Lucene增强功能：Payload的应用

有关Lucene的Payload的相关内容，可以参考如下链接，介绍的非常详细，值得参考：

http://www.ibm.com/developerworks/cn/opensource/os-cn-lucene-pl/
http://www.lucidimagination.com/blog/2009/08/05/getting-started-with-payloads/

例如，有这样的一个需求：

现在有两篇文档内容非常相似，如下所示：

文档1：egg tomato potato bread文档2：egg book potato bread

现在我想要查询食物（foods），而且是查询关键词是egg，如何能够区别出上面两个文档哪一个更是我想要的？

可以看到上面两篇文档，文档1中描述的各项都是食物，而文档2中的book不是食物，基于上述需求，应该是文档1比文档2更相关，在查询结果中，文档1排名应该更靠前。通过上面
http://www.lucidimagination.com/blog/2009/08/05/getting-started-with-payloads/中给出的方法，可以在文档中，对给定词出现在文档的出现的权重信息（egg在文档1与文档中，以foods来衡量，文档1更相关），可以在索引之前处理一下，为egg增加payload信息，例如：

文档1：egg|0.984 tomato potato bread文档2：egg|0.356 book potato bread

然后再进行索引，通过Lucene提供的PayloadTermQuery就能够分辨出上述egg这个Term的不同。在Lucene中，实际上是将我们存储的Payload数据，如上述“|”分隔后面的数字，乘到了tf上，然后在进行权重的计算。

下面，我们再看一下，增加一个Field来存储Payload数据，而源文档不需要进行修改，或者，我们可以在索引之前对文档进行一个处理，例如分类，通过分类可以给不同的文档所属类别的不同程度，计算一个Payload数值。

为了能够使用存储的Payload数据信息，结合上面提出的实例，我们需要按照如下步骤去做：

第一，待索引数据处理

例如，增加category这个Field存储类别信息，content这个Field存储上面的内容：

文档1：new Field("category", "foods|0.984 shopping|0.503", Field.Store.YES, Field.Index.ANALYZED)new Field("content", "egg tomato potato bread", Field.Store.YES, Field.Index.ANALYZED)文档2：new Field("category", "foods|0.356 shopping|0.791", Field.Store.YES, Field.Index.ANALYZED)new Field("content", "egg book potato bread", Field.Store.YES, Field.Index.ANALYZED)

 

第二，实现解析Payload数据的Analyzer

由于Payload信息存储在category这个Field中，多个类别之间使用空格分隔，每个类别内容是以“|”分隔的，所以我们的Analyzer就要能够解析它。Lucene提供了DelimitedPayloadTokenFilter，能够处理具有分隔符的情况。我们的实现如下所示：

package org.shirdrn.lucene.query.payloadquery;import java.io.Reader;import org.apache.lucene.analysis.Analyzer;import org.apache.lucene.analysis.TokenStream;import org.apache.lucene.analysis.WhitespaceTokenizer;import org.apache.lucene.analysis.payloads.DelimitedPayloadTokenFilter;import org.apache.lucene.analysis.payloads.PayloadEncoder;public class PayloadAnalyzer extends Analyzer {private PayloadEncoder encoder;PayloadAnalyzer(PayloadEncoder encoder) {this.encoder = encoder;}@SuppressWarnings("deprecation")public TokenStream tokenStream(String fieldName, Reader reader) {TokenStream result = new WhitespaceTokenizer(reader); // 用来解析空格分隔的各个类别result = new DelimitedPayloadTokenFilter(result, '|', encoder); // 在上面分词的基础上，在进行Payload数据解析return result;}}

第三， 实现Similarity计算得分

Lucene中Similarity类中提供了scorePayload方法，用于计算Payload值来对文档贡献得分，我们重写了该方法，实现如下所示：

package org.shirdrn.lucene.query.payloadquery;import org.apache.lucene.analysis.payloads.PayloadHelper;import org.apache.lucene.search.DefaultSimilarity;public class PayloadSimilarity extends DefaultSimilarity {private static final long serialVersionUID = 1L;@Overridepublic float scorePayload(int docId, String fieldName, int start, int end,byte[] payload, int offset, int length) {return PayloadHelper.decodeFloat(payload, offset);}}

通过使用PayloadHelper这个工具类可以获取到Payload值，然后在计算文档得分的时候起到作用。

第四，创建索引

在创建索引的时候，需要使用到我们上面实现的Analyzer和Similarity，代码如下所示：

package org.shirdrn.lucene.query.payloadquery;import java.io.File;import java.io.IOException;import org.apache.lucene.analysis.Analyzer;import org.apache.lucene.analysis.payloads.FloatEncoder;import org.apache.lucene.document.Document;import org.apache.lucene.document.Field;import org.apache.lucene.index.CorruptIndexException;import org.apache.lucene.index.IndexWriter;import org.apache.lucene.index.IndexWriterConfig;import org.apache.lucene.index.IndexWriterConfig.OpenMode;import org.apache.lucene.search.Similarity;import org.apache.lucene.store.FSDirectory;import org.apache.lucene.store.LockObtainFailedException;import org.apache.lucene.util.Version;public class PayloadIndexing {private IndexWriter indexWriter = null;private final Analyzer analyzer = new PayloadAnalyzer(new FloatEncoder()); // 使用PayloadAnalyzer，并指定Encoderprivate final Similarity similarity = new PayloadSimilarity(); // 实例化一个PayloadSimilarityprivate IndexWriterConfig config = null;public PayloadIndexing(String indexPath) throws CorruptIndexException, LockObtainFailedException, IOException {File indexFile = new File(indexPath);config = new IndexWriterConfig(Version.LUCENE_31, analyzer);config.setOpenMode(OpenMode.CREATE).setSimilarity(similarity); // 设置计算得分的SimilarityindexWriter = new IndexWriter(FSDirectory.open(indexFile), config);}public void index() throws CorruptIndexException, IOException {Document doc1 = new Document();doc1.add(new Field("category", "foods|0.984 shopping|0.503", Field.Store.YES, Field.Index.ANALYZED));doc1.add(new Field("content", "egg tomato potato bread", Field.Store.YES, Field.Index.ANALYZED));indexWriter.addDocument(doc1);Document doc2 = new Document();doc2.add(new Field("category", "foods|0.356 shopping|0.791", Field.Store.YES, Field.Index.ANALYZED));doc2.add(new Field("content", "egg book potato bread", Field.Store.YES, Field.Index.ANALYZED));indexWriter.addDocument(doc2);indexWriter.close();}public static void main(String[] args) throws CorruptIndexException, IOException {new PayloadIndexing("E:\\index").index();}}

第五，查询

查询的时候，我们可以构造PayloadTermQuery来进行查询。代码如下所示：

package org.shirdrn.lucene.query.payloadquery;import java.io.File;import java.io.IOException;import org.apache.lucene.document.Document;import org.apache.lucene.index.CorruptIndexException;import org.apache.lucene.index.IndexReader;import org.apache.lucene.index.Term;import org.apache.lucene.queryParser.ParseException;import org.apache.lucene.search.BooleanQuery;import org.apache.lucene.search.Explanation;import org.apache.lucene.search.IndexSearcher;import org.apache.lucene.search.ScoreDoc;import org.apache.lucene.search.TopScoreDocCollector;import org.apache.lucene.search.BooleanClause.Occur;import org.apache.lucene.search.payloads.AveragePayloadFunction;import org.apache.lucene.search.payloads.PayloadTermQuery;import org.apache.lucene.store.NIOFSDirectory;public class PayloadSearching {private IndexReader indexReader;private IndexSearcher searcher;public PayloadSearching(String indexPath) throws CorruptIndexException, IOException {indexReader = IndexReader.open(NIOFSDirectory.open(new File(indexPath)), true);searcher = new IndexSearcher(indexReader);searcher.setSimilarity(new PayloadSimilarity()); // 设置自定义的PayloadSimilarity}public ScoreDoc[] search(String qsr) throws ParseException, IOException {int hitsPerPage = 10;BooleanQuery bq = new BooleanQuery();for(String q : qsr.split(" ")) {bq.add(createPayloadTermQuery(q), Occur.MUST);}TopScoreDocCollector collector = TopScoreDocCollector.create(5 * hitsPerPage, true);searcher.search(bq, collector);ScoreDoc[] hits = collector.topDocs().scoreDocs;for (int i = 0; i < hits.length; i++) {int docId = hits[i].doc; // 文档编号Explanation  explanation  = searcher.explain(bq, docId);System.out.println(explanation.toString());}return hits;}public void display(ScoreDoc[] hits, int start, int end) throws CorruptIndexException, IOException {end = Math.min(hits.length, end);for (int i = start; i < end; i++) {Document doc = searcher.doc(hits[i].doc);int docId = hits[i].doc; // 文档编号float score = hits[i].score; // 文档得分System.out.println(docId + "\t" + score + "\t" + doc + "\t");}}public void close() throws IOException {searcher.close();indexReader.close();}private PayloadTermQuery createPayloadTermQuery(String item) {PayloadTermQuery ptq = null;if(item.indexOf("^")!=-1) {String[] a = item.split("\\^");String field = a[0].split(":")[0];String token = a[0].split(":")[1];ptq = new PayloadTermQuery(new Term(field, token), new AveragePayloadFunction());ptq.setBoost(Float.parseFloat(a[1].trim()));} else {String field = item.split(":")[0];String token = item.split(":")[1];ptq = new PayloadTermQuery(new Term(field, token), new AveragePayloadFunction());}return ptq;}public static void main(String[] args) throws ParseException, IOException {int start = 0, end = 10;//String queries = "category:foods^123.0 content:bread^987.0";String queries = "category:foods content:egg";PayloadSearching payloadSearcher = new PayloadSearching("E:\\index");payloadSearcher.display(payloadSearcher.search(queries), start, end);payloadSearcher.close();}}

我们可以看到查询结果，两个文档的相关度排序：

00.3314532Document<stored,indexed,tokenized<category:foods|0.984 shopping|0.503> stored,indexed,tokenized<content:egg tomato potato bread>>10.21477573Document<stored,indexed,tokenized<category:foods|0.356 shopping|0.791> stored,indexed,tokenized<content:egg book potato bread>>

通过输出计算得分的解释信息，如下所示：

0.3314532 = (MATCH) sum of:  0.18281947 = (MATCH) weight(category:foods in 0), product of:    0.70710677 = queryWeight(category:foods), product of:      0.5945349 = idf(category:  foods=2)      1.1893445 = queryNorm    0.2585458 = (MATCH) fieldWeight(category:foods in 0), product of:      0.6957931 = (MATCH) btq, product of:        0.70710677 = tf(phraseFreq=0.5)        0.984 = scorePayload(...)      0.5945349 = idf(category:  foods=2)      0.625 = fieldNorm(field=category, doc=0)  0.14863372 = (MATCH) weight(content:egg in 0), product of:    0.70710677 = queryWeight(content:egg), product of:      0.5945349 = idf(content:  egg=2)      1.1893445 = queryNorm    0.21019982 = (MATCH) fieldWeight(content:egg in 0), product of:      0.70710677 = (MATCH) btq, product of:        0.70710677 = tf(phraseFreq=0.5)        1.0 = scorePayload(...)      0.5945349 = idf(content:  egg=2)      0.5 = fieldNorm(field=content, doc=0)0.21477571 = (MATCH) sum of:  0.066142 = (MATCH) weight(category:foods in 1), product of:    0.70710677 = queryWeight(category:foods), product of:      0.5945349 = idf(category:  foods=2)      1.1893445 = queryNorm    0.09353892 = (MATCH) fieldWeight(category:foods in 1), product of:      0.25173002 = (MATCH) btq, product of:        0.70710677 = tf(phraseFreq=0.5)        0.356 = scorePayload(...)      0.5945349 = idf(category:  foods=2)      0.625 = fieldNorm(field=category, doc=1)  0.14863372 = (MATCH) weight(content:egg in 1), product of:    0.70710677 = queryWeight(content:egg), product of:      0.5945349 = idf(content:  egg=2)      1.1893445 = queryNorm    0.21019982 = (MATCH) fieldWeight(content:egg in 1), product of:      0.70710677 = (MATCH) btq, product of:        0.70710677 = tf(phraseFreq=0.5)        1.0 = scorePayload(...)      0.5945349 = idf(content:  egg=2)      0.5 = fieldNorm(field=content, doc=1)

我们可以看到，除了在计算category权重的时候，tf上乘了一个Payload值以外，其他的都相同，也就是说，我们预期使用的Payload为文档（ID=0）贡献了得分，排名靠前了。否则，如果不使用Payload的话，查询结果中两个文档的得分是相同的（可以模拟设置他们的Payload值相同，测试一下看看）