搜索的过程总的来说就是将词典及倒排表信息从索引中读出来，根据用户输入的查询语句合并倒排表，得到结果文档集并对文档进行打分的过程。
其可用如下图示：
总共包括以下几个过程：
IndexReader打开索引文件，读取并打开指向索引文件的流。用户输入查询语句将查询语句转换为查询对象Query对象树构造Weight对象树，用于计算词的权重Term Weight，也即计算打分公式中与仅与搜索语句相关与文档无关的部分(红色部分)。构造Scorer对象树，用于计算打分(TermScorer.score())。在构造Scorer对象树的过程中，其叶子节点的TermScorer会将词典和倒排表从索引中读出来。构造SumScorer对象树，其是为了方便合并倒排表对Scorer对象树的从新组织，它的叶子节点仍为TermScorer，包含词典和倒排表。此步将倒排表合并后得到结果文档集，并对结果文档计算打分公式中的蓝色部分。打分公式中的求和符合，并非简单的相加，而是根据子查询倒排表的合并方式(与或非)来对子查询的打分求和，计算出父查询的打分。将收集的结果集合及打分返回给用户。为了解析Lucene对索引文件搜索的过程，预先写入索引了如下几个文件：
file01.txt: apple apples cat dog
file02.txt: apple boy cat category
file03.txt: apply dog eat etc
file04.txt: apply cat foods
代码为：
IndexReader reader = IndexReader.open(FSDirectory.open(indexDir));
其实是调用了DirectoryReader.open(Directory, IndexDeletionPolicy, IndexCommit, boolean, int) 函数，其主要作用是生成一个SegmentInfos.FindSegmentsFile对象，并用它来找到此索引文件中所有的段，并打开这些段。
SegmentInfos.FindSegmentsFile.run(IndexCommit commit)主要做以下事情：
String[] files = directory.listAll();
long genA = getCurrentSegmentGeneration(files);
long getCurrentSegmentGeneration(String[] files) {
long max = -1;
for (int i = 0; i < files.length; i++) {
String file = files[i];
if (file.startsWith(IndexFileNames.SEGMENTS) //"segments_N"
&& !file.equals(IndexFileNames.SEGMENTS_GEN)) { //"segments.gen"
long gen = generationFromSegmentsFileName(file);
if (gen > max) {
max = gen;
}
}
}
return max;
}
另一方面，打开segment.gen文件，从中读出N，设为genBIndexInput genInput = directory.openInput(IndexFileNames.SEGMENTS_GEN);
int version = genInput.readInt();
long gen0 = genInput.readLong();
long gen1 = genInput.readLong();
if (gen0 == gen1) {
genB = gen0;
}
在genA和genB中去较大者，为gen，并用此gen构造要打开的segments_N的文件名if (genA > genB)
gen = genA;
else
gen = genB;
String segmentFileName = IndexFileNames.fileNameFromGeneration(IndexFileNames.SEGMENTS, "", gen); //segmentFileName "segments_4"
SegmentInfos infos = new SegmentInfos();
infos.read(directory, segmentFileName);
SegmentInfos.read(Directory, String) 代码如下：
int format = input.readInt();
version = input.readLong();
counter = input.readInt();
for (int i = input.readInt(); i > 0; i—) {
//读出每一个段，并构造SegmentInfo对象
add(new SegmentInfo(directory, format, input));
}
SegmentInfo(Directory dir, int format, IndexInput input)构造函数如下：
name = input.readString();
docCount = input.readInt();
delGen = input.readLong();
docStoreOffset = input.readInt();
if (docStoreOffset != -1) {
docStoreSegment = input.readString();
docStoreIsCompoundFile = (1 == input.readByte());
} else {
docStoreSegment = name;
docStoreIsCompoundFile = false;
}
hasSingleNormFile = (1 == input.readByte());
int numNormGen = input.readInt();
normGen = new long[numNormGen];
for(int j=0;j
normGen[j] = input.readLong();
}
isCompoundFile = input.readByte();
delCount = input.readInt();
hasProx = input.readByte() == 1;
其实不用多介绍，看过Lucene学习总结之三：Lucene的索引文件格式 (2)一章，就很容易明白。
根据生成的SegmentInfos打开各个段，并生成ReadOnlyDirectoryReaderSegmentReader[] readers = new SegmentReader[sis.size()];
for (int i = sis.size()-1; i >= 0; i—) {
//打开每一个段
readers[i] = SegmentReader.get(readOnly, sis.info(i), termInfosIndexDivisor);
}
SegmentReader.get(boolean, Directory, SegmentInfo, int, boolean, int) 代码如下：
instance.core = new CoreReaders(dir, si, readBufferSize, termInfosIndexDivisor);
instance.core.openDocStores(si); //生成用于读取存储域和词向量的对象。
instance.loadDeletedDocs(); //读取被删除文档(.del)文件
instance.openNorms(instance.core.cfsDir, readBufferSize); //读取标准化因子(.nrm)
CoreReaders(Directory dir, SegmentInfo si, int readBufferSize, int termsIndexDivisor)构造函数代码如下：
cfsReader = new CompoundFileReader(dir, segment + "." + IndexFileNames.COMPOUND_FILE_EXTENSION, readBufferSize); //读取cfs的reader
fieldInfos = new FieldInfos(cfsDir, segment + "." + IndexFileNames.FIELD_INFOS_EXTENSION); //读取段元数据信息(.fnm)
TermInfosReader reader = new TermInfosReader(cfsDir, segment, fieldInfos, readBufferSize, termsIndexDivisor); //用于读取词典信息(.tii .tis)
freqStream = cfsDir.openInput(segment + "." + IndexFileNames.FREQ_EXTENSION, readBufferSize); //用于读取freq
proxStream = cfsDir.openInput(segment + "." + IndexFileNames.PROX_EXTENSION, readBufferSize); //用于读取prox
FieldInfos(Directory d, String name)构造函数如下：
IndexInput input = d.openInput(name);
int firstInt = input.readVInt();
size = input.readVInt();
for (int i = 0; i < size; i++) {
//读取域名
String name = StringHelper.intern(input.readString());
//读取域的各种标志位
byte bits = input.readByte();
boolean isIndexed = (bits & IS_INDEXED) != 0;
boolean storeTermVector = (bits & STORE_TERMVECTOR) != 0;
boolean storePositionsWithTermVector = (bits & STORE_POSITIONS_WITH_TERMVECTOR) != 0;
boolean storeOffsetWithTermVector = (bits & STORE_OFFSET_WITH_TERMVECTOR) != 0;
boolean omitNorms = (bits & OMIT_NORMS) != 0;
boolean storePayloads = (bits & STORE_PAYLOADS) != 0;
boolean omitTermFreqAndPositions = (bits & OMIT_TERM_FREQ_AND_POSITIONS) != 0;
//将读出的域生成FieldInfo对象，加入fieldinfos进行管理
addInternal(name, isIndexed, storeTermVector, storePositionsWithTermVector, storeOffsetWithTermVector, omitNorms, storePayloads, omitTermFreqAndPositions);
}
CoreReaders.openDocStores(SegmentInfo)主要代码如下：
fieldsReaderOrig = new FieldsReader(storeDir, storesSegment, fieldInfos, readBufferSize, si.getDocStoreOffset(), si.docCount); //用于读取存储域(.fdx, .fdt)
termVectorsReaderOrig = new TermVectorsReader(storeDir, storesSegment, fieldInfos, readBufferSize, si.getDocStoreOffset(), si.docCount); //用于读取词向量(.tvx, .tvd, .tvf)
初始化生成的ReadOnlyDirectoryReader，对打开的多个SegmentReader中的文档编号
在Lucene中，每个段中的文档编号都是从0开始的，而一个索引有多个段，需要重新进行编号，于是维护数组start[]，来保存每个段的文档号的偏移量，从而第i个段的文档号是从start[i]至start[i]+Num
private void initialize(SegmentReader[] subReaders) {
this.subReaders = subReaders;
starts = new int[subReaders.length + 1];
for (int i = 0; i < subReaders.length; i++) {
starts[i] = maxDoc;
maxDoc += subReaders[i].maxDoc();
if (subReaders[i].hasDeletions())
hasDeletions = true;
}
starts[subReaders.length] = maxDoc;
}
reader ReadOnlyDirectoryReader (id=466) closed false deletionPolicy null
//索引文件夹 directory SimpleFSDirectory (id=31) checked false chunkSize 104857600 directory File (id=487) path "D://lucene-3.0.0//TestSearch//index" prefixLength 3 isOpen true lockFactory NativeFSLockFactory (id=488) hasChanges false hasDeletions false maxDoc 12 normsCache HashMap (id=483) numDocs -1 readOnly true refCount 1 rollbackHasChanges false rollbackSegmentInfos null
//段元数据信息 segmentInfos SegmentInfos (id=457) elementCount 3 elementData Object[10] (id=532) [0] SegmentInfo (id=464) delCount 0 delGen -1 diagnostics HashMap (id=537) dir SimpleFSDirectory (id=31) docCount 4 docStoreIsCompoundFile false docStoreOffset -1 docStoreSegment "_0" files null hasProx true hasSingleNormFile true isCompoundFile 1 name "_0" normGen null preLockless false sizeInBytes -1 [1] SegmentInfo (id=517) delCount 0 delGen -1 diagnostics HashMap (id=542) dir SimpleFSDirectory (id=31) docCount 4 docStoreIsCompoundFile false docStoreOffset -1 docStoreSegment "_1" files null hasProx true hasSingleNormFile true isCompoundFile 1 name "_1" normGen null preLockless false sizeInBytes -1 [2] SegmentInfo (id=470) delCount 0 delGen -1 diagnostics HashMap (id=547) dir SimpleFSDirectory (id=31) docCount 4 docStoreIsCompoundFile false docStoreOffset -1 docStoreSegment "_2" files null hasProx true hasSingleNormFile true isCompoundFile 1 name "_2" normGen null preLockless false sizeInBytes -1 generation 4 lastGeneration 4 modCount 4 pendingSegnOutput null userData HashMap (id=533) version 1268193441675 segmentInfosStart null stale false starts int[4] (id=484)
//每个段的Reader subReaders SegmentReader[3] (id=467) [0] ReadOnlySegmentReader (id=492) closed false core SegmentReader$CoreReaders (id=495) cfsDir CompoundFileReader (id=552) cfsReader CompoundFileReader (id=552) dir SimpleFSDirectory (id=31) fieldInfos FieldInfos (id=553) fieldsReaderOrig FieldsReader (id=554) freqStream CompoundFileReader$CSIndexInput (id=555) proxStream CompoundFileReader$CSIndexInput (id=556) readBufferSize 1024 ref SegmentReader$Ref (id=557) segment "_0" storeCFSReader null termsIndexDivisor 1 termVectorsReaderOrig null tis TermInfosReader (id=558) tisNoIndex null deletedDocs null deletedDocsDirty false deletedDocsRef null fieldsReaderLocal SegmentReader$FieldsReaderLocal (id=496) hasChanges false norms HashMap (id=500) normsDirty false pendingDeleteCount 0 readBufferSize 1024 readOnly true refCount 1 rollbackDeletedDocsDirty false rollbackHasChanges false rollbackNormsDirty false rollbackPendingDeleteCount 0 si SegmentInfo (id=464) singleNormRef SegmentReader$Ref (id=504) singleNormStream CompoundFileReader$CSIndexInput (id=506) termVectorsLocal CloseableThreadLocal (id=508) [1] ReadOnlySegmentReader (id=493) closed false core SegmentReader$CoreReaders (id=511) cfsDir CompoundFileReader (id=561) cfsReader CompoundFileReader (id=561) dir SimpleFSDirectory (id=31) fieldInfos FieldInfos (id=562) fieldsReaderOrig FieldsReader (id=563) freqStream CompoundFileReader$CSIndexInput (id=564) proxStream CompoundFileReader$CSIndexInput (id=565) readBufferSize 1024 ref SegmentReader$Ref (id=566) segment "_1" storeCFSReader null termsIndexDivisor 1 termVectorsReaderOrig null tis TermInfosReader (id=567) tisNoIndex null deletedDocs null deletedDocsDirty false deletedDocsRef null fieldsReaderLocal SegmentReader$FieldsReaderLocal (id=512) hasChanges false norms HashMap (id=514) normsDirty false pendingDeleteCount 0 readBufferSize 1024 readOnly true refCount 1 rollbackDeletedDocsDirty false rollbackHasChanges false rollbackNormsDirty false rollbackPendingDeleteCount 0 si SegmentInfo (id=517) singleNormRef SegmentReader$Ref (id=519) singleNormStream CompoundFileReader$CSIndexInput (id=520) termVectorsLocal CloseableThreadLocal (id=521) [2] ReadOnlySegmentReader (id=471) closed false core SegmentReader$CoreReaders (id=475) cfsDir CompoundFileReader (id=476) cfsReader CompoundFileReader (id=476) dir SimpleFSDirectory (id=31) fieldInfos FieldInfos (id=480) fieldsReaderOrig FieldsReader (id=570) freqStream CompoundFileReader$CSIndexInput (id=571) proxStream CompoundFileReader$CSIndexInput (id=572) readBufferSize 1024 ref SegmentReader$Ref (id=573) segment "_2" storeCFSReader null termsIndexDivisor 1 termVectorsReaderOrig null tis TermInfosReader (id=574) tisNoIndex null deletedDocs null deletedDocsDirty false deletedDocsRef null fieldsReaderLocal SegmentReader$FieldsReaderLocal (id=524) hasChanges false norms HashMap (id=525) normsDirty false pendingDeleteCount 0 readBufferSize 1024 readOnly true refCount 1 rollbackDeletedDocsDirty false rollbackHasChanges false rollbackNormsDirty false rollbackPendingDeleteCount 0 si SegmentInfo (id=470) singleNormRef SegmentReader$Ref (id=527) singleNormStream CompoundFileReader$CSIndexInput (id=528) termVectorsLocal CloseableThreadLocal (id=530) synced HashSet (id=485) termInfosIndexDivisor 1 writeLock null writer null
从上面的过程来看，IndexReader有以下几个特性：
段元数据信息已经被读入到内存中，因而索引文件夹中因为新添加文档而新增加的段对已经打开的reader是不可见的。.del文件已经读入内存，因而其他的reader或者writer删除的文档对打开的reader也是不可见的。打开的reader已经有inputstream指向cfs文件，从段合并的过程我们知道，一个段文件从生成起就不会改变，新添加的文档都在新的段中，删除的文档都在.del中，段之间的合并是生成新的段，而不会改变旧的段，只不过在段的合并过程中，会将旧的段文件删除，这没有问题，因为从操作系统的角度来讲，一旦一个文件被打开一个inputstream也即打开了一个文件描述符，在内核中，此文件会保持reference count，只要reader还没有关闭，文件描述符还在，文件是不会被删除的，仅仅reference count减一。以上三点保证了IndexReader的snapshot的性质，也即一个IndexReader打开一个索引，就好像对此索引照了一张像，无论背后索引如何改变，此IndexReader在被重新打开之前，看到的信息总是相同的。严格的来讲，Lucene的文档号仅仅对打开的某个reader有效，当索引发生了变化，再打开另外一个reader的时候，前面reader的文档0就不一定是后面reader的文档0了，因而我们进行查询的时候，从结果中得到文档号的时候，一定要在reader关闭之前应用，从存储域中得到真正能够唯一标识你的业务逻辑中的文档的信息，如url，md5等等，一旦reader关闭了，则文档号已经无意义，如果用其他的reader查询这些文档号，得到的可能是不期望的文档。代码为：
IndexSearcher searcher = new IndexSearcher(reader);
其过程非常简单：
private IndexSearcher(IndexReader r, boolean closeReader) {
reader = r;
//当关闭searcher的时候，是否关闭其reader
this.closeReader = closeReader;
//对文档号进行编号
List subReadersList = new ArrayList();
gatherSubReaders(subReadersList, reader);
subReaders = subReadersList.toArray(new IndexReader[subReadersList.size()]);
docStarts = new int[subReaders.length];
int maxDoc = 0;
for (int i = 0; i < subReaders.length; i++) {
docStarts[i] = maxDoc;
maxDoc += subReaders[i].maxDoc();
}
}
IndexSearcher表面上看起来好像仅仅是reader的一个封装，它的很多函数都是直接调用reader的相应函数，如：int docFreq(Term term)，Document doc(int i)，int maxDoc()。然而它提供了两个非常重要的函数：
void setSimilarity(Similarity similarity)，用户可以实现自己的Similarity对象，从而影响搜索过程的打分，详见有关Lucene的问题(4):影响Lucene对文档打分的四种方式一系列search函数，是搜索过程的关键，主要负责打分的计算和倒排表的合并。因而在某些应用之中，只想得到某个词的倒排表的时候，最好不要用IndexSearcher，而直接用IndexReader.termDocs(Term term)，则省去了打分的计算。
代码为：
QueryParser parser = new QueryParser(Version.LUCENE_CURRENT, "contents", new StandardAnalyzer(Version.LUCENE_CURRENT));
Query query = parser.parse("+(+apple* -boy) (cat* dog) -(eat~ foods)");
此过程相对复杂，涉及JavaCC，QueryParser，分词器，查询语法等，本章不会详细论述，会在后面的章节中一一说明。
此处唯一要说明的是，根据查询语句生成的是一个Query树，这棵树很重要，并且会生成其他的树，一直贯穿整个索引过程。
query BooleanQuery (id=96) | boost 1.0 | clauses ArrayList (id=98) | elementData Object[10] (id=100) |------[0] BooleanClause (id=102) | | occur BooleanClause$Occur$1 (id=106) | | name "MUST" //AND | | ordinal 0 | |---query BooleanQuery (id=108) | | boost 1.0 | | clauses ArrayList (id=112) | | elementData Object[10] (id=113) | |------[0] BooleanClause (id=114) | | | occur BooleanClause$Occur$1 (id=106) | | | name "MUST" //AND | | | ordinal 0 | | |--query PrefixQuery (id=116) | | boost 1.0 | | numberOfTerms 0 | | prefix Term (id=117) | | field "contents" | | text "apple" | | rewriteMethod MultiTermQuery$1 (id=119) | | docCountPercent 0.1 | | termCountCutoff 350 | |------[1] BooleanClause (id=115) | | occur BooleanClause$Occur$3 (id=123) | | name "MUST_NOT" //NOT | | ordinal 2 | |--query TermQuery (id=125) | boost 1.0 | term Term (id=127) | field "contents" | text "boy" | size 2 | disableCoord false | minNrShouldMatch 0 |------[1] BooleanClause (id=104) | | occur BooleanClause$Occur$2 (id=129) | | name "SHOULD" //OR | | ordinal 1 | |---query BooleanQuery (id=131) | | boost 1.0 | | clauses ArrayList (id=133) | | elementData Object[10] (id=134) | |------[0] BooleanClause (id=135) | | | occur BooleanClause$Occur$2 (id=129) | | | name "SHOULD" //OR | | | ordinal 1 | | |--query PrefixQuery (id=137) | | boost 1.0 | | numberOfTerms 0 | | prefix Term (id=138) | | field "contents" | | text "cat" | | rewriteMethod MultiTermQuery$1 (id=119) | | docCountPercent 0.1 | | termCountCutoff 350 | |------[1] BooleanClause (id=136) | | occur BooleanClause$Occur$2 (id=129) | | name "SHOULD" //OR | | ordinal 1 | |--query TermQuery (id=140) | boost 1.0 | term Term (id=141) | field "contents" | text "dog" | size 2 | disableCoord false | minNrShouldMatch 0 |------[2] BooleanClause (id=105) | occur BooleanClause$Occur$3 (id=123) | name "MUST_NOT" //NOT | ordinal 2 |---query BooleanQuery (id=143) | boost 1.0 | clauses ArrayList (id=146) | elementData Object[10] (id=147) |------[0] BooleanClause (id=148) | | occur BooleanClause$Occur$2 (id=129) | | name "SHOULD" //OR | | ordinal 1 | |--query FuzzyQuery (id=150) | boost 1.0 | minimumSimilarity 0.5 | numberOfTerms 0 | prefixLength 0 | rewriteMethod MultiTermQuery$ScoringBooleanQueryRewrite (id=152) | term Term (id=153) | field "contents" | text "eat" | termLongEnough true |------[1] BooleanClause (id=149) | occur BooleanClause$Occur$2 (id=129) | name "SHOULD" //OR | ordinal 1 |--query TermQuery (id=155) boost 1.0 term Term (id=156) field "contents" text "foods" size 2 disableCoord false minNrShouldMatch 0 size 3 disableCoord false minNrShouldMatch 0
对于Query对象有以下说明：
BooleanQuery即所有的子语句按照布尔关系合并 +也即MUST表示必须满足的语句SHOULD表示可以满足的，minNrShouldMatch表示在SHOULD中必须满足的最小语句个数，默认是0，也即既然是SHOULD，也即或的关系，可以一个也不满足(当然没有MUST的时候除外)。-也即MUST_NOT表示必须不能满足的语句树的叶子节点中： 最基本的是TermQuery，也即表示一个词当然也可以是PrefixQuery和FuzzyQuery，这些查询语句由于特殊的语法，可能对应的不是一个词，而是多个词，因而他们都有rewriteMethod对象指向MultiTermQuery的Inner Class，表示对应多个词，在查询过程中会得到特殊处理。代码为：
TopDocs docs = searcher.search(query, 50);
其最终调用search(createWeight(query), filter, n);
索引过程包含以下子过程：
创建weight树，计算term weight创建scorer及SumScorer树，为合并倒排表做准备用SumScorer进行倒排表合并收集文档结果集合及计算打分
IndexSearcher(Searcher).createWeight(Query) 代码如下：
protected Weight createWeight(Query query) throws IOException {
return query.weight(this);
}
BooleanQuery(Query).weight(Searcher) 代码为：
public Weight weight(Searcher searcher) throws IOException {
//重写Query对象树
Query query = searcher.rewrite(this);
//创建Weight对象树
Weight weight = query.createWeight(searcher);
//计算Term Weight分数
float sum = weight.sumOfSquaredWeights();
float norm = getSimilarity(searcher).queryNorm(sum);
weight.normalize(norm);
return weight;
}
此过程又包含以下过程：
重写Query对象树创建Weight对象树计算Term Weight分数从BooleanQuery的rewrite函数我们可以看出，重写过程也是一个递归的过程，一直到Query对象树的叶子节点。
BooleanQuery.rewrite(IndexReader) 代码如下：
BooleanQuery clone = null;
for (int i = 0 ; i < clauses.size(); i++) {
BooleanClause c = clauses.get(i);
//对每一个子语句的Query对象进行重写
Query query = c.getQuery().rewrite(reader);
if (query != c.getQuery()) {
if (clone == null)
clone = (BooleanQuery)this.clone();
//重写后的Query对象加入复制的新Query对象树
clone.clauses.set(i, new BooleanClause(query, c.getOccur()));
}
}
if (clone != null) {
return clone; //如果有子语句被重写，则返回复制的新Query对象树。
} else
return this; //否则将老的Query对象树返回。
让我们把目光聚集到叶子节点上，叶子节点基本是两种，或是TermQuery，或是MultiTermQuery，从Lucene的源码可以看出TermQuery的rewrite函数就是返回对象本身，也即真正需要重写的是MultiTermQuery，也即一个Query代表多个Term参与查询，如本例子中的PrefixQuery及FuzzyQuery。
对此类的Query，Lucene不能够直接进行查询，必须进行重写处理：
首先，要从索引文件的词典中，把多个Term都找出来，比如"appl*"，我们在索引文件的词典中可以找到如下Term："apple"，"apples"，"apply"，这些Term都要参与查询过程，而非原来的"appl*"参与查询过程，因为词典中根本就没有"appl*"。然后，将取出的多个Term重新组织成新的Query对象进行查询，基本有两种方式： 方式一：将多个Term看成一个Term，将包含它们的文档号取出来放在一起(DocId Set)，作为一个统一的倒排表来参与倒排表的合并。方式二：将多个Term组成一个BooleanQuery，它们之间是OR的关系。从上面的Query对象树中，我们可以看到，MultiTermQuery都有一个RewriteMethod成员变量，就是用来重写Query对象的，有以下几种：
ConstantScoreFilterRewrite采取的是方式一，其rewrite函数实现如下：public Query rewrite(IndexReader reader, MultiTermQuery query) {
Query result = new ConstantScoreQuery(new MultiTermQueryWrapperFilter(query));
result.setBoost(query.getBoost());
return result;
}
MultiTermQueryWrapperFilter中的getDocIdSet函数实现如下：
public DocIdSet getDocIdSet(IndexReader reader) throws IOException {
//得到MultiTermQuery的Term枚举器
final TermEnum enumerator = query.getEnum(reader);
try {
if (enumerator.term() == null)
return DocIdSet.EMPTY_DOCIDSET;
//创建包含多个Term的文档号集合
final OpenBitSet bitSet = new OpenBitSet(reader.maxDoc());
final int[] docs = new int[32];
final int[] freqs = new int[32];
TermDocs termDocs = reader.termDocs();
try {
int termCount = 0;
//一个循环，取出对应MultiTermQuery的所有的Term，取出他们的文档号，加入集合
do {
Term term = enumerator.term();
if (term == null)
break;
termCount++;
termDocs.seek(term);
while (true) {
final int count = termDocs.read(docs, freqs);
if (count != 0) {
for(int i=0;i
bitSet.set(docs[i]);
}
} else {
break;
}
}
} while (enumerator.next());
query.incTotalNumberOfTerms(termCount);
} finally {
termDocs.close();
}
return bitSet;
} finally {
enumerator.close();
}
}
ScoringBooleanQueryRewrite及其子类ConstantScoreBooleanQueryRewrite采取方式二，其rewrite函数代码如下：
public Query rewrite(IndexReader reader, MultiTermQuery query) throws IOException {
//得到MultiTermQuery的Term枚举器
FilteredTermEnum enumerator = query.getEnum(reader);
BooleanQuery result = new BooleanQuery(true);
int count = 0;
try {
//一个循环，取出对应MultiTermQuery的所有的Term，加入BooleanQuery
do {
Term t = enumerator.term();
if (t != null) {
TermQuery tq = new TermQuery(t);
tq.setBoost(query.getBoost() * enumerator.difference());
result.add(tq, BooleanClause.Occur.SHOULD);
count++;
}
} while (enumerator.next());
} finally {
enumerator.close();
}
query.incTotalNumberOfTerms(count);
return result;
}
以上两种方式各有优劣： 方式一使得MultiTermQuery对应的所有的Term看成一个Term，组成一个docid set，作为统一的倒排表参与倒排表的合并，这样无论这样的Term在索引中有多少，都只会有一个倒排表参与合并，不会产生TooManyClauses异常，也使得性能得到提高。但是多个Term之间的tf, idf等差别将被忽略，所以采用方式二的RewriteMethod为ConstantScoreXXX，也即除了用户指定的Query boost，其他的打分计算全部忽略。方式二使得整个Query对象树被展开，叶子节点都为TermQuery，MultiTermQuery中的多个Term可根据在索引中的tf, idf等参与打分计算，然而我们事先并不知道索引中和MultiTermQuery相对应的Term到底有多少个，因而会出现TooManyClauses异常，也即一个BooleanQuery中的子查询太多。这样会造成要合并的倒排表非常多，从而影响性能。Lucene认为对于MultiTermQuery这种查询，打分计算忽略是很合理的，因为当用户输入"appl*"的时候，他并不知道索引中有什么与此相关，也并不偏爱其中之一，因而计算这些词之间的差别对用户来讲是没有意义的。从而Lucene对方式二也提供了ConstantScoreXXX，来提高搜索过程的性能，从后面的例子来看，会影响文档打分，在实际的系统应用中，还是存在问题的。为了兼顾上述两种方式，Lucene提供了ConstantScoreAutoRewrite，来根据不同的情况，选择不同的方式。ConstantScoreAutoRewrite.rewrite代码如下：
public Query rewrite(IndexReader reader, MultiTermQuery query) throws IOException {
final Collection pendingTerms = new ArrayList();
//计算文档数目限制，docCountPercent默认为0.1，也即索引文档总数的0.1%
final int docCountCutoff = (int) ((docCountPercent / 100.) * reader.maxDoc());
//计算Term数目限制，默认为350
final int termCountLimit = Math.min(BooleanQuery.getMaxClauseCount(), termCountCutoff);
int docVisitCount = 0;
FilteredTermEnum enumerator = query.getEnum(reader);
try {
//一个循环，取出与MultiTermQuery相关的所有的Term。
while(true) {
Term t = enumerator.term();
if (t != null) {
pendingTerms.add(t);
docVisitCount += reader.docFreq(t);
}
//如果Term数目超限，或者文档数目超限，则可能非常影响倒排表合并的性能，因而选用方式一，也即ConstantScoreFilterRewrite的方式
if (pendingTerms.size() >= termCountLimit || docVisitCount >= docCountCutoff) {
Query result = new ConstantScoreQuery(new MultiTermQueryWrapperFilter(query));
result.setBoost(query.getBoost());
return result;
} else if (!enumerator.next()) {
//如果Term数目不太多，而且文档数目也不太多，不会影响倒排表合并的性能，因而选用方式二，也即ConstantScoreBooleanQueryRewrite的方式。
BooleanQuery bq = new BooleanQuery(true);
for (final Term term: pendingTerms) {
TermQuery tq = new TermQuery(term);
bq.add(tq, BooleanClause.Occur.SHOULD);
}
Query result = new ConstantScoreQuery(new QueryWrapperFilter(bq));
result.setBoost(query.getBoost());
query.incTotalNumberOfTerms(pendingTerms.size());
return result;
}
}
} finally {
enumerator.close();
}
}
从上面的叙述中，我们知道，在重写Query对象树的时候，从MultiTermQuery得到的TermEnum很重要，能够得到对应MultiTermQuery的所有的Term，这是怎么做的的呢？
MultiTermQuery的getEnum返回的是FilteredTermEnum，它有两个成员变量，其中TermEnum actualEnum是用来枚举索引中所有的Term的，而Term currentTerm指向的是当前满足条件的Term，FilteredTermEnum的next()函数如下：
public boolean next() throws IOException {
if (actualEnum == null) return false;
currentTerm = null;
//不断得到下一个索引中的Term
while (currentTerm == null) {
if (endEnum()) return false;
if (actualEnum.next()) {
Term term = actualEnum.term();
//如果当前索引中的Term满足条件，则赋值为当前的Term
if (termCompare(term)) {
currentTerm = term;
return true;
}
}
else return false;
}
currentTerm = null;
return false;
}
不同的MultiTermQuery的termCompare不同：
对于PrefixQuery的getEnum(IndexReader reader)得到的是PrefixTermEnum，其termCompare实现如下：protected boolean termCompare(Term term) {
//只要前缀相同，就满足条件
if (term.field() == prefix.field() && term.text().startsWith(prefix.text())){
return true;
}
endEnum = true;
return false;
}
对于FuzzyQuery的getEnum得到的是FuzzyTermEnum，其termCompare实现如下：protected final boolean termCompare(Term term) {
//对于FuzzyQuery，其prefix设为空""，也即这一条件一定满足，只要计算的是similarity
if (field == term.field() && term.text().startsWith(prefix)) {
final String target = term.text().substring(prefix.length());
this.similarity = similarity(target);
return (similarity > minimumSimilarity);
}
endEnum = true;
return false;
}
//计算Levenshtein distance 也即 edit distance，对于两个字符串，从一个转换成为另一个所需要的最少基本操作(添加，删除，替换)数。
private synchronized final float similarity(final String target) {
final int m = target.length();
final int n = text.length();
// init matrix d
for (int i = 0; i<=n; ++i) {
p[i] = i;
}
// start computing edit distance
for (int j = 1; j<=m; ++j) { // iterates through target
int bestPossibleEditDistance = m;
final char t_j = target.charAt(j-1); // jth character of t
d[0] = j;
for (int i=1; i<=n; ++i) { // iterates through text
// minimum of cell to the left+1, to the top+1, diagonally left and up +(0|1)
if (t_j != text.charAt(i-1)) {
d[i] = Math.min(Math.min(d[i-1], p[i]), p[i-1]) + 1;
} else {
d[i] = Math.min(Math.min(d[i-1]+1, p[i]+1), p[i-1]);
}
bestPossibleEditDistance = Math.min(bestPossibleEditDistance, d[i]);
}
// copy current distance counts to 'previous row' distance counts: swap p and d
int _d[] = p;
p = d;
d = _d;
}
return 1.0f - ((float)p[n] / (float) (Math.min(n, m)));
}
有关edit distance的算法详见http://www.merriampark.com/ld.htm
计算两个字符串s和t的edit distance算法如下：
Step 1: Set n to be the length of s. Set m to be the length of t. If n = 0, return m and exit. If m = 0, return n and exit. Construct a matrix containing 0..m rows and 0..n columns.
Step 2: Initialize the first row to 0..n. Initialize the first column to 0..m.
Step 3: Examine each character of s (i from 1 to n).
Step 4: Examine each character of t (j from 1 to m).
Step 5: If s[i] equals t[j], the cost is 0. If s[i] doesn't equal t[j], the cost is 1.
Step 6: Set cell d[i,j] of the matrix equal to the minimum of: a. The cell immediately above plus 1: d[i-1,j] + 1. b. The cell immediately to the left plus 1: d[i,j-1] + 1. c. The cell diagonally above and to the left plus the cost: d[i-1,j-1] + cost.
Step 7: After the iteration steps (3, 4, 5, 6) are complete, the distance is found in cell d[n,m].
举例说明其过程如下：
比较的两个字符串为：“GUMBO” 和 "GAMBOL".
下面做一个试验，来说明ConstantScoreXXX对评分的影响：
在索引中，添加了以下四篇文档：
file01.txt : apple other other other other
file02.txt : apple apple other other other
file03.txt : apple apple apple other other
file04.txt : apple apple apple other other
搜索"apple"结果如下：
docid : 3 score : 0.67974937 docid : 2 score : 0.58868027 docid : 1 score : 0.4806554 docid : 0 score : 0.33987468
文档按照包含"apple"的多少排序。
而搜索"apple*"结果如下：
docid : 0 score : 1.0 docid : 1 score : 1.0 docid : 2 score : 1.0 docid : 3 score : 1.0
也即Lucene放弃了对score的计算。
经过rewrite，得到的新Query对象树如下：
query BooleanQuery (id=89) | boost 1.0 | clauses ArrayList (id=90) | elementData Object[3] (id=97) |------[0] BooleanClause (id=99) | | occur BooleanClause$Occur$1 (id=103) | | name "MUST" | | ordinal 0 | |---query BooleanQuery (id=105) | | boost 1.0 | | clauses ArrayList (id=115) | | elementData Object[2] (id=120)
| | //"apple*"被用方式一重写为ConstantScoreQuery | |---[0] BooleanClause (id=121) | | | occur BooleanClause$Occur$1 (id=103) | | | name "MUST" | | | ordinal 0 | | |---query ConstantScoreQuery (id=123) | | boost 1.0 | | filter MultiTermQueryWrapperFilter (id=125) | | query PrefixQuery (id=48) | | boost 1.0 | | numberOfTerms 0 | | prefix Term (id=127) | | field "contents" | | text "apple" | | rewriteMethod MultiTermQuery$1 (id=50) | |---[1] BooleanClause (id=122) | | occur BooleanClause$Occur$3 (id=111) | | name "MUST_NOT" | | ordinal 2 | |---query TermQuery (id=124) | boost 1.0 | term Term (id=130) | field "contents" | text "boy" | modCount 0 | size 2 | disableCoord false | minNrShouldMatch 0 |------[1] BooleanClause (id=101) | | occur BooleanClause$Occur$2 (id=108) | | name "SHOULD" | | ordinal 1 | |---query BooleanQuery (id=110) | | boost 1.0 | | clauses ArrayList (id=117) | | elementData Object[2] (id=132)
| | //"cat*"被用方式一重写为ConstantScoreQuery | |------[0] BooleanClause (id=133) | | | occur BooleanClause$Occur$2 (id=108) | | | name "SHOULD" | | | ordinal 1 | | |---query ConstantScoreQuery (id=135) | | boost 1.0 | | filter MultiTermQueryWrapperFilter (id=137) | | query PrefixQuery (id=63) | | boost 1.0 | | numberOfTerms 0 | | prefix Term (id=138) | | field "contents" | | text "cat" | | rewriteMethod MultiTermQuery$1 (id=50) | |------[1] BooleanClause (id=134) | | occur BooleanClause$Occur$2 (id=108) | | name "SHOULD" | | ordinal 1 | |---query TermQuery (id=136) | boost 1.0 | term Term (id=140) | field "contents" | text "dog" | modCount 0 | size 2 | disableCoord false | minNrShouldMatch 0 |------[2] BooleanClause (id=102) | occur BooleanClause$Occur$3 (id=111) | name "MUST_NOT" | ordinal 2 |---query BooleanQuery (id=113) | boost 1.0 | clauses ArrayList (id=119) | elementData Object[2] (id=142) |------[0] BooleanClause (id=143) | | occur BooleanClause$Occur$2 (id=108) | | name "SHOULD" | | ordinal 1
| | //"eat~"作为FuzzyQuery，被重写成BooleanQuery， | | 索引中满足 条件的Term有"eat"和"cat"。FuzzyQuery | | 不用上述的任何一种RewriteMethod，而是用方式二自己 | | 实现了rewrite函数，是将同"eat"的edit distance最近的 | | 最多maxClauseCount(默认1024)个Term组成BooleanQuery。 | |---query BooleanQuery (id=145) | | boost 1.0 | | clauses ArrayList (id=146) | | elementData Object[10] (id=147) | |------[0] BooleanClause (id=148) | | | occur BooleanClause$Occur$2 (id=108) | | | name "SHOULD" | | | ordinal 1 | | |---query TermQuery (id=150) | | boost 1.0 | | term Term (id=152) | | field "contents" | | text "eat" | |------[1] BooleanClause (id=149) | | occur BooleanClause$Occur$2 (id=108) | | name "SHOULD" | | ordinal 1 | |---query TermQuery (id=151) | boost 0.33333325 | term Term (id=153) | field "contents" | text "cat" | modCount 2 | size 2 | disableCoord true | minNrShouldMatch 0 |------[1] BooleanClause (id=144) | occur BooleanClause$Occur$2 (id=108) | name "SHOULD" | ordinal 1 |---query TermQuery (id=154) boost 1.0 term Term (id=155) field "contents" text "foods" modCount 0 size 2 disableCoord false minNrShouldMatch 0 modCount 0 size 3 disableCoord false minNrShouldMatch 0
BooleanQuery.createWeight(Searcher) 最终返回return new BooleanWeight(searcher)，BooleanWeight构造函数的具体实现如下：
public BooleanWeight(Searcher searcher) {
this.similarity = getSimilarity(searcher);
weights = new ArrayList(clauses.size());
//也是一个递归的过程，沿着新的Query对象树一直到叶子节点
for (int i = 0 ; i < clauses.size(); i++) {
weights.add(clauses.get(i).getQuery().createWeight(searcher));
}
}
对于TermQuery的叶子节点，其TermQuery.createWeight(Searcher) 返回return new TermWeight(searcher)对象，TermWeight构造函数如下：
public TermWeight(Searcher searcher) {
this.similarity = getSimilarity(searcher);
//此处计算了idf
idfExp = similarity.idfExplain(term, searcher);
idf = idfExp.getIdf();
}
//idf的计算完全符合文档中的公式：
public IDFExplanation idfExplain(final Term term, final Searcher searcher) {
final int df = searcher.docFreq(term);
final int max = searcher.maxDoc();
final float idf = idf(df, max);
return new IDFExplanation() {
public float getIdf() {
return idf;
}};
}
public float idf(int docFreq, int numDocs) {
return (float)(Math.log(numDocs/(double)(docFreq+1)) + 1.0);
}
而ConstantScoreQuery.createWeight(Searcher) 除了创建ConstantScoreQuery.ConstantWeight(searcher)对象外，没有计算idf。
由此创建的Weight对象树如下：
weight BooleanQuery$BooleanWeight (id=169) | similarity DefaultSimilarity (id=177) | this$0 BooleanQuery (id=89) | weights ArrayList (id=188) | elementData Object[3] (id=190) |------[0] BooleanQuery$BooleanWeight (id=171) | | similarity DefaultSimilarity (id=177) | | this$0 BooleanQuery (id=105) | | weights ArrayList (id=193) | | elementData Object[2] (id=199) | |------[0] ConstantScoreQuery$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$0 BooleanQuery (id=110) | | weights ArrayList (id=195) | | elementData Object[2] (id=204) | |------[0] ConstantScoreQuery$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$0 BooleanQuery (id=113) | weights ArrayList (id=197) | elementData Object[2] (id=216) |------[0] BooleanQuery$BooleanWeight (id=181) | | similarity BooleanQuery$1 (id=220) | | this$0 BooleanQuery (id=145) | | weights ArrayList (id=221) | | elementData Object[2] (id=224) | |------[0] TermQuery$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$0 TermQuery (id=154) value 0.0 modCount 2 size 2 modCount 3 size 3
(1) 首先计算sumOfSquaredWeights
按照公式：
代码如下：
float sum = weight.sumOfSquaredWeights();
//可以看出，也是一个递归的过程
public float sumOfSquaredWeights() throws IOException {
float sum = 0.0f;
for (int i = 0 ; i < weights.size(); i++) {
float s = weights.get(i).sumOfSquaredWeights();
if (!clauses.get(i).isProhibited())
sum += s;
}
sum *= getBoost() * getBoost(); //乘以query boost
return sum ;
}
对于叶子节点TermWeight来讲，其TermQuery$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：
当创建完Weight对象树的时候，调用IndexSearcher.search(Weight, Filter, int)，代码如下：
//(a)创建文档号收集器
TopScoreDocCollector collector = TopScoreDocCollector.create(nDocs, !weight.scoresDocsOutOfOrder());
search(weight, filter, collector);
//(b)返回搜索结果
return collector.topDocs();
public void search(Weight weight, Filter filter, Collector collector)
throws IOException {
if (filter == null) {
for (int i = 0; i < subReaders.length; i++) {
collector.setNextReader(subReaders[i], docStarts[i]);
//(c)创建Scorer对象树，以及SumScorer树用来合并倒排表
Scorer scorer = weight.scorer(subReaders[i], !collector.acceptsDocsOutOfOrder(), true);
if (scorer != null) {
//(d)合并倒排表，(e)收集文档号
scorer.score(collector);
}
}
} else {
for (int i = 0; i < subReaders.length; i++) {
collector.setNextReader(subReaders[i], docStarts[i]);
searchWithFilter(subReaders[i], weight, filter, collector);
}
}
}
在本节中，重点分析(c)创建Scorer对象树，以及SumScorer树用来合并倒排表，在2.4.3节中，分析 (d)合并倒排表，在2.4.4节中，分析文档结果收集器的创建(a)，结果文档的收集(e)，以及文档的返回(b)。
BooleanQuery$BooleanWeight.scorer(IndexReader, boolean, boolean) 代码如下：
public Scorer scorer(IndexReader reader, boolean scoreDocsInOrder, boolean topScorer){
//存放对应于MUST语句的Scorer
List required = new ArrayList();
//存放对应于MUST_NOT语句的Scorer
List prohibited = new ArrayList();
//存放对应于SHOULD语句的Scorer
List optional = new ArrayList();
//遍历每一个子语句，生成子Scorer对象，并加入相应的集合，这是一个递归的过程。
Iterator cIter = clauses.iterator();
for (Weight w : weights) {
BooleanClause c = cIter.next();
Scorer subScorer = w.scorer(reader, true, false);
if (subScorer == null) {
if (c.isRequired()) {
return null;
}
} else if (c.isRequired()) {
required.add(subScorer);
} else if (c.isProhibited()) {
prohibited.add(subScorer);
} else {
optional.add(subScorer);
}
}
//此处在有关BooleanScorer及scoreDocsInOrder一节会详细描述
if (!scoreDocsInOrder && topScorer && required.size() == 0 && prohibited.size() < 32) { return new BooleanScorer(similarity, minNrShouldMatch, optional, prohibited); }
//生成Scorer对象树，同时生成SumScorer对象树
return new BooleanScorer2(similarity, minNrShouldMatch, required, prohibited, optional);
}
对其叶子节点TermWeight来说，TermQuery$TermWeight.scorer(IndexReader, boolean, boolean) 代码如下：
public Scorer scorer(IndexReader reader, boolean scoreDocsInOrder, boolean topScorer) throws IOException {
//此Term的倒排表
TermDocs termDocs = reader.termDocs(term);
if (termDocs == null)
return null;
return new TermScorer(this, termDocs, similarity, reader.norms(term.field()));
}
TermScorer(Weight weight, TermDocs td, Similarity similarity, byte[] norms) {
super(similarity);
this.weight = weight;
this.termDocs = td;
//得到标准化因子
this.norms = norms;
//得到原来计算得的打分：queryNorm*idf^2*t.getBoost()
this.weightValue = weight.getValue();
for (int i = 0; i < SCORE_CACHE_SIZE; i++)
scoreCache[i] = getSimilarity().tf(i) * weightValue;
}
对其叶子节点ConstantWeight来说，ConstantScoreQuery$ConstantWeight.scorer(IndexReader, boolean, boolean) 代码如下：
public ConstantScorer(Similarity similarity, IndexReader reader, Weight w) {
super(similarity);
theScore = w.getValue();
//得到所有的文档号，形成统一的倒排表，参与倒排表合并。
DocIdSet docIdSet = filter.getDocIdSet(reader);
DocIdSetIterator docIdSetIterator = docIdSet.iterator();
}
对于BooleanWeight，最后要产生的是BooleanScorer2，其构造函数代码如下：
public BooleanScorer2(Similarity similarity, int minNrShouldMatch,
List required, List prohibited, List optional) {
super(similarity);
//为了计算打分公式中的coord项做统计
coordinator = new Coordinator();
this.minNrShouldMatch = minNrShouldMatch;
//SHOULD的部分
optionalScorers = optional;
coordinator.maxCoord += optional.size();
//MUST的部分
requiredScorers = required;
coordinator.maxCoord += required.size();
//MUST_NOT的部分
prohibitedScorers = prohibited;
//事先计算好各种情况的coord值
coordinator.init();
//创建SumScorer为倒排表合并做准备
countingSumScorer = makeCountingSumScorer();
}
Coordinator.init() {
coordFactors = new float[maxCoord + 1];
Similarity sim = getSimilarity();
for (int i = 0; i <= maxCoord; i++) {
//计算总的子语句的个数和一个文档满足的子语句的个数之间的关系，自然是一篇文档满足的子语句个个数越多，打分越高。
coordFactors[i] = sim.coord(i, maxCoord);
}
}
在生成Scorer对象树之外，还会生成SumScorer对象树，来表示各个语句之间的关系，为合并倒排表做准备。
在解析BooleanScorer2.makeCountingSumScorer() 之前，我们先来看不同的语句之间都存在什么样的关系，又将如何影响倒排表合并呢？
语句主要分三类：MUST，SHOULD，MUST_NOT
语句之间的组合主要有以下几种情况：
多个MUST，如"(+apple +boy +dog)"，则会生成ConjunctionScorer(Conjunction 交集)，也即倒排表取交集MUST和SHOULD，如"(+apple boy)"，则会生成ReqOptSumScorer(required optional)，也即MUST的倒排表返回，如果文档包括SHOULD的部分，则增加打分。MUST和MUST_NOT，如"(+apple –boy)"，则会生成ReqExclScorer(required exclusive)，也即返回MUST的倒排表，但扣除MUST_NOT的倒排表中的文档。多个SHOULD，如"(apple boy dog)"，则会生成DisjunctionSumScorer(Disjunction 并集)，也即倒排表去并集SHOULD和MUST_NOT，如"(apple –boy)"，则SHOULD被认为成MUST，会生成ReqExclScorerMUST，SHOULD，MUST_NOT同时出现，则MUST首先和MUST_NOT组合成ReqExclScorer，SHOULD单独成为SingleMatchScorer，然后两者组合成ReqOptSumScorer。下面分析生成SumScorer的过程：
BooleanScorer2.makeCountingSumScorer() 分两种情况：
当有MUST的语句的时候，则调用makeCountingSumScorerSomeReq()当没有MUST的语句的时候，则调用makeCountingSumScorerNoReq()首先来看makeCountingSumScorerSomeReq代码如下：
private Scorer makeCountingSumScorerSomeReq() {
if (optionalScorers.size() == minNrShouldMatch) {
//如果optional的语句个数恰好等于最少需满足的optional的个数，则所有的optional都变成required。于是首先所有的optional生成ConjunctionScorer(交集)，然后再通过addProhibitedScorers将prohibited加入，生成ReqExclScorer(required exclusive)
ArrayList allReq = new ArrayList(requiredScorers);
allReq.addAll(optionalScorers);
return addProhibitedScorers(countingConjunctionSumScorer(allReq));
} else {
//首先所有的required的语句生成ConjunctionScorer(交集)
Scorer requiredCountingSumScorer =
requiredScorers.size() == 1
? new SingleMatchScorer(requiredScorers.get(0))
: countingConjunctionSumScorer(requiredScorers);
if (minNrShouldMatch > 0) {
//如果最少需满足的optional的个数有一定的限制，则意味着optional中有一部分要相当于required，会影响倒排表的合并。因而required生成的ConjunctionScorer(交集)和optional生成的DisjunctionSumScorer(并集)共同组合成一个ConjunctionScorer(交集)，然后再加入prohibited，生成ReqExclScorer
return addProhibitedScorers(
dualConjunctionSumScorer(
requiredCountingSumScorer,
countingDisjunctionSumScorer(
optionalScorers,
minNrShouldMatch)));
} else { // minNrShouldMatch == 0
//如果最少需满足的optional的个数没有一定的限制，则optional并不影响倒排表的合并，仅仅在文档包含optional部分的时候增加打分。所以required和prohibited首先生成ReqExclScorer，然后再加入optional，生成ReqOptSumScorer(required optional)
return new ReqOptSumScorer(
addProhibitedScorers(requiredCountingSumScorer),
optionalScorers.size() == 1
? new SingleMatchScorer(optionalScorers.get(0))
: countingDisjunctionSumScorer(optionalScorers, 1));
}
}
}
然后我们来看makeCountingSumScorerNoReq代码如下：
private Scorer makeCountingSumScorerNoReq() {
// minNrShouldMatch optional scorers are required, but at least 1
int nrOptRequired = (minNrShouldMatch < 1) ? 1 : minNrShouldMatch;
Scorer requiredCountingSumScorer;
if (optionalScorers.size() > nrOptRequired)
//如果optional的语句个数多于最少需满足的optional的个数，则optional中一部分相当required，影响倒排表的合并，所以生成DisjunctionSumScorer
requiredCountingSumScorer = countingDisjunctionSumScorer(optionalScorers, nrOptRequired);
else if (optionalScorers.size() == 1)
//如果optional的语句只有一个，则返回SingleMatchScorer，不存在倒排表合并的问题。
requiredCountingSumScorer = new SingleMatchScorer(optionalScorers.get(0));
else
//如果optional的语句个数少于等于最少需满足的optional的个数，则所有的optional都算required，所以生成ConjunctionScorer
requiredCountingSumScorer = countingConjunctionSumScorer(optionalScorers);
//将prohibited加入，生成ReqExclScorer
return addProhibitedScorers(requiredCountingSumScorer);
}
经过此步骤，生成的Scorer对象树如下：
scorer BooleanScorer2 (id=50) | coordinator BooleanScorer2$Coordinator (id=53) | countingSumScorer ReqOptSumScorer (id=54) | minNrShouldMatch 0 |---optionalScorers ArrayList (id=55) | | elementData Object[10] (id=69) | |---[0] BooleanScorer2 (id=73) | | coordinator BooleanScorer2$Coordinator (id=74) | | countingSumScorer BooleanScorer2$1 (id=75) | | minNrShouldMatch 0 | |---optionalScorers ArrayList (id=76) | | | elementData Object[10] (id=83) | | |---[0] ConstantScoreQuery$ConstantScorer (id=86) | | | docIdSetIterator OpenBitSetIterator (id=88) | | | similarity DefaultSimilarity (id=64) | | | theScore 0.47844642
| | | //ConstantScore(contents:cat*) | | | this$0 ConstantScoreQuery (id=90) | | |---[1] TermScorer (id=87) | | doc -1 | | doc 0 | | docs int[32] (id=93) | | freqs int[32] (id=95) | | norms byte[4] (id=96) | | pointer 0 | | pointerMax 2 | | scoreCache float[32] (id=98) | | similarity DefaultSimilarity (id=64) | | termDocs SegmentTermDocs (id=103)
| | //weight(contents:dog) | | weight TermQuery$TermWeight (id=106) | | weightValue 1.1332052 | | modCount 2 | | size 2 | |---prohibitedScorers ArrayList (id=77) | | elementData Object[10] (id=84) | | size 0 | |---requiredScorers ArrayList (id=78) | elementData Object[10] (id=85) | size 0 | similarity DefaultSimilarity (id=64) | size 1 |---prohibitedScorers ArrayList (id=60) | | elementData Object[10] (id=71) | |---[0] BooleanScorer2 (id=81) | | coordinator BooleanScorer2$Coordinator (id=114) | | countingSumScorer BooleanScorer2$1 (id=115) | | minNrShouldMatch 0 | |---optionalScorers ArrayList (id=116) | | | elementData Object[10] (id=119) | | |---[0] BooleanScorer2 (id=122) | | | | coordinator BooleanScorer2$Coordinator (id=124) | | | | countingSumScorer BooleanScorer2$1 (id=125) | | | | minNrShouldMatch 0 | | | |---optionalScorers ArrayList (id=126) | | | | | elementData Object[10] (id=138) | | | | |---[0] TermScorer (id=156) | | | | | docs int[32] (id=162) | | | | | freqs int[32] (id=163) | | | | | norms byte[4] (id=96) | | | | | pointer 0 | | | | | pointerMax 1 | | | | | scoreCache float[32] (id=164) | | | | | similarity DefaultSimilarity (id=64) | | | | | termDocs SegmentTermDocs (id=165)
| | | | | //weight(contents:eat) | | | | | weight TermQuery$TermWeight (id=166) | | | | | weightValue 2.107161 | | | | |---[1] TermScorer (id=157) | | | | doc -1 | | | | doc 1 | | | | docs int[32] (id=171) | | | | freqs int[32] (id=172) | | | | norms byte[4] (id=96) | | | | pointer 1 | | | | pointerMax 3 | | | | scoreCache float[32] (id=173) | | | | similarity DefaultSimilarity (id=64) | | | | termDocs SegmentTermDocs (id=180)
| | | | //weight(contents:cat^0.33333325) | | | | weight TermQuery$TermWeight (id=181) | | | | weightValue 0.22293752 | | | | size 2 | | | |---prohibitedScorers ArrayList (id=127) | | | | elementData Object[10] (id=140) | | | | modCount 0 | | | | size 0 | | | |---requiredScorers ArrayList (id=128) | | | elementData Object[10] (id=142) | | | modCount 0 | | | size 0 | | | similarity BooleanQuery$1 (id=129) | | |---[1] TermScorer (id=123) | | doc -1 | | doc 3 | | docs int[32] (id=131) | | freqs int[32] (id=132) | | norms byte[4] (id=96) | | pointer 0 | | pointerMax 1 | | scoreCache float[32] (id=133) | | similarity DefaultSimilarity (id=64) | | termDocs SegmentTermDocs (id=134)
| | //weight(contents:foods) | | weight TermQuery$TermWeight (id=135) | | weightValue 2.107161 | | size 2 | |---prohibitedScorers ArrayList (id=117) | | elementData Object[10] (id=120) | | size 0 | |---requiredScorers ArrayList (id=118) | elementData Object[10] (id=121) | size 0 | similarity DefaultSimilarity (id=64) | size 1 |---requiredScorers ArrayList (id=63) | elementData Object[10] (id=72) |---[0] BooleanScorer2 (id=82) | coordinator BooleanScorer2$Coordinator (id=183) | countingSumScorer ReqExclScorer (id=184) | minNrShouldMatch 0 |---optionalScorers ArrayList (id=185) | elementData Object[10] (id=189) | size 0 |---prohibitedScorers ArrayList (id=186) | | elementData Object[10] (id=191) | |---[0] TermScorer (id=195) | docs int[32] (id=197) | freqs int[32] (id=198) | norms byte[4] (id=96) | pointer 0 | pointerMax 0 | scoreCache float[32] (id=199) | similarity DefaultSimilarity (id=64) | termDocs SegmentTermDocs (id=200)
| //weight(contents:boy) | weight TermQuery$TermWeight (id=201) | weightValue 2.107161 | size 1 |---requiredScorers ArrayList (id=187) | elementData Object[10] (id=193) |---[0] ConstantScoreQuery$ConstantScorer (id=203) docIdSetIterator OpenBitSetIterator (id=206) similarity DefaultSimilarity (id=64) theScore 0.47844642
//ConstantScore(contents:apple*) this$0 ConstantScoreQuery (id=207) size 1 similarity DefaultSimilarity (id=64) size 1 similarity DefaultSimilarity (id=64)
生成的SumScorer对象树如下：
scorer BooleanScorer2 (id=50) | coordinator BooleanScorer2$Coordinator (id=53) |---countingSumScorer ReqOptSumScorer (id=54) |---optScorer BooleanScorer2$SingleMatchScorer (id=79) | | lastDocScore NaN | | lastScoredDoc -1 | |---scorer BooleanScorer2 (id=73) | | coordinator BooleanScorer2$Coordinator (id=74) | |---countingSumScorer BooleanScorer2$1(DisjunctionSumScorer) (id=75) | | currentDoc -1 | | currentScore NaN | | doc -1 | | lastDocScore NaN | | lastScoredDoc -1 | | minimumNrMatchers 1 | | nrMatchers -1 | | nrScorers 2 | | scorerDocQueue ScorerDocQueue (id=243) | | similarity null | |---subScorers ArrayList (id=76) | | elementData Object[10] (id=83) | |---[0] ConstantScoreQuery$ConstantScorer (id=86) | | doc -1 | | doc -1 | | docIdSetIterator OpenBitSetIterator (id=88) | | similarity DefaultSimilarity (id=64) | | theScore 0.47844642
| | //ConstantScore(contents:cat*) | | this$0 ConstantScoreQuery (id=90) | |---[1] TermScorer (id=87) | doc -1 | doc 0 | docs int[32] (id=93) | freqs int[32] (id=95) | norms byte[4] (id=96) | pointer 0 | pointerMax 2 | scoreCache float[32] (id=98) | similarity DefaultSimilarity (id=64) | termDocs SegmentTermDocs (id=103)
| //weight(contents:dog) | weight TermQuery$TermWeight (id=106) | weightValue 1.1332052 | size 2 | this$0 BooleanScorer2 (id=73) | minNrShouldMatch 0 | optionalScorers ArrayList (id=76) | prohibitedScorers ArrayList (id=77) | requiredScorers ArrayList (id=78) | similarity DefaultSimilarity (id=64) | similarity DefaultSimilarity (id=64) | this$0 BooleanScorer2 (id=50) |---reqScorer ReqExclScorer (id=80) |---exclDisi BooleanScorer2 (id=81) | | coordinator BooleanScorer2$Coordinator (id=114) | |---countingSumScorer BooleanScorer2$1(DisjunctionSumScorer) (id=115) | | currentDoc -1 | | currentScore NaN | | doc -1 | | lastDocScore NaN | | lastScoredDoc -1 | | minimumNrMatchers 1 | | nrMatchers -1 | | nrScorers 2 | | scorerDocQueue ScorerDocQueue (id=260) | | similarity null | |---subScorers ArrayList (id=116) | | elementData Object[10] (id=119) | |---[0] BooleanScorer2 (id=122) | | | coordinator BooleanScorer2$Coordinator (id=124) | | |---countingSumScorer BooleanScorer2$1(DisjunctionSumScorer) (id=125) | | | currentDoc 0 | | | currentScore 0.11146876 | | | doc -1 | | | lastDocScore NaN | | | lastScoredDoc -1 | | | minimumNrMatchers 1 | | | nrMatchers 1 | | | nrScorers 2 | | | scorerDocQueue ScorerDocQueue (id=270) | | | similarity null | | |---subScorers ArrayList (id=126) | | | elementData Object[10] (id=138) | | |---[0] TermScorer (id=156) | | | doc -1 | | | doc 2 | | | docs int[32] (id=162) | | | freqs int[32] (id=163) | | | norms byte[4] (id=96) | | | pointer 0 | | | pointerMax 1 | | | scoreCache float[32] (id=164) | | | similarity DefaultSimilarity (id=64) | | | termDocs SegmentTermDocs (id=165)
| | | //weight(contents:eat) | | | weight TermQuery$TermWeight (id=166) | | | weightValue 2.107161 | | |---[1] TermScorer (id=157) | | doc -1 | | doc 1 | | docs int[32] (id=171) | | freqs int[32] (id=172) | | norms byte[4] (id=96) | | pointer 1 | | pointerMax 3 | | scoreCache float[32] (id=173) | | similarity DefaultSimilarity (id=64) | | termDocs SegmentTermDocs (id=180)
| | //weight(contents:cat^0.33333325) | | weight TermQuery$TermWeight (id=181) | | weightValue 0.22293752 | | size 2 | | this$0 BooleanScorer2 (id=122) | | doc -1 | | doc 0 | | minNrShouldMatch 0 | | optionalScorers ArrayList (id=126) | | prohibitedScorers ArrayList (id=127) | | requiredScorers ArrayList (id=128) | | similarity BooleanQuery$1 (id=129) | |---[1] TermScorer (id=123) | doc -1 | doc 3 | docs int[32] (id=131) | freqs int[32] (id=132) | norms byte[4] (id=96) | pointer 0 | pointerMax 1 | scoreCache float[32] (id=133) | similarity DefaultSimilarity (id=64) | termDocs SegmentTermDocs (id=134)
| //weight(contents:foods) | weight TermQuery$TermWeight (id=135) | weightValue 2.107161 | size 2 | this$0 BooleanScorer2 (id=81) | doc -1 | doc -1 | minNrShouldMatch 0 | optionalScorers ArrayList (id=116) | prohibitedScorers ArrayList (id=117) | requiredScorers ArrayList (id=118) | similarity DefaultSimilarity (id=64) |---reqScorer BooleanScorer2$SingleMatchScorer (id=237) | doc -1 | lastDocScore NaN | lastScoredDoc -1 |---scorer BooleanScorer2 (id=82) | coordinator BooleanScorer2$Coordinator (id=183) |---countingSumScorer ReqExclScorer (id=184) |---exclDisi TermScorer (id=195) | doc -1 | doc -1 | docs int[32] (id=197) | freqs int[32] (id=198) | norms byte[4] (id=96) | pointer 0 | pointerMax 0 | scoreCache float[32] (id=199) | similarity DefaultSimilarity (id=64) | termDocs SegmentTermDocs (id=200)
| //weight(contents:boy) | weight TermQuery$TermWeight (id=201) | weightValue 2.107161 |---reqScorer BooleanScorer2$2(ConjunctionScorer) (id=281) | coord 1.0 | doc -1 | lastDoc -1 | lastDocScore NaN | lastScoredDoc -1 |---scorers Scorer[1] (id=283) |---[0] ConstantScoreQuery$ConstantScorer (id=203) doc -1 doc -1 docIdSetIterator OpenBitSetIterator (id=206) similarity DefaultSimilarity (id=64) theScore 0.47844642
//ConstantScore(contents:apple*) this$0 ConstantScoreQuery (id=207) similarity DefaultSimilarity (id=64) this$0 BooleanScorer2 (id=82) val$requiredNrMatchers 1 similarity null minNrShouldMatch 0 optionalScorers ArrayList (id=185) prohibitedScorers ArrayList (id=186) requiredScorers ArrayList (id=187) similarity DefaultSimilarity (id=64) similarity DefaultSimilarity (id=64) this$0 BooleanScorer2 (id=50) similarity null similarity null minNrShouldMatch 0 optionalScorers ArrayList (id=55) prohibitedScorers ArrayList (id=60) requiredScorers ArrayList (id=63) similarity DefaultSimilarity (id=64)
在得到了Scorer对象树以及SumScorer对象树后，便是倒排表的合并以及打分计算的过程。
合并倒排表在此节中进行分析，而Scorer对象树来进行打分的计算则在下一节分析。
BooleanScorer2.score(Collector) 代码如下：
public void score(Collector collector) throws IOException {
collector.setScorer(this);
while ((doc = countingSumScorer.nextDoc()) != NO_MORE_DOCS) {
collector.collect(doc);
}
}
从代码我们可以看出，此过程就是不断的取下一篇文档号，然后加入文档结果集。
取下一篇文档的过程，就是合并倒排表的过程，也就是对多个查询条件进行综合考虑后的下一篇文档的编号。
由于SumScorer是一棵树，因而合并倒排表也是按照树的结构进行的，先合并子树，然后子树与子树再进行合并，直到根。
按照上一节的分析，倒排表的合并主要用了以下几个SumScorer:
交集ConjunctionScorer并集DisjunctionSumScorer差集ReqExclScorerReqOptSumScorer下面我们一一分析：
ConjunctionScorer中有成员变量Scorer[] scorers，是一个Scorer的数组，每一项代表一个倒排表，ConjunctionScorer就是对这些倒排表取交集，然后将交集中的文档号在nextDoc()函数中依次返回。
为了描述清楚此过程，下面举一个具体的例子来解释倒排表合并的过程：
(1) 倒排表最初如下：
(2) 在ConjunctionScorer的构造函数中，首先调用每个Scorer的nextDoc()函数，使得每个Scorer得到自己的第一篇文档号。
for (int i = 0; i < scorers.length; i++) {
if (scorers[i].nextDoc() == NO_MORE_DOCS) {
//由于是取交集，因而任何一个倒排表没有文档，交集就为空。
lastDoc = NO_MORE_DOCS;
return;
}
}
(3) 在ConjunctionScorer的构造函数中，将Scorer按照第一篇的文档号从小到大进行排列。
Arrays.sort(scorers, new Comparator() {
public int compare(Scorer o1, Scorer o2) {
return o1.docID() - o2.docID();
}
});
倒排表如下：
(4) 在ConjunctionScorer的构造函数中，第一次调用doNext()函数。
if (doNext() == NO_MORE_DOCS) {
lastDoc = NO_MORE_DOCS;
return;
}
private int doNext() throws IOException {
int first = 0;
int doc = scorers[scorers.length - 1].docID();
Scorer firstScorer;
while ((firstScorer = scorers[first]).docID() < doc) {
doc = firstScorer.advance(doc);
first = first == scorers.length - 1 ? 0 : first + 1;
}
return doc;
}
姑且我们称拥有最小文档号的倒排表称为first，其实从doNext()函数中的first = first == scorers.length - 1 ? 0 : first + 1;我们可以看出，在处理过程中，Scorer数组被看成一个循环数组(Ring)。
而此时scorer[scorers.length - 1]拥有最大的文档号，doNext()中的循环，将所有的小于当前数组中最大文档号的文档全部用firstScorer.advance(doc)(其跳到大于或等于doc的文档)函数跳过，因为既然它们小于最大的文档号，而ConjunctionScorer又是取交集，它们当然不会在交集中。
此过程如下：
doc = 8，first指向第0项，advance到大于8的第一篇文档，也即文档10，然后设doc = 10，first指向第1项。 doc = 10，first指向第1项，advance到文档11，然后设doc = 11，first指向第2项。 doc = 11，first指向第2项，advance到文档11，然后设doc = 11，first指向第3项。 doc = 11，first指向第3项，advance到文档11，然后设doc = 11，first指向第4项。 doc = 11，first指向第4项，advance到文档11，然后设doc = 11，first指向第5项。 doc = 11，first指向第5项，advance到文档11，然后设doc = 11，first指向第6项。 doc = 11，first指向第6项，advance到文档11，然后设doc = 11，first指向第7项。 doc = 11，first指向第7项，advance到文档11，然后设doc = 11，first指向第0项。 doc = 11，first指向第0项，advance到文档11，然后设doc = 11，first指向第1项。 doc = 11，first指向第1项。因为11 < 11为false，因而结束循环，返回doc = 11。这时候我们会发现，在循环退出的时候，所有的倒排表的第一篇文档都是11。(5) 当BooleanScorer2.score(Collector)中第一次调用ConjunctionScorer.nextDoc()的时候，lastDoc为-1，根据nextDoc函数的实现，返回lastDoc = scorers[scorers.length - 1].docID()也即返回11，lastDoc也设为11。
public int nextDoc() throws IOException {
if (lastDoc == NO_MORE_DOCS) {
return lastDoc;
} else if (lastDoc == -1) {
return lastDoc = scorers[scorers.length - 1].docID();
}
scorers[(scorers.length - 1)].nextDoc();
return lastDoc = doNext();
}
(6) 在BooleanScorer2.score(Collector)中，调用nextDoc()后，collector.collect(doc)来收集文档号(收集过程下节分析)，在收集文档的过程中，ConjunctionScorer.docID()会被调用，返回lastDoc，也即当前的文档号为11。
(7) 当BooleanScorer2.score(Collector)第二次调用ConjunctionScorer.nextDoc()时：
根据nextDoc函数的实现，首先调用scorers[(scorers.length - 1)].nextDoc()，取最后一项的下一篇文档13。 然后调用lastDoc = doNext()，设doc = 13，first = 0，进入循环。doc = 13，first指向第0项，advance到文档13，然后设doc = 13，first指向第1项。 doc = 13，first指向第1项，advance到文档13，然后设doc = 13，first指向第2项。 doc = 13，first指向第2项，advance到文档13，然后设doc = 13，first指向第3项。 doc = 13，first指向第3项，advance到文档13，然后设doc = 13，first指向第4项。 doc = 13，first指向第4项，advance到文档13，然后设doc = 13，first指向第5项。 doc = 13，first指向第5项，advance到文档13，然后设doc = 13，first指向第6项。 doc = 13，first指向第6项，advance到文档13，然后设doc = 13，first指向第7项。 doc = 13，first指向第7项，advance到文档13，然后设doc = 13，first指向第0项。 doc = 13，first指向第0项。因为13 < 13为false，因而结束循环，返回doc = 13。在循环退出的时候，所有的倒排表的第一篇文档都是13。(8) lastDoc设为13，在收集文档的过程中，ConjunctionScorer.docID()会被调用，返回lastDoc，也即当前的文档号为13。
(9) 当再次调用nextDoc()的时候，返回NO_MORE_DOCS，倒排表合并结束。
DisjunctionSumScorer中有成员变量List subScorers，是一个Scorer的链表，每一项代表一个倒排表，DisjunctionSumScorer就是对这些倒排表取并集，然后将并集中的文档号在nextDoc()函数中依次返回。
DisjunctionSumScorer还有一个成员变量minimumNrMatchers，表示最少需满足的子条件的个数，也即subScorer中，必须有至少minimumNrMatchers个Scorer都包含某个文档号，此文档号才能够返回。
为了描述清楚此过程，下面举一个具体的例子来解释倒排表合并的过程：
(1) 假设minimumNrMatchers = 4，倒排表最初如下：
(2) 在DisjunctionSumScorer的构造函数中，将倒排表放入一个优先级队列scorerDocQueue中(scorerDocQueue的实现是一个最小堆)，队列中的Scorer按照第一篇文档的大小排序。
private void initScorerDocQueue() throws IOException {
scorerDocQueue = new ScorerDocQueue(nrScorers);
for (Scorer se : subScorers) {
if (se.nextDoc() != NO_MORE_DOCS) { //此处的nextDoc使得每个Scorer得到第一篇文档号。
scorerDocQueue.insert(se);
}
}
}
(3) 当BooleanScorer2.score(Collector)中第一次调用nextDoc()的时候，advanceAfterCurrent被调用。
public int nextDoc() throws IOException {
if (scorerDocQueue.size() < minimumNrMatchers || !advanceAfterCurrent()) {
currentDoc = NO_MORE_DOCS;
}
return currentDoc;
}
protected boolean advanceAfterCurrent() throws IOException {
do {
currentDoc = scorerDocQueue.topDoc(); //当前的文档号为最顶层
currentScore = scorerDocQueue.topScore(); //当前文档的打分
nrMatchers = 1; //当前文档满足的子条件的个数，也即包含当前文档号的Scorer的个数
do {
//所谓topNextAndAdjustElsePop是指，最顶层(top)的Scorer取下一篇文档(Next)，如果能够取到，则最小堆的堆顶可能不再是最小值了，需要调整(Adjust，其实是downHeap())，如果不能够取到，则最顶层的Scorer已经为空，则弹出队列(Pop)。
if (!scorerDocQueue.topNextAndAdjustElsePop()) {
if (scorerDocQueue.size() == 0) {
break; // nothing more to advance, check for last match.
}
}
//当最顶层的Scorer取到下一篇文档，并且调整完毕后，再取出此时最上层的Scorer的第一篇文档，如果不是currentDoc，说明currentDoc此文档号已经统计完毕nrMatchers，则退出内层循环。
if (scorerDocQueue.topDoc() != currentDoc) {
break; // All remaining subscorers are after currentDoc.
}
//否则nrMatchers加一，也即又多了一个Scorer也包含此文档号。
currentScore += scorerDocQueue.topScore();
nrMatchers++;
} while (true);
//如果统计出的nrMatchers大于最少需满足的子条件的个数，则此currentDoc就是满足条件的文档，则返回true，在收集文档的过程中，DisjunctionSumScorer.docID()会被调用，返回currentDoc。
if (nrMatchers >= minimumNrMatchers) {
return true;
} else if (scorerDocQueue.size() < minimumNrMatchers) {
return false;
}
} while (true);
}
advanceAfterCurrent具体过程如下：
最初，currentDoc=2，文档2的nrMatchers=1 最顶层的Scorer 0取得下一篇文档，为文档3，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 1的第一篇文档号，都为2，文档2的nrMatchers为2。 最顶层的Scorer 1取得下一篇文档，为文档8，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 3的第一篇文档号，都为2，文档2的nrMatchers为3。 最顶层的Scorer 3取得下一篇文档，为文档7，重新调整最小堆后如下图。此时currentDoc还为2，不等于最顶层Scorer 2的第一篇文档3，于是退出内循环。此时检查，发现文档2的nrMatchers为3，小于minimumNrMatchers，不满足条件。于是currentDoc设为最顶层Scorer 2的第一篇文档3，nrMatchers设为1，重新进入下一轮循环。 最顶层的Scorer 2取得下一篇文档，为文档5，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 4的第一篇文档号，都为3，文档3的nrMatchers为2。 最顶层的Scorer 4取得下一篇文档，为文档7，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 0的第一篇文档号，都为3，文档3的nrMatchers为3。 最顶层的Scorer 0取得下一篇文档，为文档5，重新调整最小堆后如下图。此时currentDoc还为3，不等于最顶层Scorer 0的第一篇文档5，于是退出内循环。此时检查，发现文档3的nrMatchers为3，小于minimumNrMatchers，不满足条件。于是currentDoc设为最顶层Scorer 0的第一篇文档5，nrMatchers设为1，重新进入下一轮循环。 最顶层的Scorer 0取得下一篇文档，为文档7，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 2的第一篇文档号，都为5，文档5的nrMatchers为2。最顶层的Scorer 2取得下一篇文档，为文档7，重新调整最小堆后如下图。此时currentDoc还为5，不等于最顶层Scorer 2的第一篇文档7，于是退出内循环。此时检查，发现文档5的nrMatchers为2，小于minimumNrMatchers，不满足条件。于是currentDoc设为最顶层Scorer 2的第一篇文档7，nrMatchers设为1，重新进入下一轮循环。 最顶层的Scorer 2取得下一篇文档，为文档8，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 3的第一篇文档号，都为7，文档7的nrMatchers为2。 最顶层的Scorer 3取得下一篇文档，为文档9，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 4的第一篇文档号，都为7，文档7的nrMatchers为3。 最顶层的Scorer 4取得下一篇文档，结果为空，Scorer 4所有的文档遍历完毕，弹出队列，重新调整最小堆后如下图。此时currentDoc等于最顶层Scorer 0的第一篇文档号，都为7，文档7的nrMatchers为4。 最顶层的Scorer 0取得下一篇文档，为文档9，重新调整最小堆后如下图。此时currentDoc还为7，不等于最顶层Scorer 1的第一篇文档8，于是退出内循环。此时检查，发现文档7的nrMatchers为4，大于等于minimumNrMatchers，满足条件，返回true，退出外循环。
(4) currentDoc设为7，在收集文档的过程中，DisjunctionSumScorer.docID()会被调用，返回currentDoc，也即当前的文档号为7。
(5) 当再次调用nextDoc()的时候，文档8, 9, 11都不满足要求，最后返回NO_MORE_DOCS，倒排表合并结束。
ReqExclScorer有成员变量Scorer reqScorer表示必须满足的部分(required)，成员变量DocIdSetIterator exclDisi表示必须不能满足的部分，ReqExclScorer就是返回reqScorer和exclDisi的倒排表的差集，也即在reqScorer的倒排表中排除exclDisi中的文档号。
当nextDoc()调用的时候，首先取得reqScorer的第一个文档号，然后toNonExcluded()函数则判断此文档号是否被exclDisi排除掉，如果没有，则返回此文档号，如果排除掉，则取下一个文档号，看是否被排除掉，依次类推，直到找到一个文档号，或者返回NO_MORE_DOCS。
public int nextDoc() throws IOException {
if (reqScorer == null) {
return doc;
}
doc = reqScorer.nextDoc();
if (doc == NO_MORE_DOCS) {
reqScorer = null;
return doc;
}
if (exclDisi == null) {
return doc;
}
return doc = toNonExcluded();
}
private int toNonExcluded() throws IOException {
//取得被排除的文档号
int exclDoc = exclDisi.docID();
//取得当前required文档号
int reqDoc = reqScorer.docID();
do {
//如果required文档号小于被排除的文档号，由于倒排表是按照从小到大的顺序排列的，因而此required文档号不会被排除，返回。
if (reqDoc < exclDoc) {
return reqDoc;
} else if (reqDoc > exclDoc) {
//如果required文档号大于被排除的文档号，则此required文档号有可能被排除。于是exclDisi移动到大于或者等于required文档号的文档。
exclDoc = exclDisi.advance(reqDoc);
//如果被排除的倒排表遍历结束，则required文档号不会被排除，返回。
if (exclDoc == NO_MORE_DOCS) {
exclDisi = null;
return reqDoc;
}
//如果exclDisi移动后，大于required文档号，则required文档号不会被排除，返回。
if (exclDoc > reqDoc) {
return reqDoc; // not excluded
}
}
//如果required文档号等于被排除的文档号，则被排除，取下一个required文档号。
} while ((reqDoc = reqScorer.nextDoc()) != NO_MORE_DOCS);
reqScorer = null;
return NO_MORE_DOCS;
}
ReqOptSumScorer包含两个成员变量，Scorer reqScorer代表必须(required)满足的文档倒排表，Scorer optScorer代表可以(optional)满足的文档倒排表。
如代码显示，在nextDoc()中，返回的就是required的文档倒排表，只不过在计算score的时候打分更高。
public int nextDoc() throws IOException {
return reqScorer.nextDoc();
}
在BooleanWeight.scorer生成Scorer树的时候，除了生成上述的BooleanScorer2外， 还会生成BooleanScorer，是在以下的条件下：
!scoreDocsInOrder：根据2.4.2节的步骤(c)，scoreDocsInOrder = !collector.acceptsDocsOutOfOrder()，此值是在search中调用TopScoreDocCollector.create(nDocs, !weight.scoresDocsOutOfOrder())的时候设定的，scoreDocsInOrder = !weight.scoresDocsOutOfOrder()，其代码如下：public boolean scoresDocsOutOfOrder() {
int numProhibited = 0;
for (BooleanClause c : clauses) {
if (c.isRequired()) {
return false;
} else if (c.isProhibited()) {
++numProhibited;
}
}
if (numProhibited > 32) {
return false;
}
return true;
}
topScorer：根据2.4.2节的步骤(c)，此值为true。required.size() == 0，没有必须满足的子语句。prohibited.size() < 32，不需不能满足的子语句小于32。从上面可以看出，最后两个条件和scoresDocsOutOfOrder函数中的逻辑是一致的。
下面我们看看BooleanScorer如何合并倒排表的：
public int nextDoc() throws IOException {
boolean more;
do {
//bucketTable等于是存放合并后的倒排表的文档队列
while (bucketTable.first != null) {
//从队列中取出第一篇文档，返回
current = bucketTable.first;
bucketTable.first = current.next;
if ((current.bits & prohibitedMask) == 0 &&
(current.bits & requiredMask) == requiredMask &&
current.coord >= minNrShouldMatch) {
return doc = current.doc;
}
}
//如果队列为空，则填充队列。
more = false;
end += BucketTable.SIZE;
//按照Scorer的顺序，依次用Scorer中的倒排表填充队列，填满为止。
for (SubScorer sub = scorers; sub != null; sub = sub.next) {
Scorer scorer = sub.scorer;
sub.collector.setScorer(scorer);
int doc = scorer.docID();
while (doc < end) {
sub.collector.collect(doc);
doc = scorer.nextDoc();
}
more |= (doc != NO_MORE_DOCS);
}
} while (bucketTable.first != null || more);
return doc = NO_MORE_DOCS;
}
public final void collect(final int doc) throws IOException {
final BucketTable table = bucketTable;
final int i = doc & BucketTable.MASK;
Bucket bucket = table.buckets[i];
if (bucket == null)
table.buckets[i] = bucket = new Bucket();
if (bucket.doc != doc) {
bucket.doc = doc;
bucket.score = scorer.score();
bucket.bits = mask;
bucket.coord = 1;
bucket.next = table.first;
table.first = bucket;
} else {
bucket.score += scorer.score();
bucket.bits |= mask;
bucket.coord++;
}
}
从上面的实现我们可以看出，BooleanScorer合并倒排表的时候，并不是按照文档号从小到大的顺序排列的。
从原理上我们可以理解，在AND的查询条件下，倒排表的合并按照算法需要按照文档号从小到大的顺序排列。然而在没有AND的查询条件下，如果都是OR，则文档号是否按照顺序返回就不重要了，因而scoreDocsInOrder就是false。
因而上面的DisjunctionSumScorer，其实"apple boy dog"是不能产生DisjunctionSumScorer的，而仅有在有AND的查询条件下，才产生DisjunctionSumScorer。
我们做实验如下：
对于查询语句"apple boy dog"，生成的Scorer如下：
scorer BooleanScorer (id=34) bucketTable BooleanScorer$BucketTable (id=39) coordFactors float[4] (id=41) current null doc -1 doc -1 end 0 maxCoord 4 minNrShouldMatch 0 nextMask 1 prohibitedMask 0 requiredMask 0 scorers BooleanScorer$SubScorer (id=43) collector BooleanScorer$BooleanScorerCollector (id=49) next BooleanScorer$SubScorer (id=51) collector BooleanScorer$BooleanScorerCollector (id=68) next BooleanScorer$SubScorer (id=69) collector BooleanScorer$BooleanScorerCollector (id=76) next null prohibited false required false scorer TermScorer (id=77) doc -1 doc 0 docs int[32] (id=79) freqs int[32] (id=80) norms byte[4] (id=58) pointer 0 pointerMax 2 scoreCache float[32] (id=81) similarity DefaultSimilarity (id=45) termDocs SegmentTermDocs (id=82) weight TermQuery$TermWeight (id=84) //weight(contents:apple) weightValue 0.828608 prohibited false required false scorer TermScorer (id=70) doc -1 doc 1 docs int[32] (id=72) freqs int[32] (id=73) norms byte[4] (id=58) pointer 0 pointerMax 1 scoreCache float[32] (id=74) similarity DefaultSimilarity (id=45) termDocs SegmentTermDocs (id=86) weight TermQuery$TermWeight (id=87) //weight(contents:boy) weightValue 1.5407716 prohibited false required false scorer TermScorer (id=52) doc -1 doc 0 docs int[32] (id=54) freqs int[32] (id=56) norms byte[4] (id=58) pointer 0 pointerMax 3 scoreCache float[32] (id=61) similarity DefaultSimilarity (id=45) termDocs SegmentTermDocs (id=62) weight TermQuery$TermWeight (id=66) //weight(contents:cat) weightValue 0.48904076 similarity DefaultSimilarity (id=45)
对于查询语句"+hello (apple boy dog)"，生成的Scorer对象如下：
scorer BooleanScorer2 (id=40) coordinator BooleanScorer2$Coordinator (id=42) countingSumScorer ReqOptSumScorer (id=43) minNrShouldMatch 0 optionalScorers ArrayList (id=44) elementData Object[10] (id=62) [0] BooleanScorer2 (id=84) coordinator BooleanScorer2$Coordinator (id=87) countingSumScorer BooleanScorer2$1 (id=88) minNrShouldMatch 0 optionalScorers ArrayList (id=89) elementData Object[10] (id=95) [0] TermScorer (id=97) docs int[32] (id=101) freqs int[32] (id=102) norms byte[4] (id=71) pointer 0 pointerMax 2 scoreCache float[32] (id=103) similarity DefaultSimilarity (id=48) termDocs SegmentTermDocs (id=104)
//weight(contents:apple) weight TermQuery$TermWeight (id=105) weightValue 0.525491 [1] TermScorer (id=98) docs int[32] (id=107) freqs int[32] (id=108) norms byte[4] (id=71) pointer 0 pointerMax 1 scoreCache float[32] (id=110) similarity DefaultSimilarity (id=48) termDocs SegmentTermDocs (id=111)
//weight(contents:boy) weight TermQuery$TermWeight (id=112) weightValue 0.9771348 [2] TermScorer (id=99) docs int[32] (id=114) freqs int[32] (id=118) norms byte[4] (id=71) pointer 0 pointerMax 3 scoreCache float[32] (id=119) similarity DefaultSimilarity (id=48) termDocs SegmentTermDocs (id=120)
//weight(contents:cat) weight TermQuery$TermWeight (id=121) weightValue 0.3101425 size 3 prohibitedScorers ArrayList (id=90) requiredScorers ArrayList (id=91) similarity DefaultSimilarity (id=48) size 1 prohibitedScorers ArrayList (id=46) requiredScorers ArrayList (id=47) elementData Object[10] (id=59) [0] TermScorer (id=66) docs int[32] (id=68) freqs int[32] (id=70) norms byte[4] (id=71) pointer 0 pointerMax 0 scoreCache float[32] (id=73) similarity DefaultSimilarity (id=48) termDocs SegmentTermDocs (id=76) weight TermQuery$TermWeight (id=78) //weight(contents:hello) weightValue 2.6944637 size 1 similarity DefaultSimilarity (id=48)
在函数IndexSearcher.search(Weight, Filter, int) 中，有如下代码：
TopScoreDocCollector collector = TopScoreDocCollector.create(nDocs, !weight.scoresDocsOutOfOrder());
search(weight, filter, collector);
return collector.topDocs();
TopScoreDocCollector collector = TopScoreDocCollector.create(nDocs, !weight.scoresDocsOutOfOrder());
public static TopScoreDocCollector create(int numHits, boolean docsScoredInOrder) {
if (docsScoredInOrder) {
return new InOrderTopScoreDocCollector(numHits);
} else {
return new OutOfOrderTopScoreDocCollector(numHits);
}
}
其根据是否按照文档号从小到大返回文档而创建InOrderTopScoreDocCollector或者OutOfOrderTopScoreDocCollector，两者的不同在于收集文档的方式不同。
当创建完毕Scorer对象树和SumScorer对象树后，IndexSearcher.search(Weight, Filter, Collector) 有以下调用：
scorer.score(collector) ，如下代码所示，其不断的得到合并的倒排表后的文档号，并收集它们。
public void score(Collector collector) throws IOException {
collector.setScorer(this);
while ((doc = countingSumScorer.nextDoc()) != NO_MORE_DOCS) {
collector.collect(doc);
}
}
InOrderTopScoreDocCollector的collect函数如下：
public void collect(int doc) throws IOException {
float score = scorer.score();
totalHits++;
if (score <= pqTop.score) {
return;
}
pqTop.doc = doc + docBase;
pqTop.score = score;
pqTop = pq.updateTop();
}
OutOfOrderTopScoreDocCollector的collect函数如下：
public void collect(int doc) throws IOException {
float score = scorer.score();
totalHits++;
doc += docBase;
if (score < pqTop.score || (score == pqTop.score && doc > pqTop.doc)) {
return;
}
pqTop.doc = doc;
pqTop.score = score;
pqTop = pq.updateTop();
}
从上面的代码可以看出，collector的作用就是首先计算文档的打分，然后根据打分，将文档放入优先级队列(最小堆)中，最后在优先级队列中取前N篇文档。
然而存在一个问题，如果要取10篇文档，而第8,9,10,11,12篇文档的打分都相同，则抛弃那些呢？Lucene的策略是，在文档打分相同的情况下，文档号小的优先。
也即8,9,10被保留，11,12被抛弃。
由上面的叙述可知，创建collector的时候，根据文档是否将按照文档号从小到大的顺序返回而创建InOrderTopScoreDocCollector或者OutOfOrderTopScoreDocCollector。
对于InOrderTopScoreDocCollector，由于文档是按照顺序返回的，后来的文档号肯定大于前面的文档号，因而当score <= pqTop.score的时候，直接抛弃。
对于OutOfOrderTopScoreDocCollector，由于文档不是按顺序返回的，因而当score
BooleanScorer2的打分函数如下：
将子语句的打分乘以coordpublic float score() throws IOException {
coordinator.nrMatchers = 0;
float sum = countingSumScorer.score();
return sum * coordinator.coordFactors[coordinator.nrMatchers];
}
ConjunctionScorer的打分函数如下：
将取交集的子语句的打分相加，然后乘以coordpublic float score() throws IOException {
float sum = 0.0f;
for (int i = 0; i < scorers.length; i++) {
sum += scorers[i].score();
}
return sum * coord;
}
DisjunctionSumScorer的打分函数如下：
public float score() throws IOException { return currentScore; }
currentScore计算如下：
currentScore += scorerDocQueue.topScore();
以上计算是在DisjunctionSumScorer的倒排表合并算法中进行的，其是取堆顶的打分函数。
public final float topScore() throws IOException {
return topHSD.scorer.score();
}
ReqExclScorer的打分函数如下：
仅仅取required语句的打分public float score() throws IOException {
return reqScorer.score();
}
ReqOptSumScorer的打分函数如下：
上面曾经指出，ReqOptSumScorer的nextDoc()函数仅仅返回required语句的文档号。而optional的部分仅仅在打分的时候有所体现，从下面的实现可以看出optional的语句的分数加到required语句的分数上，也即文档还是required语句包含的文档，只不过是当此文档能够满足optional的语句的时候，打分得到增加。public float score() throws IOException {
int curDoc = reqScorer.docID();
float reqScore = reqScorer.score();
if (optScorer == null) {
return reqScore;
}
int optScorerDoc = optScorer.docID();
if (optScorerDoc < curDoc && (optScorerDoc = optScorer.advance(curDoc)) == NO_MORE_DOCS) {
optScorer = null;
return reqScore;
}
return optScorerDoc == curDoc ? reqScore + optScorer.score() : reqScore;
}
TermScorer的打分函数如下：
整个Scorer及SumScorer对象树的打分计算，最终都会源自叶子节点TermScorer上。从TermScorer的计算可以看出，它计算出tf * norm * weightValue = tf * norm * queryNorm * idf^2 * t.getBoost()public float score() {
int f = freqs[pointer];
float raw = f < SCORE_CACHE_SIZE ? scoreCache[f] : getSimilarity().tf(f)*weightValue;
return norms == null ? raw : raw * SIM_NORM_DECODER[norms[doc] & 0xFF];
}
Lucene的打分公式整体如下，2.4.1计算了图中的红色的部分，此步计算了蓝色的部分：
打分计算到此结束。
IndexSearcher.search(Weight, Filter, int)中，在收集完文档后，调用collector.topDocs()返回打分最高的N篇文档：
public final TopDocs topDocs() {
return topDocs(0, totalHits < pq.size() ? totalHits : pq.size());
}
public final TopDocs topDocs(int start, int howMany) {
int size = totalHits < pq.size() ? totalHits : pq.size();
howMany = Math.min(size - start, howMany);
ScoreDoc[] results = new ScoreDoc[howMany];
//由于pq是最小堆，因而要首先弹出最小的文档。比如qp中总共有50篇文档，想取第5到10篇文档，则应该先弹出打分最小的40篇文档。
for (int i = pq.size() - start - howMany; i > 0; i--) { pq.pop(); }
populateResults(results, howMany);
return newTopDocs(results, start);
}
protected void populateResults(ScoreDoc[] results, int howMany) {
//然后再从pq弹出第5到10篇文档，并按照打分从大到小的顺序放入results中。
for (int i = howMany - 1; i >= 0; i--) {
results[i] = pq.pop();
}
}
protected TopDocs newTopDocs(ScoreDoc[] results, int start) {
return results == null ? EMPTY_TOPDOCS : new TopDocs(totalHits, results);
}
以上叙述的是搜索过程中如何进行倒排表合并以及计算打分。然而索引信息是从索引文件中读出来的，下面分析如何读取这些信息。
其实读取的信息无非是两种信息，一个是词典信息，一个是倒排表信息。
词典信息的读取是在Scorer对象树生成的时候进行的，真正读取这些信息的是叶子节点TermScorer。
倒排表信息的读取时在合并倒排表的时候进行的，真正读取这些信息的也是叶子节点TermScorer.nextDoc()。
此步是在TermWeight.scorer(IndexReader, boolean, boolean) 中进行的，其代码如下：
public Scorer scorer(IndexReader reader, boolean scoreDocsInOrder, boolean topScorer) {
TermDocs termDocs = reader.termDocs(term);
if (termDocs == null)
return null;
return new TermScorer(this, termDocs, similarity, reader.norms(term.field()));
}
ReadOnlySegmentReader.termDocs(Term)是找到Term并生成用来读倒排表的TermDocs对象：
public TermDocs termDocs(Term term) throws IOException {
ensureOpen();
TermDocs termDocs = termDocs();
termDocs.seek(term);
return termDocs;
}
termDocs()函数首先生成SegmentTermDocs对象，用于读取倒排表：
protected SegmentTermDocs(SegmentReader parent) {
this.parent = parent;
this.freqStream = (IndexInput) parent.core.freqStream.clone();//用于读取freq
synchronized (parent) {
this.deletedDocs = parent.deletedDocs;
}
this.skipInterval = parent.core.getTermsReader().getSkipInterval();
this.maxSkipLevels = parent.core.getTermsReader().getMaxSkipLevels();
}
SegmentTermDocs.seek(Term)是读取词典中的Term，并将freqStream指向此Term对应的倒排表：
public void seek(Term term) throws IOException {
TermInfo ti = parent.core.getTermsReader().get(term);
seek(ti, term);
}
TermInfosReader.get(Term, boolean)主要是读取词典中的Term得到TermInfo，代码如下：
private TermInfo get(Term term, boolean useCache) {
if (size == 0) return null;
ensureIndexIsRead();
TermInfo ti;
ThreadResources resources = getThreadResources();
SegmentTermEnum enumerator = resources.termEnum;
seekEnum(enumerator, getIndexOffset(term));
enumerator.scanTo(term);
if (enumerator.term() != null && term.compareTo(enumerator.term()) == 0) {
ti = enumerator.termInfo();
} else {
ti = null;
}
return ti;
}
在IndexReader打开一个索引文件夹的时候，会从tii文件中读出的Term index到indexPointers数组中，TermInfosReader.seekEnum(SegmentTermEnum enumerator, int indexOffset)负责在indexPointers数组中找Term对应的tis文件中所在的跳表区域的位置。
private final void seekEnum(SegmentTermEnum enumerator, int indexOffset) throws IOException {
enumerator.seek(indexPointers[indexOffset],
(indexOffset * totalIndexInterval) - 1,
indexTerms[indexOffset], indexInfos[indexOffset]);
}
final void SegmentTermEnum.seek(long pointer, int p, Term t, TermInfo ti) {
input.seek(pointer);
position = p;
termBuffer.set(t);
prevBuffer.reset();
termInfo.set(ti);
}
SegmentTermEnum.scanTo(Term)在跳表区域中，一个一个往下找，直到找到Term：
final int scanTo(Term term) throws IOException {
scanBuffer.set(term);
int count = 0;
//不断取得下一个term到termBuffer中，目标term放入scanBuffer中，当两者相等的时候，目标Term找到。
while (scanBuffer.compareTo(termBuffer) > 0 && next()) {
count++;
}
return count;
}
public final boolean next() throws IOException {
if (position++ >= size - 1) {
prevBuffer.set(termBuffer);
termBuffer.reset();
return false;
}
prevBuffer.set(termBuffer);
//读取Term的字符串
termBuffer.read(input, fieldInfos);
//读取docFreq，也即多少文档包含此Term
termInfo.docFreq = input.readVInt();
//读取偏移量
termInfo.freqPointer += input.readVLong();
termInfo.proxPointer += input.readVLong();
if (termInfo.docFreq >= skipInterval)
termInfo.skipOffset = input.readVInt();
indexPointer += input.readVLong();
return true;
}
TermBuffer.read(IndexInput, FieldInfos) 代码如下：
public final void read(IndexInput input, FieldInfos fieldInfos) {
this.term = null;
int start = input.readVInt();
int length = input.readVInt();
int totalLength = start + length;
text.setLength(totalLength);
input.readChars(text.result, start, length);
this.field = fieldInfos.fieldName(input.readVInt());
}
SegmentTermDocs.seek(TermInfo ti, Term term)根据TermInfo，将freqStream指向此Term对应的倒排表位置：
void seek(TermInfo ti, Term term) {
count = 0;
FieldInfo fi = parent.core.fieldInfos.fieldInfo(term.field);
df = ti.docFreq;
doc = 0;
freqBasePointer = ti.freqPointer;
proxBasePointer = ti.proxPointer;
skipPointer = freqBasePointer + ti.skipOffset;
freqStream.seek(freqBasePointer);
haveSkipped = false;
}
当读出Term的信息得到TermInfo后，并且freqStream指向此Term的倒排表位置的时候，下面就是在TermScorer.nextDoc()函数中读取倒排表信息：
public int nextDoc() throws IOException {
pointer++;
if (pointer >= pointerMax) {
pointerMax = termDocs.read(docs, freqs);
if (pointerMax != 0) {
pointer = 0;
} else {
termDocs.close();
return doc = NO_MORE_DOCS;
}
}
doc = docs[pointer];
return doc;
}
SegmentTermDocs.read(int[], int[]) 代码如下：
public int read(final int[] docs, final int[] freqs) {
final int length = docs.length;
int i = 0;
while (i < length && count < df) {
//读取docid
final int docCode = freqStream.readVInt();
doc += docCode >>> 1;
if ((docCode & 1) != 0)
freq = 1;
else
freq = freqStream.readVInt(); //读取freq
count++;
if (deletedDocs == null || !deletedDocs.get(doc)) {
docs[i] = doc;
freqs[i] = freq;
++i;
}
return i;
}
}
转载于:https://www.cnblogs.com/jinhong-lu/p/4559431.html
