目录
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<Object> response = client.search(searchRequest, Object.class);
System.out.println(response);
}
一些注意事项都在注释中。
4.3 运行结果
5、max_doc_count 和 search.max_buckets
6、注意事项
rare terms统计返回的数据没有大小限制，而且受max_doc_count参数的限制，比如：如果复合 max_doc_count 的分组有60个，那么这60个分组会直接返回。max_doc_count的值最大为100，貌似不能修改。- 如果一台节点聚合收集的结果过多，那么很容易超过
search.max_buckets的值，此时就需要修改这个值。
# 临时修改
PUT /_cluster/settings
{"transient": {"search.max_buckets": 65536}}
# 永久修改
PUT /_cluster/settings
{"persistent": {"search.max_buckets": 65536}}