Lucene 6.6.1源码分析---倒排索引

叁歲伎倆 2022-04-15 04:17 221阅读 0赞

之前分析Lucene的添加文档过程是已经知道，文档的添加可分解为域的添加，而域的添加过程就是倒排索引的过程。本文将以域的添加作为入口来分析倒排索引的过程。首先看添加域的入口方法：

private int processField(IndexableField field, long fieldGen, int fieldCount) throws IOException, AbortingException {
        String fieldName = field.name();
        IndexableFieldType fieldType = field.fieldType();
    
        PerField fp = null;
    
        if (fieldType.indexOptions() == null) {
          throw new NullPointerException("IndexOptions must not be null (field: \"" + field.name() + "\")");
        }
    
        // Invert indexed fields:
        if (fieldType.indexOptions() != IndexOptions.NONE) {
          
          // if the field omits norms, the boost cannot be indexed.
          if (fieldType.omitNorms() && field.boost() != 1.0f) {
            throw new UnsupportedOperationException("You cannot set an index-time boost: norms are omitted for field '" + field.name() + "'");
          }
          
          fp = getOrAddField(fieldName, fieldType, true);
          boolean first = fp.fieldGen != fieldGen;
          fp.invert(field, first);
    
          if (first) {
            fields[fieldCount++] = fp;
            fp.fieldGen = fieldGen;
          }
        } else {
          verifyUnIndexedFieldType(fieldName, fieldType);
        }
    
        // Add stored fields:
        if (fieldType.stored()) {
          if (fp == null) {
            fp = getOrAddField(fieldName, fieldType, false);
          }
          if (fieldType.stored()) {
            String value = field.stringValue();
            if (value != null && value.length() > IndexWriter.MAX_STORED_STRING_LENGTH) {
              throw new IllegalArgumentException("stored field \"" + field.name() + "\" is too large (" + value.length() + " characters) to store");
            }
            try {
              storedFieldsConsumer.writeField(fp.fieldInfo, field);
            } catch (Throwable th) {
              throw AbortingException.wrap(th);
            }
          }
        }
    
        DocValuesType dvType = fieldType.docValuesType();
        if (dvType == null) {
          throw new NullPointerException("docValuesType must not be null (field: \"" + fieldName + "\")");
        }
        if (dvType != DocValuesType.NONE) {
          if (fp == null) {
            fp = getOrAddField(fieldName, fieldType, false);
          }
          indexDocValue(fp, dvType, field);
        }
        if (fieldType.pointDimensionCount() != 0) {
          if (fp == null) {
            fp = getOrAddField(fieldName, fieldType, false);
          }
          indexPoint(fp, field);
        }
        
        return fieldCount;
      }

## 1、 获取域名称fieldName、域类型fieldType。fieldType中包含着索引类型IndexOptions，索引类型总共有以下5钟： ##

### a) NONE：不索引 ###

### b) DOCS：仅索引文档 ###

### c) DOCS\_AND\_FREQS：索引文档，词项频率 ###

### d) DOCS\_AND\_FREQS\_AND\_POSITIONS：索引文档，词项频率，词项位置 ###

### e) DOCS\_AND\_FREQS\_AND\_POSITIONS\_AND\_OFFSETS：索引文档，词项频率，词项位置，词项偏移量 ###

## 2、 如果索引类型为NONE，则该域不做任何索引操作。也就不可能以这个域作为查询条件去查询相关文档了；如果该域不为NONE，则需要做索引。根据fieldName从PerField数组中获取对应的PerField（如果没有就先新建再获取），PerField中保存了域的信息与索引信息。调用fp.invert(field, first)做倒排索引，然后将倒排索引的数据保存在PerField中。倒排索引是本文重点，后面会重点研究。 ##

## 3、 用fieldType.stored判断这个域是否需要存储，如果需要存储，根据fieldName从PerField数组中获取对应的PerField（如果没有就先新建再获取），PerField中保存了域的信息与索引信息。再调用storedFieldsConsumer.writeField(fp.fieldInfo, field)将这个域的数据写到内存中。 ##

## 4、 待所有域都经过以上步骤处理过后，调用finishStoredFields();将需要存储的域的数据从内存中写到.fdx文件中 ##

接下来我们来分析一下倒排索引的过程

public void invert(IndexableField field, boolean first) throws IOException, AbortingException {
          if (first) {
            // First time we're seeing this field (indexed) in
            // this document:
            invertState.reset();
          }
    
          IndexableFieldType fieldType = field.fieldType();
    
          IndexOptions indexOptions = fieldType.indexOptions();
          fieldInfo.setIndexOptions(indexOptions);
    
          if (fieldType.omitNorms()) {
            fieldInfo.setOmitsNorms();
          }
    
          final boolean analyzed = fieldType.tokenized() && docState.analyzer != null;
            
          // only bother checking offsets if something will consume them.
          // TODO: after we fix analyzers, also check if termVectorOffsets will be indexed.
          final boolean checkOffsets = indexOptions == IndexOptions.DOCS_AND_FREQS_AND_POSITIONS_AND_OFFSETS;
    
          /*
           * To assist people in tracking down problems in analysis components, we wish to write the field name to the infostream
           * when we fail. We expect some caller to eventually deal with the real exception, so we don't want any 'catch' clauses,
           * but rather a finally that takes note of the problem.
           */
          boolean succeededInProcessingField = false;
          try (TokenStream stream = tokenStream = field.tokenStream(docState.analyzer, tokenStream)) {
            // reset the TokenStream to the first token
            stream.reset();
            invertState.setAttributeSource(stream);
            termsHashPerField.start(field, first);
    
            while (stream.incrementToken()) {
    
              // If we hit an exception in stream.next below
              // (which is fairly common, e.g. if analyzer
              // chokes on a given document), then it's
              // non-aborting and (above) this one document
              // will be marked as deleted, but still
              // consume a docID
    
              int posIncr = invertState.posIncrAttribute.getPositionIncrement();
              invertState.position += posIncr;
              if (invertState.position < invertState.lastPosition) {
                if (posIncr == 0) {
                  throw new IllegalArgumentException("first position increment must be > 0 (got 0) for field '" + field.name() + "'");
                } else if (posIncr < 0) {
                  throw new IllegalArgumentException("position increment must be >= 0 (got " + posIncr + ") for field '" + field.name() + "'");
                } else {
                  throw new IllegalArgumentException("position overflowed Integer.MAX_VALUE (got posIncr=" + posIncr + " lastPosition=" + invertState.lastPosition + " position=" + invertState.position + ") for field '" + field.name() + "'");
                }
              } else if (invertState.position > IndexWriter.MAX_POSITION) {
                throw new IllegalArgumentException("position " + invertState.position + " is too large for field '" + field.name() + "': max allowed position is " + IndexWriter.MAX_POSITION);
              }
              invertState.lastPosition = invertState.position;
              if (posIncr == 0) {
                invertState.numOverlap++;
              }
                  
              if (checkOffsets) {
                int startOffset = invertState.offset + invertState.offsetAttribute.startOffset();
                int endOffset = invertState.offset + invertState.offsetAttribute.endOffset();
                if (startOffset < invertState.lastStartOffset || endOffset < startOffset) {
                  throw new IllegalArgumentException("startOffset must be non-negative, and endOffset must be >= startOffset, and offsets must not go backwards "
                                                     + "startOffset=" + startOffset + ",endOffset=" + endOffset + ",lastStartOffset=" + invertState.lastStartOffset + " for field '" + field.name() + "'");
                }
                invertState.lastStartOffset = startOffset;
              }
    
              invertState.length++;
              if (invertState.length < 0) {
                throw new IllegalArgumentException("too many tokens in field '" + field.name() + "'");
              }
              //System.out.println("  term=" + invertState.termAttribute);
    
              // If we hit an exception in here, we abort
              // all buffered documents since the last
              // flush, on the likelihood that the
              // internal state of the terms hash is now
              // corrupt and should not be flushed to a
              // new segment:
              try {
                termsHashPerField.add();
              } catch (MaxBytesLengthExceededException e) {
                byte[] prefix = new byte[30];
                BytesRef bigTerm = invertState.termAttribute.getBytesRef();
                System.arraycopy(bigTerm.bytes, bigTerm.offset, prefix, 0, 30);
                String msg = "Document contains at least one immense term in field=\"" + fieldInfo.name + "\" (whose UTF8 encoding is longer than the max length " + DocumentsWriterPerThread.MAX_TERM_LENGTH_UTF8 + "), all of which were skipped.  Please correct the analyzer to not produce such terms.  The prefix of the first immense term is: '" + Arrays.toString(prefix) + "...', original message: " + e.getMessage();
                if (docState.infoStream.isEnabled("IW")) {
                  docState.infoStream.message("IW", "ERROR: " + msg);
                }
                // Document will be deleted above:
                throw new IllegalArgumentException(msg, e);
              } catch (Throwable th) {
                throw AbortingException.wrap(th);
              }
            }
    
            // trigger streams to perform end-of-stream operations
            stream.end();
    
            // TODO: maybe add some safety? then again, it's already checked 
            // when we come back around to the field...
            invertState.position += invertState.posIncrAttribute.getPositionIncrement();
            invertState.offset += invertState.offsetAttribute.endOffset();
    
            /* if there is an exception coming through, we won't set this to true here:*/
            succeededInProcessingField = true;
          } finally {
            if (!succeededInProcessingField && docState.infoStream.isEnabled("DW")) {
              docState.infoStream.message("DW", "An exception was thrown while processing field " + fieldInfo.name);
            }
          }
    
          if (analyzed) {
            invertState.position += docState.analyzer.getPositionIncrementGap(fieldInfo.name);
            invertState.offset += docState.analyzer.getOffsetGap(fieldInfo.name);
          }
    
          invertState.boost *= field.boost();
    }

## 1、 tokenStream = field.tokenStream(docState.analyzer, tokenStream)用分析器analyzer将该域的数据分析为词汇单元流tokenStream，它就像一个枚举类一样，通过stream.incrementToken()获取下一个词汇单元。 ##

## 2、 FieldInvertState invertState，TermsHashPerField termsHashPerField 是 被PerField所持有的对象，invertState用于记录该域中词汇单元的数量以及位置信息。 ##

invertState.posIncrAttribute 是指前后词汇单元位置的增量，一般为1，说明前后两个词汇单元中间没有空位。如果大于1，说明前后两个词汇单元中间存在空位，这可能是分析器将停词删除后留下的空位。如果如果等于0，有可能是存在同义词的情况了。这时候记录词汇覆盖数的invertState.numOverlap就会加1。invertState.length用于记录词汇数量。

## 3、 termsHashPerField.add()则用于完成词项索引过程，将索引内容存储于内存缓冲之中。 ##

### a) bytesHash.add将词项的文本转成字节数组及数组长度存在bytesHash中。完了会返回一个termId。如果termId<0表示这个词项不是第一次添加。 ###

### b) newTerm(termID) 根据termID创建词项对象，在创建过程中，把词频、文档ID、偏移量、位置等信息都记录在了FreqProxPostingsArray之中。在FreqProxPostingsArray中维护了词频、文档ID、偏移量、位置等信息的数组，数组的每一个元素就是一个词项所对应的信息。下面是FreqProxPostingsArray的代码： ###

static final class FreqProxPostingsArray extends ParallelPostingsArray {
        public FreqProxPostingsArray(int size, boolean writeFreqs, boolean writeProx, boolean writeOffsets) {
          super(size);
          if (writeFreqs) {
            termFreqs = new int[size];
          }
          lastDocIDs = new int[size];
          lastDocCodes = new int[size];
          if (writeProx) {
            lastPositions = new int[size];
            if (writeOffsets) {
              lastOffsets = new int[size];
            }
          } else {
            assert !writeOffsets;
          }
          //System.out.println("PA init freqs=" + writeFreqs + " pos=" + writeProx + " offs=" + writeOffsets);
        }
    
        int termFreqs[];                                   // # times this term occurs in the current doc
        int lastDocIDs[];                                  // Last docID where this term occurred
        int lastDocCodes[];                                // Code for prior doc
        int lastPositions[];                               // Last position where this term occurred
        int lastOffsets[];                                 // Last endOffset where this term occurred

参考 [Lucene索引过程分析][Lucene]

[Lucene]: https://blog.csdn.net/jediael_lu/article/details/34433835