一、聚合分析简介

1. ES聚合分析是什么？

聚合分析是数据库中重要的功能特性，完成对一个查询的数据集中数据的聚合计算，如：找出某字段（或计算表达式的结果）的最大值、最小值，计算和、平均值等。ES作为搜索引擎兼数据库，同样提供了强大的聚合分析能力。

对一个数据集求最大、最小、和、平均值等指标的聚合，在ES中称为指标聚合   metric

而关系型数据库中除了有聚合函数外，还可以对查询出的数据进行分组group by，再在组上进行指标聚合。在 ES 中group by 称为分桶，桶聚合 bucketing

ES中还提供了矩阵聚合（matrix）、管道聚合（pipleline），但还在完善中。

2. ES聚合分析查询的写法

在查询请求体中以aggregations节点按如下语法定义聚合分析：

  1. "aggregations" : {
  2.     "<aggregation_name>" : { <!--聚合的名字 -->
  3.         "<aggregation_type>" : { <!--聚合的类型 -->
  4.             <aggregation_body> <!--聚合体：对哪些字段进行聚合 -->
  5.         }
  6.         [,"meta" : {  [<meta_data_body>] } ]? <!--元 -->
  7.         [,"aggregations" : { [<sub_aggregation>]+ } ]? <!--在聚合里面在定义子聚合 -->
  8.     }
  9.     [,"<aggregation_name_2>" : { ... } ]*<!--聚合的名字 -->
  10. }

 说明：

aggregations 也可简写为 aggs

3. 聚合分析的值来源

聚合计算的值可以取字段的值，也可是脚本计算的结果。

二、指标聚合

1. max min sum avg

示例1：查询所有客户中余额的最大值

  1. POST /bank/_search?
  2. {
  3.   "size": 0,
  4.   "aggs": {
  5.     "masssbalance": {
  6.       "max": {
  7.         "field": "balance"
  8.       }
  9.     }
  10.   }
  11. }

结果1：

  1. {
  2.   "took": 2080,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "masssbalance": {
  17.       "value": 49989
  18.     }
  19.   }
  20. }

示例2：查询年龄为24岁的客户中的余额最大值

  1. POST /bank/_search?
  2. {
  3.   "size": 2,
  4.   "query": {
  5.     "match": {
  6.       "age": 24
  7.     }
  8.   },
  9.   "sort": [
  10.     {
  11.       "balance": {
  12.         "order": "desc"
  13.       }
  14.     }
  15.   ],
  16.   "aggs": {
  17.     "max_balance": {
  18.       "max": {
  19.         "field": "balance"
  20.       }
  21.     }
  22.   }
  23. }

结果2：

  1. {
  2.   "took": 5,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 42,
  12.     "max_score": null,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "697",
  18.         "_score": null,
  19.         "_source": {
  20.           "account_number": 697,
  21.           "balance": 48745,
  22.           "firstname": "Mallory",
  23.           "lastname": "Emerson",
  24.           "age": 24,
  25.           "gender": "F",
  26.           "address": "318 Dunne Court",
  27.           "employer": "Exoplode",
  28.           "email": "malloryemerson@exoplode.com",
  29.           "city": "Montura",
  30.           "state": "LA"
  31.         },
  32.         "sort": [
  33.           48745
  34.         ]
  35.       },
  36.       {
  37.         "_index": "bank",
  38.         "_type": "_doc",
  39.         "_id": "917",
  40.         "_score": null,
  41.         "_source": {
  42.           "account_number": 917,
  43.           "balance": 47782,
  44.           "firstname": "Parks",
  45.           "lastname": "Hurst",
  46.           "age": 24,
  47.           "gender": "M",
  48.           "address": "933 Cozine Avenue",
  49.           "employer": "Pyramis",
  50.           "email": "parkshurst@pyramis.com",
  51.           "city": "Lindcove",
  52.           "state": "GA"
  53.         },
  54.         "sort": [
  55.           47782
  56.         ]
  57.       }
  58.     ]
  59.   },
  60.   "aggregations": {
  61.     "max_balance": {
  62.       "value": 48745
  63.     }
  64.   }
  65. }

示例3：值来源于脚本，查询所有客户的平均年龄是多少，并对平均年龄加10

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "avg_age": {
  5.       "avg": {
  6.         "script": {
  7.           "source": "doc.age.value"
  8.         }
  9.       }
  10.     },
  11.     "avg_age10": {
  12.       "avg": {
  13.         "script": {
  14.           "source": "doc.age.value + 10"
  15.         }
  16.       }
  17.     }
  18.   }
  19. }

结果3：

  1. {
  2.   "took": 86,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "avg_age": {
  17.       "value": 30.171
  18.     },
  19.     "avg_age10": {
  20.       "value": 40.171
  21.     }
  22.   }
  23. }

示例4：指定field，在脚本中用_value 取字段的值

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "sum_balance": {
  5.       "sum": {
  6.         "field": "balance",
  7.         "script": {
  8.             "source": "_value * 1.03"
  9.         }
  10.       }
  11.     }
  12.   }
  13. }

结果4：

  1. {
  2.   "took": 165,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "sum_balance": {
  17.       "value": 26486282.11
  18.     }
  19.   }
  20. }

示例5：为没有值字段指定值。如未指定，缺失该字段值的文档将被忽略。

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "avg_age": {
  5.       "avg": {
  6.         "field": "age",
  7.         "missing": 18
  8.       }
  9.     }
  10.   }
  11. }

2. 文档计数 count

示例1：统计银行索引bank下年龄为24的文档数量

  1. POST /bank/_doc/_count
  2. {
  3.   "query": {
  4.     "match": {
  5.       "age" : 24
  6.     }
  7.   }
  8. }

结果1：

  1. {
  2.   "count": 42,
  3.   "_shards": {
  4.     "total": 5,
  5.     "successful": 5,
  6.     "skipped": 0,
  7.     "failed": 0
  8.   }
  9. }

3. Value count 统计某字段有值的文档数

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_count": {
  5.       "value_count": {
  6.         "field": "age"
  7.       }
  8.     }
  9.   }
  10. }

结果1：

  1. {
  2.   "took": 2022,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_count": {
  17.       "value": 1000
  18.     }
  19.   }
  20. }

4. cardinality  值去重计数

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_count": {
  5.       "cardinality": {
  6.         "field": "age"
  7.       }
  8.     },
  9.     "state_count": {
  10.       "cardinality": {
  11.         "field": "state.keyword"
  12.       }
  13.     }
  14.   }
  15. }

 说明：state的使用它的keyword版

结果1：

  1. {
  2.   "took": 2074,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "state_count": {
  17.       "value": 51
  18.     },
  19.     "age_count": {
  20.       "value": 21
  21.     }
  22.   }
  23. }

5. stats 统计 count max min avg sum 5个值

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_stats": {
  5.       "stats": {
  6.         "field": "age"
  7.       }
  8.     }
  9.   }
  10. }

结果1：

  1. {
  2.   "took": 7,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_stats": {
  17.       "count": 1000,
  18.       "min": 20,
  19.       "max": 40,
  20.       "avg": 30.171,
  21.       "sum": 30171
  22.     }
  23.   }
  24. }

6. Extended stats

高级统计，比stats多4个统计结果： 平方和、方差、标准差、平均值加/减两个标准差的区间

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_stats": {
  5.       "extended_stats": {
  6.         "field": "age"
  7.       }
  8.     }
  9.   }
  10. }

结果1：

  1. {
  2.   "took": 7,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_stats": {
  17.       "count": 1000,
  18.       "min": 20,
  19.       "max": 40,
  20.       "avg": 30.171,
  21.       "sum": 30171,
  22.       "sum_of_squares": 946393,
  23.       "variance": 36.10375899999996,
  24.       "std_deviation": 6.008640362012022,
  25.       "std_deviation_bounds": {
  26.         "upper": 42.18828072402404,
  27.         "lower": 18.153719275975956
  28.       }
  29.     }
  30.   }
  31. }

7. Percentiles 占比百分位对应的值统计

对指定字段（脚本）的值按从小到大累计每个值对应的文档数的占比（占所有命中文档数的百分比），返回指定占比比例对应的值。默认返回[ 1, 5, 25, 50, 75, 95, 99 ]分位上的值。如下中间的结果，可以理解为：占比为50%的文档的age值 <= 31，或反过来：age<=31的文档数占总命中文档数的50%

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_percents": {
  5.       "percentiles": {
  6.         "field": "age"
  7.       }
  8.     }
  9.   }
  10. }

结果1：

  1. {
  2.   "took": 87,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_percents": {
  17.       "values": {
  18.         "1.0": 20,
  19.         "5.0": 21,
  20.         "25.0": 25,
  21.         "50.0": 31,
  22.         "75.0": 35.00000000000001,
  23.         "95.0": 39,
  24.         "99.0": 40
  25.       }
  26.     }
  27.   }
  28. }

结果说明:

占比为50%的文档的age值 <= 31，或反过来：age<=31的文档数占总命中文档数的50%

示例2：指定分位值

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_percents": {
  5.       "percentiles": {
  6.         "field": "age",
  7.         "percents" : [95, 99, 99.9]
  8.       }
  9.     }
  10.   }
  11. }

结果2：

  1. {
  2.   "took": 8,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_percents": {
  17.       "values": {
  18.         "95.0": 39,
  19.         "99.0": 40,
  20.         "99.9": 40
  21.       }
  22.     }
  23.   }
  24. }

8. Percentiles rank 统计值小于等于指定值的文档占比

示例1：统计年龄小于25和30的文档的占比，和第7项相反

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "gge_perc_rank": {
  5.       "percentile_ranks": {
  6.         "field": "age",
  7.         "values": [
  8.           25,
  9.           30
  10.         ]
  11.       }
  12.     }
  13.   }
  14. }

结果2：

  1. {
  2.   "took": 8,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "gge_perc_rank": {
  17.       "values": {
  18.         "25.0": 26.1,
  19.         "30.0": 49.2
  20.       }
  21.     }
  22.   }
  23. }

结果说明：年龄小于25的文档占比为26.1%，年龄小于30的文档占比为49.2%，

9. Geo Bounds aggregation 求文档集中的地理位置坐标点的范围

参考官网链接：

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geobounds-aggregation.html

10. Geo Centroid aggregation  求地理位置中心点坐标值

参考官网链接：

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geocentroid-aggregation.html

三、桶聚合

1. Terms Aggregation  根据字段值项分组聚合

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age"
  7.       }
  8.     }
  9.   }
  10. }

结果1：

  1. {
  2.   "took": 2000,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 0,
  18.       "sum_other_doc_count": 463,
  19.       "buckets": [
  20.         {
  21.           "key": 31,
  22.           "doc_count": 61
  23.         },
  24.         {
  25.           "key": 39,
  26.           "doc_count": 60
  27.         },
  28.         {
  29.           "key": 26,
  30.           "doc_count": 59
  31.         },
  32.         {
  33.           "key": 32,
  34.           "doc_count": 52
  35.         },
  36.         {
  37.           "key": 35,
  38.           "doc_count": 52
  39.         },
  40.         {
  41.           "key": 36,
  42.           "doc_count": 52
  43.         },
  44.         {
  45.           "key": 22,
  46.           "doc_count": 51
  47.         },
  48.         {
  49.           "key": 28,
  50.           "doc_count": 51
  51.         },
  52.         {
  53.           "key": 33,
  54.           "doc_count": 50
  55.         },
  56.         {
  57.           "key": 34,
  58.           "doc_count": 49
  59.         }
  60.       ]
  61.     }
  62.   }
  63. }

结果说明：

"doc_count_error_upper_bound": 0：文档计数的最大偏差值

"sum_other_doc_count": 463：未返回的其他项的文档数

默认情况下返回按文档计数从高到低的前10个分组：

  1.  "buckets": [
  2.         {
  3.           "key": 31,
  4.           "doc_count": 61
  5.         },
  6.         {
  7.           "key": 39,
  8.           "doc_count": 60
  9.         },
  10.     .............
  11. ]

年龄为31的文档有61个，年龄为39的文档有60个

 size 指定返回多少个分组：

示例2：指定返回20个分组

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age",
  7.         "size": 20
  8.       }
  9.     }
  10.   }
  11. }

结果2：

  1. {
  2.   "took": 9,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 0,
  18.       "sum_other_doc_count": 35,
  19.       "buckets": [
  20.         {
  21.           "key": 31,
  22.           "doc_count": 61
  23.         },
  24.         {
  25.           "key": 39,
  26.           "doc_count": 60
  27.         },
  28.         {
  29.           "key": 26,
  30.           "doc_count": 59
  31.         },
  32.         {
  33.           "key": 32,
  34.           "doc_count": 52
  35.         },
  36.         {
  37.           "key": 35,
  38.           "doc_count": 52
  39.         },
  40.         {
  41.           "key": 36,
  42.           "doc_count": 52
  43.         },
  44.         {
  45.           "key": 22,
  46.           "doc_count": 51
  47.         },
  48.         {
  49.           "key": 28,
  50.           "doc_count": 51
  51.         },
  52.         {
  53.           "key": 33,
  54.           "doc_count": 50
  55.         },
  56.         {
  57.           "key": 34,
  58.           "doc_count": 49
  59.         },
  60.         {
  61.           "key": 30,
  62.           "doc_count": 47
  63.         },
  64.         {
  65.           "key": 21,
  66.           "doc_count": 46
  67.         },
  68.         {
  69.           "key": 40,
  70.           "doc_count": 45
  71.         },
  72.         {
  73.           "key": 20,
  74.           "doc_count": 44
  75.         },
  76.         {
  77.           "key": 23,
  78.           "doc_count": 42
  79.         },
  80.         {
  81.           "key": 24,
  82.           "doc_count": 42
  83.         },
  84.         {
  85.           "key": 25,
  86.           "doc_count": 42
  87.         },
  88.         {
  89.           "key": 37,
  90.           "doc_count": 42
  91.         },
  92.         {
  93.           "key": 27,
  94.           "doc_count": 39
  95.         },
  96.         {
  97.           "key": 38,
  98.           "doc_count": 39
  99.         }
  100.       ]
  101.     }
  102.   }
  103. }

示例3：每个分组上显示偏差值

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age",
  7.         "size": 5,
  8.         "shard_size": 20,
  9.         "show_term_doc_count_error": true
  10.       }
  11.     }
  12.   }
  13. }

结果3：

  1. {
  2.   "took": 8,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 25,
  18.       "sum_other_doc_count": 716,
  19.       "buckets": [
  20.         {
  21.           "key": 31,
  22.           "doc_count": 61,
  23.           "doc_count_error_upper_bound": 0
  24.         },
  25.         {
  26.           "key": 39,
  27.           "doc_count": 60,
  28.           "doc_count_error_upper_bound": 0
  29.         },
  30.         {
  31.           "key": 26,
  32.           "doc_count": 59,
  33.           "doc_count_error_upper_bound": 0
  34.         },
  35.         {
  36.           "key": 32,
  37.           "doc_count": 52,
  38.           "doc_count_error_upper_bound": 0
  39.         },
  40.         {
  41.           "key": 36,
  42.           "doc_count": 52,
  43.           "doc_count_error_upper_bound": 0
  44.         }
  45.       ]
  46.     }
  47.   }
  48. }

示例4：shard_size 指定每个分片上返回多少个分组

shard_size 的默认值为：
索引只有一个分片：= size
多分片：= size * 1.5 + 10

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age",
  7.         "size": 5,
  8.         "shard_size": 20
  9.       }
  10.     }
  11.   }
  12. }

结果4：

  1. {
  2.   "took": 8,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 25,
  18.       "sum_other_doc_count": 716,
  19.       "buckets": [
  20.         {
  21.           "key": 31,
  22.           "doc_count": 61
  23.         },
  24.         {
  25.           "key": 39,
  26.           "doc_count": 60
  27.         },
  28.         {
  29.           "key": 26,
  30.           "doc_count": 59
  31.         },
  32.         {
  33.           "key": 32,
  34.           "doc_count": 52
  35.         },
  36.         {
  37.           "key": 36,
  38.           "doc_count": 52
  39.         }
  40.       ]
  41.     }
  42.   }
  43. }

 order  指定分组的排序

示例5：根据文档计数排序

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age",
  7.         "order" : { "_count" : "asc" }
  8.       }
  9.     }
  10.   }
  11. }

结果5：

  1. {
  2.   "took": 3,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 0,
  18.       "sum_other_doc_count": 584,
  19.       "buckets": [
  20.         {
  21.           "key": 29,
  22.           "doc_count": 35
  23.         },
  24.         {
  25.           "key": 27,
  26.           "doc_count": 39
  27.         },
  28.         {
  29.           "key": 38,
  30.           "doc_count": 39
  31.         },
  32.         {
  33.           "key": 23,
  34.           "doc_count": 42
  35.         },
  36.         {
  37.           "key": 24,
  38.           "doc_count": 42
  39.         },
  40.         {
  41.           "key": 25,
  42.           "doc_count": 42
  43.         },
  44.         {
  45.           "key": 37,
  46.           "doc_count": 42
  47.         },
  48.         {
  49.           "key": 20,
  50.           "doc_count": 44
  51.         },
  52.         {
  53.           "key": 40,
  54.           "doc_count": 45
  55.         },
  56.         {
  57.           "key": 21,
  58.           "doc_count": 46
  59.         }
  60.       ]
  61.     }
  62.   }
  63. }

示例6：根据分组值排序

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age",
  7.         "order" : { "_key" : "asc" }
  8.       }
  9.     }
  10.   }
  11. }

结果6：

  1. {
  2.   "took": 10,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 0,
  18.       "sum_other_doc_count": 549,
  19.       "buckets": [
  20.         {
  21.           "key": 20,
  22.           "doc_count": 44
  23.         },
  24.         {
  25.           "key": 21,
  26.           "doc_count": 46
  27.         },
  28.         {
  29.           "key": 22,
  30.           "doc_count": 51
  31.         },
  32.         {
  33.           "key": 23,
  34.           "doc_count": 42
  35.         },
  36.         {
  37.           "key": 24,
  38.           "doc_count": 42
  39.         },
  40.         {
  41.           "key": 25,
  42.           "doc_count": 42
  43.         },
  44.         {
  45.           "key": 26,
  46.           "doc_count": 59
  47.         },
  48.         {
  49.           "key": 27,
  50.           "doc_count": 39
  51.         },
  52.         {
  53.           "key": 28,
  54.           "doc_count": 51
  55.         },
  56.         {
  57.           "key": 29,
  58.           "doc_count": 35
  59.         }
  60.       ]
  61.     }
  62.   }
  63. }

示例7：取分组指标值排序

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "terms": {
  6.         "field": "age",
  7.         "order": {
  8.           "max_balance": "asc"
  9.         }
  10.       },
  11.       "aggs": {
  12.         "max_balance": {
  13.           "max": {
  14.             "field": "balance"
  15.           }
  16.         },
  17.         "min_balance": {
  18.           "min": {
  19.             "field": "balance"
  20.           }
  21.         }
  22.       }
  23.     }
  24.   }
  25. }

结果7：

  1. {
  2.   "took": 28,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count_error_upper_bound": 0,
  18.       "sum_other_doc_count": 511,
  19.       "buckets": [
  20.         {
  21.           "key": 27,
  22.           "doc_count": 39,
  23.           "min_balance": {
  24.             "value": 1110
  25.           },
  26.           "max_balance": {
  27.             "value": 46868
  28.           }
  29.         },
  30.         {
  31.           "key": 39,
  32.           "doc_count": 60,
  33.           "min_balance": {
  34.             "value": 3589
  35.           },
  36.           "max_balance": {
  37.             "value": 47257
  38.           }
  39.         },
  40.         {
  41.           "key": 37,
  42.           "doc_count": 42,
  43.           "min_balance": {
  44.             "value": 1360
  45.           },
  46.           "max_balance": {
  47.             "value": 47546
  48.           }
  49.         },
  50.         {
  51.           "key": 32,
  52.           "doc_count": 52,
  53.           "min_balance": {
  54.             "value": 1031
  55.           },
  56.           "max_balance": {
  57.             "value": 48294
  58.           }
  59.         },
  60.         {
  61.           "key": 26,
  62.           "doc_count": 59,
  63.           "min_balance": {
  64.             "value": 1447
  65.           },
  66.           "max_balance": {
  67.             "value": 48466
  68.           }
  69.         },
  70.         {
  71.           "key": 33,
  72.           "doc_count": 50,
  73.           "min_balance": {
  74.             "value": 1314
  75.           },
  76.           "max_balance": {
  77.             "value": 48734
  78.           }
  79.         },
  80.         {
  81.           "key": 24,
  82.           "doc_count": 42,
  83.           "min_balance": {
  84.             "value": 1011
  85.           },
  86.           "max_balance": {
  87.             "value": 48745
  88.           }
  89.         },
  90.         {
  91.           "key": 31,
  92.           "doc_count": 61,
  93.           "min_balance": {
  94.             "value": 2384
  95.           },
  96.           "max_balance": {
  97.             "value": 48758
  98.           }
  99.         },
  100.         {
  101.           "key": 34,
  102.           "doc_count": 49,
  103.           "min_balance": {
  104.             "value": 3001
  105.           },
  106.           "max_balance": {
  107.             "value": 48997
  108.           }
  109.         },
  110.         {
  111.           "key": 29,
  112.           "doc_count": 35,
  113.           "min_balance": {
  114.             "value": 3596
  115.           },
  116.           "max_balance": {
  117.             "value": 49119
  118.           }
  119.         }
  120.       ]
  121.     }
  122.   }
  123. }

示例8：筛选分组-正则表达式匹配值

  1. GET /_search
  2. {
  3.     "aggs" : {
  4.         "tags" : {
  5.             "terms" : {
  6.                 "field" : "tags",
  7.                 "include" : ".*sport.*",
  8.                 "exclude" : "water_.*"
  9.             }
  10.         }
  11.     }
  12. }

示例9：筛选分组-指定值列表

  1. GET /_search
  2. {
  3.     "aggs" : {
  4.         "JapaneseCars" : {
  5.              "terms" : {
  6.                  "field" : "make",
  7.                  "include" : ["mazda", "honda"]
  8.              }
  9.          },
  10.         "ActiveCarManufacturers" : {
  11.              "terms" : {
  12.                  "field" : "make",
  13.                  "exclude" : ["rover", "jensen"]
  14.              }
  15.          }
  16.     }
  17. }

示例10：根据脚本计算值分组

  1. GET /_search
  2. {
  3.     "aggs" : {
  4.         "genres" : {
  5.             "terms" : {
  6.                 "script" : {
  7.                     "source": "doc['genre'].value",
  8.                     "lang": "painless"
  9.                 }
  10.             }
  11.         }
  12.     }
  13. }

示例1：缺失值处理

  1. GET /_search
  2. {
  3.     "aggs" : {
  4.         "tags" : {
  5.              "terms" : {
  6.                  "field" : "tags",
  7.                  "missing": "N/A"
  8.              }
  9.          }
  10.     }
  11. }

结果10：

  1. {
  2.   "took": 2059,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 58,
  6.     "successful": 58,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1015,
  12.     "max_score": 1,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "25",
  18.         "_score": 1,
  19.         "_source": {
  20.           "account_number": 25,
  21.           "balance": 40540,
  22.           "firstname": "Virginia",
  23.           "lastname": "Ayala",
  24.           "age": 39,
  25.           "gender": "F",
  26.           "address": "171 Putnam Avenue",
  27.           "employer": "Filodyne",
  28.           "email": "virginiaayala@filodyne.com",
  29.           "city": "Nicholson",
  30.           "state": "PA"
  31.         }
  32.       },
  33.       {
  34.         "_index": "bank",
  35.         "_type": "_doc",
  36.         "_id": "44",
  37.         "_score": 1,
  38.         "_source": {
  39.           "account_number": 44,
  40.           "balance": 34487,
  41.           "firstname": "Aurelia",
  42.           "lastname": "Harding",
  43.           "age": 37,
  44.           "gender": "M",
  45.           "address": "502 Baycliff Terrace",
  46.           "employer": "Orbalix",
  47.           "email": "aureliaharding@orbalix.com",
  48.           "city": "Yardville",
  49.           "state": "DE"
  50.         }
  51.       },
  52.       {
  53.         "_index": "bank",
  54.         "_type": "_doc",
  55.         "_id": "99",
  56.         "_score": 1,
  57.         "_source": {
  58.           "account_number": 99,
  59.           "balance": 47159,
  60.           "firstname": "Ratliff",
  61.           "lastname": "Heath",
  62.           "age": 39,
  63.           "gender": "F",
  64.           "address": "806 Rockwell Place",
  65.           "employer": "Zappix",
  66.           "email": "ratliffheath@zappix.com",
  67.           "city": "Shaft",
  68.           "state": "ND"
  69.         }
  70.       },
  71.       {
  72.         "_index": "bank",
  73.         "_type": "_doc",
  74.         "_id": "119",
  75.         "_score": 1,
  76.         "_source": {
  77.           "account_number": 119,
  78.           "balance": 49222,
  79.           "firstname": "Laverne",
  80.           "lastname": "Johnson",
  81.           "age": 28,
  82.           "gender": "F",
  83.           "address": "302 Howard Place",
  84.           "employer": "Senmei",
  85.           "email": "lavernejohnson@senmei.com",
  86.           "city": "Herlong",
  87.           "state": "DC"
  88.         }
  89.       },
  90.       {
  91.         "_index": "bank",
  92.         "_type": "_doc",
  93.         "_id": "126",
  94.         "_score": 1,
  95.         "_source": {
  96.           "account_number": 126,
  97.           "balance": 3607,
  98.           "firstname": "Effie",
  99.           "lastname": "Gates",
  100.           "age": 39,
  101.           "gender": "F",
  102.           "address": "620 National Drive",
  103.           "employer": "Digitalus",
  104.           "email": "effiegates@digitalus.com",
  105.           "city": "Blodgett",
  106.           "state": "MD"
  107.         }
  108.       },
  109.       {
  110.         "_index": "bank",
  111.         "_type": "_doc",
  112.         "_id": "145",
  113.         "_score": 1,
  114.         "_source": {
  115.           "account_number": 145,
  116.           "balance": 47406,
  117.           "firstname": "Rowena",
  118.           "lastname": "Wilkinson",
  119.           "age": 32,
  120.           "gender": "M",
  121.           "address": "891 Elton Street",
  122.           "employer": "Asimiline",
  123.           "email": "rowenawilkinson@asimiline.com",
  124.           "city": "Ripley",
  125.           "state": "NH"
  126.         }
  127.       },
  128.       {
  129.         "_index": "bank",
  130.         "_type": "_doc",
  131.         "_id": "183",
  132.         "_score": 1,
  133.         "_source": {
  134.           "account_number": 183,
  135.           "balance": 14223,
  136.           "firstname": "Hudson",
  137.           "lastname": "English",
  138.           "age": 26,
  139.           "gender": "F",
  140.           "address": "823 Herkimer Place",
  141.           "employer": "Xinware",
  142.           "email": "hudsonenglish@xinware.com",
  143.           "city": "Robbins",
  144.           "state": "ND"
  145.         }
  146.       },
  147.       {
  148.         "_index": "bank",
  149.         "_type": "_doc",
  150.         "_id": "190",
  151.         "_score": 1,
  152.         "_source": {
  153.           "account_number": 190,
  154.           "balance": 3150,
  155.           "firstname": "Blake",
  156.           "lastname": "Davidson",
  157.           "age": 30,
  158.           "gender": "F",
  159.           "address": "636 Diamond Street",
  160.           "employer": "Quantasis",
  161.           "email": "blakedavidson@quantasis.com",
  162.           "city": "Crumpler",
  163.           "state": "KY"
  164.         }
  165.       },
  166.       {
  167.         "_index": "bank",
  168.         "_type": "_doc",
  169.         "_id": "208",
  170.         "_score": 1,
  171.         "_source": {
  172.           "account_number": 208,
  173.           "balance": 40760,
  174.           "firstname": "Garcia",
  175.           "lastname": "Hess",
  176.           "age": 26,
  177.           "gender": "F",
  178.           "address": "810 Nostrand Avenue",
  179.           "employer": "Quiltigen",
  180.           "email": "garciahess@quiltigen.com",
  181.           "city": "Brooktrails",
  182.           "state": "GA"
  183.         }
  184.       },
  185.       {
  186.         "_index": "bank",
  187.         "_type": "_doc",
  188.         "_id": "222",
  189.         "_score": 1,
  190.         "_source": {
  191.           "account_number": 222,
  192.           "balance": 14764,
  193.           "firstname": "Rachelle",
  194.           "lastname": "Rice",
  195.           "age": 36,
  196.           "gender": "M",
  197.           "address": "333 Narrows Avenue",
  198.           "employer": "Enaut",
  199.           "email": "rachellerice@enaut.com",
  200.           "city": "Wright",
  201.           "state": "AZ"
  202.         }
  203.       }
  204.     ]
  205.   },
  206.   "aggregations": {
  207.     "tags": {
  208.       "doc_count_error_upper_bound": 0,
  209.       "sum_other_doc_count": 0,
  210.       "buckets": [
  211.         {
  212.           "key": "N/A",
  213.           "doc_count": 1014
  214.         },
  215.         {
  216.           "key": "red",
  217.           "doc_count": 1
  218.         }
  219.       ]
  220.     }
  221.   }
  222. }

2.  filter Aggregation  对满足过滤查询的文档进行聚合计算

在查询命中的文档中选取符合过滤条件的文档进行聚合，先过滤再聚合

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_terms": {
  5.       "filter": {"match":{"gender":"F"}},
  6.       "aggs": {
  7.         "avg_age": {
  8.           "avg": {
  9.             "field": "age"
  10.           }
  11.         }
  12.       }
  13.     }
  14.   }
  15. }

结果1：

  1. {
  2.   "took": 163,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_terms": {
  17.       "doc_count": 493,
  18.       "avg_age": {
  19.         "value": 30.3184584178499
  20.       }
  21.     }
  22.   }
  23. }

3. Filters Aggregation  多个过滤组聚合计算

示例1：

准备数据：

  1. PUT /logs/_doc/_bulk?refresh
  2. {"index":{"_id":1}}
  3. {"body":"warning: page could not be rendered"}
  4. {"index":{"_id":2}}
  5. {"body":"authentication error"}
  6. {"index":{"_id":3}}
  7. {"body":"warning: connection timed out"}

获取组合过滤后聚合的结果：

  1. GET logs/_search
  2. {
  3.   "size": 0,
  4.   "aggs": {
  5.     "messages": {
  6.       "filters": {
  7.         "filters": {
  8.           "errors": {
  9.             "match": {
  10.               "body": "error"
  11.             }
  12.           },
  13.           "warnings": {
  14.             "match": {
  15.               "body": "warning"
  16.             }
  17.           }
  18.         }
  19.       }
  20.     }
  21.   }
  22. }

上面的结果：

  1. {
  2.   "took": 18,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 3,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "messages": {
  17.       "buckets": {
  18.         "errors": {
  19.           "doc_count": 1
  20.         },
  21.         "warnings": {
  22.           "doc_count": 2
  23.         }
  24.       }
  25.     }
  26.   }
  27. }

示例2：为其他值组指定key

  1. GET logs/_search
  2. {
  3.   "size": 0,
  4.   "aggs": {
  5.     "messages": {
  6.       "filters": {
  7.         "other_bucket_key": "other_messages",
  8.         "filters": {
  9.           "errors": {
  10.             "match": {
  11.               "body": "error"
  12.             }
  13.           },
  14.           "warnings": {
  15.             "match": {
  16.               "body": "warning"
  17.             }
  18.           }
  19.         }
  20.       }
  21.     }
  22.   }
  23. }

结果2：

  1. {
  2.   "took": 5,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 3,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "messages": {
  17.       "buckets": {
  18.         "errors": {
  19.           "doc_count": 1
  20.         },
  21.         "warnings": {
  22.           "doc_count": 2
  23.         },
  24.         "other_messages": {
  25.           "doc_count": 0
  26.         }
  27.       }
  28.     }
  29.   }
  30. }

4. Range Aggregation 范围分组聚合

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_range": {
  5.       "range": {
  6.         "field": "age",
  7.         "ranges": [
  8.           {
  9.             "to": 25
  10.           },
  11.           {
  12.             "from": 25,
  13.             "to": 35
  14.           },
  15.           {
  16.             "from": 35
  17.           }
  18.         ]
  19.       },
  20.       "aggs": {
  21.         "bmax": {
  22.           "max": {
  23.             "field": "balance"
  24.           }
  25.         }
  26.       }
  27.     }
  28.   }
  29. }

结果1：

  1. {
  2.   "took": 7,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_range": {
  17.       "buckets": [
  18.         {
  19.           "key": "*-25.0",
  20.           "to": 25,
  21.           "doc_count": 225,
  22.           "bmax": {
  23.             "value": 49587
  24.           }
  25.         },
  26.         {
  27.           "key": "25.0-35.0",
  28.           "from": 25,
  29.           "to": 35,
  30.           "doc_count": 485,
  31.           "bmax": {
  32.             "value": 49795
  33.           }
  34.         },
  35.         {
  36.           "key": "35.0-*",
  37.           "from": 35,
  38.           "doc_count": 290,
  39.           "bmax": {
  40.             "value": 49989
  41.           }
  42.         }
  43.       ]
  44.     }
  45.   }
  46. }

示例2：为组指定key

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "age_range": {
  5.       "range": {
  6.         "field": "age",
  7.         "keyed": true,
  8.         "ranges": [
  9.           {
  10.             "to": 25,
  11.             "key": "Ld"
  12.           },
  13.           {
  14.             "from": 25,
  15.             "to": 35,
  16.             "key": "Md"
  17.           },
  18.           {
  19.             "from": 35,
  20.             "key": "Od"
  21.           }
  22.         ]
  23.       }
  24.     }
  25.   }
  26. }

结果2：

  1. {
  2.   "took": 2,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "age_range": {
  17.       "buckets": {
  18.         "Ld": {
  19.           "to": 25,
  20.           "doc_count": 225
  21.         },
  22.         "Md": {
  23.           "from": 25,
  24.           "to": 35,
  25.           "doc_count": 485
  26.         },
  27.         "Od": {
  28.           "from": 35,
  29.           "doc_count": 290
  30.         }
  31.       }
  32.     }
  33.   }
  34. }

5. Date Range Aggregation  时间范围分组聚合

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "range": {
  5.       "date_range": {
  6.         "field": "date",
  7.         "format": "MM-yyy",
  8.         "ranges": [
  9.           {
  10.             "to": "now-10M/M"
  11.           },
  12.           {
  13.             "from": "now-10M/M"
  14.           }
  15.         ]
  16.       }
  17.     }
  18.   }
  19. }

结果1：

  1. {
  2.   "took": 115,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "range": {
  17.       "buckets": [
  18.         {
  19.           "key": "*-2017-08-01T00:00:00.000Z",
  20.           "to": 1501545600000,
  21.           "to_as_string": "2017-08-01T00:00:00.000Z",
  22.           "doc_count": 0
  23.         },
  24.         {
  25.           "key": "2017-08-01T00:00:00.000Z-*",
  26.           "from": 1501545600000,
  27.           "from_as_string": "2017-08-01T00:00:00.000Z",
  28.           "doc_count": 0
  29.         }
  30.       ]
  31.     }
  32.   }
  33. }

6. Date Histogram Aggregation  时间直方图（柱状）聚合

就是按天、月、年等进行聚合统计。可按 year (1y), quarter (1q), month (1M), week (1w), day (1d), hour (1h), minute (1m), second (1s) 间隔聚合或指定的时间间隔聚合。

示例1：

  1. POST /bank/_search?size=0
  2. {
  3.   "aggs": {
  4.     "sales_over_time": {
  5.       "date_histogram": {
  6.         "field": "date",
  7.         "interval": "month"
  8.       }
  9.     }
  10.   }
  11. }

结果1：

  1. {
  2.   "took": 9,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 0,
  13.     "hits": []
  14.   },
  15.   "aggregations": {
  16.     "sales_over_time": {
  17.       "buckets": []
  18.     }
  19.   }
  20. }

7. Missing Aggregation  缺失值的桶聚合

  1. POST /bank/_search?size=0
  2. {
  3.     "aggs" : {
  4.         "account_without_a_age" : {
  5.             "missing" : { "field" : "age" }
  6.         }
  7.     }
  8. }

8. Geo Distance Aggregation  地理距离分区聚合

参考官网链接：

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-geodistance-aggregation.html

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