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

     ... 
     
        
      
       2
         
      
       15
       
     ...
    

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

"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
     
       queryNewsType(            ReportQuery reportQuery) {            HttpSolrServer solrServer = SolrServer.getInstance().getServer();        SolrQuery sQuery = new SolrQuery();        List
      
        list = new ArrayList
       
        ();        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
        
         
          > 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
          
            pivotList = null; for(int i=0;i
           
             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。