Lucene学习总结之七:Lucene搜索过程解析

时间:2021-09-05 15:47:46

一、Lucene搜索过程总论

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:

Lucene学习总结之七:Lucene搜索过程解析

总共包括以下几个过程:

  1. IndexReader打开索引文件,读取并打开指向索引文件的流。
  2. 用户输入查询语句
  3. 将查询语句转换为查询对象Query对象树
  4. 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)。
  5. 构造Scorer对象树,用于计算打分(TermScorer.score())。
  6. 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。
  7. 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。
  8. 将收集的结果集合及打分返回给用户。

二、Lucene搜索详细过程

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

2.1、打开IndexReader指向索引文件夹

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:

2.1.1、找到最新的segment_N文件

  • 由于segment_N是整个索引中总的元数据,因而正确的选择segment_N更加重要。
  • 然而有时候为了使得索引能够保存在另外的存储系统上,有时候需要用NFS mount一个远程的磁盘来存放索引,然而NFS为了提高性能,在本地有Cache,因而有可能使得此次打开的索引不是另外的writer写入的最新信息,所以在此处用了双保险。
  • 一方面,列出所有的segment_N,并取出其中的最大的N,设为genA

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genB

IndexInput 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"

2.1.2、通过segment_N文件中保存的各个段的信息打开各个段

  • 从segment_N中读出段的元数据信息,生成SegmentInfos

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ReadOnlyDirectoryReader

SegmentReader[] 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;

}

2.1.3、得到的IndexReader对象如下

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。

2.2、打开IndexSearcher

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:

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。

2.3、QueryParser解析查询语句生成查询对象

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

Lucene学习总结之七:Lucene搜索过程解析

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。

2.4、搜索查询对象

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

2.4.1、创建Weight对象树,计算Term Weight

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
2.4.1.1、重写Query对象树

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".

Lucene学习总结之七:Lucene搜索过程解析

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

Lucene学习总结之七:Lucene搜索过程解析

2.4、搜索查询对象

2.4.1.2、创建Weight对象树

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

public BooleanWeight(Searcher searcher) {

this.similarity = getSimilarity(searcher);

weights = new ArrayList(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:

Lucene学习总结之七:Lucene搜索过程解析

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

Lucene学习总结之七:Lucene搜索过程解析

2.4.1.3、计算Term Weight分数

(1) 首先计算sumOfSquaredWeights

按照公式:

Lucene学习总结之七:Lucene搜索过程解析

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:

Lucene学习总结之七:Lucene搜索过程解析

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:

Lucene学习总结之七:Lucene搜索过程解析

2.4.2、创建Scorer及SumScorer对象树

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ReqExclScorer
  • MUST,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)

Lucene学习总结之七:Lucene搜索过程解析

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)

Lucene学习总结之七:Lucene搜索过程解析

2.4、搜索查询对象

2.4.3、进行倒排表合并

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ReqExclScorer
  • ReqOptSumScorer

下面我们一一分析:

2.4.3.1、交集ConjunctionScorer(+A +B)

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:

Lucene学习总结之七:Lucene搜索过程解析

(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:

Lucene学习总结之七:Lucene搜索过程解析

(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。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 10,first指向第1项,advance到文档11,然后设doc = 11,first指向第2项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第2项,advance到文档11,然后设doc = 11,first指向第3项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第3项,advance到文档11,然后设doc = 11,first指向第4项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第4项,advance到文档11,然后设doc = 11,first指向第5项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第5项,advance到文档11,然后设doc = 11,first指向第6项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第6项,advance到文档11,然后设doc = 11,first指向第7项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第7项,advance到文档11,然后设doc = 11,first指向第0项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 11,first指向第0项,advance到文档11,然后设doc = 11,first指向第1项。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

(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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第1项,advance到文档13,然后设doc = 13,first指向第2项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第2项,advance到文档13,然后设doc = 13,first指向第3项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第3项,advance到文档13,然后设doc = 13,first指向第4项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第4项,advance到文档13,然后设doc = 13,first指向第5项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第5项,advance到文档13,然后设doc = 13,first指向第6项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第6项,advance到文档13,然后设doc = 13,first指向第7项。

Lucene学习总结之七:Lucene搜索过程解析

  • doc = 13,first指向第7项,advance到文档13,然后设doc = 13,first指向第0项。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

(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。

2.4.3.2、并集DisjunctionSumScorer(A OR B)

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:

Lucene学习总结之七:Lucene搜索过程解析

(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);

}

}

}

Lucene学习总结之七:Lucene搜索过程解析

(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

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

  • 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。

Lucene学习总结之七:Lucene搜索过程解析

(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。

2.4.3.3、差集ReqExclScorer(+A -B)

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;

}

2.4.3.4、ReqOptSumScorer(+A B)

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();

}

2.4.3.5、有关BooleanScorer及scoresDocsOutOfOrder

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)

2.4、搜索查询对象

2.4.4、收集文档结果集合及计算打分

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();

2.4.4.1、创建结果文档收集器

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。

2.4.4.2、收集文档号

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

2.4.4.3、打分计算

BooleanScorer2的打分函数如下:

  • 将子语句的打分乘以coord

public 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coord

public 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:

Lucene学习总结之七:Lucene搜索过程解析

打分计算到此结束。

2.4.4.4、返回打分最高的N篇文档

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);

}

2.4.5、Lucene如何在搜索阶段读取索引信息

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()。

2.4.5.1、读取词典信息

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;

}

2.4.5.2、读取倒排表信息

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;

}

}