关于后端:elasticsearch-bucket-之rare-terms聚合

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