ElasticSearch6.0 高级应用之 多字段聚合Aggregation（二）

ElasticSearch6.0 多字段聚合网上完整的资料很少 ，所以作者经过查阅资料，编写了聚合高级使用例子

例子是根据电商搜索实际场景模拟出来的

希望给大家带来帮助！

下面我们开始吧！

1. 创建索引的映射关系

 PUT gome_market
 {
   "mappings": {
     "goods": {
       "dynamic_templates": [
         {
           "dynamicFields": {
             "match_mapping_type": "string",
                   "path_match":"dynamicFields.*_sku_attr",
             "mapping":{
               "type": "keyword"
             }
           }
         }
       ],
        "properties":{
         "id":{
            "type": "keyword"
         },
         "category_first_id":{
            "type": "keyword"
         },
         "category_first":{
            "type": "keyword"
         },
         "category_second_id":{
            "type": "keyword"
         },
         "category_second":{
            "type": "keyword"
         },
         "category_third_id":{
            "type": "keyword"
         },
         "category_third":{
            "type": "keyword"
         },
         "brand_id":{
            "type": "keyword"
         },
         "brand":{
            "type": "text",
            "analyzer":"ik_max_word",
            "search_analyzer":"ik_max_word",
            "copy_to":"full_name"
         },
         "shop":{
            "type": "keyword"
         },
         "attr_name":{
            "type": "keyword",
            "index":"true"

         },
         "sku":{
            "type": "keyword",
            "index":"true"
         },
         "spu":{
            "type": "keyword",
            "index":"true"
         },
         "gome_sku":{
            "type": "keyword"
         },
         "product_ch":{
            "type": "text",
            "analyzer":"ik_max_word",
            "search_analyzer":"ik_max_word",
            "copy_to":"full_name"
         },
         "adver":{
            "type": "keyword"
         },
         "product_img":{
            "type": "keyword"
         },
         "product_proto_price":{
            "type": "double"
         },
         "product_sale_price":{
            "type": "double"
         },
         "is_sku":{
            "type": "boolean"
         },
         "is_self":{
            "type": "boolean"
         },
         "shop_flag":{
            "type": "long"
         },
         "is_in_store":{
            "type": "boolean"
         },
         "is_shelves":{
            "type": "boolean"
         },
         "is_suit":{
            "type": "boolean"
         },
         "good_comment_rate":{
            "type": "long"
         },
         "sale_num":{
            "type": "long"
         },
         "spu_score":{
            "type": "long"
         },
         "dynamic_attrs":{
            "type": "keyword"
         },
         "full_name":{
            "type": "text",
            "store":"true"
         },
     "create_time":{
           "type":"date"
         }
       }
     }
   }
 }

2.创建索引数据

请参照上一篇文章

3.Controller层  创建  SuperMarketSearchController

 package com.elastic.controller;

 import com.alibaba.fastjson.JSONObject;

 import com.elastic.service.inter.SuperMarketSearchService;
 import com.elastic.service.pojo.SearchBean;
 import org.apache.commons.lang.StringUtils;
 import org.apache.log4j.Logger;
 import org.springframework.stereotype.Controller;
 import org.springframework.web.bind.annotation.RequestMapping;

 import javax.annotation.Resource;
 import javax.servlet.http.HttpServletRequest;
 import javax.servlet.http.HttpServletResponse;
 import java.io.*;
 import java.net.URLDecoder;
 import java.nio.charset.Charset;

 @Controller
 public class SuperMarketSearchController extends BaseController {

     private static final Logger logger = Logger.getLogger(SuperMarketSearchController.class);

     @Resource(name = "superMarketSearchServiceImpl")
     private SuperMarketSearchService searchService;

     @RequestMapping(value = "/s.action")
     public String searchForPost(HttpServletRequest request, HttpServletResponse response) throws IOException {
         try {
             String param = getParam(request);
             logger.info("请求参数：" + param);
             SearchBean bean = JSONObject.toJavaObject(JSONObject.parseObject(param), SearchBean.class);
             bean.setQuery(bean.getQuery() != null ? URLDecoder.decode(bean.getQuery(), "UTF-8") : null);
             String re = searchService.searchByBean(bean);
             printReturnVal(response, re);
         } catch (Exception e) {
             e.printStackTrace();
             logger.error(e.getMessage());
             printReturnVal(response, assemReturn("[]", 500, "系统异常,请检查参数：" + e.getMessage()));
         }
         return null;
     }

     @RequestMapping(value = "/suggest")
     public void suggest(HttpServletRequest request, HttpServletResponse response) throws Exception {
         String keyword = request.getParameter("keyword");
 //        String newKeyWord="";
 //        if (!StringUtils.isEmpty(keyword)){
 //             newKeyWord = new String(keyword.getBytes("ISO-8859-1"), "UTF-8");
 //        }
         SearchBean bean = new SearchBean();
        // bean.setQuery(newKeyWord==""?"********":newKeyWord);
         bean.setQuery(keyword);
         PrintWriter out = null;
         String suggests = searchService.suggestByBean(bean);
         printReturnVal(response, suggests);
     }

     private void printReturnVal(HttpServletResponse response, String returnVal) {
         PrintWriter out = null;
         try {
             response.setCharacterEncoding("UTF-8");
             response.setContentType("application/json");
             out = response.getWriter();
             System.out.println(returnVal);
             out.print(returnVal);
             out.flush();
         } catch (IOException e) {
             e.printStackTrace();
         }
     }

     private String getParam(HttpServletRequest request) throws IOException {
         StringBuffer sb = new StringBuffer();
         InputStream is = request.getInputStream();
         InputStreamReader isr = new InputStreamReader(is, Charset.forName("UTF-8"));
         BufferedReader br = new BufferedReader(isr);
         String s = "";
         while ((s = br.readLine()) != null) {
             sb.append(s);
         }
         isr.close();
         br.close();
         String str = sb.toString();
         logger.info("requstParam=" + str);
         String param = "";
         JSONObject jsonObject = JSONObject.parseObject(str);
         if (jsonObject.getString("body") != null && !jsonObject.getString("body").isEmpty()) {
             param = jsonObject.getString("body");
         } else {
             param = str;
         }
         return param;
     }

 }

4. Service层  interface  SuperMarketSearchService

 package com.elastic.service.inter;

 import com.elastic.service.pojo.SearchBean;

 public interface SuperMarketSearchService {

     public String searchByBean(SearchBean bean);

     public String suggestByBean(SearchBean bean);

 }

Service层  Impl 实现  SuperMarketSearchServiceImpl

 package com.elastic.service.impl;

 import com.elastic.common.conn.EsClient;
 import com.elastic.service.inter.SuperMarketSearchService;
 import com.elastic.service.pojo.Filter;
 import com.elastic.service.pojo.SearchBean;
 import com.elastic.service.vo.SearchParams;
 import com.elastic.service.vo.SearchReturn;
 import com.elastic.util.SpringApplicationUtils;
 import org.apache.commons.lang.StringUtils;
 import org.elasticsearch.action.search.SearchRequestBuilder;
 import org.elasticsearch.action.search.SearchResponse;
 import org.elasticsearch.client.transport.TransportClient;
 import org.elasticsearch.index.query.BoolQueryBuilder;
 import org.elasticsearch.index.query.Operator;
 import org.elasticsearch.index.query.QueryBuilder;
 import org.elasticsearch.index.query.QueryBuilders;
 import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilders;
 import org.elasticsearch.index.query.functionscore.ScriptScoreFunctionBuilder;
 import org.elasticsearch.script.Script;
 import org.elasticsearch.script.ScriptType;
 import org.elasticsearch.search.aggregations.*;
 import org.elasticsearch.search.aggregations.bucket.filter.InternalFilter;
 import org.elasticsearch.search.aggregations.bucket.range.Range;
 import org.elasticsearch.search.aggregations.bucket.range.RangeAggregationBuilder;
 import org.elasticsearch.search.aggregations.bucket.terms.*;
 import org.springframework.stereotype.Service;

 import java.math.BigDecimal;
 import java.util.*;
 import java.util.function.BiConsumer;

 import static org.elasticsearch.index.query.QueryBuilders.functionScoreQuery;

 /**
  * Created by xiaotian on 2017/12/23.
  */
 @Service
 public class SuperMarketSearchServiceImpl implements SuperMarketSearchService {

     private EsClient esClient;

     @Override
     public String searchByBean(SearchBean bean) {
         try {
             esClient = SpringApplicationUtils.getBean(EsClient.class);
             SearchRequestBuilder searchRequestBuilder = esClient.getConnection().prepareSearch("gome_market").setTypes("goods");
             long s = System.currentTimeMillis();

             bean.setIsFacet(bean.getIsFacet() == null ? true : bean.getIsFacet());
             if (bean.getIsFacet()){
                 //聚合
                 String[] attrFacet = new String[]{"attr_name"};
                 bean.setFacetFields(attrFacet);

             }
             SearchResponse searchResponse = assemQueryByBean(searchRequestBuilder, esClient, bean);

             bean.setFacetFields(null);
             if (bean.getIsFacet()){
                 String[] docAttrFields = getDocAttrFields(searchResponse);
                 bean.setFacetFields(docAttrFields);
                 SearchRequestBuilder requestBuilder = esClient.getConnection().prepareSearch("gome_market").setTypes("goods");
                 SearchResponse response = assemQueryByBean(requestBuilder, esClient, bean);

                 Aggregations aggregations = response.getAggregations();
                 Map<String, Aggregation> asMap = aggregations.getAsMap();
                 List<String> list = new ArrayList<>();
                 for (String key: bean.getFacetFields()) {
                     InternalFilter filter = aggregations.get(key);
                     Map<String, Aggregation> aggMap = filter.getAggregations().getAsMap();
                     Iterator<String> iterator = filter.getAggregations().getAsMap().keySet().iterator();
                     while (iterator.hasNext()){
                         String keys = iterator.next();
                         if ("range".equals(keys)){
                             continue;
                         }
                         if (aggMap.get(keys)==null|| aggMap.get(keys) instanceof UnmappedTerms){
                             continue;
                         }
                         StringTerms aggregation = (StringTerms)aggMap.get(keys);
                         aggregation.getBuckets().forEach(bucket -> {
                             System.out.println(keys+"-->"+bucket.getKey()+":"+bucket.getDocCount());
                             list.add(keys+"-->"+bucket.getKey()+":"+bucket.getDocCount());
                         });

                     }
                 }
                 System.out.println("list="+list);

                 Range range = response.getAggregations().get("range");

                 range.getBuckets().forEach(bucket ->{

                     System.out.println(String.format("key [%s], from [%s], to [%s], doc_count [%d]", bucket.getKey(), bucket.getFrom(),bucket.getTo(),bucket.getDocCount()));

                 });
             }else {

             }

         } catch (Exception e) {
             e.printStackTrace();
         } finally {
         }

         return null;
     }

     private String[] getDocAttrFields(SearchResponse searchResponse) {
         List<String> attrFieldList = new ArrayList<>();
         attrFieldList.add("category_third");
         attrFieldList.add("category_third_id");
         if (searchResponse.getAggregations().get("attr_name")!=null){
             InternalFilter aggFilter= searchResponse.getAggregations().get("attr_name");
             Map<String, Aggregation> aggMap = aggFilter.getAggregations().getAsMap();
             Iterator<String> iterator = aggFilter.getAggregations().getAsMap().keySet().iterator();
             while (iterator.hasNext()){
                 String keys = iterator.next();
                 StringTerms aggregation = (StringTerms)aggMap.get(keys);
                 aggregation.getBuckets().forEach(bucket -> {
                     attrFieldList.add("dynamicFields."+bucket.getKey()+"");
                     System.out.println(keys+"-->"+bucket.getKey()+":"+bucket.getDocCount());
                 });

             }
         }

        return attrFieldList.toArray(new String[attrFieldList.size()]);
     }

     @Override
     public String suggestByBean(SearchBean bean) {
         return null;
     }

     private SearchResponse assemQueryByBean(SearchRequestBuilder searchRequest, EsClient esClient, SearchBean bean) throws Exception {

         Map<String, Object> params = new HashMap<>();
         params.put("num1", 1);
         params.put("num2", 2);

         //String inlineScript = "long age;if (doc['age'].value < 45)  age = doc['age'].value + 50; return age * params.num1;";
         String inlineScript = " return doc['age'].value * params.num1;";
         // + "return (diff +num1+num2)";
         Script script = new Script(ScriptType.INLINE,"painless",inlineScript , params);
         ScriptScoreFunctionBuilder scriptScoreFunctionBuilder = ScoreFunctionBuilders.scriptFunction(script);
         //searchRequest.setQuery(functionScoreQuery(QueryBuilders.matchQuery("name","中华").operator(Operator.AND),scriptScoreFunctionBuilder));
         searchRequest.setQuery(bean.getQuery()!=null?QueryBuilders.matchQuery("full_name",bean.getQuery()):QueryBuilders.matchAllQuery());

         BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
         BoolQueryBuilder queryBuilder = QueryBuilders.boolQuery();
         //条件
         boolQueryBuilder.must(queryBuilder);
          //动态添加聚合项目
         if (bean.getFacetFields()!=null){

             for (String field : bean.getFacetFields()) {
                 TermsAggregationBuilder aggFieldBuilder = AggregationBuilders.terms(field).field(field);
                 aggFieldBuilder.size(1000);
                 AggregationBuilder filter = AggregationBuilders.filter(field, boolQueryBuilder);
                 filter.subAggregation(aggFieldBuilder);
                 searchRequest.addAggregation(filter);
             }
         }

         RangeAggregationBuilder rangeAggregationBuilder = AggregationBuilders.range("range").field("product_sale_price").addRange(0, 30).addRange(30,50).addRange(50,100);
         searchRequest.addAggregation(rangeAggregationBuilder);
         searchRequest.setPostFilter(boolQueryBuilder);
         searchRequest.setFrom(0);
         searchRequest.setSize(10);
         searchRequest.setExplain(true);
         System.out.println("param:"+searchRequest);
         SearchResponse searchResponse = searchRequest.get();
         return searchResponse;

     }

     private QueryBuilder getFilterQuery(String fieldName, Object[] fieldValues, String andor, BoolQueryBuilder queryBuilder) {
         //BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();

         for (int i = 0; i < fieldValues.length; i++) {
             if ("OR".equals(andor)){
                 queryBuilder.should(QueryBuilders.matchQuery(fieldName,fieldValues[i]).operator(Operator.OR));
             }else if ("AND".equals(andor)){
                 queryBuilder.must(QueryBuilders.matchQuery(fieldName,fieldValues[i]).operator(Operator.AND));
             }
         }

         return  queryBuilder;

     }

     private void assemQueryParam(SearchBean bean, SearchReturn returns) {
         SearchParams params = new SearchParams();
         params.setKeyword(bean.getQuery());
         Filter f = bean.getFilter();
         if (f != null) {
             String[] catFirst = f.getCatFirst();
             String[] catSecond = f.getCatSecond();
             String[] catThird = f.getCatThird();
             Integer[] brand = f.getBrand();
             Integer[] country = f.getCountry();
             String[] fgCatFirst = f.getFgCatFirst();
             String[] fgCatSecond = f.getFgCatSecond();
             String[] fgCatThird = f.getFgCatThird();
             String[] whiteShopIds = f.getWhiteShopIds();
             String[] whiteCateIds = f.getWhiteCateIds();
             String[] whiteBrandIds = f.getWhiteBrandIds();
             String[] whiteProdIds = f.getWhiteProdIds();
             String[] blackShopIds = f.getBlackShopIds();
             String[] blackCateIds = f.getBlackCateIds();
             String[] blackBrandIds = f.getBlackBrandIds();
             String[] blackProdIds = f.getBlackProdIds();
             String[] activityIds = f.getActivityIds();

             if (catFirst != null && catFirst.length > 0) {
                 params.setCatFirst(catFirst);
             }
             if (catSecond != null && catSecond.length > 0) {
                 params.setCatSecond(catSecond);
             }
             if (catThird != null && catThird.length > 0) {
                 params.setCatThird(catThird);
             }
             if (fgCatFirst != null && fgCatFirst.length > 0) {
                 params.setFgCatFirst(fgCatFirst);
             }
             if (fgCatSecond != null && fgCatSecond.length > 0) {
                 params.setFgCatSecond(fgCatSecond);
             }
             if (fgCatThird != null && fgCatThird.length > 0) {
                 params.setFgCatThird(fgCatThird);
             }
             if (brand != null && brand.length > 0) {
                 params.setBrand(brand);
             }
             if (country != null && country.length > 0) {
                 params.setCountry(country);
             }
             if (activityIds!=null&&activityIds.length>0){
                 params.setActivityIds(activityIds);
             }
         }
         returns.setSearchParams(params);
     }
 }

5.通过kibana查询结果

查询条件 ： 跟java执行是一样的参数

 GET gome_market/_search
 {
   "from" : 0,
   "size" : 10,
   "query" : {
     "match" : {
       "full_name" : {
         "query" : "多颜色多版本",
         "operator" : "OR",
         "prefix_length" : 0,
         "max_expansions" : 50,
         "fuzzy_transpositions" : true,
         "lenient" : false,
         "zero_terms_query" : "NONE",
         "boost" : 1.0
       }
     }
   },
   "post_filter" : {
     "bool" : {
       "must" : [
         {
           "bool" : {
             "adjust_pure_negative" : true,
             "boost" : 1.0
           }
         }
       ],
       "adjust_pure_negative" : true,
       "boost" : 1.0
     }
   },
   "explain" : true,
   "aggregations" : {
     "category_third" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "category_third" : {
           "terms" : {
             "field" : "category_third",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "category_third_id" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "category_third_id" : {
           "terms" : {
             "field" : "category_third_id",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "dynamicFields.sku级多选_sku_attr" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "dynamicFields.sku级多选_sku_attr" : {
           "terms" : {
             "field" : "dynamicFields.sku级多选_sku_attr",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "dynamicFields.sku级枚举_sku_attr" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "dynamicFields.sku级枚举_sku_attr" : {
           "terms" : {
             "field" : "dynamicFields.sku级枚举_sku_attr",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "dynamicFields.品牌_sku_attr" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "dynamicFields.品牌_sku_attr" : {
           "terms" : {
             "field" : "dynamicFields.品牌_sku_attr",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "dynamicFields.类别-模板2使用_sku_attr" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "dynamicFields.类别-模板2使用_sku_attr" : {
           "terms" : {
             "field" : "dynamicFields.类别-模板2使用_sku_attr",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "dynamicFields.颜色-模板2使用_sku_attr" : {
       "filter" : {
         "bool" : {
           "must" : [
             {
               "bool" : {
                 "adjust_pure_negative" : true,
                 "boost" : 1.0
               }
             }
           ],
           "adjust_pure_negative" : true,
           "boost" : 1.0
         }
       },
       "aggregations" : {
         "dynamicFields.颜色-模板2使用_sku_attr" : {
           "terms" : {
             "field" : "dynamicFields.颜色-模板2使用_sku_attr",
             "size" : 1000,
             "min_doc_count" : 1,
             "shard_min_doc_count" : 0,
             "show_term_doc_count_error" : false,
             "order" : [
               {
                 "_count" : "desc"
               },
               {
                 "_key" : "asc"
               }
             ]
           }
         }
       }
     },
     "range" : {
       "range" : {
         "field" : "product_sale_price",
         "ranges" : [
           {
             "from" : 0.0,
             "to" : 30.0
           },
           {
             "from" : 30.0,
             "to" : 50.0
           },
           {
             "from" : 50.0,
             "to" : 100.0
           }
         ],
         "keyed" : false
       }
     }
   }
 }

查询结果：

 {
   "took": 9,
   "timed_out": false,
   "_shards": {
     "total": 5,
     "successful": 5,
     "skipped": 0,
     "failed": 0
   },
   "hits": {
     "total": 3,
     "max_score": 4.531806,
     "hits": [
       {
         "_shard": "[gome_market][0]",
         "_node": "WBp9VADzRG2Jr-yIKi8h6w",
         "_index": "gome_market",
         "_type": "goods",
         "_id": "100253641",
         "_score": 4.531806,
         "_source": {
           "is_shelves": false,
           "shop_flag": 2,
           "category_third": "536_耳机/耳麦",
           "shop": "",
           "product_ch": "zyl-多颜色多版本",
           "spu_score": 0,
           "category_second": "311_时尚数码",
           "category_third_id": "536",
           "id": "100253641",
           "is_in_store": false,
           "sku": "100253641",
           "brand": "10000073_诺基亚（NOKIA）",
           "is_self": false,
           "is_suit": false,
           "product_proto_price": 0,
           "create_time": "2017-12-25T07:20:37.510Z",
           "good_comment_rate": 0,
           "sale_num": 0,
           "adver": "",
           "attr_name": [
             "品牌_sku_attr",
             "颜色-模板2使用_sku_attr",
             "类别-模板2使用_sku_attr",
             "sku级枚举_sku_attr",
             "sku级多选_sku_attr"
           ],
           "category_first_id": "286",
           "is_sku": true,
           "dynamicFields": {
             "品牌_sku_attr": [
               "诺基亚（NOKIA）"
             ],
             "颜色-模板2使用_sku_attr": [
               "黑色"
             ],
             "适用人群-模板2使用_sku_attr": [
               "人群"
             ],
             "类别-模板2使用_sku_attr": [
               "手提包, 洗漱包"
             ]
           },
           "brand_id": 10000073,
           "product_img": "//gfs17.atguat.net.cn/T1.FKTBvVT1RCvBVdK",
           "product_sale_price": 2223,
           "category_first": "286_数码",
           "shop_id": "null",
           "category_second_id": "311",
           "gome_sku": "1000101403",
           "spu": "9010006069"
         },
         "_explanation": {
           "value": 4.531806,
           "description": "sum of:",
           "details": [
             {
               "value": 0.9293165,
               "description": "weight(full_name:多 in 0) [PerFieldSimilarity], result of:",
               "details": [
                 {
                   "value": 0.9293165,
                   "description": "score(doc=0,freq=2.0 = termFreq=2.0\n), product of:",
                   "details": [
                     {
                       "value": 0.6931472,
                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "docFreq",
                           "details": []
                         },
                         {
                           "value": 2,
                           "description": "docCount",
                           "details": []
                         }
                       ]
                     },
                     {
                       "value": 1.3407203,
                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 2,
                           "description": "termFreq=2.0",
                           "details": []
                         },
                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
                         },
                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
                         },
                         {
                           "value": 11,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 12,
                           "description": "fieldLength",
                           "details": []
                         }
                       ]
                     }
                   ]
                 }
               ]
             },
             {
               "value": 0.6682933,
               "description": "weight(full_name:颜 in 0) [PerFieldSimilarity], result of:",
               "details": [
                 {
                   "value": 0.6682933,
                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
                   "details": [
                     {
                       "value": 0.6931472,
                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "docFreq",
                           "details": []
                         },
                         {
                           "value": 2,
                           "description": "docCount",
                           "details": []
                         }
                       ]
                     },
                     {
                       "value": 0.96414346,
                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "termFreq=1.0",
                           "details": []
                         },
                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
                         },
                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
                         },
                         {
                           "value": 11,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 12,
                           "description": "fieldLength",
                           "details": []
                         }
                       ]
                     }
                   ]
                 }
               ]
             },
             {
               "value": 0.6682933,
               "description": "weight(full_name:色 in 0) [PerFieldSimilarity], result of:",
               "details": [
                 {
                   "value": 0.6682933,
                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
                   "details": [
                     {
                       "value": 0.6931472,
                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "docFreq",
                           "details": []
                         },
                         {
                           "value": 2,
                           "description": "docCount",
                           "details": []
                         }
                       ]
                     },
                     {
                       "value": 0.96414346,
                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "termFreq=1.0",
                           "details": []
                         },
                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
                         },
                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
                         },
                         {
                           "value": 11,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 12,
                           "description": "fieldLength",
                           "details": []
                         }
                       ]
                     }
                   ]
                 }
               ]
             },
             {
               "value": 0.9293165,
               "description": "weight(full_name:多 in 0) [PerFieldSimilarity], result of:",
               "details": [
                 {
                   "value": 0.9293165,
                   "description": "score(doc=0,freq=2.0 = termFreq=2.0\n), product of:",
                   "details": [
                     {
                       "value": 0.6931472,
                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "docFreq",
                           "details": []
                         },
                         {
                           "value": 2,
                           "description": "docCount",
                           "details": []
                         }
                       ]
                     },
                     {
                       "value": 1.3407203,
                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 2,
                           "description": "termFreq=2.0",
                           "details": []
                         },
                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
                         },
                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
                         },
                         {
                           "value": 11,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 12,
                           "description": "fieldLength",
                           "details": []
                         }
                       ]
                     }
                   ]
                 }
               ]
             },
             {
               "value": 0.6682933,
               "description": "weight(full_name:版 in 0) [PerFieldSimilarity], result of:",
               "details": [
                 {
                   "value": 0.6682933,
                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
                   "details": [
                     {
                       "value": 0.6931472,
                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "docFreq",
                           "details": []
                         },
                         {
                           "value": 2,
                           "description": "docCount",
                           "details": []
                         }
                       ]
                     },
                     {
                       "value": 0.96414346,
                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "termFreq=1.0",
                           "details": []
                         },
                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
                         },
                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
                         },
                         {
                           "value": 11,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 12,
                           "description": "fieldLength",
                           "details": []
                         }
                       ]
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                   ]
                 }
               ]
             },
             {
               "value": 0.6682933,
               "description": "weight(full_name:本 in 0) [PerFieldSimilarity], result of:",
               "details": [
                 {
                   "value": 0.6682933,
                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
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                     {
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                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "docFreq",
                           "details": []
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                         {
                           "value": 2,
                           "description": "docCount",
                           "details": []
                         }
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                     },
                     {
                       "value": 0.96414346,
                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "termFreq=1.0",
                           "details": []
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                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
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                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
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                         {
                           "value": 11,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 12,
                           "description": "fieldLength",
                           "details": []
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                   ]
                 }
               ]
             }
           ]
         }
       },
       {
         "_shard": "[gome_market][1]",
         "_node": "WBp9VADzRG2Jr-yIKi8h6w",
         "_index": "gome_market",
         "_type": "goods",
         "_id": "1000122353",
         "_score": 0.2876821,
         "_source": {
           "is_shelves": false,
           "shop_flag": 2,
           "category_third": "536_耳机/耳麦",
           "shop": "",
           "product_ch": "诺基亚（NOKIA）C5-05手机（黑银色）非定制机-------9010006069这个product改的",
           "spu_score": 0,
           "category_second": "311_时尚数码",
           "category_third_id": "536",
           "id": "1000122353",
           "is_in_store": true,
           "sku": "1000122353",
           "brand": "10000073_诺基亚（NOKIA）",
           "is_self": false,
           "is_suit": false,
           "product_proto_price": 0,
           "create_time": "2017-12-25T07:20:36.931Z",
           "good_comment_rate": 0,
           "sale_num": 0,
           "adver": "%E7%99%BD%E6%8B%BF009%20%E6%B1%89%E5%AD%97",
           "attr_name": [
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             "颜色-模板2使用_sku_attr",
             "类别-模板2使用_sku_attr",
             "sku级枚举_sku_attr",
             "sku级多选_sku_attr"
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           "category_first_id": "286",
           "is_sku": true,
           "dynamicFields": {
             "sku级枚举_sku_attr": [
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             ],
             "sku级多选_sku_attr": [
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             ],
             "品牌_sku_attr": [
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             "cy-屏幕尺寸_sku_attr": [
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             ],
             "0122-cy-多选使用_sku_attr": [
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             ],
             "0121-cy-枚举使用_sku_attr": [
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             ],
             "0121-cy-多选使用_sku_attr": [
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             "product级枚举_sku_attr": [
               "可组合"
             ],
             "product级描述_sku_attr": [
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           "product_sale_price": 1311,
           "category_first": "286_数码",
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           "description": "sum of:",
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               "description": "weight(full_name:色 in 0) [PerFieldSimilarity], result of:",
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                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
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                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
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                           "description": "docFreq",
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                         {
                           "value": 1,
                           "description": "docCount",
                           "details": []
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                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
                       "details": [
                         {
                           "value": 1,
                           "description": "termFreq=1.0",
                           "details": []
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                         {
                           "value": 1.2,
                           "description": "parameter k1",
                           "details": []
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                         {
                           "value": 0.75,
                           "description": "parameter b",
                           "details": []
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                         {
                           "value": 26,
                           "description": "avgFieldLength",
                           "details": []
                         },
                         {
                           "value": 26,
                           "description": "fieldLength",
                           "details": []
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                 }
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       },
       {
         "_shard": "[gome_market][3]",
         "_node": "WBp9VADzRG2Jr-yIKi8h6w",
         "_index": "gome_market",
         "_type": "goods",
         "_id": "1000122354",
         "_score": 0.2876821,
         "_source": {
           "is_shelves": false,
           "shop_flag": 2,
           "category_third": "536_耳机/耳麦",
           "shop": "",
           "product_ch": "诺基亚（NOKIA）C5-05手机（黑红色）searchadmin",
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           "category_second": "311_时尚数码",
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           "id": "1000122354",
           "is_in_store": true,
           "sku": "1000122354",
           "brand": "10000073_诺基亚（NOKIA）",
           "is_self": false,
           "is_suit": false,
           "product_proto_price": 0,
           "create_time": "2017-12-25T07:20:38.606Z",
           "good_comment_rate": 0,
           "sale_num": 0,
           "adver": "%E9%95%BF%E6%9C%9F%E4%BF%83%E9%94%80%E8%AF%AD",
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             "颜色-模板2使用_sku_attr",
             "类别-模板2使用_sku_attr",
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           "category_first_id": "286",
           "is_sku": true,
           "dynamicFields": {
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             "颜色-模板2使用_sku_attr": [
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             ],
             "适用人群-模板2使用_sku_attr": [
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             ],
             "类别-模板2使用_sku_attr": [
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           "description": "sum of:",
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                           "description": "termFreq=1.0",
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                           "description": "parameter b",
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                           "description": "avgFieldLength",
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                           "description": "fieldLength",
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       "dynamicFields.sku级枚举_sku_attr": {
         "doc_count_error_upper_bound": 0,
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         "buckets": [
           {
             "key": "洁厕剂",
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       }
     },
     "dynamicFields.类别-模板2使用_sku_attr": {
       "doc_count": 3,
       "dynamicFields.类别-模板2使用_sku_attr": {
         "doc_count_error_upper_bound": 0,
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         "buckets": [
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           },
           {
             "key": "马夹包, 腰挂, 手提包, 洗漱包, 化妆包, 折叠包, 其他",
             "doc_count": 1
           }
         ]
       }
     },
     "category_third_id": {
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       "category_third_id": {
         "doc_count_error_upper_bound": 0,
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         "buckets": [
           {
             "key": "536",
             "doc_count": 3
           }
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       }
     },
     "category_third": {
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       "category_third": {
         "doc_count_error_upper_bound": 0,
         "sum_other_doc_count": 0,
         "buckets": [
           {
             "key": "536_耳机/耳麦",
             "doc_count": 3
           }
         ]
       }
     },
     "dynamicFields.sku级多选_sku_attr": {
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       "dynamicFields.sku级多选_sku_attr": {
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         "sum_other_doc_count": 0,
         "buckets": [
           {
             "key": "中端高性价比",
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           }
         ]
       }
     },
     "dynamicFields.品牌_sku_attr": {
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       "dynamicFields.品牌_sku_attr": {
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     "range": {
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           "from": 0,
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         {
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           "to": 50,
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           "from": 50,
           "to": 100,
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     },
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       "dynamicFields.颜色-模板2使用_sku_attr": {
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         "buckets": [
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         ]
       }
     }
   }
 }