subScorer = w.scorer(context, true, false, acceptDocs); required.add(subScorer); return new BooleanScorer2(this, disableCoord, minNrShouldMatch, required, prohibited, optional, maxCoord); }//在创建BooleanScore2的过程中，计算coordBooleanQuery$BooleanWeight,coord， public float coord(int overlap, int maxOverlap) { return maxOverlap == 1 ? 1F : similarity.coord(overlap, maxOverlap); }//最终调用TermWeight.scorer方法，创建score对象 public Scorer scorer(AtomicReaderContext context, boolean scoreDocsInOrder, boolean topScorer, Bits acceptDocs) throws IOException { assert termStates.topReaderContext == ReaderUtil.getTopLevelContext(context) : "The top-reader used to create Weight (" + termStates.topReaderContext + ") is not the same as the current reader's top-reader (" + ReaderUtil.getTopLevelContext(context); final TermsEnum termsEnum = getTermsEnum(context); if (termsEnum == null) { return null; }//Term对应的docs DocsEnum docs = termsEnum.docs(acceptDocs, null); assert docs != null;//TermScorer负责doc的打分 return new TermScorer(this, docs, similarity.simScorer(stats, context)); } TermScorer(Weight weight, DocsEnum td, Similarity.SimScorer docScorer) { super(weight); this.docScorer = docScorer; this.docsEnum = td;}
3、根据SumScorer对象树，进行文档的合并，收集文档结果结合，并进行打分排名 scorer.score(collector);public void score(Collector collector) throws IOException { assert docID() == -1; // not started collector.setScorer(this); int doc;//在nextDoc的过程中合并document，合并倒排表是按照树的结构进行，先合并子树，子树与子树合并，一直到根 while ((doc = nextDoc()) != NO_MORE_DOCS) {//收集doc，并打分，根据文档的打分，放入优先级队列（最小堆）中 collector.collect(doc); }}//整个Score以及SumScorer对象数的打分计算，最终会落到叶子节点TermScorer上TermScorer： @Overridepublic float score() throws IOException { assert docID() != NO_MORE_DOCS; return docScorer.score(docsEnum.docID(), docsEnum.freq()); }
//打分计算公式：tf * norm * weightValue = tf * norm *queryNorm * idf^2 * t.getBoost()TFIDFSimilarity$TFIDFSimScorer @Override public float score(int doc, float freq) {//weight是在创建weight阶段的query分词的打分， //这一部分计算打分公式的蓝色部分，再乘以weight final float raw = tf(freq) * weightValue; // compute tf(f)*weight，weight=queryNorm * idf^2 * t.getBoost() return norms == null ? raw : raw * decodeNormValue(norms.get(doc));// normalize for field, norm部分 }
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