elasticsearch-dsl聚合-1

接續上篇，本篇介紹elasticsearch聚合查詢，使用python庫elasticsearch-dsl進行聚合查詢操做。

7.三、聚合查詢

高階概念

• Buckets(桶/集合):知足特定條件的文檔的集合

• Metrics(指標):對桶內的文檔進行統計計算(例如最小值,求和,最大值等)

  • 新建一張測試表

     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 }

    插入數據

     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" }

  • 查詢哪一個顏色的汽車銷量最好（按顏色分類）

     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")

  • 查詢每種顏色車的平均價格

     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()

  • 先按顏色分，再按品牌分，再求每種品牌的均價

     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()

  • 先按顏色分，再按品牌分，再求每種品牌的最高和最低價

     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()

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