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Elasticsearch 第六篇:聚合統計查詢

2020-11-06 20:10:11 itread01

      前面一直沒有記錄 Elasticsearch 的聚合查詢或者其它複雜的查詢。本篇做一下筆記,為了方便測試,索引資料依然是第五篇生成的測試索引庫 db_student_test ,別名是 student_test 

第一部分 基本聚合

1、最大值 max、最小值 min、平均值 avg 、總和 sum

場景:查詢語文、數學、英語 這三科的最大值、最小值、平均值

POST  http://localhost:9200/student_test1/_search?size=0
{
    "aggs" : {
        "max_chinese" : { "max" : { "field" : "chinese" } },
        "min_chinese" : { "min" : { "field" : "chinese" } },
        "avg_chinese" : { "avg" : { "field" : "chinese" } },
        "max_math": { "max" : { "field" : "math" } },
        "min_math": { "min" : { "field" : "math" } },
        "avg_math": { "avg" : { "field" : "math" } },
        "max_english": { "max" : { "field" : "english" } },
        "min_english": { "min" : { "field" : "english" } },
        "avg_english": { "avg" : { "field" : "english" } }
    }
}

查詢結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "avg_english": {
            "value": 57.78366490546798
        },
        "max_chinese": {
            "value": 98
        },
        "min_chinese": {
            "value": 25
        },
        "min_math": {
            "value": 15
        },
        "max_english": {
            "value": 98
        },
        "avg_chinese": {
            "value": 59.353859695794505
        },
        "avg_math": {
            "value": 56.92907568735187
        },
        "min_english": {
            "value": 21
        },
        "max_math": {
            "value": 99
        }
    }
}

也可以來查詢語文科目分數總和,相當於 sql 的 sum 邏輯,雖然在這裡並沒有什麼意義:

POST  http://localhost:9200/student_test1/_search?size=0
{
    "aggs" : {
        "sum_chinese" : { "sum" : { "field" : "chinese" } }
    }
}

2、求個數,相當於 sql 的 count 邏輯

場景:查詢所有學生總數,這裡隨便 count 一個 欄位就可以,例如數學這個欄位

POST  http://localhost:9200/student_test1/_search?size=0
{
  "aggs": {
    "age_count": {
      "value_count": {
        "field": "math"
      }
    }
  }
}

返回結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "age_count": {
            "value": 50084828
        }
    }
}

課間總數是:50084828 跟第五篇我們生成的資料總量一致

3、distinct 聚合,相當於 sql  的  count ( distinct )

場景:統計語文成績有多少種值

POST  http://localhost:9200/student_test1/_search?size=0
{
    "aggs" : {
        "type_count" : {
            "cardinality" : {
                "field" : "chinese"
            }
        }
    }
}

返回結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "type_count": {
            "value": 74
        }
    }
}

從結果上看,只有74個不同的分數,與第五篇隨機生成資料的規則匹配

4、統計聚合

場景:查詢語文成績 總個數、最大值、最小值、平均值、總和等

POST  http://localhost:9200/student_test1/_search?size=0
{
  "aggs": {
    "chinese_stats": {
      "stats": {
        "field": "chinese"
      }
    }
  }
}

返回結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "chinese_stats": {
            "count": 50084828,
            "min": 25,
            "max": 98,
            "avg": 59.353859695794505,
            "sum": 2972727854
        }
    }
}

5、加強版統計聚合,查詢結果在上面的基礎上,加上方差等統計學上的資料

POST  http://localhost:9200/student_test1/_search?size=0 
{
  "aggs": {
    "chinese_stats": {
      "extended_stats": {
        "field": "chinese"
      }
    }
  }
}

6、分位聚合統計

預設的分位是 1%  5%  25%  50%  75%  95%  99%  《= 的概念

分位數的概念:25% 的分位數是 54,意思是小於等於 54 的樣本佔據了總樣本的 25% ,即是 54 這個數將最底層的1/4 的資料分割出來。

POST  http://localhost:9200/student_test1/_search?size=0 
{
  "aggs": {
    "chinese_percents": {
      "percentiles": {
        "field": "chinese"
      }
    }
  }
}

也可以自定義分位:

POST  http://localhost:9200/student_test1/_search?size=0 
{
  "aggs": {
    "chinese_percents": {
      "percentiles": {
        "field": "chinese",
        "percents" : [10,20,30,40,50,60,70,80,90] 
      }
    }
  }
}

7、範圍聚合統計

場景:分別查詢語文成績小於40分、小於50分、小於60分的比例

POST  http://localhost:9200/student_test1/_search?size=0 
{
  "aggs": {
    "gge_perc_rank": {
      "percentile_ranks": {
        "field": "chinese",
        "values": [40,50,60]
      }
    }
  }
}

以上是查詢成績小於40,小於50,小於60的佔比,得到的資料是: 21.29%   36.09%   51.12%  可以看到這是一個接近等差的數列,可見測試資料的隨機性還是很好的。

第二部分 其它聚合方式

1、Term 聚合

場景:想知道學生的語文成績,在所有分數值上的個數

POST  http://localhost:9200/student_test1/_search?size=0
{
    "aggs" : {
        "genres" : {
            "terms" : { 
                "field" : "chinese"
            }
        }
    }
}

這個查詢會將欄位Chinese進行聚合,例如87分聚合成一個組,88分聚合成一個組,等等;

但是這裡預設是按組的大小排序,而且不會將所有的組都顯示出來,數量太小的組可能被忽略,查詢結果如下:

{
    "took": 1,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "genres": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 42560269,
            "buckets": [
                {
                    "key": 61,
                    "doc_count": 752863
                },
                {
                    "key": 68,
                    "doc_count": 752835
                },
                {
                    "key": 55,
                    "doc_count": 752749
                },
                {
                    "key": 59,
                    "doc_count": 752444
                },
                {
                    "key": 76,
                    "doc_count": 752405
                },
                {
                    "key": 74,
                    "doc_count": 752309
                },
                {
                    "key": 56,
                    "doc_count": 752283
                },
                {
                    "key": 49,
                    "doc_count": 752273
                },
                {
                    "key": 52,
                    "doc_count": 752201
                },
                {
                    "key": 50,
                    "doc_count": 752197
                }
            ]
        }
    }
}

如果想要自定義篩選條件,Term聚合還可以按照以下設定來查詢:

post  http://localhost:9200/student_test1/_search?size=0
{
    "aggs" : {
        "genres" : {
            "terms" : { 
                "field" : "chinese",
                 "size" : 100,                     // 可能有100個不用的分數,我們將全部都展示出來
                 "order" : { "_count" : "asc" },   // 按照組數由小到大排序
                  "min_doc_count": 752200          //過濾條件:組數最小值是752200
            }
        }
    }
}

查詢結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "genres": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": 52,
                    "doc_count": 752201
                },
                {
                    "key": 49,
                    "doc_count": 752273
                },
                {
                    "key": 56,
                    "doc_count": 752283
                },
                {
                    "key": 74,
                    "doc_count": 752309
                },
                {
                    "key": 76,
                    "doc_count": 752405
                },
                {
                    "key": 59,
                    "doc_count": 752444
                },
                {
                    "key": 55,
                    "doc_count": 752749
                },
                {
                    "key": 68,
                    "doc_count": 752835
                },
                {
                    "key": 61,
                    "doc_count": 752863
                }
            ]
        }
    }
}

 2、Filter 聚合

Filter 聚合會先進行條件過濾,在進行聚合

場景:查詢華南理工大學的學生的數學科目平均分(先篩選學校,再進行分數統計聚合)

{
    "aggs" : {
        "scut_math_avg" : {
            "filter" : { "term": { "school": "華南理工大學" } },
            "aggs" : {
                "avg_price" : { "avg" : { "field" : "math" } }
            }
        }
    }
}

查詢結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "scut_math_avg": {
            "doc_count": 1854993,
            "avg_price": {
                "value": 56.93080027795253
            }
        }
    }
}

 3、Filters 多重聚合

場景:查詢各個學校,語文、數學、英語的平均分都是多少,可以採用多重聚合,速度可能有點慢,如下

POST  http://localhost:9200/student_test1/_search?size=0
{
  "aggs" : {
    "messages" : {
      "filters" : {
        "filters" : {
          "school_1" :   { "term" : { "school" : "華南理工大學" }},
          "school_2" : { "term" : { "school" : "中山大學" }},
          "school_3" : { "match" : { "school" : "暨南大學" }}
        }
      },
      "aggs" : {
           "avg_chinese" : { "avg" : { "field" : "chinese" } },
           "avg_math" : { "avg" : { "field" : "math" } }
      }
    }
  }
}

於是得到結果:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "messages": {
            "buckets": {
                "school_1": {
                    "doc_count": 1854993,
                    "avg_chinese": {
                        "value": 59.353236912484306
                    },
                    "avg_math": {
                        "value": 56.93080027795253
                    }
                },
                "school_2": {
                    "doc_count": 1855016,
                    "avg_chinese": {
                        "value": 59.349129064115886
                    },
                    "avg_math": {
                        "value": 56.93540918245449
                    }
                },
                "school_3": {
                    "doc_count": 44519876,
                    "avg_chinese": {
                        "value": 59.35397212247402
                    },
                    "avg_math": {
                        "value": 56.92948502372289
                    }
                }
            }
        }
    }
}

 4、Range 範圍聚合

場景:想要查詢語文成績各個分數段的人數,可以這樣查詢

POST  http://localhost:9200/student_test1/_search?size=0
{ "aggs" : { "chinese_ranges" : { "range" : { "field" : "chinese", "ranges" : [ { "to" : 60 }, { "from" : 60, "to" : 75 }, { "from" : 75, "to" : 85 }, { "from" : 85 } ] } } } }

查詢結果是:

{
    "took": 0,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "chinese_ranges": {
            "buckets": [
                {
                    "key": "*-60.0",
                    "to": 60,
                    "doc_count": 25096839
                },
                {
                    "key": "60.0-75.0",
                    "from": 60,
                    "to": 75,
                    "doc_count": 11278543
                },
                {
                    "key": "75.0-85.0",
                    "from": 75,
                    "to": 85,
                    "doc_count": 7424634
                },
                {
                    "key": "85.0-*",
                    "from": 85,
                    "doc_count": 6284812
                }
            ]
        }
    }
}

這個返回結果的組名分別是 *-60.0 60.0-75.0 75.0-85.0 85.0-*
如果我們不想要這樣的組名,可以自定義組名,例如:

POST  http://localhost:9200/student_test1/_search?size=0
{
    "aggs" : {
        "chinese_ranges" : {
            "range" : {
                "field" : "chinese",
                "keyed" : true,
                "ranges" : [
                    { "key" : "不及格", "to" : 60 },
                    { "key" : "及格", "from" : 60, "to" : 75 },
                    { "key" : "良好", "from" : 75, "to" : 85 },
                    { "key" : "優秀", "from" : 85 }
                ]
            }
        }
    }
}

查詢結果將會是:

{
    "took": 1675,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 10000,
            "relation": "gte"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "chinese_ranges": {
            "buckets": {
                "不及格": {
                    "to": 60,
                    "doc_count": 25096839
                },
                "及格": {
                    "from": 60,
                    "to": 75,
                    "doc_count": 11278543
                },
                "良好": {
                    "from": 75,
                    "to": 85,
                    "doc_count": 7424634
                },
                "優秀": {
                    "from": 85,
                    "doc_count": 6284812
                }
            }
        }
    }
}

 還有其它各種各樣的、複雜的聚合查詢,都是可以網上查資料,甚至還支援推薦系統的一些計算方法,例如矩陣的概念等等。

 還可以參考 https://blog.csdn.net/alex_xfboy/article/details/8610

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