elasticsearch-dsl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...

 

转载于:https://www.cnblogs.com/dowi/p/10100878.html