1、背景

我们知道当我们使用 terms聚合时，当修改默认顺序为_count asc时，统计的结果是不准备的，而且官方也不推荐我们这样做，而是推荐使用rare terms聚合。rare terms是一个稀少的term聚合，可以一定程度的解决升序问题。

2、需求

统计province字段中包含上和湖的term数据，并且最多只能出现2次。获取到聚合后的结果。

3、前置准备

3.1 准备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 准备数据

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，类似按照 _count asc 排序的terms聚合，但是terms聚合中按照_count asc的结果是不准的，需要使用 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 ->
                            // 稀有词 的字段
                            rare.field("province")
                                    // 该稀有词最多可以出现在几个文档中，最大值为100，如果要调整，需要修改search.max_buckets参数的值(尝试修改这个值，不生效)
                                    // 在该例子中，只要是出现的次数<=2的聚合都会返回
                                    .maxDocCount(2L)
                                    // 内部布谷鸟过滤器的精度，精度越小越准，但是相应的消耗内存也越多，最小值为 0.00001，默认值为 0.01
                                    .precision(0.01)
                                    // 应该包含在聚合的term, 当是单个字段是，可以写正则表达式
                                    .include(include -> include.regexp("(.*上.*|.*湖.*|.*江.*)"))
                                    // 排出在聚合中的term，当是集合时，需要写准确的值
                                    .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