目标

ES版本信息

官方文档

实战

新增测试数据

基本语法

先过滤再分桶聚合

按照范围聚合

Histogram（直方图/柱状图）

嵌套分桶聚合

Date range aggregation（日期范围聚合）

Filter aggregation

Filters aggregation

Missing aggregation

Multi Terms aggregation（多字段聚合）

目标

掌握Bucket aggregations（分桶聚合，相当于MySQL中的分组聚合）语法，通过本文列举的各种案例举一反三。具体会涉及以下内容：

Multi Terms aggregation（多字段聚合）；
分桶聚合排序；
分桶聚合前先过滤数据；
根据范围分桶聚合；
Histogram（直方图/柱状图）；
嵌套分桶聚合；
对日期分桶聚合；
单过滤器和多过滤器；
missing聚合。

ES版本信息

7.17.5

官方文档

Bucket aggregationshttps://www.elastic.co/guide/en/elasticsearch/reference/7.17/search-aggregations-bucket.html

实战

新增测试数据

PUT /library_db
{"settings": {"index": {"analysis.analyzer.default.type": "ik_max_word"}}
}PUT /library_db/_bulk
{"index":{"_id":"1"}}
{"id":1,"type":"玄幻","name":"诛仙","words_num":120,"chapter_num":600,"completion_time":"2000-09-01","author":"萧鼎","prices":32.12}
{"index":{"_id":"2"}}
{"id":2,"type":"玄幻","name":"诛仙前传：蛮荒行","words_num":30,"chapter_num":67,"completion_time":"2020-09-01","author":"萧鼎","prices":23.12}
{"index":{"_id":"3"}}
{"id":3,"type":"武侠","name":"天龙八部","words_num":80,"chapter_num":120,"completion_time":"1995-09-01","author":"金庸","prices":52.1}
{"index":{"_id":"4"}}
{"id":4,"type":"武侠","name":"射雕英雄传","words_num":67,"chapter_num":95,"completion_time":"1998-01-01","author":"金庸","prices":4.12}
{"index":{"_id":"5"}}
{"id":5,"type":"武侠","name":"神雕侠侣","words_num":75,"chapter_num":76,"completion_time":"2000-01-01","author":"金庸","prices":32.8}
{"index":{"_id":"6"}}
{"id":5,"type":"武侠","name":"倚天屠龙记","words_num":83,"chapter_num":130,"completion_time":"2003-01-01","author":"金庸","prices":100.12}
{"index":{"_id":"7"}}
{"id":7,"type":"玄幻","name":"凡人修仙传","words_num":600,"chapter_num":3000,"completion_time":"2018-01-01","author":"忘语","prices":120.12}
{"index":{"_id":"8"}}
{"id":8,"type":"玄幻","name":"魔天记","words_num":159,"chapter_num":400,"completion_time":"2019-01-01","author":"忘语","prices":11.12}
{"index":{"_id":"9"}}
{"id":9,"type":"都市异能","name":"黄金瞳","words_num":220,"chapter_num":400,"completion_time":"2019-01-01","author":"打眼","prices":74.5}
{"index":{"_id":"10"}}
{"id":10,"type":"玄幻","name":"将夜","words_num":210,"chapter_num":600,"completion_time":"2014-01-01","author":"血红","prices":32.0}
{"index":{"_id":"11"}}
{"id":11,"type":"军事","name":"亮剑","words_num":120,"chapter_num":100,"completion_time":"2012-01-01","author":"都梁","prices":15.0}

基本语法

需求一：求图书馆中每个小说类型的小说数量。

#size=10，表示聚合后展示10条数据。
GET /library_db/_search
{"size": 0,"aggs": {"type_count": {"terms": {"field": "type.keyword","size": 10}}}
}

需求二：求图书馆中每个小说类型的小说数量。按照升序排序。

#同理，降序排序就用desc，这和MySQL是一样的语法。
GET /library_db/_search
{"size": 0,"aggs": {"type_count": {"terms": {"field": "type.keyword","size": 10,"order": {"_count": "asc"}}}}
}

先过滤再分桶聚合

需求：求图书馆中每个小说类型的小说数量，要求小说字数大于等于100万。

GET /library_db/_search
{"size": 0,"query": {"range": {"words_num": {"gte": 100}}}, "aggs": {"type_count": {"terms": {"field": "type.keyword","size": 10}}}
}

按照范围聚合

需求：求小说字数在0-30万、30万-50万、50万-100万、100万-200万、大于等于200万区间的数量。


GET /library_db/_search
{"size": 0,"aggs": {"words_num_count": {"range": {"field": "words_num","ranges": [{"from": 0, "to": 30},{"to": 50,"from": 30},{"to": 100,"from": 50},{"to": 200,"from": 100},{"key": ">200","from": 200}]}}}
}

Histogram（直方图/柱状图）

需求：求图书馆中，各个字数区间的小说数量，每个区间50万字。

#这里会查出来很多空区间。设置min_doc_count=1表示为空区间的数据不返回。
GET /library_db/_search
{"size": 0,"aggs": {"type_count": {"histogram": {"field": "words_num","interval": 50,"min_doc_count": 1}}}
}

嵌套分桶聚合

需求：求图书馆中，每种类型的小说的平均价格。

GET /library_db/_search
{"size": 0,"aggs": {"type_group": {"terms": {"field": "type.keyword"},"aggs": {"prices_avg": {"avg": {"field": "prices"}}}}}
}

Date range aggregation（日期范围聚合）

分析

from和to分别表示开始时间和结束时间；
from相当于>=；
to相当于<；
missing：为缺少的字段设置代替值的作用。

需求一：求图书馆中，以当前时间后推一年的时间间隔，完本的小说数量。注意：这里按照东八区（北京时间）来计算，所以+8h。

GET /library_db/_search
{"size": 0,"aggs": {"range": {"date_range": {"field": "completion_time","ranges": [{"from": "now+8h-2y/d","to": "now+8h-1y/d"}]}}}
}

需求二：求图书馆中，以2019年为分界线，完本的小说的数据量。没有时间字段，则默认时间字段值为1976-11-30，它们会被划分到Older中。

GET /library_db/_search
{"size": 0,"aggs": {"range": {"date_range": {"field": "completion_time","missing": "1976-11-30","ranges": [{"key": "Older","to": "2019-01-01"},{"key": "Newer","from": "2019-01-01","to": "now+8h/d"}]}}}
}

Filter aggregation

需求：查询所有小说的平均价格和武侠小说的平均价格。

POST /library_db/_search?size=0&filter_path=aggregations
{"aggs": {"avg_price": {"avg": {"field": "prices"}},"wx": {"filter": {"term": {"type": "武侠"}},"aggs": {"wx_avg_prices": {"avg": {"field": "prices"}}}}}
}#如果单纯地查询武侠小说的平均价格，query比filter效率更高。
POST /library_db/_search?size=0&filter_path=aggregations
{"query": { "term": { "type": "武侠" } },"aggs": {"wx": { "avg": { "field": "prices" } }}
}#不推荐
POST /library_db/_search?size=0&filter_path=aggregations
{"aggs": {"wx": {"filter": { "term": { "type": "武侠" } },"aggs": {"wx_avg_prices": { "avg": { "field": "prices" } }}}}
}

Filters aggregation

分析

多个过滤条件时，考虑到效率问题，推荐用多过滤器而不是单过滤器。
other_bucket_key表示其他，相当于java中的else。

需求一：查询武侠、玄幻、军事、都市类小说的数量。

GET library_db/_search
{"size": 0,"aggs": {"type_avg": {"filters": {"filters": {"wx": {"term": {"type.keyword": "武侠"}},"xh": {"term": {"type.keyword": "玄幻"}},"js": {"term": {"type.keyword": "军事"}},"ds": {"term": {"type.keyword": "都市"}}}}}}
}

需求二：查询武侠、玄幻、其他类小说的数量。这里的其他类是只除了玄幻和武侠小说的类别。

GET library_db/_search
{"size": 0,"aggs": {"type_avg": {"filters": {"filters": {"wx": {"term": {"type.keyword": "武侠"}},"xh": {"term": {"type.keyword": "玄幻"}}},"other_bucket_key": "other_type"}}}
}

Missing aggregation

需求：搜索没有价格字段的小说数量。

POST /library_db/_search?size=0
{"aggs": {"without_prices": {"missing": { "field": "prices" }}}
}

Multi Terms aggregation（多字段聚合）

需求：根据作者和小说类型字段分组，统计小说数量。

分析：相当于MySQL中对多个字段进行分组。Multi Terms aggregation与terms aggregation相似，但是前者效率低于后者，所以官方文档中建议：如果经常使用的同一组字段，则将这些字段的组合键索引设置为单独的字段并在此字段上使用terms aggregation。

GET /library_db/_search
{"size": 0, "aggs": {"genres_and_products": {"multi_terms": {"terms": [{"field": "type.keyword" }, {"field": "author.keyword"}]}}}
}

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ES聚合之Bucket聚合语法讲解

目标

ES版本信息

官方文档

实战

新增测试数据

基本语法

先过滤再分桶聚合

按照范围聚合

Histogram（直方图/柱状图）

嵌套分桶聚合

Date range aggregation（日期范围聚合）

Filter aggregation

Filters aggregation

Missing aggregation

Multi Terms aggregation（多字段聚合）

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