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

  1 PUT gome_market 
  2 {
  3   "mappings": {
  4     "goods": {
  5       "dynamic_templates": [
  6         {
  7           "dynamicFields": {
  8             "match_mapping_type": "string",
  9                   "path_match":"dynamicFields.*_sku_attr",
 10             "mapping":{
 11               "type": "keyword"
 12             }
 13           }
 14         }
 15       ],
 16        "properties":{ 
 17         "id":{
 18            "type": "keyword"
 19         }, 
 20         "category_first_id":{
 21            "type": "keyword"
 22         }, 
 23         "category_first":{
 24            "type": "keyword"
 25         },
 26         "category_second_id":{
 27            "type": "keyword"
 28         },
 29         "category_second":{
 30            "type": "keyword"
 31         },
 32         "category_third_id":{
 33            "type": "keyword"
 34         },
 35         "category_third":{
 36            "type": "keyword"
 37         },
 38         "brand_id":{
 39            "type": "keyword"
 40         },
 41         "brand":{
 42            "type": "text",
 43            "analyzer":"ik_max_word",
 44            "search_analyzer":"ik_max_word",
 45            "copy_to":"full_name"
 46         },
 47         "shop":{
 48            "type": "keyword"
 49         },
 50         "attr_name":{
 51            "type": "keyword",
 52            "index":"true"
 53          
 54         },
 55         "sku":{
 56            "type": "keyword",
 57            "index":"true"
 58         },
 59         "spu":{
 60            "type": "keyword",
 61            "index":"true"
 62         },
 63         "gome_sku":{
 64            "type": "keyword"
 65         },
 66         "product_ch":{
 67            "type": "text",
 68            "analyzer":"ik_max_word",
 69            "search_analyzer":"ik_max_word",
 70            "copy_to":"full_name"
 71         },
 72         "adver":{
 73            "type": "keyword"
 74         },
 75         "product_img":{
 76            "type": "keyword"
 77         },
 78         "product_proto_price":{ 
 79            "type": "double"
 80         },
 81         "product_sale_price":{ 
 82            "type": "double"
 83         },
 84         "is_sku":{
 85            "type": "boolean"
 86         },
 87         "is_self":{
 88            "type": "boolean"
 89         },
 90         "shop_flag":{
 91            "type": "long"
 92         },
 93         "is_in_store":{
 94            "type": "boolean"
 95         },
 96         "is_shelves":{
 97            "type": "boolean"
 98         },
 99         "is_suit":{
100            "type": "boolean"
101         },
102         "good_comment_rate":{ 
103            "type": "long"
104         },
105         "sale_num":{
106            "type": "long"
107         },
108         "spu_score":{
109            "type": "long"
110         },
111         "dynamic_attrs":{
112            "type": "keyword"
113         },
114         "full_name":{ 
115            "type": "text",
116            "store":"true"
117         },
118     "create_time":{
119           "type":"date"
120         }
121       }
122     }
123   }
124 }

2.创建索引数据

请参照上一篇文章

3.Controller层创建 SuperMarketSearchController

 1 package com.elastic.controller;
 2 
 3 import com.alibaba.fastjson.JSONObject;
 4 
 5 import com.elastic.service.inter.SuperMarketSearchService;
 6 import com.elastic.service.pojo.SearchBean;
 7 import org.apache.commons.lang.StringUtils;
 8 import org.apache.log4j.Logger;
 9 import org.springframework.stereotype.Controller;
10 import org.springframework.web.bind.annotation.RequestMapping;
11 
12 import javax.annotation.Resource;
13 import javax.servlet.http.HttpServletRequest;
14 import javax.servlet.http.HttpServletResponse;
15 import java.io.*;
16 import java.net.URLDecoder;
17 import java.nio.charset.Charset;
18 
19 @Controller
20 public class SuperMarketSearchController extends BaseController {
21 
22     private static final Logger logger = Logger.getLogger(SuperMarketSearchController.class);
23 
24     @Resource(name = "superMarketSearchServiceImpl")
25     private SuperMarketSearchService searchService;
26 
27     @RequestMapping(value = "/s.action")
28     public String searchForPost(HttpServletRequest request, HttpServletResponse response) throws IOException {
29         try {
30             String param = getParam(request);
31             logger.info("请求参数：" + param);
32             SearchBean bean = JSONObject.toJavaObject(JSONObject.parseObject(param), SearchBean.class);
33             bean.setQuery(bean.getQuery() != null ? URLDecoder.decode(bean.getQuery(), "UTF-8") : null);
34             String re = searchService.searchByBean(bean);
35             printReturnVal(response, re);
36         } catch (Exception e) {
37             e.printStackTrace();
38             logger.error(e.getMessage());
39             printReturnVal(response, assemReturn("[]", 500, "系统异常,请检查参数：" + e.getMessage()));
40         }
41         return null;
42     }
43 
44 
45 
46     @RequestMapping(value = "/suggest")
47     public void suggest(HttpServletRequest request, HttpServletResponse response) throws Exception {
48         String keyword = request.getParameter("keyword");
49 //        String newKeyWord="";
50 //        if (!StringUtils.isEmpty(keyword)){
51 //             newKeyWord = new String(keyword.getBytes("ISO-8859-1"), "UTF-8");
52 //        }
53         SearchBean bean = new SearchBean();
54        // bean.setQuery(newKeyWord==""?"********":newKeyWord);
55         bean.setQuery(keyword);
56         PrintWriter out = null;
57         String suggests = searchService.suggestByBean(bean);
58         printReturnVal(response, suggests);
59     }
60 
61     private void printReturnVal(HttpServletResponse response, String returnVal) {
62         PrintWriter out = null;
63         try {
64             response.setCharacterEncoding("UTF-8");
65             response.setContentType("application/json");
66             out = response.getWriter();
67             System.out.println(returnVal);
68             out.print(returnVal);
69             out.flush();
70         } catch (IOException e) {
71             e.printStackTrace();
72         }
73     }
74 
75     private String getParam(HttpServletRequest request) throws IOException {
76         StringBuffer sb = new StringBuffer();
77         InputStream is = request.getInputStream();
78         InputStreamReader isr = new InputStreamReader(is, Charset.forName("UTF-8"));
79         BufferedReader br = new BufferedReader(isr);
80         String s = "";
81         while ((s = br.readLine()) != null) {
82             sb.append(s);
83         }
84         isr.close();
85         br.close();
86         String str = sb.toString();
87         logger.info("requstParam=" + str);
88         String param = "";
89         JSONObject jsonObject = JSONObject.parseObject(str);
90         if (jsonObject.getString("body") != null && !jsonObject.getString("body").isEmpty()) {
91             param = jsonObject.getString("body");
92         } else {
93             param = str;
94         }
95         return param;
96     }
97 
98 }

4. Service层 interface SuperMarketSearchService

 1 package com.elastic.service.inter;
 2 
 3 
 4 import com.elastic.service.pojo.SearchBean;
 5 
 6 public interface SuperMarketSearchService {
 7 
 8     public String searchByBean(SearchBean bean);
 9 
10     public String suggestByBean(SearchBean bean);
11 
12 }

Service层 Impl 实现 SuperMarketSearchServiceImpl

  1 package com.elastic.service.impl;
  2 
  3 import com.elastic.common.conn.EsClient;
  4 import com.elastic.service.inter.SuperMarketSearchService;
  5 import com.elastic.service.pojo.Filter;
  6 import com.elastic.service.pojo.SearchBean;
  7 import com.elastic.service.vo.SearchParams;
  8 import com.elastic.service.vo.SearchReturn;
  9 import com.elastic.util.SpringApplicationUtils;
 10 import org.apache.commons.lang.StringUtils;
 11 import org.elasticsearch.action.search.SearchRequestBuilder;
 12 import org.elasticsearch.action.search.SearchResponse;
 13 import org.elasticsearch.client.transport.TransportClient;
 14 import org.elasticsearch.index.query.BoolQueryBuilder;
 15 import org.elasticsearch.index.query.Operator;
 16 import org.elasticsearch.index.query.QueryBuilder;
 17 import org.elasticsearch.index.query.QueryBuilders;
 18 import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilders;
 19 import org.elasticsearch.index.query.functionscore.ScriptScoreFunctionBuilder;
 20 import org.elasticsearch.script.Script;
 21 import org.elasticsearch.script.ScriptType;
 22 import org.elasticsearch.search.aggregations.*;
 23 import org.elasticsearch.search.aggregations.bucket.filter.InternalFilter;
 24 import org.elasticsearch.search.aggregations.bucket.range.Range;
 25 import org.elasticsearch.search.aggregations.bucket.range.RangeAggregationBuilder;
 26 import org.elasticsearch.search.aggregations.bucket.terms.*;
 27 import org.springframework.stereotype.Service;
 28 
 29 import java.math.BigDecimal;
 30 import java.util.*;
 31 import java.util.function.BiConsumer;
 32 
 33 import static org.elasticsearch.index.query.QueryBuilders.functionScoreQuery;
 34 
 35 /**
 36  * Created by xiaotian on 2017/12/23.
 37  */
 38 @Service
 39 public class SuperMarketSearchServiceImpl implements SuperMarketSearchService {
 40 
 41     private EsClient esClient;
 42 
 43     @Override
 44     public String searchByBean(SearchBean bean) {
 45         try {
 46             esClient = SpringApplicationUtils.getBean(EsClient.class);
 47             SearchRequestBuilder searchRequestBuilder = esClient.getConnection().prepareSearch("gome_market").setTypes("goods");
 48             long s = System.currentTimeMillis();
 49 
 50             bean.setIsFacet(bean.getIsFacet() == null ? true : bean.getIsFacet());
 51             if (bean.getIsFacet()){
 52                 //聚合
 53                 String[] attrFacet = new String[]{"attr_name"};
 54                 bean.setFacetFields(attrFacet);
 55 
 56             }
 57             SearchResponse searchResponse = assemQueryByBean(searchRequestBuilder, esClient, bean);
 58 
 59             bean.setFacetFields(null);
 60             if (bean.getIsFacet()){
 61                 String[] docAttrFields = getDocAttrFields(searchResponse);
 62                 bean.setFacetFields(docAttrFields);
 63                 SearchRequestBuilder requestBuilder = esClient.getConnection().prepareSearch("gome_market").setTypes("goods");
 64                 SearchResponse response = assemQueryByBean(requestBuilder, esClient, bean);
 65 
 66                 Aggregations aggregations = response.getAggregations();
 67                 Map<String, Aggregation> asMap = aggregations.getAsMap();
 68                 List<String> list = new ArrayList<>();
 69                 for (String key: bean.getFacetFields()) {
 70                     InternalFilter filter = aggregations.get(key);
 71                     Map<String, Aggregation> aggMap = filter.getAggregations().getAsMap();
 72                     Iterator<String> iterator = filter.getAggregations().getAsMap().keySet().iterator();
 73                     while (iterator.hasNext()){
 74                         String keys = iterator.next();
 75                         if ("range".equals(keys)){
 76                             continue;
 77                         }
 78                         if (aggMap.get(keys)==null|| aggMap.get(keys) instanceof UnmappedTerms){
 79                             continue;
 80                         }
 81                         StringTerms aggregation = (StringTerms)aggMap.get(keys);
 82                         aggregation.getBuckets().forEach(bucket -> {
 83                             System.out.println(keys+"-->"+bucket.getKey()+":"+bucket.getDocCount());
 84                             list.add(keys+"-->"+bucket.getKey()+":"+bucket.getDocCount());
 85                         });
 86 
 87                     }
 88                 }
 89                 System.out.println("list="+list);
 9
 97                 Range range = response.getAggregations().get("range");
 98 
 99                 range.getBuckets().forEach(bucket ->{
100       
102                     System.out.println(String.format("key [%s], from [%s], to [%s], doc_count [%d]", bucket.getKey(), bucket.getFrom(),bucket.getTo(),bucket.getDocCount()));
103 
104                 });
105             }else {
106 
107             }
108             
109 
110         } catch (Exception e) {
111             e.printStackTrace();
112         } finally {
113         }
114 
115         return null;
116     }
117 
118     private String[] getDocAttrFields(SearchResponse searchResponse) {
119         List<String> attrFieldList = new ArrayList<>();
120         attrFieldList.add("category_third");
121         attrFieldList.add("category_third_id");
122         if (searchResponse.getAggregations().get("attr_name")!=null){
123             InternalFilter aggFilter= searchResponse.getAggregations().get("attr_name");
124             Map<String, Aggregation> aggMap = aggFilter.getAggregations().getAsMap();
125             Iterator<String> iterator = aggFilter.getAggregations().getAsMap().keySet().iterator();
126             while (iterator.hasNext()){
127                 String keys = iterator.next();
128                 StringTerms aggregation = (StringTerms)aggMap.get(keys);
129                 aggregation.getBuckets().forEach(bucket -> {
130                     attrFieldList.add("dynamicFields."+bucket.getKey()+"");
131                     System.out.println(keys+"-->"+bucket.getKey()+":"+bucket.getDocCount());
132                 });
133 
134             }
135         }
136 
137        return attrFieldList.toArray(new String[attrFieldList.size()]);
138     }
139 
140     @Override
141     public String suggestByBean(SearchBean bean) {
142         return null;
143     }
144 
145 
146     private SearchResponse assemQueryByBean(SearchRequestBuilder searchRequest, EsClient esClient, SearchBean bean) throws Exception {
147 
148         Map<String, Object> params = new HashMap<>();
149         params.put("num1", 1);
150         params.put("num2", 2);
151 
152         //String inlineScript = "long age;if (doc['age'].value < 45)  age = doc['age'].value + 50; return age * params.num1;";
153         String inlineScript = " return doc['age'].value * params.num1;";
154         // + "return (diff +num1+num2)";
155         Script script = new Script(ScriptType.INLINE,"painless",inlineScript , params);
156         ScriptScoreFunctionBuilder scriptScoreFunctionBuilder = ScoreFunctionBuilders.scriptFunction(script);
157         //searchRequest.setQuery(functionScoreQuery(QueryBuilders.matchQuery("name","中华").operator(Operator.AND),scriptScoreFunctionBuilder));
158         searchRequest.setQuery(bean.getQuery()!=null?QueryBuilders.matchQuery("full_name",bean.getQuery()):QueryBuilders.matchAllQuery());
159 
160         BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
161         BoolQueryBuilder queryBuilder = QueryBuilders.boolQuery();
162         //条件
163         boolQueryBuilder.must(queryBuilder);
164          //动态添加聚合项目
165         if (bean.getFacetFields()!=null){
166 
167             for (String field : bean.getFacetFields()) {
168                 TermsAggregationBuilder aggFieldBuilder = AggregationBuilders.terms(field).field(field);
169                 aggFieldBuilder.size(1000);
170                 AggregationBuilder filter = AggregationBuilders.filter(field, boolQueryBuilder);
171                 filter.subAggregation(aggFieldBuilder);
172                 searchRequest.addAggregation(filter);
173             }
174         }
175 
176 
177         RangeAggregationBuilder rangeAggregationBuilder = AggregationBuilders.range("range").field("product_sale_price").addRange(0, 30).addRange(30,50).addRange(50,100);
178         searchRequest.addAggregation(rangeAggregationBuilder);
179         searchRequest.setPostFilter(boolQueryBuilder);
180         searchRequest.setFrom(0);
181         searchRequest.setSize(10);
182         searchRequest.setExplain(true);
183         System.out.println("param:"+searchRequest);
184         SearchResponse searchResponse = searchRequest.get();
185         return searchResponse;
186 
187     }
188 
189     private QueryBuilder getFilterQuery(String fieldName, Object[] fieldValues, String andor, BoolQueryBuilder queryBuilder) {
190         //BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
191 
192         for (int i = 0; i < fieldValues.length; i++) {
193             if ("OR".equals(andor)){
194                 queryBuilder.should(QueryBuilders.matchQuery(fieldName,fieldValues[i]).operator(Operator.OR));
195             }else if ("AND".equals(andor)){
196                 queryBuilder.must(QueryBuilders.matchQuery(fieldName,fieldValues[i]).operator(Operator.AND));
197             }
198         }
199 
200         return  queryBuilder;
201 
202     }
203 
204     private void assemQueryParam(SearchBean bean, SearchReturn returns) {
205         SearchParams params = new SearchParams();
206         params.setKeyword(bean.getQuery());
207         Filter f = bean.getFilter();
208         if (f != null) {
209             String[] catFirst = f.getCatFirst();
210             String[] catSecond = f.getCatSecond();
211             String[] catThird = f.getCatThird();
212             Integer[] brand = f.getBrand();
213             Integer[] country = f.getCountry();
214             String[] fgCatFirst = f.getFgCatFirst();
215             String[] fgCatSecond = f.getFgCatSecond();
216             String[] fgCatThird = f.getFgCatThird();
217             String[] whiteShopIds = f.getWhiteShopIds();
218             String[] whiteCateIds = f.getWhiteCateIds();
219             String[] whiteBrandIds = f.getWhiteBrandIds();
220             String[] whiteProdIds = f.getWhiteProdIds();
221             String[] blackShopIds = f.getBlackShopIds();
222             String[] blackCateIds = f.getBlackCateIds();
223             String[] blackBrandIds = f.getBlackBrandIds();
224             String[] blackProdIds = f.getBlackProdIds();
225             String[] activityIds = f.getActivityIds();
226 
227             if (catFirst != null && catFirst.length > 0) {
228                 params.setCatFirst(catFirst);
229             }
230             if (catSecond != null && catSecond.length > 0) {
231                 params.setCatSecond(catSecond);
232             }
233             if (catThird != null && catThird.length > 0) {
234                 params.setCatThird(catThird);
235             }
236             if (fgCatFirst != null && fgCatFirst.length > 0) {
237                 params.setFgCatFirst(fgCatFirst);
238             }
239             if (fgCatSecond != null && fgCatSecond.length > 0) {
240                 params.setFgCatSecond(fgCatSecond);
241             }
242             if (fgCatThird != null && fgCatThird.length > 0) {
243                 params.setFgCatThird(fgCatThird);
244             }
245             if (brand != null && brand.length > 0) {
246                 params.setBrand(brand);
247             }
248             if (country != null && country.length > 0) {
249                 params.setCountry(country);
250             }
251             if (activityIds!=null&&activityIds.length>0){
252                 params.setActivityIds(activityIds);
253             }
254         }
255         returns.setSearchParams(params);
256     }
257 }

5.通过kibana查询结果

查询条件：跟java执行是一样的参数

  1 GET gome_market/_search
  2 {
  3   "from" : 0,
  4   "size" : 10,
  5   "query" : {
  6     "match" : {
  7       "full_name" : {
  8         "query" : "多颜色多版本",
  9         "operator" : "OR",
 10         "prefix_length" : 0,
 11         "max_expansions" : 50,
 12         "fuzzy_transpositions" : true,
 13         "lenient" : false,
 14         "zero_terms_query" : "NONE",
 15         "boost" : 1.0
 16       }
 17     }
 18   },
 19   "post_filter" : {
 20     "bool" : {
 21       "must" : [
 22         {
 23           "bool" : {
 24             "adjust_pure_negative" : true,
 25             "boost" : 1.0
 26           }
 27         }
 28       ],
 29       "adjust_pure_negative" : true,
 30       "boost" : 1.0
 31     }
 32   },
 33   "explain" : true,
 34   "aggregations" : {
 35     "category_third" : {
 36       "filter" : {
 37         "bool" : {
 38           "must" : [
 39             {
 40               "bool" : {
 41                 "adjust_pure_negative" : true,
 42                 "boost" : 1.0
 43               }
 44             }
 45           ],
 46           "adjust_pure_negative" : true,
 47           "boost" : 1.0
 48         }
 49       },
 50       "aggregations" : {
 51         "category_third" : {
 52           "terms" : {
 53             "field" : "category_third",
 54             "size" : 1000,
 55             "min_doc_count" : 1,
 56             "shard_min_doc_count" : 0,
 57             "show_term_doc_count_error" : false,
 58             "order" : [
 59               {
 60                 "_count" : "desc"
 61               },
 62               {
 63                 "_key" : "asc"
 64               }
 65             ]
 66           }
 67         }
 68       }
 69     },
 70     "category_third_id" : {
 71       "filter" : {
 72         "bool" : {
 73           "must" : [
 74             {
 75               "bool" : {
 76                 "adjust_pure_negative" : true,
 77                 "boost" : 1.0
 78               }
 79             }
 80           ],
 81           "adjust_pure_negative" : true,
 82           "boost" : 1.0
 83         }
 84       },
 85       "aggregations" : {
 86         "category_third_id" : {
 87           "terms" : {
 88             "field" : "category_third_id",
 89             "size" : 1000,
 90             "min_doc_count" : 1,
 91             "shard_min_doc_count" : 0,
 92             "show_term_doc_count_error" : false,
 93             "order" : [
 94               {
 95                 "_count" : "desc"
 96               },
 97               {
 98                 "_key" : "asc"
 99               }
100             ]
101           }
102         }
103       }
104     },
105     "dynamicFields.sku级多选_sku_attr" : {
106       "filter" : {
107         "bool" : {
108           "must" : [
109             {
110               "bool" : {
111                 "adjust_pure_negative" : true,
112                 "boost" : 1.0
113               }
114             }
115           ],
116           "adjust_pure_negative" : true,
117           "boost" : 1.0
118         }
119       },
120       "aggregations" : {
121         "dynamicFields.sku级多选_sku_attr" : {
122           "terms" : {
123             "field" : "dynamicFields.sku级多选_sku_attr",
124             "size" : 1000,
125             "min_doc_count" : 1,
126             "shard_min_doc_count" : 0,
127             "show_term_doc_count_error" : false,
128             "order" : [
129               {
130                 "_count" : "desc"
131               },
132               {
133                 "_key" : "asc"
134               }
135             ]
136           }
137         }
138       }
139     },
140     "dynamicFields.sku级枚举_sku_attr" : {
141       "filter" : {
142         "bool" : {
143           "must" : [
144             {
145               "bool" : {
146                 "adjust_pure_negative" : true,
147                 "boost" : 1.0
148               }
149             }
150           ],
151           "adjust_pure_negative" : true,
152           "boost" : 1.0
153         }
154       },
155       "aggregations" : {
156         "dynamicFields.sku级枚举_sku_attr" : {
157           "terms" : {
158             "field" : "dynamicFields.sku级枚举_sku_attr",
159             "size" : 1000,
160             "min_doc_count" : 1,
161             "shard_min_doc_count" : 0,
162             "show_term_doc_count_error" : false,
163             "order" : [
164               {
165                 "_count" : "desc"
166               },
167               {
168                 "_key" : "asc"
169               }
170             ]
171           }
172         }
173       }
174     },
175     "dynamicFields.品牌_sku_attr" : {
176       "filter" : {
177         "bool" : {
178           "must" : [
179             {
180               "bool" : {
181                 "adjust_pure_negative" : true,
182                 "boost" : 1.0
183               }
184             }
185           ],
186           "adjust_pure_negative" : true,
187           "boost" : 1.0
188         }
189       },
190       "aggregations" : {
191         "dynamicFields.品牌_sku_attr" : {
192           "terms" : {
193             "field" : "dynamicFields.品牌_sku_attr",
194             "size" : 1000,
195             "min_doc_count" : 1,
196             "shard_min_doc_count" : 0,
197             "show_term_doc_count_error" : false,
198             "order" : [
199               {
200                 "_count" : "desc"
201               },
202               {
203                 "_key" : "asc"
204               }
205             ]
206           }
207         }
208       }
209     },
210     "dynamicFields.类别-模板2使用_sku_attr" : {
211       "filter" : {
212         "bool" : {
213           "must" : [
214             {
215               "bool" : {
216                 "adjust_pure_negative" : true,
217                 "boost" : 1.0
218               }
219             }
220           ],
221           "adjust_pure_negative" : true,
222           "boost" : 1.0
223         }
224       },
225       "aggregations" : {
226         "dynamicFields.类别-模板2使用_sku_attr" : {
227           "terms" : {
228             "field" : "dynamicFields.类别-模板2使用_sku_attr",
229             "size" : 1000,
230             "min_doc_count" : 1,
231             "shard_min_doc_count" : 0,
232             "show_term_doc_count_error" : false,
233             "order" : [
234               {
235                 "_count" : "desc"
236               },
237               {
238                 "_key" : "asc"
239               }
240             ]
241           }
242         }
243       }
244     },
245     "dynamicFields.颜色-模板2使用_sku_attr" : {
246       "filter" : {
247         "bool" : {
248           "must" : [
249             {
250               "bool" : {
251                 "adjust_pure_negative" : true,
252                 "boost" : 1.0
253               }
254             }
255           ],
256           "adjust_pure_negative" : true,
257           "boost" : 1.0
258         }
259       },
260       "aggregations" : {
261         "dynamicFields.颜色-模板2使用_sku_attr" : {
262           "terms" : {
263             "field" : "dynamicFields.颜色-模板2使用_sku_attr",
264             "size" : 1000,
265             "min_doc_count" : 1,
266             "shard_min_doc_count" : 0,
267             "show_term_doc_count_error" : false,
268             "order" : [
269               {
270                 "_count" : "desc"
271               },
272               {
273                 "_key" : "asc"
274               }
275             ]
276           }
277         }
278       }
279     },
280     "range" : {
281       "range" : {
282         "field" : "product_sale_price",
283         "ranges" : [
284           {
285             "from" : 0.0,
286             "to" : 30.0
287           },
288           {
289             "from" : 30.0,
290             "to" : 50.0
291           },
292           {
293             "from" : 50.0,
294             "to" : 100.0
295           }
296         ],
297         "keyed" : false
298       }
299     }
300   }
301 }

查询结果：

  1 {
  2   "took": 9,
  3   "timed_out": false,
  4   "_shards": {
  5     "total": 5,
  6     "successful": 5,
  7     "skipped": 0,
  8     "failed": 0
  9   },
 10   "hits": {
 11     "total": 3,
 12     "max_score": 4.531806,
 13     "hits": [
 14       {
 15         "_shard": "[gome_market][0]",
 16         "_node": "WBp9VADzRG2Jr-yIKi8h6w",
 17         "_index": "gome_market",
 18         "_type": "goods",
 19         "_id": "100253641",
 20         "_score": 4.531806,
 21         "_source": {
 22           "is_shelves": false,
 23           "shop_flag": 2,
 24           "category_third": "536_耳机/耳麦",
 25           "shop": "",
 26           "product_ch": "zyl-多颜色多版本",
 27           "spu_score": 0,
 28           "category_second": "311_时尚数码",
 29           "category_third_id": "536",
 30           "id": "100253641",
 31           "is_in_store": false,
 32           "sku": "100253641",
 33           "brand": "10000073_诺基亚（NOKIA）",
 34           "is_self": false,
 35           "is_suit": false,
 36           "product_proto_price": 0,
 37           "create_time": "2017-12-25T07:20:37.510Z",
 38           "good_comment_rate": 0,
 39           "sale_num": 0,
 40           "adver": "",
 41           "attr_name": [
 42             "品牌_sku_attr",
 43             "颜色-模板2使用_sku_attr",
 44             "类别-模板2使用_sku_attr",
 45             "sku级枚举_sku_attr",
 46             "sku级多选_sku_attr"
 47           ],
 48           "category_first_id": "286",
 49           "is_sku": true,
 50           "dynamicFields": {
 51             "品牌_sku_attr": [
 52               "诺基亚（NOKIA）"
 53             ],
 54             "颜色-模板2使用_sku_attr": [
 55               "黑色"
 56             ],
 57             "适用人群-模板2使用_sku_attr": [
 58               "人群"
 59             ],
 60             "类别-模板2使用_sku_attr": [
 61               "手提包, 洗漱包"
 62             ]
 63           },
 64           "brand_id": 10000073,
 65           "product_img": "//gfs17.atguat.net.cn/T1.FKTBvVT1RCvBVdK",
 66           "product_sale_price": 2223,
 67           "category_first": "286_数码",
 68           "shop_id": "null",
 69           "category_second_id": "311",
 70           "gome_sku": "1000101403",
 71           "spu": "9010006069"
 72         },
 73         "_explanation": {
 74           "value": 4.531806,
 75           "description": "sum of:",
 76           "details": [
 77             {
 78               "value": 0.9293165,
 79               "description": "weight(full_name:多 in 0) [PerFieldSimilarity], result of:",
 80               "details": [
 81                 {
 82                   "value": 0.9293165,
 83                   "description": "score(doc=0,freq=2.0 = termFreq=2.0\n), product of:",
 84                   "details": [
 85                     {
 86                       "value": 0.6931472,
 87                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
 88                       "details": [
 89                         {
 90                           "value": 1,
 91                           "description": "docFreq",
 92                           "details": []
 93                         },
 94                         {
 95                           "value": 2,
 96                           "description": "docCount",
 97                           "details": []
 98                         }
 99                       ]
100                     },
101                     {
102                       "value": 1.3407203,
103                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
104                       "details": [
105                         {
106                           "value": 2,
107                           "description": "termFreq=2.0",
108                           "details": []
109                         },
110                         {
111                           "value": 1.2,
112                           "description": "parameter k1",
113                           "details": []
114                         },
115                         {
116                           "value": 0.75,
117                           "description": "parameter b",
118                           "details": []
119                         },
120                         {
121                           "value": 11,
122                           "description": "avgFieldLength",
123                           "details": []
124                         },
125                         {
126                           "value": 12,
127                           "description": "fieldLength",
128                           "details": []
129                         }
130                       ]
131                     }
132                   ]
133                 }
134               ]
135             },
136             {
137               "value": 0.6682933,
138               "description": "weight(full_name:颜 in 0) [PerFieldSimilarity], result of:",
139               "details": [
140                 {
141                   "value": 0.6682933,
142                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
143                   "details": [
144                     {
145                       "value": 0.6931472,
146                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
147                       "details": [
148                         {
149                           "value": 1,
150                           "description": "docFreq",
151                           "details": []
152                         },
153                         {
154                           "value": 2,
155                           "description": "docCount",
156                           "details": []
157                         }
158                       ]
159                     },
160                     {
161                       "value": 0.96414346,
162                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
163                       "details": [
164                         {
165                           "value": 1,
166                           "description": "termFreq=1.0",
167                           "details": []
168                         },
169                         {
170                           "value": 1.2,
171                           "description": "parameter k1",
172                           "details": []
173                         },
174                         {
175                           "value": 0.75,
176                           "description": "parameter b",
177                           "details": []
178                         },
179                         {
180                           "value": 11,
181                           "description": "avgFieldLength",
182                           "details": []
183                         },
184                         {
185                           "value": 12,
186                           "description": "fieldLength",
187                           "details": []
188                         }
189                       ]
190                     }
191                   ]
192                 }
193               ]
194             },
195             {
196               "value": 0.6682933,
197               "description": "weight(full_name:色 in 0) [PerFieldSimilarity], result of:",
198               "details": [
199                 {
200                   "value": 0.6682933,
201                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
202                   "details": [
203                     {
204                       "value": 0.6931472,
205                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
206                       "details": [
207                         {
208                           "value": 1,
209                           "description": "docFreq",
210                           "details": []
211                         },
212                         {
213                           "value": 2,
214                           "description": "docCount",
215                           "details": []
216                         }
217                       ]
218                     },
219                     {
220                       "value": 0.96414346,
221                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
222                       "details": [
223                         {
224                           "value": 1,
225                           "description": "termFreq=1.0",
226                           "details": []
227                         },
228                         {
229                           "value": 1.2,
230                           "description": "parameter k1",
231                           "details": []
232                         },
233                         {
234                           "value": 0.75,
235                           "description": "parameter b",
236                           "details": []
237                         },
238                         {
239                           "value": 11,
240                           "description": "avgFieldLength",
241                           "details": []
242                         },
243                         {
244                           "value": 12,
245                           "description": "fieldLength",
246                           "details": []
247                         }
248                       ]
249                     }
250                   ]
251                 }
252               ]
253             },
254             {
255               "value": 0.9293165,
256               "description": "weight(full_name:多 in 0) [PerFieldSimilarity], result of:",
257               "details": [
258                 {
259                   "value": 0.9293165,
260                   "description": "score(doc=0,freq=2.0 = termFreq=2.0\n), product of:",
261                   "details": [
262                     {
263                       "value": 0.6931472,
264                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
265                       "details": [
266                         {
267                           "value": 1,
268                           "description": "docFreq",
269                           "details": []
270                         },
271                         {
272                           "value": 2,
273                           "description": "docCount",
274                           "details": []
275                         }
276                       ]
277                     },
278                     {
279                       "value": 1.3407203,
280                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
281                       "details": [
282                         {
283                           "value": 2,
284                           "description": "termFreq=2.0",
285                           "details": []
286                         },
287                         {
288                           "value": 1.2,
289                           "description": "parameter k1",
290                           "details": []
291                         },
292                         {
293                           "value": 0.75,
294                           "description": "parameter b",
295                           "details": []
296                         },
297                         {
298                           "value": 11,
299                           "description": "avgFieldLength",
300                           "details": []
301                         },
302                         {
303                           "value": 12,
304                           "description": "fieldLength",
305                           "details": []
306                         }
307                       ]
308                     }
309                   ]
310                 }
311               ]
312             },
313             {
314               "value": 0.6682933,
315               "description": "weight(full_name:版 in 0) [PerFieldSimilarity], result of:",
316               "details": [
317                 {
318                   "value": 0.6682933,
319                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
320                   "details": [
321                     {
322                       "value": 0.6931472,
323                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
324                       "details": [
325                         {
326                           "value": 1,
327                           "description": "docFreq",
328                           "details": []
329                         },
330                         {
331                           "value": 2,
332                           "description": "docCount",
333                           "details": []
334                         }
335                       ]
336                     },
337                     {
338                       "value": 0.96414346,
339                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
340                       "details": [
341                         {
342                           "value": 1,
343                           "description": "termFreq=1.0",
344                           "details": []
345                         },
346                         {
347                           "value": 1.2,
348                           "description": "parameter k1",
349                           "details": []
350                         },
351                         {
352                           "value": 0.75,
353                           "description": "parameter b",
354                           "details": []
355                         },
356                         {
357                           "value": 11,
358                           "description": "avgFieldLength",
359                           "details": []
360                         },
361                         {
362                           "value": 12,
363                           "description": "fieldLength",
364                           "details": []
365                         }
366                       ]
367                     }
368                   ]
369                 }
370               ]
371             },
372             {
373               "value": 0.6682933,
374               "description": "weight(full_name:本 in 0) [PerFieldSimilarity], result of:",
375               "details": [
376                 {
377                   "value": 0.6682933,
378                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
379                   "details": [
380                     {
381                       "value": 0.6931472,
382                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
383                       "details": [
384                         {
385                           "value": 1,
386                           "description": "docFreq",
387                           "details": []
388                         },
389                         {
390                           "value": 2,
391                           "description": "docCount",
392                           "details": []
393                         }
394                       ]
395                     },
396                     {
397                       "value": 0.96414346,
398                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
399                       "details": [
400                         {
401                           "value": 1,
402                           "description": "termFreq=1.0",
403                           "details": []
404                         },
405                         {
406                           "value": 1.2,
407                           "description": "parameter k1",
408                           "details": []
409                         },
410                         {
411                           "value": 0.75,
412                           "description": "parameter b",
413                           "details": []
414                         },
415                         {
416                           "value": 11,
417                           "description": "avgFieldLength",
418                           "details": []
419                         },
420                         {
421                           "value": 12,
422                           "description": "fieldLength",
423                           "details": []
424                         }
425                       ]
426                     }
427                   ]
428                 }
429               ]
430             }
431           ]
432         }
433       },
434       {
435         "_shard": "[gome_market][1]",
436         "_node": "WBp9VADzRG2Jr-yIKi8h6w",
437         "_index": "gome_market",
438         "_type": "goods",
439         "_id": "1000122353",
440         "_score": 0.2876821,
441         "_source": {
442           "is_shelves": false,
443           "shop_flag": 2,
444           "category_third": "536_耳机/耳麦",
445           "shop": "",
446           "product_ch": "诺基亚（NOKIA）C5-05手机（黑银色）非定制机-------9010006069这个product改的",
447           "spu_score": 0,
448           "category_second": "311_时尚数码",
449           "category_third_id": "536",
450           "id": "1000122353",
451           "is_in_store": true,
452           "sku": "1000122353",
453           "brand": "10000073_诺基亚（NOKIA）",
454           "is_self": false,
455           "is_suit": false,
456           "product_proto_price": 0,
457           "create_time": "2017-12-25T07:20:36.931Z",
458           "good_comment_rate": 0,
459           "sale_num": 0,
460           "adver": "%E7%99%BD%E6%8B%BF009%20%E6%B1%89%E5%AD%97",
461           "attr_name": [
462             "品牌_sku_attr",
463             "颜色-模板2使用_sku_attr",
464             "类别-模板2使用_sku_attr",
465             "sku级枚举_sku_attr",
466             "sku级多选_sku_attr"
467           ],
468           "category_first_id": "286",
469           "is_sku": true,
470           "dynamicFields": {
471             "sku级枚举_sku_attr": [
472               "洁厕剂"
473             ],
474             "sku级多选_sku_attr": [
475               "中端高性价比"
476             ],
477             "品牌_sku_attr": [
478               "诺基亚（NOKIA）"
479             ],
480             "cy-屏幕尺寸_sku_attr": [
481               "测试测试"
482             ],
483             "0122-cy-多选使用_sku_attr": [
484               "手柄可折叠"
485             ],
486             "0121-cy-枚举使用_sku_attr": [
487               "1千米"
488             ],
489             "0121-cy-多选使用_sku_attr": [
490               "5ml及以下"
491             ],
492             "product级枚举_sku_attr": [
493               "可组合"
494             ],
495             "product级描述_sku_attr": [
496               "测试"
497             ]
498           },
499           "brand_id": 10000073,
500           "product_img": "//gfs11.atguat.net.cn/T1jzhTByY_1RCvBVdK",
501           "product_sale_price": 1311,
502           "category_first": "286_数码",
503           "shop_id": "null",
504           "category_second_id": "311",
505           "gome_sku": "1000046768",
506           "spu": "9010006069"
507         },
508         "_explanation": {
509           "value": 0.2876821,
510           "description": "sum of:",
511           "details": [
512             {
513               "value": 0.2876821,
514               "description": "weight(full_name:色 in 0) [PerFieldSimilarity], result of:",
515               "details": [
516                 {
517                   "value": 0.2876821,
518                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
519                   "details": [
520                     {
521                       "value": 0.2876821,
522                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
523                       "details": [
524                         {
525                           "value": 1,
526                           "description": "docFreq",
527                           "details": []
528                         },
529                         {
530                           "value": 1,
531                           "description": "docCount",
532                           "details": []
533                         }
534                       ]
535                     },
536                     {
537                       "value": 1,
538                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
539                       "details": [
540                         {
541                           "value": 1,
542                           "description": "termFreq=1.0",
543                           "details": []
544                         },
545                         {
546                           "value": 1.2,
547                           "description": "parameter k1",
548                           "details": []
549                         },
550                         {
551                           "value": 0.75,
552                           "description": "parameter b",
553                           "details": []
554                         },
555                         {
556                           "value": 26,
557                           "description": "avgFieldLength",
558                           "details": []
559                         },
560                         {
561                           "value": 26,
562                           "description": "fieldLength",
563                           "details": []
564                         }
565                       ]
566                     }
567                   ]
568                 }
569               ]
570             }
571           ]
572         }
573       },
574       {
575         "_shard": "[gome_market][3]",
576         "_node": "WBp9VADzRG2Jr-yIKi8h6w",
577         "_index": "gome_market",
578         "_type": "goods",
579         "_id": "1000122354",
580         "_score": 0.2876821,
581         "_source": {
582           "is_shelves": false,
583           "shop_flag": 2,
584           "category_third": "536_耳机/耳麦",
585           "shop": "",
586           "product_ch": "诺基亚（NOKIA）C5-05手机（黑红色）searchadmin",
587           "spu_score": 0,
588           "category_second": "311_时尚数码",
589           "category_third_id": "536",
590           "id": "1000122354",
591           "is_in_store": true,
592           "sku": "1000122354",
593           "brand": "10000073_诺基亚（NOKIA）",
594           "is_self": false,
595           "is_suit": false,
596           "product_proto_price": 0,
597           "create_time": "2017-12-25T07:20:38.606Z",
598           "good_comment_rate": 0,
599           "sale_num": 0,
600           "adver": "%E9%95%BF%E6%9C%9F%E4%BF%83%E9%94%80%E8%AF%AD",
601           "attr_name": [
602             "品牌_sku_attr",
603             "颜色-模板2使用_sku_attr",
604             "类别-模板2使用_sku_attr",
605             "sku级枚举_sku_attr",
606             "sku级多选_sku_attr"
607           ],
608           "category_first_id": "286",
609           "is_sku": true,
610           "dynamicFields": {
611             "品牌_sku_attr": [
612               "诺基亚（NOKIA）"
613             ],
614             "颜色-模板2使用_sku_attr": [
615               "黑色"
616             ],
617             "适用人群-模板2使用_sku_attr": [
618               "人群"
619             ],
620             "类别-模板2使用_sku_attr": [
621               "马夹包, 腰挂, 手提包, 洗漱包, 化妆包, 折叠包, 其他"
622             ]
623           },
624           "brand_id": 10000073,
625           "product_img": "//gfs17.atguat.net.cn/T1hNJTB5Av1RCvBVdK",
626           "product_sale_price": 849,
627           "category_first": "286_数码",
628           "shop_id": "null",
629           "category_second_id": "311",
630           "gome_sku": "1000046769",
631           "spu": "9010006069"
632         },
633         "_explanation": {
634           "value": 0.2876821,
635           "description": "sum of:",
636           "details": [
637             {
638               "value": 0.2876821,
639               "description": "weight(full_name:色 in 0) [PerFieldSimilarity], result of:",
640               "details": [
641                 {
642                   "value": 0.2876821,
643                   "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
644                   "details": [
645                     {
646                       "value": 0.2876821,
647                       "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
648                       "details": [
649                         {
650                           "value": 1,
651                           "description": "docFreq",
652                           "details": []
653                         },
654                         {
655                           "value": 1,
656                           "description": "docCount",
657                           "details": []
658                         }
659                       ]
660                     },
661                     {
662                       "value": 1,
663                       "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
664                       "details": [
665                         {
666                           "value": 1,
667                           "description": "termFreq=1.0",
668                           "details": []
669                         },
670                         {
671                           "value": 1.2,
672                           "description": "parameter k1",
673                           "details": []
674                         },
675                         {
676                           "value": 0.75,
677                           "description": "parameter b",
678                           "details": []
679                         },
680                         {
681                           "value": 17,
682                           "description": "avgFieldLength",
683                           "details": []
684                         },
685                         {
686                           "value": 17,
687                           "description": "fieldLength",
688                           "details": []
689                         }
690                       ]
691                     }
692                   ]
693                 }
694               ]
695             }
696           ]
697         }
698       }
699     ]
700   },
701   "aggregations": {
702     "dynamicFields.sku级枚举_sku_attr": {
703       "doc_count": 3,
704       "dynamicFields.sku级枚举_sku_attr": {
705         "doc_count_error_upper_bound": 0,
706         "sum_other_doc_count": 0,
707         "buckets": [
708           {
709             "key": "洁厕剂",
710             "doc_count": 1
711           }
712         ]
713       }
714     },
715     "dynamicFields.类别-模板2使用_sku_attr": {
716       "doc_count": 3,
717       "dynamicFields.类别-模板2使用_sku_attr": {
718         "doc_count_error_upper_bound": 0,
719         "sum_other_doc_count": 0,
720         "buckets": [
721           {
722             "key": "手提包, 洗漱包",
723             "doc_count": 1
724           },
725           {
726             "key": "马夹包, 腰挂, 手提包, 洗漱包, 化妆包, 折叠包, 其他",
727             "doc_count": 1
728           }
729         ]
730       }
731     },
732     "category_third_id": {
733       "doc_count": 3,
734       "category_third_id": {
735         "doc_count_error_upper_bound": 0,
736         "sum_other_doc_count": 0,
737         "buckets": [
738           {
739             "key": "536",
740             "doc_count": 3
741           }
742         ]
743       }
744     },
745     "category_third": {
746       "doc_count": 3,
747       "category_third": {
748         "doc_count_error_upper_bound": 0,
749         "sum_other_doc_count": 0,
750         "buckets": [
751           {
752             "key": "536_耳机/耳麦",
753             "doc_count": 3
754           }
755         ]
756       }
757     },
758     "dynamicFields.sku级多选_sku_attr": {
759       "doc_count": 3,
760       "dynamicFields.sku级多选_sku_attr": {
761         "doc_count_error_upper_bound": 0,
762         "sum_other_doc_count": 0,
763         "buckets": [
764           {
765             "key": "中端高性价比",
766             "doc_count": 1
767           }
768         ]
769       }
770     },
771     "dynamicFields.品牌_sku_attr": {
772       "doc_count": 3,
773       "dynamicFields.品牌_sku_attr": {
774         "doc_count_error_upper_bound": 0,
775         "sum_other_doc_count": 0,
776         "buckets": [
777           {
778             "key": "诺基亚（NOKIA）",
779             "doc_count": 3
780           }
781         ]
782       }
783     },
784     "range": {
785       "buckets": [
786         {
787           "key": "0.0-30.0",
788           "from": 0,
789           "to": 30,
790           "doc_count": 0
791         },
792         {
793           "key": "30.0-50.0",
794           "from": 30,
795           "to": 50,
796           "doc_count": 0
797         },
798         {
799           "key": "50.0-100.0",
800           "from": 50,
801           "to": 100,
802           "doc_count": 0
803         }
804       ]
805     },
806     "dynamicFields.颜色-模板2使用_sku_attr": {
807       "doc_count": 3,
808       "dynamicFields.颜色-模板2使用_sku_attr": {
809         "doc_count_error_upper_bound": 0,
810         "sum_other_doc_count": 0,
811         "buckets": [
812           {
813             "key": "黑色",
814             "doc_count": 2
815           }
816         ]
817       }
818     }
819   }
820 }

posted @ 2017-12-25 15:38 霄九天阅读(19738) 评论(2) 编辑收藏举报

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

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