bobo-browse为Lucene添加分组统计
Bobo-browse是一个基于lucene的搜索结果分组统计开源插件，可以完成对搜索结果的分组面统计，比如在淘宝上搜索“衬衣”，在搜索结果顶上显示 “长袖衬衫（10321） 短袖衬衫（32561） ”等。
虽然Lucene 在3.2.0也提供了Grouping组件来提供分组统计功能，另作讨论，这里不做讨论。
据Bobo-browse项目介绍, Linkedin.com使用了该组件。
Bobo-browse仅关注搜索，对索引创建与它无关，索引的创建，继续使用标准的Lucene索引创建方法创建。
Bobo-browse项目地址为 http://sna-projects.com/bobo/
以下是测试代码使用的组件及版本：
Bobo-browse: 2.5.0-rc1
Lucene: 3.4.0
log4j: 1.2.16
Fastutil: 6.4
测试代码：
import java.util.*;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.standard.StandardAnalyzer;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriter.MaxFieldLength;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TopScoreDocCollector;
import org.apache.lucene.index.*;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.RAMDirectory;
import org.apache.lucene.util.Version;
import org.apache.lucene.search.*;
import org.apache.lucene.queryParser.*;
import com.browseengine.bobo.api.BoboBrowser;
import com.browseengine.bobo.api.BoboIndexReader;
import com.browseengine.bobo.api.Browsable;
import com.browseengine.bobo.api.BrowseFacet;
import com.browseengine.bobo.api.BrowseHit;
import com.browseengine.bobo.api.BrowseRequest;
import com.browseengine.bobo.api.BrowseResult;
import com.browseengine.bobo.api.FacetAccessible;
import com.browseengine.bobo.api.FacetSpec;
import com.browseengine.bobo.api.FacetSpec.FacetSortSpec;
import com.browseengine.bobo.facets.FacetHandler;
import com.browseengine.bobo.facets.impl.*;
import com.browseengine.bobo.facets.impl.SimpleFacetHandler;
import com.browseengine.bobo.facets.data.PredefinedTermListFactory;
public class TestBoboBrowse {
private Directory indexDir = null;
private Version luceneVersion = Version.LUCENE_34;
/**
* @param args
*/
public static void main(String[] args) {
// TODO Auto-generated method stub
TestBoboBrowse app = new TestBoboBrowse();
app.test1();
}
public void test1(){
try{
indexDir = new RAMDirectory();
createIndex();
searchTest();
indexDir.close();
}catch(Exception ex){
ex.printStackTrace();
}
}
public void createIndex(){
String[][] data = new String[][]{
new String[] {"lenovo", "Intel", "PC Core2 E8200 2GB DDR2 250GB 7200RPM 22LCD", "08998"},
new String[] {"lenovo", "Intel", "PC Core2 E8300 2GB DDR2 320GB 7200RPM 22LCD", "09998"},
new String[] {"lenovo", "Intel", "PC Core2 Q6600 2GB DDR2 320GB 7200RPM 22LCD", "11998"},
new String[] {"lenovo", "Intel", "PC Core2 QX9770 4GB DDR2 320GB 7200RPM RAID-1 22LCD", "19998"},
new String[] {"lenovo", "Intel", "PC pentium E2200 1GB DDR2 160GB 5400RPM 19LCD", "05998"},
new String[] {"hp", "Intel", "PC pentium E2180 1GB DDR2 160GB 7200RPM 20LCD", "06398"},
new String[] {"hp", "Intel", "PC Core2 E8200 2GB DDR2 250GB 5400RPM 22LCD", "08998"},
new String[] {"hp", "Intel", "PC Core2 E6550 2GB DDR2 250GB 7200RPM 20LCD", "07398"},
new String[] {"hp", "Intel", "PC Core2 QX6850 4GB DDR2 320GB 5400RPM 22LCD", "13998"},
new String[] {"asus", "AMD", "PC Core2 QX9650 4GB DDR2 450GB 7200RPM 22LCD", "17998"},
new String[] {"dell", "AMD", "PC Core2 athlon FX76 4GB DDR2 450GB 7200RPM 22LCD", "12998"}
};
try{
Analyzer analyzer = new StandardAnalyzer(luceneVersion);
// Lucene Version >= 3.2.0 (Version.LUCENE_32)
IndexWriterConfig indexConfig = new IndexWriterConfig(luceneVersion, analyzer);
indexConfig.setOpenMode(IndexWriterConfig.OpenMode.CREATE_OR_APPEND);
IndexWriter indexWriter = new IndexWriter(indexDir, indexConfig);
// Lucene Version < 3.2.0 (Version.LUCENE_32)
// IndexWriter indexWriter = new IndexWriter(indexDir, analyzer, true, MaxFieldLength.LIMITED);
for(int i = 0; i < data.length; i++){
Document doc = new Document();
doc.add(new Field("vendor",data[i][0], Field.Store.YES, Field.Index.NOT_ANALYZED));
doc.add(new Field("cpu",data[i][1], Field.Store.YES, Field.Index.NOT_ANALYZED));
doc.add(new Field("desc",data[i][2], Field.Store.YES, Field.Index.ANALYZED));
doc.add(new Field("price",data[i][3], Field.Store.YES, Field.Index.NOT_ANALYZED));
indexWriter.addDocument(doc);
}
indexWriter.optimize();
indexWriter.commit();
indexWriter.close();
}catch(Exception ex){
ex.printStackTrace();
}
}
private void searchTest(){
try{
String fieldName = "desc";
String keywords = "Core2";
QueryParser queryParser = new QueryParser(luceneVersion, fieldName, new StandardAnalyzer(luceneVersion));
Query query = queryParser.parse(keywords);
IndexReader indexReader = IndexReader.open(indexDir,true);
List<FacetHandler<?>> facetHandlers = new ArrayList<FacetHandler<?>>();
facetHandlers.add(new SimpleFacetHandler("vendor"));
facetHandlers.add(new SimpleFacetHandler("cpu"));
//facetHandlers.add(new RangeFacetHandler("price", Arrays.asList(new String[]{"[* TO 09998]", "[09999 TO 11998]", "[11999 TO *]"})));
String[] ranges = new String[]{"[00000 TO 09999]", "[10000 TO 11998]", "[11999 TO 30000]"};
facetHandlers.add(new RangeFacetHandler("price", new PredefinedTermListFactory(Integer.class, "0"), Arrays.asList(ranges)));
BoboIndexReader boboIndexReader = BoboIndexReader.getInstance(indexReader,facetHandlers);
BrowseRequest browseRequest = new BrowseRequest();
browseRequest.setCount(10);
browseRequest.setOffset(0);
browseRequest.setQuery(query);
//SortField[] sortFields = new SortField[2]; //排序
//sortFields[0] = new SortField("vendor", SortField.STRING, true);
//sortFields[1] = new SortField("price", SortField.INT, true);
//browseRequest.setSort(sortFields);
FacetSpec facetSpec = new FacetSpec();
facetSpec.setMaxCount(10);// 搜索出来的标签数目
facetSpec.setOrderBy(FacetSortSpec.OrderHitsDesc);
browseRequest.setFacetSpec("vendor", facetSpec);
browseRequest.setFacetSpec("cpu", facetSpec);
browseRequest.setFacetSpec("price", facetSpec);
Browsable browser = new BoboBrowser(boboIndexReader);
BrowseResult browseResult = browser.browse(browseRequest);
int totalHits = browseResult.getNumHits();
BrowseHit[] browseHit = browseResult.getHits();
System.out.println("=====Total records: "+totalHits);
// 获取分组统计结果
Map<String,FacetAccessible> facetMap = browseResult.getFacetMap();
if(facetMap.size() > 0){
System.out.println("-------Vendor-----------------------");
FacetAccessible vendorFacets = facetMap.get("vendor");
List<BrowseFacet> facetVals = vendorFacets.getFacets();
for(BrowseFacet f:facetVals){
System.out.println(f.getValue() + "(" + f.getHitCount() + ")");
}
System.out.println("-------CPU----------------");
FacetAccessible cpuFacets = facetMap.get("cpu");
facetVals = cpuFacets.getFacets();
for(BrowseFacet f:facetVals){
System.out.println(f.getValue() + "(" + f.getHitCount() + ")");
}
System.out.println("-------Price----------------");
FacetAccessible priceFacets = facetMap.get("price");
facetVals = priceFacets.getFacets();
for(BrowseFacet f:facetVals){
System.out.println(f.getValue() + "(" + f.getHitCount() + ")");
}
}
boboIndexReader.close();
indexReader.close();
}catch(Exception ex){
ex.printStackTrace();
}
}
}
输出结果：
Total records: 9
-------Vendor-----------------------
lenovo(4)
hp(3)
asus(1)
dell(1)
-------CPU----------------
Intel(7)
AMD(2)
-------Price----------------
[00000 TO 09999](4)
[11999 TO 30000](4)
[10000 TO 11998](1)
