elasticsearchdsl聚合1

接续上篇，本篇介绍elasticsearch聚合查询，使用python库elasticsearch-dsl进行聚合查询操作。

7.3、聚合查询

高阶概念

Buckets(桶/集合):满足特定条件的文档的集合

Metrics(指标):对桶内的文档进行统计计算(例如最小值,求和,最大值等)

新建一张测试表

 1 PUT cars
 2 {
 3   "mappings": {
 4     "transactions":{
 5       "properties": {
 6         "price":{
 7           "type": "integer"
 8         },
 9         "color":{
10           "type": "text",
11           "fielddata": true
12         },
13         "make":{
14           "type": "text",
15           "fielddata": true
16         },
17         "sold":{
18           "type": "date",
19           "format": "yyyy-MM-dd"
20         }
21       }
22     }
23   }
24 }

插入数据

 1 POST /cars/transactions/_bulk
 2 { "index": {"_index": "cars", "_type": "transactions"}} 
 3 { "price" : 10000, "color" : "red", "make" : "honda", "sold" : "2014-10-28" }
 4 { "index": {"_index": "cars", "_type": "transactions"}} 
 5 { "price" : 20000, "color" : "red", "make" : "honda", "sold" : "2014-11-05" } 
 6 { "index": {"_index": "cars", "_type": "transactions"}} 
 7 { "price" : 30000, "color" : "green", "make" : "ford", "sold" : "2014-05-18" }
 8 { "index": {"_index": "cars", "_type": "transactions"}} 
 9 { "price" : 15000, "color" : "blue", "make" : "toyota", "sold" : "2014-07-02" } 
10 { "index": {"_index": "cars", "_type": "transactions"}} 
11 { "price" : 12000, "color" : "green", "make" : "toyota", "sold" : "2014-08-19" }
12 { "index": {"_index": "cars", "_type": "transactions"}} 
13 { "price" : 20000, "color" : "red", "make" : "honda", "sold" : "2014-11-05" } 
14 { "index": {"_index": "cars", "_type": "transactions"}} 
15 { "price" : 80000, "color" : "red", "make" : "bmw", "sold" : "2014-01-01" } 
16 { "index": {"_index": "cars", "_type": "transactions"}} 
17 { "price" : 25000, "color" : "blue", "make" : "ford", "sold" : "2014-02-12" }

查询哪个颜色的汽车销量最好（按颜色分类）

 1 GET cars/transactions/_search
 2 {
 3   "size": 0,
 4   "aggs": {
 5     "popular_colors": {
 6       "terms": {
 7         "field": "color"
 8       }
 9     }
10   }
11 }

1 s = Search(index='cars')
2 a = A("terms", field="color")
3 s.aggs.bucket("popular_color", a)
4 response = s.execute()

或者

1 s.aggs.bucket("popular_color", "terms", field="color")

查询每种颜色车的平均价格

 1 GET cars/transactions/_search
 2 {
 3   "size": 0,
 4   "aggs": {
 5     "colors": {
 6       "terms": {
 7         "field": "color"
 8       },
 9       "aggs": {
10         "avg_price": {
11           "avg": {
12             "field": "price"
13           }
14         }
15       }
16     }
17   }
18 }

1 s = Search(index='cars')
2 a1 = A("terms", field="color")
3 a2 = A("avg", field="price")
4 s.aggs.bucket("colors", a1).metric("avg_price", a2)
5 response = s.execute()

或者

1 s = Search(index='cars')
2 s.aggs.bucket("colors", "terms", field="color").metric("avg_price", "avg", field="price")
3 response = s.execute()

先按颜色分，再按品牌分，再求每种品牌的均价

 1 GET cars/transactions/_search
 2 {
 3   "size": 0,
 4   "aggs": {
 5     "colors": {
 6       "terms": {
 7         "field": "color"
 8       },
 9       "aggs": {
10         "make": {
11           "terms": {
12             "field": "make"
13           },
14           "aggs": {
15             "avg_price": {
16               "avg": {
17                 "field": "price"
18               }
19             }
20           }
21         }
22       }
23     }
24   }
25 }

1 s = Search(index='cars')
2 s.aggs.bucket("colors", "terms", field="color")
3 s.aggs["colors"].bucket("make", "terms", field="make")
4 s.aggs["colors"].aggs["make"].metric("avg_price", "avg", field="price")
5 response = s.execute()

先按颜色分，再按品牌分，再求每种品牌的最高和最低价

 1 GET cars/transactions/_search
 2 {
 3   "size": 0,
 4   "aggs": {
 5     "colors": {
 6       "terms": {
 7         "field": "color"
 8       },
 9       "aggs": {
10         "make": {
11           "terms": {
12             "field": "make"
13           },
14           "aggs": {
15             "min_price": {
16               "min": {
17                 "field": "price"
18               }
19             },
20             "max_price": {
21               "max": {
22                 "field": "price"
23               }
24             }
25           }
26         }
27       }
28     }
29   }
30 }

1 s = Search(index='cars')
2 s.aggs.bucket("colors", "terms", field="color")
3 s.aggs["colors"].bucket("make", "terms", field="make")
4 s.aggs["colors"].aggs["make"].metric("min_price", "min", field="price")
5 s.aggs["colors"].aggs["make"].metric("max_price", "max", field="price")
6 response = s.execute()

未完待续...

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原文地址：https://www.cnblogs.com/dowi/p/10100878.html