Facet['fæsɪt]很难翻译，只能靠例子来理解了。Solr作者Yonik Seeley也给出更为直接的名字：导航（Guided Navigation）、参数化查询（Paramatic Search）。
上面是比较直接的Faceted Search例子，品牌、产品特征、卖家，均是 Facet 。而Apple、Lenovo等品牌，就是 Facet values 或者说 Constraints ，而Facet values所带的统计值就是 Facet count/Constraint count 。
2 、Facet 使用
q = 超级本
facet = true
facet.field = 产品特性
facet.field = 品牌
facet.field = 卖家
http://…/select?q=超级本&facet=true&wt=json
&facet.field=品牌&facet.field=产品特性&facet.field=卖家
也可以提交查询条件，设置fq(filter query)。
q = 电脑
facet = true
fq = 价格:[8000 TO *]
facet.mincount = 1 // fq将不符合的字段过滤后，会显示count为0
facet.field = 产品特性
facet.field = 品牌
facet.field = 卖家
http://…/select?q=超级本&facet=true&wt=json
&fq=价格:[8000 TO *]&facet.mincount=1
&facet.field=品牌&facet.field=产品特性&facet.field=卖家
"facet_counts": {
"facet_fields": {
"品牌": [
"Apple", 4,
"Lenovo", 39
…]
"产品特性": [
"显卡", 42,
"酷睿", 38
…]
…}}
如果用户选择了Apple这个分类，查询条件中需要添加另外一个fq查询条件，并移除Apple所在的facet.field。
http://…/select?q=超级本&facet=true&wt=json
&fq=价格:[8000 TO *]&fq=品牌:Apple&facet.mincount=1
&facet.field= 品牌&facet.field=产品特性&facet.field=卖家
3 、Facet 参数
facet.prefix – 限制constaints的前缀
facet.mincount=0 – 限制constants count的最小返回值，默认为0
facet.sort=count – 排序的方式，根据count或者index
facet.offset=0 – 表示在当前排序情况下的偏移，可以做分页
facet.limit=100 – constraints返回的数目
facet.missing=false – 是否返回没有值的field
facet.date – Deprecated, use facet.range
facet.query
指定一个查询字符串作为Facet Constraint
facet.query = rank:[* TO 20]
facet.query = rank:[21 TO *]
"facet_counts": {
"facet_fields": {
"品牌": [
"Apple", 4,
"Lenovo", 10
…]
"产品特性": [
"显卡", 11,
"酷睿", 20
…]
…}}
facet.range
http://…/select?&facet=true
&facet.range=price
&facet.range.start=5000
&facet.range.end=8000
&facet.range.gap=1000
<result numFound="27" ... />
...
<lst name="facet_counts">
<lst name="facet_queries">
<int name="rank:[* TO 20]">2</int>
<int name="rank:[21 TO *]">15</int>
</lst>
...
WARNING: range范围是左闭右开，[start, end)
facet.pivot
这个是Solr 4.0的新特性，pivot和facet一样难理解，还是用例子来讲吧。
Syntax: facet.pivot=field1,field2,field3...
e.g. facet.pivot=comment_user, grade
|
#docs |
#docs grade:好 |
#docs 等级:中 |
#docs 等级:差 |
|
|
comment_user:1 |
10 |
8 |
1 |
1 |
|
comment_user:2 |
20 |
18 |
2 |
0 |
|
comment_user:3 |
15 |
12 |
2 |
1 |
|
comment_user:4 |
18 |
15 |
2 |
1 |
"facet_counts":{
"facet_pivot":{
"comment_user, grade ":[{
"field":"comment_user",
"value":"1",
"count":10,
"pivot":[{
"field":"grade",
"value":"好",
"count":8}, {
"field":"grade",
"value":"中",
"count":1}, {
"field":"grade",
"value":"差",
"count":1}]
}, {
"field":" comment_user ",
"value":"2",
"count":20,
"pivot":[{
…
没有pivot机制的话，要做到上面那点可能需要多次查询：
http://...q= comment&fq= grade:好&facet=true&facet.field=comment_user
http://...q=comment&fq=grade:中&facet=true&facet.field=comment_user
http://...q=comment&fq=grade:差&facet=true&facet.field=comment_user
Facet.pivot - Computes a Matrix of Constraint Counts across multiple Facet Fields. by Yonik Seeley.
上面那个解释很不错，只能理解不能翻译。
facet.pivot自己的理解，就是按照多个维度进行分组查询，以下是自己的实战代码，按照newsType，property两个维度统计：
public List<ReportNewsTypeDTO> queryNewsType( ReportQuery reportQuery) { HttpSolrServer solrServer = SolrServer.getInstance().getServer(); SolrQuery sQuery = new SolrQuery(); List<ReportNewsTypeDTO> list = new ArrayList<ReportNewsTypeDTO>(); try { String para = this.initReportQueryPara(reportQuery, 0); sQuery.setFacet(true); sQuery.add("facet.pivot", "newsType,property");//根据这两维度来分组查询 sQuery.setQuery(para); QueryResponse response = solrServer.query(sQuery,SolrRequest.METHOD.POST); NamedList<List<PivotField>> namedList = response.getFacetPivot(); System.out.println(namedList);//底下为啥要这样判断，把这个值打印出来，你就明白了 if(namedList != null){ List<PivotField> pivotList = null; for(int i=0;i<namedList.size();i++){ pivotList = namedList.getVal(i); if(pivotList != null){ ReportNewsTypeDTO dto = null; for(PivotField pivot:pivotList){ dto = new ReportNewsTypeDTO(); dto.setNewsTypeId((Integer)pivot.getValue()); dto.setNewsTypeName(News.newsTypeMap.get((Integer)pivot.getValue())); int pos = 0; int neg = 0; List<PivotField> fieldList = pivot.getPivot(); if(fieldList != null){ for(PivotField field:fieldList){ int proValue = (Integer) field.getValue(); int count = field.getCount(); if(proValue == 1){ pos = count; }else{ neg = count; } } } dto.setPositiveCount(pos); dto.setNegativeCount(neg); list.add(dto); } } } } return list; } catch (SolrServerException e) { log.error("查询solr失败", e); e.printStackTrace(); } finally{ solrServer.shutdown(); solrServer = null; } return list; }namedList打印结果: {newsType,property= [ newsType:8 [4260] [property:1 [3698] null, property:0 [562] null], newsType:1 [1507] [property:1 [1389] null, property:0 [118] null], newsType:2 [1054] [property:1 [909] null, property:0 [145] null], newsType:6 [715] [property:1 [581] null, property:0 [134] null], newsType:4 [675] [property:1 [466] null, property:0 [209] null], newsType:3 [486] [property:1 [397] null, property:0 [89] null], newsType:7 [458] [property:1 [395] null, property:0 [63] null], newsType:5 [289] [property:1 [263] null, property:0 [26] null], newsType:9 [143] [property:1 [138] null, property:0 [5] null] ] } 这下应该明白了。写到这里，突然想到一个，所有的分组查询统计，不管是一个维度两个维度都可以使用face.pivot来统计，不错的东东。
