1、背景

咱们晓得当咱们应用 terms聚合时，当批改默认程序为_count asc时，统计的后果是不筹备的，而且官网也不举荐咱们这样做，而是举荐应用rare terms聚合。rare terms是一个稀少的term聚合，能够肯定水平的解决升序问题。

2、需要

统计province字段中蕴含上和湖的term数据，并且最多只能呈现2次。获取到聚合后的后果。

3、前置筹备

3.1 筹备mapping

PUT /index_person{  "settings": {    "number_of_shards": 1  },  "mappings": {    "properties": {      "id": {        "type": "long"      },      "name": {        "type": "keyword"      },      "province": {        "type": "keyword"      },      "sex": {        "type": "keyword"      },      "age": {        "type": "integer"      },      "pipeline_province_sex":{        "type": "keyword"      },      "address": {        "type": "text",        "analyzer": "ik_max_word",        "fields": {          "keyword": {            "type": "keyword",            "ignore_above": 256          }        }      }    }  }}

3.2 筹备数据

PUT /_bulk{"create":{"_index":"index_person","_id":1}}{"id":1,"name":"张三","sex":"男","age":20,"province":"湖北","address":"湖北省黄冈市罗田县匡河镇"}{"create":{"_index":"index_person","_id":2}}{"id":2,"name":"李四","sex":"男","age":19,"province":"江苏","address":"江苏省南京市"}{"create":{"_index":"index_person","_id":3}}{"id":3,"name":"王武","sex":"女","age":25,"province":"湖北","address":"湖北省武汉市江汉区"}{"create":{"_index":"index_person","_id":4}}{"id":4,"name":"赵六","sex":"女","age":30,"province":"北京","address":"北京市东城区"}{"create":{"_index":"index_person","_id":5}}{"id":5,"name":"钱七","sex":"女","age":16,"province":"北京","address":"北京市西城区"}{"create":{"_index":"index_person","_id":6}}{"id":6,"name":"王八","sex":"女","age":45,"province":"北京","address":"北京市朝阳区"}{"create":{"_index":"index_person","_id":7}}{"id":7,"name":"九哥","sex":"男","age":25,"province":"上海市","address":"上海市嘉定区"}

4、实现需求

4.1 dsl

GET /index_person/_search{  "size": 0,  "aggs": {    "agg_province": {      "rare_terms": {        "field": "province",        "max_doc_count": 2,        "precision": 0.01,        "include": "(.*上.*|.*湖.*|.*江.*)",        "exclude": ["江苏"],        "missing": "default省"      }    }  }}

4.2 java代码

@Test@DisplayName("稀少的term聚合，相似依照 _count asc 排序的terms聚合，然而terms聚合中依照_count asc的后果是不准的，须要应用 rare terms 聚合")public void agg01() throws IOException {    SearchRequest searchRequest = new SearchRequest.Builder()            .size(0)            .index("index_person")            .aggregations("agg_province", agg ->                    agg.rareTerms(rare ->                            // 罕见词 的字段                            rare.field("province")                                    // 该罕见词最多能够呈现在几个文档中，最大值为100，如果要调整，须要批改search.max_buckets参数的值(尝试批改这个值，不失效)                                    // 在该例子中，只有是呈现的次数<=2的聚合都会返回                                    .maxDocCount(2L)                                    // 外部布谷鸟过滤器的精度，精度越小越准，然而相应的耗费内存也越多，最小值为 0.00001，默认值为 0.01                                    .precision(0.01)                                    // 应该蕴含在聚合的term, 当是单个字段是，能够写正则表达式                                    .include(include -> include.regexp("(.*上.*|.*湖.*|.*江.*)"))                                    // 排出在聚合中的term，当是汇合时，须要写精确的值                                    .exclude(exclude -> exclude.terms(Collections.singletonList("江苏")))                                    // 当文档中缺失province字段时，给默认值                                    .missing("default省")                    )            )            .build();    System.out.println(searchRequest);    SearchResponse<Object> response = client.search(searchRequest, Object.class);    System.out.println(response);}

一些注意事项都在正文中。

4.3 运行后果

5、max_doc_count 和 search.max_buckets

6、注意事项

  1. rare terms统计返回的数据没有大小限度，而且受max_doc_count参数的限度，比方：如果复合 max_doc_count 的分组有60个，那么这60个分组会间接返回。
  2. max_doc_count的值最大为100，貌似不能批改。
  3. 如果一台节点聚合收集的后果过多，那么很容易超过 search.max_buckets的值，此时就须要批改这个值。
# 长期批改PUT /_cluster/settings{"transient": {"search.max_buckets": 65536}}# 永恒批改PUT /_cluster/settings{"persistent": {"search.max_buckets": 65536}}

7、残缺代码

https://gitee.com/huan1993/spring-cloud-parent/blob/master/es/es8-api/src/main/java/com/huan/es8/aggregations/bucket/RareTermsAggs.java

8、参考文档

  1. https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-rare-terms-aggregation.html