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  • 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(/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
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