本篇是接一步一步跟我学习lucene（14）---lucene搜索之facet索引原理和facet查询实例
（http://blog.csdn.net/wuyinggui10000/article/details/45973769），上篇主要是统计facet的dim和每个种类对应的数量，个人感觉这个跟lucene的group不同的在于facet的存储类似于hash（key-field-value）形式的，而group则是单一的map（key-value）形式的，虽然都可以统计某一品类的数量，显然facet更具扩展性。
key-field-value查询
facet可以对某一个维度的满足某个条件的结果进行统计，如下：
[java]
view plain copy@Test
public void testDrillDownSlide(){
try {
DirectoryReader indexReader = DirectoryReader.open(directory);
IndexSearcher searcher = new IndexSearcher(indexReader);
DrillSideways ds = new DrillSideways(searcher, config, taxoReader);
DrillDownQuery ddq = new DrillDownQuery(config);
ddq.add("filePath", "ik");
DrillSidewaysResult r = ds.search(ddq, 10);
TopDocs hits = r.hits;
for (ScoreDoc scoreDoc : hits.scoreDocs) {
Document doc = searcher.doc(scoreDoc.doc);
System.out.println(doc.get("path"));
}
} catch (Exception e) {
e.printStackTrace();
}
}
这里我们搜索的dim是filePath，查找的范围是ik相关联的数据，对应的查询结果就是所有包含在IK文件夹下的数据
[java]
view plain copyC:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\dist\IKAnalyzer.cfg.xml
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\dist\IKAnalyzer2012FF_u1.jar
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\dist\IKAnalyzer2015.jar
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\dist\LICENSE.txt
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\dist\NOTICE.txt
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\dist\stopword.dic
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\doc\allclasses-frame.html
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\doc\allclasses-noframe.html
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\doc\constant-values.html
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\doc\deprecated-list.html
range查询
facet还支持range查询，range查询的类型包括DoubleRange和LongRange；其对应的Facets为DoubleRangeFacets和LongRangeFacets;
以LongRangeFacetCounts为例，LongRangeFacetCounts可以对long类型的数值进行整理查询
这里我们对每个文档的单词数量进行区间的分组，range查询示例如下：
[java]
view plain copy@Test
public void testOverlappedEndStart(){
try {
IndexReader reader = DirectoryReader.open(directory);
FacetsCollector fc = new FacetsCollector();
IndexSearcher s = new IndexSearcher(reader);
s.search(new MatchAllDocsQuery(), fc);
Facets facets = new LongRangeFacetCounts("contentLength", fc,
new LongRange("0-100", 0L, true, 100L, true),
new LongRange("100-200", 100L, true, 200L, true),
new LongRange("200-300", 200L, true, 300L, true),
new LongRange("300-400", 300L, true, 400L, true));
FacetResult result = facets.getTopChildren(10, "contentLength");
System.out.println(result.toString());
} catch (Exception e) {
e.printStackTrace();
}
}
其执行结果为：
[html]
view plain copydim=contentLength path=[] value=22 childCount=4
0-100 (7)
100-200 (9)
200-300 (3)
300-400 (3)
多个dim查询
facet里DrillSideways可以定义多个facetCount的查询，这时返回的结果为各个facet对应的统计数
[java]
view plain copy@Test
public void testMixedRangeAndNonRangeTaxonomy(){
try {
IndexReader reader = DirectoryReader.open(directory);
IndexSearcher s = new IndexSearcher(reader);
DrillSideways ds = new DrillSideways(s, config, taxoReader){
@Override
protected Facets buildFacetsResult(FacetsCollector drillDowns, FacetsCollector[] drillSideways, String[] drillSidewaysDims) throws IOException {
FacetsCollector fieldFC = drillDowns;
FacetsCollector dimFC = drillDowns;
if (drillSideways != null) {
for(int i=0;i<drillSideways.length;i++) {
String dim = drillSidewaysDims[i];
if (dim.equals("contentLength")) {
fieldFC = drillSideways[i];
} else {
dimFC = drillSideways[i];
}
}
}
Map<String,Facets> byDim = new HashMap<String,Facets>();
byDim.put("contentLength",new LongRangeFacetCounts("contentLength", fieldFC,
new LongRange("less than 100", 0L, true, 100L, false),
new LongRange("between 100 and 200", 100L, true, 200L, false),
new LongRange("over 200", 200L, true, Integer.MAX_VALUE, false)));
byDim.put("dim", new FastTaxonomyFacetCounts(taxoReader, config, dimFC));
return new MultiFacets(byDim);
}
@Override
protected boolean scoreSubDocsAtOnce() {
return false;
}
};
DrillDownQuery ddq = new DrillDownQuery(config);
DrillSidewaysResult dsr = ds.search(ddq, 10);
Facets facet = dsr.facets;
List<FacetResult> results = facet.getAllDims(reader.maxDoc());
for (FacetResult facetResult : results) {
System.out.println(facetResult.dim);
LabelAndValue[] values = facetResult.labelValues;
for (LabelAndValue labelAndValue : values) {
System.out.println("\t"+labelAndValue.label +" "+labelAndValue.value);
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
程序运行结果如下：
[html]
view plain copydim
odd 126
even 119
contentLength
less than 100 7
between 100 and 200 9
over 200 229
对单个range的列表查询支持
facet支持单个range的区间查询，这样可以查询出此range对饮的TopDocs列表，等同于返回了document对象列表；
这里我们查询内容长度在0到100之间的数据
[java]
view plain copy@Test
public void testDrillDownQueryWithRange(){
try {
IndexReader reader = DirectoryReader.open(directory);
IndexSearcher s = new IndexSearcher(reader);
DrillDownQuery ddq = new DrillDownQuery(config);
ddq.add("contentLength", NumericRangeQuery.newLongRange("contentLength", 0l, 100l, true, false));//;
TopDocs docs = s.search(ddq, reader.maxDoc());
System.out.println("查询到的数据总数："+docs.totalHits);
for (ScoreDoc scoreDoc : docs.scoreDocs) {
System.out.println(s.doc(scoreDoc.doc).get("path"));
}
} catch (Exception e) {
e.printStackTrace();
}
}
其运行结果如下：
[html]
view plain copy查询到的数据总数：7
C:\Users\lenovo\Desktop\lucene\jcseg\DONATE.txt
C:\Users\lenovo\Desktop\lucene\jcseg\jcseg-elasticsearch\src\main\resources\es-plugin.properties
C:\Users\lenovo\Desktop\lucene\jcseg\lexicon\lex-autoload.todo
C:\Users\lenovo\Desktop\lucene\jcseg\lexicon\lex-en-pun.lex
C:\Users\lenovo\Desktop\lucene\jcseg\lexicon\lex-ln-adorn.lex
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\doc\resources\inherit.gif
C:\Users\lenovo\Desktop\lucene\IK-Analyzer-2012FF\src\ext.dic
