Solr4.8.0源码分析(6)之非排序查询

Solr4.8.0源码分析(6)之非排序查询

上篇文章简单介绍了Solr的查询流程，本文开始将详细介绍下查询的细节。查询主要分为排序查询和非排序查询，由于两者走的是两个分支，所以本文先介绍下非排序的查询。

查询的流程主要在SolrIndexSearch.getDocListC(QueryResult qr, QueryCommand cmd),顾名思义该函数对queryResultCache进行处理，并根据查询条件选择进入排序查询还是非排序查询。

1   /**
  2    * getDocList version that uses+populates query and filter caches.
    * In the event of a timeout, the cache is not populated.
    */
   private void getDocListC(QueryResult qr, QueryCommand cmd) throws IOException {
     DocListAndSet out = new DocListAndSet();
     qr.setDocListAndSet(out);
     QueryResultKey key=null;
     int maxDocRequested = cmd.getOffset() + cmd.getLen(); //当有偏移的查询产生，Solr首先会获取cmd.getOffset()+cmd.getLen()个的doc id然后　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　//再根据偏移量获取子集，所以maxDocRequested是实际的查询个数。
     // check for overflow, and check for # docs in index
     if (maxDocRequested < 0 || maxDocRequested > maxDoc()) maxDocRequested = maxDoc();// 最多的情况获取所有doc id
     int supersetMaxDoc= maxDocRequested;
     DocList superset = null;

     int flags = cmd.getFlags();
     Query q = cmd.getQuery();
     if (q instanceof ExtendedQuery) {
       ExtendedQuery eq = (ExtendedQuery)q;
       if (!eq.getCache()) {
         flags |= (NO_CHECK_QCACHE | NO_SET_QCACHE | NO_CHECK_FILTERCACHE);
       }
     }

     // we can try and look up the complete query in the cache.
     // we can't do that if filter!=null though (we don't want to
     // do hashCode() and equals() for a big DocSet).
        // 先从查询结果的缓存区查找是否出现过该条件的查询，若出现过则返回缓存的结果.关于缓存的内容将会独立写一篇文章
     if (queryResultCache != null && cmd.getFilter()==null
         && (flags & (NO_CHECK_QCACHE|NO_SET_QCACHE)) != ((NO_CHECK_QCACHE|NO_SET_QCACHE)))
     {
         // all of the current flags can be reused during warming,
         // so set all of them on the cache key.
         key = new QueryResultKey(q, cmd.getFilterList(), cmd.getSort(), flags);
         if ((flags & NO_CHECK_QCACHE)==0) {
           superset = queryResultCache.get(key);

           if (superset != null) {
             // check that the cache entry has scores recorded if we need them
             if ((flags & GET_SCORES)==0 || superset.hasScores()) {
               // NOTE: subset() returns null if the DocList has fewer docs than
               // requested
               out.docList = superset.subset(cmd.getOffset(),cmd.getLen()); //如果有缓存，就从中去除一部分子集
             }
           }
           if (out.docList != null) {
             // found the docList in the cache... now check if we need the docset too.
             // OPT: possible future optimization - if the doclist contains all the matches,
             // use it to make the docset instead of rerunning the query.
                //获取缓存中的docSet，并传给result。
             if (out.docSet==null && ((flags & GET_DOCSET)!=0) ) {
               if (cmd.getFilterList()==null) {
                 out.docSet = getDocSet(cmd.getQuery());
               } else {
                 List<Query> newList = new ArrayList<>(cmd.getFilterList().size()+1);
                 newList.add(cmd.getQuery());
                 newList.addAll(cmd.getFilterList());
                 out.docSet = getDocSet(newList);
               }
             }
             return;
           }
         }

       // If we are going to generate the result, bump up to the
       // next resultWindowSize for better caching.
       // 修改supersetMaxDoc为queryResultWindwSize的整数倍
       if ((flags & NO_SET_QCACHE) == 0) {
         // handle 0 special case as well as avoid idiv in the common case.
         if (maxDocRequested < queryResultWindowSize) {
           supersetMaxDoc=queryResultWindowSize;
         } else {
           supersetMaxDoc = ((maxDocRequested -1)/queryResultWindowSize + 1)*queryResultWindowSize;
           if (supersetMaxDoc < 0) supersetMaxDoc=maxDocRequested;
         }
       } else {
         key = null;  // we won't be caching the result
       }
     }
     cmd.setSupersetMaxDoc(supersetMaxDoc);

     // OK, so now we need to generate an answer.
     // One way to do that would be to check if we have an unordered list
     // of results for the base query.  If so, we can apply the filters and then
     // sort by the resulting set.  This can only be used if:
     // - the sort doesn't contain score
     // - we don't want score returned.

     // check if we should try and use the filter cache
     boolean useFilterCache=false;
     if ((flags & (GET_SCORES|NO_CHECK_FILTERCACHE))==0 && useFilterForSortedQuery && cmd.getSort() != null && filterCache != null) {
       useFilterCache=true;
       SortField[] sfields = cmd.getSort().getSort();
       for (SortField sf : sfields) {
         if (sf.getType() == SortField.Type.SCORE) {
           useFilterCache=false;
           break;
         }
       }
     }

     if (useFilterCache) {
       // now actually use the filter cache.
       // for large filters that match few documents, this may be
       // slower than simply re-executing the query.
       if (out.docSet == null) {
         out.docSet = getDocSet(cmd.getQuery(),cmd.getFilter());
         DocSet bigFilt = getDocSet(cmd.getFilterList());
         if (bigFilt != null) out.docSet = out.docSet.intersection(bigFilt);
       }
       // todo: there could be a sortDocSet that could take a list of
       // the filters instead of anding them first...
       // perhaps there should be a multi-docset-iterator
       sortDocSet(qr, cmd);  //排序查询
     } else {
       // do it the normal way...
       if ((flags & GET_DOCSET)!=0) {
         // this currently conflates returning the docset for the base query vs
         // the base query and all filters.
         DocSet qDocSet = getDocListAndSetNC(qr,cmd);
         // cache the docSet matching the query w/o filtering
         if (qDocSet!=null && filterCache!=null && !qr.isPartialResults()) filterCache.put(cmd.getQuery(),qDocSet);
       } else {
         getDocListNC(qr,cmd); //非排序查询，这也是本文的流程。
       }
       assert null != out.docList : "docList is null";
     }

     if (null == cmd.getCursorMark()) {
       // Kludge...
       // we can't use DocSlice.subset, even though it should be an identity op
       // because it gets confused by situations where there are lots of matches, but
       // less docs in the slice then were requested, (due to the cursor)
       // so we have to short circuit the call.
       // None of which is really a problem since we can't use caching with
       // cursors anyway, but it still looks weird to have to special case this
       // behavior based on this condition - hence the long explanation.
       superset = out.docList; //根据offset和len截取查询结果
       out.docList = superset.subset(cmd.getOffset(),cmd.getLen());
     } else {
       // sanity check our cursor assumptions
       assert null == superset : "cursor: superset isn't null";
       assert 0 == cmd.getOffset() : "cursor: command offset mismatch";
       assert 0 == out.docList.offset() : "cursor: docList offset mismatch";
       assert cmd.getLen() >= supersetMaxDoc : "cursor: superset len mismatch: " +
         cmd.getLen() + " vs " + supersetMaxDoc;
     }

     // lastly, put the superset in the cache if the size is less than or equal
     // to queryResultMaxDocsCached
     if (key != null && superset.size() <= queryResultMaxDocsCached && !qr.isPartialResults()) {
       queryResultCache.put(key, superset);    //如果结果的个数小于或者等于queryResultMaxDocsCached则将本次查询结果放入缓存
     }
   }

进入非排序查询分支getDocListNC(),该函数内部分直接调用Lucene的IndexSearch.Search()

       final TopDocsCollector topCollector = buildTopDocsCollector(len, cmd); //新建TopDocsCollector对象，里面会新建(offset + len(查询条          //件的len))的HitQueue，每当获取到一个符合查询条件的doc，就会将该doc id放入HitQueue,并totalhit计数加一，这个totalhit变量也就是查询结果的数量
       Collector collector = topCollector;
       if (terminateEarly) {
         collector = new EarlyTerminatingCollector(collector, cmd.len);
       }
       if( timeAllowed > 0 ) {
         collector = new TimeLimitingCollector(collector, TimeLimitingCollector.getGlobalCounter(), timeAllowed); 
           //TimeLimitingCollector的实现原理很简单，从第一个找到符合查询条件的doc id开始计时，在达到timeAllowed之前，会想查询得到的doc id放入HitQue           //ue,一旦timeAllowed到了，就会立即扔出错误，中断后续的查询。这对于我们优化查询是个重要的提示
       }
       if (pf.postFilter != null) {
         pf.postFilter.setLastDelegate(collector);
         collector = pf.postFilter;
       }
       try {
           // 进入Lucene的IndexSearch.Search()
         super.search(query, luceneFilter, collector);
         if(collector instanceof DelegatingCollector) {
           ((DelegatingCollector)collector).finish();
         }
       }
       catch( TimeLimitingCollector.TimeExceededException x ) {
         log.warn( "Query: " + query + "; " + x.getMessage() );
         qr.setPartialResults(true);
       }

       totalHits = topCollector.getTotalHits();           //返回totalhit的结果
       TopDocs topDocs = topCollector.topDocs(0, len);    //返回优先级队列hitqueue的doc id
       populateNextCursorMarkFromTopDocs(qr, cmd, topDocs);

       maxScore = totalHits>0 ? topDocs.getMaxScore() : 0.0f;
       nDocsReturned = topDocs.scoreDocs.length;
       ids = new int[nDocsReturned];
       scores = (cmd.getFlags()&GET_SCORES)!=0 ? new float[nDocsReturned] : null;
       for (int i=0; i<nDocsReturned; i++) {
         ScoreDoc scoreDoc = topDocs.scoreDocs[i];
         ids[i] = scoreDoc.doc;
         if (scores != null) scores[i] = scoreDoc.score;
       }

TimeLimitingCollector统计查询结果的方法，一旦timeAllowed到了，就会立即扔出错误，中断后续的查询

  /**
   * Calls {@link Collector#collect(int)} on the decorated {@link Collector}
   * unless the allowed time has passed, in which case it throws an exception.
   *
   * @throws TimeExceededException
   *           if the time allowed has exceeded.
   */
  @Override
  public void collect(final int doc) throws IOException {
    final long time = clock.get();
    if (timeout < time) {
      if (greedy) {
        //System.out.println(this+"  greedy: before failing, collecting doc: "+(docBase + doc)+"  "+(time-t0));
        collector.collect(doc);
      }
      //System.out.println(this+"  failing on:  "+(docBase + doc)+"  "+(time-t0));
      throw new TimeExceededException( timeout-t0, time-t0, docBase + doc );
    }
    //System.out.println(this+"  collecting: "+(docBase + doc)+"  "+(time-t0));
    collector.collect(doc);
  }

接下来开始lucece的查询过程，

1. 首先会为每一个查询条件新建一个Weight的对象，最后将所有Weight对象放入ArrayList<Weight> weights。该过程给出每个查询条件的权重，并用于后续的评分过程。

     public BooleanWeight(IndexSearcher searcher, boolean disableCoord)
       throws IOException {
       this.similarity = searcher.getSimilarity();
       this.disableCoord = disableCoord;
       weights = new ArrayList<>(clauses.size());
       for (int i = 0 ; i < clauses.size(); i++) {
         BooleanClause c = clauses.get(i);
         Weight w = c.getQuery().createWeight(searcher);
         weights.add(w);
         if (!c.isProhibited()) {
           maxCoord++;
         }
       }
     }

2. 遍历所有sgement，一个接一个的查找符合查询条件的doc id。AtomicReaderContext 是包含segment的具体信息，包括doc base，num docs，这些信息室非常有用的，在实现查询优化时候很有帮助。这里需要注意的是这个collector是TopDocsCollector类型的对象，这在上面的代码中已经赋值过了。

 /**
    * Lower-level search API.
    *
    * <p>
    * {@link Collector#collect(int)} is called for every document. <br>
    *
    * <p>
    * NOTE: this method executes the searches on all given leaves exclusively.
    * To search across all the searchers leaves use {@link #leafContexts}.
    *
    * @param leaves
    *          the searchers leaves to execute the searches on
    * @param weight
    *          to match documents
    * @param collector
    *          to receive hits
    * @throws BooleanQuery.TooManyClauses If a query would exceed
    *         {@link BooleanQuery#getMaxClauseCount()} clauses.
    */
   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
       try {
         collector.setNextReader(ctx);
       } catch (CollectionTerminatedException e) {
         // there is no doc of interest in this reader context
         // continue with the following leaf
         continue;
       }
       BulkScorer scorer = weight.bulkScorer(ctx, !collector.acceptsDocsOutOfOrder(), ctx.reader().getLiveDocs());
       if (scorer != null) {
         try {
           scorer.score(collector);
         } catch (CollectionTerminatedException e) {
           // collection was terminated prematurely
           // continue with the following leaf
         }
       }
     }
   }

3. Weight.bulkScorer对查询条件进行评分，Lucene的多条件查询优化还是写的很不错的。Lucece会根据每个查询条件的词频对查询条件进行排序，词频小的排在前面，词频大的排在后面。这大大优化了多条件的查询。多条件查询的优化会在下文中详细介绍。

4. 最后Lucene会使用scorer.score(collector)这个过程真正的进行查询。看下Weight的两个函数，就能明白Lucene怎么进行查询统计。

  @Override
     public boolean score(Collector collector, int max) throws IOException {
       // TODO: this may be sort of weird, when we are
       // embedded in a BooleanScorer, because we are
       // called for every chunk of 2048 documents.  But,
       // then, scorer is a FakeScorer in that case, so any
       // Collector doing something "interesting" in
       // setScorer will be forced to use BS2 anyways:
       collector.setScorer(scorer);
       if (max == DocIdSetIterator.NO_MORE_DOCS) {
         scoreAll(collector, scorer);
         return false;
       } else {
         int doc = scorer.docID();
         if (doc < 0) {
           doc = scorer.nextDoc();
         }
         return scoreRange(collector, scorer, doc, max);
       }
     }

Lucece会不停的从segment获取符合查询条件的doc，并放入collector的hitqueue里面。需要注意的是这里的collector是Collector类型，是TopDocsCollector等类的父类，所以scoreAll不仅能实现获取TopDocsCollector的doc is也能获取其他查询方式的doc id。

     static void scoreAll(Collector collector, Scorer scorer) throws IOException {
       int doc;
       while ((doc = scorer.nextDoc()) != DocIdSetIterator.NO_MORE_DOCS) {
         collector.collect(doc);
       }
     }

进入collector.collect(doc)查看TopDocsCollector的统计doc id的方式，就跟之前说的一样。

     @Override
     public void collect(int doc) throws IOException {
       float score = scorer.score();

       // This collector cannot handle these scores:
       assert score != Float.NEGATIVE_INFINITY;
       assert !Float.isNaN(score);

       totalHits++;
       if (score <= pqTop.score) {
         // Since docs are returned in-order (i.e., increasing doc Id), a document
         // with equal score to pqTop.score cannot compete since HitQueue favors
         // documents with lower doc Ids. Therefore reject those docs too.
         return;
       }
       pqTop.doc = doc + docBase;
       pqTop.score = score;
       pqTop = pq.updateTop();
     }

总结：本章详细的介绍了非排序查询的流程，主要涉及了以下几个类QueryComponent,SolrIndexSearch,TimeLimitingCollector,TopDocsCollector,IndexSearch,BulkScore,Weight. 篇幅原因，并没有将如何从segment里面获取doc id以及多条件查询是怎么实现的，这将是下一问多条件查询中详细介绍。