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.
3 * In the event of a timeout, the cache is not populated.
4 */
5 private void getDocListC(QueryResult qr, QueryCommand cmd) throws IOException {
6 DocListAndSet out = new DocListAndSet();
7 qr.setDocListAndSet(out);
8 QueryResultKey key=null;
9 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。
10 // check for overflow, and check for # docs in index
11 if (maxDocRequested < 0 || maxDocRequested > maxDoc()) maxDocRequested = maxDoc();// 最多的情况获取所有doc id
12 int supersetMaxDoc= maxDocRequested;
13 DocList superset = null;
14
15 int flags = cmd.getFlags();
16 Query q = cmd.getQuery();
17 if (q instanceof ExtendedQuery) {
18 ExtendedQuery eq = (ExtendedQuery)q;
19 if (!eq.getCache()) {
20 flags |= (NO_CHECK_QCACHE | NO_SET_QCACHE | NO_CHECK_FILTERCACHE);
21 }
22 }
23
24
25 // we can try and look up the complete query in the cache.
26 // we can‘t do that if filter!=null though (we don‘t want to
27 // 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
28 if (queryResultCache != null && cmd.getFilter()==null
29 && (flags & (NO_CHECK_QCACHE|NO_SET_QCACHE)) != ((NO_CHECK_QCACHE|NO_SET_QCACHE)))
30 {
31 // all of the current flags can be reused during warming,
32 // so set all of them on the cache key.
33 key = new QueryResultKey(q, cmd.getFilterList(), cmd.getSort(), flags);
34 if ((flags & NO_CHECK_QCACHE)==0) {
35 superset = queryResultCache.get(key);
36
37 if (superset != null) {
38 // check that the cache entry has scores recorded if we need them
39 if ((flags & GET_SCORES)==0 || superset.hasScores()) {
40 // NOTE: subset() returns null if the DocList has fewer docs than
41 // requested
42 out.docList = superset.subset(cmd.getOffset(),cmd.getLen()); //如果有缓存，就从中去除一部分子集
43 }
44 }
45 if (out.docList != null) {
46 // found the docList in the cache... now check if we need the docset too.
47 // OPT: possible future optimization - if the doclist contains all the matches,
48 // use it to make the docset instead of rerunning the query. //获取缓存中的docSet，并传给result。
49 if (out.docSet==null && ((flags & GET_DOCSET)!=0) ) {
50 if (cmd.getFilterList()==null) {
51 out.docSet = getDocSet(cmd.getQuery());
52 } else {
53 List<Query> newList = new ArrayList<>(cmd.getFilterList().size()+1);
54 newList.add(cmd.getQuery());
55 newList.addAll(cmd.getFilterList());
56 out.docSet = getDocSet(newList);
57 }
58 }
59 return;
60 }
61 }
62
63 // If we are going to generate the result, bump up to the
64 // next resultWindowSize for better caching.
65 // 修改supersetMaxDoc为queryResultWindwSize的整数倍
66 if ((flags & NO_SET_QCACHE) == 0) {
67 // handle 0 special case as well as avoid idiv in the common case.
68 if (maxDocRequested < queryResultWindowSize) {
69 supersetMaxDoc=queryResultWindowSize;
70 } else {
71 supersetMaxDoc = ((maxDocRequested -1)/queryResultWindowSize + 1)*queryResultWindowSize;
72 if (supersetMaxDoc < 0) supersetMaxDoc=maxDocRequested;
73 }
74 } else {
75 key = null; // we won‘t be caching the result
76 }
77 }
78 cmd.setSupersetMaxDoc(supersetMaxDoc);
79
80
81 // OK, so now we need to generate an answer.
82 // One way to do that would be to check if we have an unordered list
83 // of results for the base query. If so, we can apply the filters and then
84 // sort by the resulting set. This can only be used if:
85 // - the sort doesn‘t contain score
86 // - we don‘t want score returned.
87
88 // check if we should try and use the filter cache
89 boolean useFilterCache=false;
90 if ((flags & (GET_SCORES|NO_CHECK_FILTERCACHE))==0 && useFilterForSortedQuery && cmd.getSort() != null && filterCache != null) {
91 useFilterCache=true;
92 SortField[] sfields = cmd.getSort().getSort();
93 for (SortField sf : sfields) {
94 if (sf.getType() == SortField.Type.SCORE) {
95 useFilterCache=false;
96 break;
97 }
98 }
99 }
100
101 if (useFilterCache) {
102 // now actually use the filter cache.
103 // for large filters that match few documents, this may be
104 // slower than simply re-executing the query.
105 if (out.docSet == null) {
106 out.docSet = getDocSet(cmd.getQuery(),cmd.getFilter());
107 DocSet bigFilt = getDocSet(cmd.getFilterList());
108 if (bigFilt != null) out.docSet = out.docSet.intersection(bigFilt);
109 }
110 // todo: there could be a sortDocSet that could take a list of
111 // the filters instead of anding them first...
112 // perhaps there should be a multi-docset-iterator
113 sortDocSet(qr, cmd); //排序查询
114 } else {
115 // do it the normal way...
116 if ((flags & GET_DOCSET)!=0) {
117 // this currently conflates returning the docset for the base query vs
118 // the base query and all filters.
119 DocSet qDocSet = getDocListAndSetNC(qr,cmd);
120 // cache the docSet matching the query w/o filtering
121 if (qDocSet!=null && filterCache!=null && !qr.isPartialResults()) filterCache.put(cmd.getQuery(),qDocSet);
122 } else {
123 getDocListNC(qr,cmd); //非排序查询，这也是本文的流程。
124 }
125 assert null != out.docList : "docList is null";
126 }
127
128 if (null == cmd.getCursorMark()) {
129 // Kludge...
130 // we can‘t use DocSlice.subset, even though it should be an identity op
131 // because it gets confused by situations where there are lots of matches, but
132 // less docs in the slice then were requested, (due to the cursor)
133 // so we have to short circuit the call.
134 // None of which is really a problem since we can‘t use caching with
135 // cursors anyway, but it still looks weird to have to special case this
136 // behavior based on this condition - hence the long explanation.
137 superset = out.docList; //根据offset和len截取查询结果
138 out.docList = superset.subset(cmd.getOffset(),cmd.getLen());
139 } else {
140 // sanity check our cursor assumptions
141 assert null == superset : "cursor: superset isn‘t null";
142 assert 0 == cmd.getOffset() : "cursor: command offset mismatch";
143 assert 0 == out.docList.offset() : "cursor: docList offset mismatch";
144 assert cmd.getLen() >= supersetMaxDoc : "cursor: superset len mismatch: " +
145 cmd.getLen() + " vs " + supersetMaxDoc;
146 }
147
148 // lastly, put the superset in the cache if the size is less than or equal
149 // to queryResultMaxDocsCached
150 if (key != null && superset.size() <= queryResultMaxDocsCached && !qr.isPartialResults()) {
151 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
152 }
153 }
进入非排序查询分支getDocListNC(),该函数内部分直接调用Lucene的IndexSearch.Search()
1 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
2 Collector collector = topCollector;
3 if (terminateEarly) {
4 collector = new EarlyTerminatingCollector(collector, cmd.len);
5 }
6 if( timeAllowed > 0 ) {
7 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
8 }
9 if (pf.postFilter != null) {
10 pf.postFilter.setLastDelegate(collector);
11 collector = pf.postFilter;
12 }
13 try { // 进入Lucene的IndexSearch.Search()
14 super.search(query, luceneFilter, collector);
15 if(collector instanceof DelegatingCollector) {
16 ((DelegatingCollector)collector).finish();
17 }
18 }
19 catch( TimeLimitingCollector.TimeExceededException x ) {
20 log.warn( "Query: " + query + "; " + x.getMessage() );
21 qr.setPartialResults(true);
22 }
23
24 totalHits = topCollector.getTotalHits(); //返回totalhit的结果
25 TopDocs topDocs = topCollector.topDocs(0, len); //返回优先级队列hitqueue的doc id
26 populateNextCursorMarkFromTopDocs(qr, cmd, topDocs);
27
28 maxScore = totalHits>0 ? topDocs.getMaxScore() : 0.0f;
29 nDocsReturned = topDocs.scoreDocs.length;
30 ids = new int[nDocsReturned];
31 scores = (cmd.getFlags()&GET_SCORES)!=0 ? new float[nDocsReturned] : null;
32 for (int i=0; i<nDocsReturned; i++) {
33 ScoreDoc scoreDoc = topDocs.scoreDocs[i];
34 ids[i] = scoreDoc.doc;
35 if (scores != null) scores[i] = scoreDoc.score;
36 }
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。
1 public BooleanWeight(IndexSearcher searcher, boolean disableCoord)
2 throws IOException {
3 this.similarity = searcher.getSimilarity();
4 this.disableCoord = disableCoord;
5 weights = new ArrayList<>(clauses.size());
6 for (int i = 0 ; i < clauses.size(); i++) {
7 BooleanClause c = clauses.get(i);
8 Weight w = c.getQuery().createWeight(searcher);
9 weights.add(w);
10 if (!c.isProhibited()) {
11 maxCoord++;
12 }
13 }
14 }
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。
1 /**
2 * Lower-level search API.
3 *
4 * <p>
5 * {@link Collector#collect(int)} is called for every document. <br>
6 *
7 * <p>
8 * NOTE: this method executes the searches on all given leaves exclusively.
9 * To search across all the searchers leaves use {@link #leafContexts}.
10 *
11 * @param leaves
12 * the searchers leaves to execute the searches on
13 * @param weight
14 * to match documents
15 * @param collector
16 * to receive hits
17 * @throws BooleanQuery.TooManyClauses If a query would exceed
18 * {@link BooleanQuery#getMaxClauseCount()} clauses.
19 */
20 protected void search(List<AtomicReaderContext> leaves, Weight weight, Collector collector)
21 throws IOException {
22
23 // TODO: should we make this
24 // threaded...? the Collector could be sync‘d?
25 // always use single thread:
26 for (AtomicReaderContext ctx : leaves) { // search each subreader
27 try {
28 collector.setNextReader(ctx);
29 } catch (CollectionTerminatedException e) {
30 // there is no doc of interest in this reader context
31 // continue with the following leaf
32 continue;
33 }
34 BulkScorer scorer = weight.bulkScorer(ctx, !collector.acceptsDocsOutOfOrder(), ctx.reader().getLiveDocs());
35 if (scorer != null) {
36 try {
37 scorer.score(collector);
38 } catch (CollectionTerminatedException e) {
39 // collection was terminated prematurely
40 // continue with the following leaf
41 }
42 }
43 }
44 }
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。
1 @Override
2 public boolean score(Collector collector, int max) throws IOException {
3 // TODO: this may be sort of weird, when we are
4 // embedded in a BooleanScorer, because we are
5 // called for every chunk of 2048 documents. But,
6 // then, scorer is a FakeScorer in that case, so any
7 // Collector doing something "interesting" in
8 // setScorer will be forced to use BS2 anyways:
9 collector.setScorer(scorer);
10 if (max == DocIdSetIterator.NO_MORE_DOCS) {
11 scoreAll(collector, scorer);
12 return false;
13 } else {
14 int doc = scorer.docID();
15 if (doc < 0) {
16 doc = scorer.nextDoc();
17 }
18 return scoreRange(collector, scorer, doc, max);
19 }
20 }
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。
1 static void scoreAll(Collector collector, Scorer scorer) throws IOException {
2 int doc;
3 while ((doc = scorer.nextDoc()) != DocIdSetIterator.NO_MORE_DOCS) {
4 collector.collect(doc);
5 }
6 }
进入collector.collect(doc)查看TopDocsCollector的统计doc id的方式，就跟之前说的一样。
1 @Override
2 public void collect(int doc) throws IOException {
3 float score = scorer.score();
4
5 // This collector cannot handle these scores:
6 assert score != Float.NEGATIVE_INFINITY;
7 assert !Float.isNaN(score);
8
9 totalHits++;
10 if (score <= pqTop.score) {
11 // Since docs are returned in-order (i.e., increasing doc Id), a document
12 // with equal score to pqTop.score cannot compete since HitQueue favors
13 // documents with lower doc Ids. Therefore reject those docs too.
14 return;
15 }
16 pqTop.doc = doc + docBase;
17 pqTop.score = score;
18 pqTop = pq.updateTop();
19 }
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。
Solr4.8.0源码分析(6)之非排序查询,布布扣,bubuko.com
时间： 2024-08-07 00:11:27