ElasticSearch6.x版本聚合分析整理

ES将聚合分析主要分为如下4类

  1. Bucket,分桶类型,类似SQL中的GROUP BY语法
  2. Metric,指标分析类型,如计算最大值 , 最小值,平均值等
  3. Pipeline,管道分析类型,基于上一级的聚合分析结果进行再分析
  4. Matrix,矩阵分析类型

Metric聚合分析

主要分如下两类:

1.单值分析,只输出一个分析结果

​ min,max,avg,sum

​ cardinality

2.多值分析,输出多个分析结果

​ stats,extended stats

​ percentile,percentile rank

​ top hits

需要使用到的数据:

  1. POST test_search_index/doc/_bulk
  2. {"index":{"_id":"1"}}
  3. {"username":"alfred way","job":"java engineer","age":18,"birth":"1990-01-02","isMarried":false,"salary":10000}
  4. {"index":{"_id":"2"}}
  5. {"username":"tom","job":"java senior engineer","age":28,"birth":"1980-05-07","isMarried":true,"salary":30000}
  6. {"index":{"_id":"3"}}
  7. {"username":"lee","job":"ruby engineer","age":22,"birth":"1985-08-07","isMarried":false,"salary":15000}
  8. {"index":{"_id":"4"}}
  9. {"username":"Nick","job":"web engineer","age":23,"birth":"1989-08-07","isMarried":false,"salary":8000}
  10. {"index":{"_id":"5"}}
  11. {"username":"Niko","job":"web engineer","age":18,"birth":"1994-08-07","isMarried":false,"salary":5000}
  12. {"index":{"_id":"6"}}
  13. {"username":"Michell","job":"ruby engineer","age":26,"birth":"1987-08-07","isMarried":false,"salary":12000}
Metric聚合分析

返回数值类字段的平均值

  1. GET  test_search_index/_search
  2. {
  3. 	#不需要返回文档列表
  4.     "size":0,
  5.     "aggs":{
  6.   		#名字
  7.         "min_age":{
  8.   			#关键词
  9.             "min":{
  10.                 "field":"age"
  11.             }
  12.         }
  13.     }
  14. }
  16. #返回结果:
  17. {
  18.     "took": 5,
  19.     "timed_out": false,
  20.     "_shards": {
  21.         "total": 5,
  22.         "successful": 5,
  23.         "skipped": 0,
  24.         "failed": 0
  25.     },
  26.     "hits": {
  27.         "total": 6,
  28.         "max_score": 0,
  29.         "hits": []
  30.     },
  31.     "aggregations": {
  32.         "min_age": {
  33.             "value": 18
  34.         }
  35.     }
  36. }
  38. #返回数值类字段的最大值
  39. GET  test_search_index/_search
  40. {
  41.     "size":0,
  42.     "aggs":{
  43.         "max_age":{
  44.             "max":{
  45.                 "field":"age"
  46.             }
  47.         }
  48.     }
  49. }
  51. #返回结果
  52. {
  53.     "took": 7,
  54.     "timed_out": false,
  55.     "_shards": {
  56.         "total": 5,
  57.         "successful": 5,
  58.         "skipped": 0,
  59.         "failed": 0
  60.     },
  61.     "hits": {
  62.         "total": 6,
  63.         "max_score": 0,
  64.         "hits": []
  65.     },
  66.     "aggregations": {
  67.         "max_age": {
  68.             "value": 28
  69.         }
  70.     }
  71. }
  73. #返回数值类字段的平均值
  74. GET  test_search_index/_search
  75. {
  76.     "size":0,
  77.     "aggs":{
  78.         "avg_age":{
  79.             "avg":{
  80.                 "field":"age"
  81.             }
  82.         }
  83.     }
  84. }
  86. #返回结果
  87. {
  88.     "took": 2,
  89.     "timed_out": false,
  90.     "_shards": {
  91.         "total": 5,
  92.         "successful": 5,
  93.         "skipped": 0,
  94.         "failed": 0
  95.     },
  96.     "hits": {
  97.         "total": 6,
  98.         "max_score": 0,
  99.         "hits": []
  100.     },
  101.     "aggregations": {
  102.         "avg_age": {
  103.             "value": 22.5
  104.         }
  105.     }
  106. }
  108. #返回数值字段的总和
  109. GET  test_search_index/_search
  110. {
  111.     "size":0,
  112.     "aggs":{
  113.         "sum_age":{
  114.             "sum":{
  115.                 "field":"age"
  116.             }
  117.         }
  118.     }
  119. }
  121. {
  122.     "took": 3,
  123.     "timed_out": false,
  124.     "_shards": {
  125.         "total": 5,
  126.         "successful": 5,
  127.         "skipped": 0,
  128.         "failed": 0
  129.     },
  130.     "hits": {
  131.         "total": 6,
  132.         "max_score": 0,
  133.         "hits": []
  134.     },
  135.     "aggregations": {
  136.         "sum_age": {
  137.             "value": 135
  138.         }
  139.     }
  140. }
  142. #一次返回多个结果
  143. {
  144.     "size":0,
  145.     "aggs":{
  146.     	"min_age":{
  147.     		"min":{
  148.     			"field":"age"
  149.     		}
  150.     	},
  151.     	"max_age":{
  152.     		"max":{
  153.     			"field":"age"
  154.     		}
  155.     	},
  156.         "sum_age":{
  157.             "sum":{
  158.                 "field":"age"
  159.             }
  160.         }
  161.     }
  162. }
  164. #返回结果
  165. {
  166.     "took": 2,
  167.     "timed_out": false,
  168.     "_shards": {
  169.         "total": 5,
  170.         "successful": 5,
  171.         "skipped": 0,
  172.         "failed": 0
  173.     },
  174.     "hits": {
  175.         "total": 6,
  176.         "max_score": 0,
  177.         "hits": []
  178.     },
  179.     "aggregations": {
  180.         "max_age": {
  181.             "value": 28
  182.         },
  183.         "sum_age": {
  184.             "value": 135
  185.         },
  186.         "min_age": {
  187.             "value": 18
  188.         }
  189.     }
  190. }

Metric聚合分析--Cardinality

Cardinality,意为集合的势,或者基数,是指不同数值的个数,类似SQL中的distinct count概念

  1. GET  test_search_index/_search
  2. {
  3.     "size":10,
  4.     "aggs":{
  5.     	"count_of_job":{
  6.     		"cardinality":{
  7.     			"field":"job.keyword"
  8.     		}
  9.     	}
  10.     }
  11. }
  13. #返回
  14. {
  15.     "took": 4,
  16.     "timed_out": false,
  17.     "_shards": {
  18.         "total": 5,
  19.         "successful": 5,
  20.         "skipped": 0,
  21.         "failed": 0
  22.     },
  23.     "hits": {
  24.         "total": 6,
  25.         "max_score": 1,
  26.         "hits": [
  27.             {
  28.                 "_index": "test_search_index",
  29.                 "_type": "doc",
  30.                 "_id": "5",
  31.                 "_score": 1,
  32.                 "_source": {
  33.                     "username": "Niko",
  34.                     "job": "web engineer",
  35.                     "age": 18,
  36.                     "birth": "1994-08-07",
  37.                     "isMarried": false,
  38.                     "salary": 5000
  39.                 }
  40.             },
  41.             {
  42.                 "_index": "test_search_index",
  43.                 "_type": "doc",
  44.                 "_id": "2",
  45.                 "_score": 1,
  46.                 "_source": {
  47.                     "username": "tom",
  48.                     "job": "java senior engineer",
  49.                     "age": 28,
  50.                     "birth": "1980-05-07",
  51.                     "isMarried": true,
  52.                     "salary": 30000
  53.                 }
  54.             },
  55.             {
  56.                 "_index": "test_search_index",
  57.                 "_type": "doc",
  58.                 "_id": "4",
  59.                 "_score": 1,
  60.                 "_source": {
  61.                     "username": "Nick",
  62.                     "job": "web engineer",
  63.                     "age": 23,
  64.                     "birth": "1989-08-07",
  65.                     "isMarried": false,
  66.                     "salary": 8000
  67.                 }
  68.             },
  69.             {
  70.                 "_index": "test_search_index",
  71.                 "_type": "doc",
  72.                 "_id": "6",
  73.                 "_score": 1,
  74.                 "_source": {
  75.                     "username": "Michell",
  76.                     "job": "ruby engineer",
  77.                     "age": 26,
  78.                     "birth": "1987-08-07",
  79.                     "isMarried": false,
  80.                     "salary": 12000
  81.                 }
  82.             },
  83.             {
  84.                 "_index": "test_search_index",
  85.                 "_type": "doc",
  86.                 "_id": "1",
  87.                 "_score": 1,
  88.                 "_source": {
  89.                     "username": "alfred way",
  90.                     "job": "java engineer",
  91.                     "age": 18,
  92.                     "birth": "1990-01-02",
  93.                     "isMarried": false,
  94.                     "salary": 10000
  95.                 }
  96.             },
  97.             {
  98.                 "_index": "test_search_index",
  99.                 "_type": "doc",
  100.                 "_id": "3",
  101.                 "_score": 1,
  102.                 "_source": {
  103.                     "username": "lee",
  104.                     "job": "ruby engineer",
  105.                     "age": 22,
  106.                     "birth": "1985-08-07",
  107.                     "isMarried": false,
  108.                     "salary": 15000
  109.                 }
  110.             }
  111.         ]
  112.     },
  113.     "aggregations": {
  114.         "count_of_job": {
  115.             "value": 4
  116.         }
  117.     }
  118. }

Metric聚合分析-Stats

返回一系列数值类型的统计值,包含min,max,avg,sum和count

  1. GET  test_search_index/_search
  2. {
  3.     "size":0,
  4.     "aggs":{
  5.     	"stats_age":{
  6.     		"stats":{
  7.     			"field":"age"
  8.     		}
  9.     	}
  10.     }
  11. }
  13. #返回
  14. {
  15.     "took": 1,
  16.     "timed_out": false,
  17.     "_shards": {
  18.         "total": 5,
  19.         "successful": 5,
  20.         "skipped": 0,
  21.         "failed": 0
  22.     },
  23.     "hits": {
  24.         "total": 6,
  25.         "max_score": 0,
  26.         "hits": []
  27.     },
  28.     "aggregations": {
  29.         "stats_age": {
  30.             "count": 6,
  31.             "min": 18,
  32.             "max": 28,
  33.             "avg": 22.5,
  34.             "sum": 135
  35.         }
  36.     }
  37. }

Metric聚合分析-Extended Stats

对stats的扩展,包含了更多的统计数据,如方差,标准差等

  1. GET  test_search_index/_search
  2. {
  3.     "size":0,
  4.     "aggs":{
  5.     	"stats_age":{
  6.     		"extended_stats":{
  7.     			"field":"age"
  8.     		}
  9.     	}
  10.     }
  11. }
  13. #返回
  14. {
  15.     "took": 2,
  16.     "timed_out": false,
  17.     "_shards": {
  18.         "total": 5,
  19.         "successful": 5,
  20.         "skipped": 0,
  21.         "failed": 0
  22.     },
  23.     "hits": {
  24.         "total": 6,
  25.         "max_score": 0,
  26.         "hits": []
  27.     },
  28.     "aggregations": {
  29.         "stats_age": {
  30.             "count": 6,
  31.             "min": 18,
  32.             "max": 28,
  33.             "avg": 22.5,
  34.             "sum": 135,
  35.             "sum_of_squares": 3121,
  36.             "variance": 13.916666666666666,
  37.             "std_deviation": 3.730504880933232,
  38.             "std_deviation_bounds": {
  39.                 "upper": 29.961009761866464,
  40.                 "lower": 15.038990238133536
  41.             }
  42.         }
  43.     }
  44. }

Metric聚合分析-Percentile

百分位数统计

  1. GET  test_search_index/_search
  2. {
  3.     "size":0,
  4.     "aggs":{
  5.     	"per_age":{
  6.     		"percentiles":{
  7.     			"field":"salary"
  8.     		}
  9.     	}
  10.     }
  11. }
  13. #返回
  14. {
  15.     "took": 6,
  16.     "timed_out": false,
  17.     "_shards": {
  18.         "total": 5,
  19.         "successful": 5,
  20.         "skipped": 0,
  21.         "failed": 0
  22.     },
  23.     "hits": {
  24.         "total": 6,
  25.         "max_score": 0,
  26.         "hits": []
  27.     },
  28.     "aggregations": {
  29.         "per_age": {
  30.           	#代表有百分之一的人工资在5000以下,百分之二十五的人工资在8000以下....
  31.             "values": {
  32.                 "1.0": 5000,
  33.                 "5.0": 5000,
  34.                 "25.0": 8000,
  35.                 "50.0": 11000,
  36.                 "75.0": 15000,
  37.                 "95.0": 30000,
  38.                 "99.0": 30000
  39.             }
  40.         }
  41.     }
  42. }
  44. GET  test_search_index/_search
  45. {
  46.     "size":0,
  47.     "aggs":{
  48.     	"per_age":{
  49.     		"percentile_ranks":{
  50.     			"field":"salary",
  51.     			"values":[
  52.     				11000,
  53.     				30000
  54.     			]
  55.     		}
  56.     	}
  57.     }
  58. }
  60. #返回
  61. {
  62.     "took": 2,
  63.     "timed_out": false,
  64.     "_shards": {
  65.         "total": 5,
  66.         "successful": 5,
  67.         "skipped": 0,
  68.         "failed": 0
  69.     },
  70.     "hits": {
  71.         "total": 6,
  72.         "max_score": 0,
  73.         "hits": []
  74.     },
  75.     "aggregations": {
  76.         "per_age": {
  77.             "values": {
  78.                 "11000.0": 50,
  79.                 "30000.0": 100
  80.             }
  81.         }
  82.     }
  83. }

Metric聚合分析-Top Hits

一般用于分桶后获取该桶内最匹配的顶部文档列表,即详情数据

  1. #先按照job分桶, 然后在桶内做年龄的排序
  2. GET  test_search_index/_search
  3. {
  4.     "size":0,
  5.     "aggs":{
  6.     	"jobs":{
  7.     		"terms":{
  8.     			"field":"job.keyword",
  9.     			"size":10
  10.     		},
  11.     		"aggs":{
  12.     			"top_employee":{
  13.     				"top_hits":{
  14.     					"size":10,
  15.     					"sort":[
  16.     					{
  17.     						"age":{
  18.     							"order":"desc"
  19.     						}
  20.     					}
  21.     					]
  22.     				}
  23.     			}
  24.     		}
  25.     	}
  26.     }
  27. }
  29. #返回
  30. {
  31.     "took": 42,
  32.     "timed_out": false,
  33.     "_shards": {
  34.         "total": 5,
  35.         "successful": 5,
  36.         "skipped": 0,
  37.         "failed": 0
  38.     },
  39.     "hits": {
  40.         "total": 6,
  41.         "max_score": 0,
  42.         "hits": []
  43.     },
  44.     "aggregations": {
  45.         "jobs": {
  46.             "doc_count_error_upper_bound": 0,
  47.             "sum_other_doc_count": 0,
  48.             "buckets": [
  49.                 {
  50.                     "key": "ruby engineer",
  51.                     "doc_count": 2,
  52.                     "top_employee": {
  53.                         "hits": {
  54.                             "total": 2,
  55.                             "max_score": null,
  56.                             "hits": [
  57.                                 {
  58.                                     "_index": "test_search_index",
  59.                                     "_type": "doc",
  60.                                     "_id": "6",
  61.                                     "_score": null,
  62.                                     "_source": {
  63.                                         "username": "Michell",
  64.                                         "job": "ruby engineer",
  65.                                         "age": 26,
  66.                                         "birth": "1987-08-07",
  67.                                         "isMarried": false,
  68.                                         "salary": 12000
  69.                                     },
  70.                                     "sort": [
  71.                                         26
  72.                                     ]
  73.                                 },
  74.                                 {
  75.                                     "_index": "test_search_index",
  76.                                     "_type": "doc",
  77.                                     "_id": "3",
  78.                                     "_score": null,
  79.                                     "_source": {
  80.                                         "username": "lee",
  81.                                         "job": "ruby engineer",
  82.                                         "age": 22,
  83.                                         "birth": "1985-08-07",
  84.                                         "isMarried": false,
  85.                                         "salary": 15000
  86.                                     },
  87.                                     "sort": [
  88.                                         22
  89.                                     ]
  90.                                 }
  91.                             ]
  92.                         }
  93.                     }
  94.                 },
  95.                 {
  96.                     "key": "web engineer",
  97.                     "doc_count": 2,
  98.                     "top_employee": {
  99.                         "hits": {
  100.                             "total": 2,
  101.                             "max_score": null,
  102.                             "hits": [
  103.                                 {
  104.                                     "_index": "test_search_index",
  105.                                     "_type": "doc",
  106.                                     "_id": "4",
  107.                                     "_score": null,
  108.                                     "_source": {
  109.                                         "username": "Nick",
  110.                                         "job": "web engineer",
  111.                                         "age": 23,
  112.                                         "birth": "1989-08-07",
  113.                                         "isMarried": false,
  114.                                         "salary": 8000
  115.                                     },
  116.                                     "sort": [
  117.                                         23
  118.                                     ]
  119.                                 },
  120.                                 {
  121.                                     "_index": "test_search_index",
  122.                                     "_type": "doc",
  123.                                     "_id": "5",
  124.                                     "_score": null,
  125.                                     "_source": {
  126.                                         "username": "Niko",
  127.                                         "job": "web engineer",
  128.                                         "age": 18,
  129.                                         "birth": "1994-08-07",
  130.                                         "isMarried": false,
  131.                                         "salary": 5000
  132.                                     },
  133.                                     "sort": [
  134.                                         18
  135.                                     ]
  136.                                 }
  137.                             ]
  138.                         }
  139.                     }
  140.                 },
  141.                 {
  142.                     "key": "java engineer",
  143.                     "doc_count": 1,
  144.                     "top_employee": {
  145.                         "hits": {
  146.                             "total": 1,
  147.                             "max_score": null,
  148.                             "hits": [
  149.                                 {
  150.                                     "_index": "test_search_index",
  151.                                     "_type": "doc",
  152.                                     "_id": "1",
  153.                                     "_score": null,
  154.                                     "_source": {
  155.                                         "username": "alfred way",
  156.                                         "job": "java engineer",
  157.                                         "age": 18,
  158.                                         "birth": "1990-01-02",
  159.                                         "isMarried": false,
  160.                                         "salary": 10000
  161.                                     },
  162.                                     "sort": [
  163.                                         18
  164.                                     ]
  165.                                 }
  166.                             ]
  167.                         }
  168.                     }
  169.                 },
  170.                 {
  171.                     "key": "java senior engineer",
  172.                     "doc_count": 1,
  173.                     "top_employee": {
  174.                         "hits": {
  175.                             "total": 1,
  176.                             "max_score": null,
  177.                             "hits": [
  178.                                 {
  179.                                     "_index": "test_search_index",
  180.                                     "_type": "doc",
  181.                                     "_id": "2",
  182.                                     "_score": null,
  183.                                     "_source": {
  184.                                         "username": "tom",
  185.                                         "job": "java senior engineer",
  186.                                         "age": 28,
  187.                                         "birth": "1980-05-07",
  188.                                         "isMarried": true,
  189.                                         "salary": 30000
  190.                                     },
  191.                                     "sort": [
  192.                                         28
  193.                                     ]
  194.                                 }
  195.                             ]
  196.                         }
  197.                     }
  198.                 }
  199.             ]
  200.         }
  201.     }
  202. }
Bucket聚合分析

Terms

该分桶策略最简单,直接按照term来分桶,如果是text类型,则按照分次后的结果分桶

  1. GET  test_search_index/_search
  2. {
  3. 	"size":0,
  4. 	"aggs":{
  5. 		"jobs":{
  6. 			"terms":{
  7. 				"field":"job.keyword",
  8. 				"size":10
  9. 			}
  10. 		}
  11. 	}
  12. }
  14. #返回
  15. {
  16.     "took": 2,
  17.     "timed_out": false,
  18.     "_shards": {
  19.         "total": 5,
  20.         "successful": 5,
  21.         "skipped": 0,
  22.         "failed": 0
  23.     },
  24.     "hits": {
  25.         "total": 6,
  26.         "max_score": 0,
  27.         "hits": []
  28.     },
  29.     "aggregations": {
  30.         "jobs": {
  31.             "doc_count_error_upper_bound": 0,
  32.             "sum_other_doc_count": 0,
  33.             "buckets": [
  34.                 {
  35.                     "key": "ruby engineer",
  36.                     "doc_count": 2
  37.                 },
  38.                 {
  39.                     "key": "web engineer",
  40.                     "doc_count": 2
  41.                 },
  42.                 {
  43.                     "key": "java engineer",
  44.                     "doc_count": 1
  45.                 },
  46.                 {
  47.                     "key": "java senior engineer",
  48.                     "doc_count": 1
  49.                 }
  50.             ]
  51.         }
  52.     }
  53. }

Range

通过制定数值的范围来设定分桶规则

  1. GET  test_search_index/_search
  2. {
  3. 	"size":0,
  4. 	"aggs":{
  5. 		"salary_range":{
  6. 			"range":{
  7. 				"field":"salary",
  8. 				"ranges":[
  9. 				{
  10. 					"to":10000
  11. 				},
  12. 				{
  13. 					"from":10000,
  14. 					"to":20000
  15. 				},
  16. 				{
  17. 					"from":20000
  18. 				}
  19. 				]
  20. 			}
  21. 		}
  22. 	}
  23. }
  25. #返回
  26. {
  27.     "took": 3,
  28.     "timed_out": false,
  29.     "_shards": {
  30.         "total": 5,
  31.         "successful": 5,
  32.         "skipped": 0,
  33.         "failed": 0
  34.     },
  35.     "hits": {
  36.         "total": 6,
  37.         "max_score": 0,
  38.         "hits": []
  39.     },
  40.     "aggregations": {
  41.         "salary_range": {
  42.             "buckets": [
  43.                 {
  44.                     "key": "*-10000.0",
  45.                     "to": 10000,
  46.                     "doc_count": 2
  47.                 },
  48.                 {
  49.                     "key": "10000.0-20000.0",
  50.                     "from": 10000,
  51.                     "to": 20000,
  52.                     "doc_count": 3
  53.                 },
  54.                 {
  55.                     "key": "20000.0-*",
  56.                     "from": 20000,
  57.                     "doc_count": 1
  58.                 }
  59.             ]
  60.         }
  61.     }
  62. }

Date Range

通过制定日期的范围来设定分桶规则

  1. GET  test_search_index/_search
  2. {
  3. 	"size":0,
  4. 	"aggs":{
  5. 		"date_range":{
  6. 			"range":{
  7. 				"field":"birth",
  8. 				"format":"yyyy",
  9. 				"ranges":[
  10. 				{
  11. 					"from":"1980",
  12. 					"to":"1990"
  13. 				},
  14. 				{
  15. 					"from":"1990",
  16. 					"to":"2000"
  17. 				},
  18. 				{
  19. 					"from":"2000"
  20. 				}
  21. 				]
  22. 			}
  23. 		}
  24. 	}
  25. }
  27. #返回
  28. {
  29.     "took": 3,
  30.     "timed_out": false,
  31.     "_shards": {
  32.         "total": 5,
  33.         "successful": 5,
  34.         "skipped": 0,
  35.         "failed": 0
  36.     },
  37.     "hits": {
  38.         "total": 6,
  39.         "max_score": 0,
  40.         "hits": []
  41.     },
  42.     "aggregations": {
  43.         "date_range": {
  44.             "buckets": [
  45.                 {
  46.                     "key": "1980-1990",
  47.                     "from": 315532800000,
  48.                     "from_as_string": "1980",
  49.                     "to": 631152000000,
  50.                     "to_as_string": "1990",
  51.                     "doc_count": 4
  52.                 },
  53.                 {
  54.                     "key": "1990-2000",
  55.                     "from": 631152000000,
  56.                     "from_as_string": "1990",
  57.                     "to": 946684800000,
  58.                     "to_as_string": "2000",
  59.                     "doc_count": 2
  60.                 },
  61.                 {
  62.                     "key": "2000-*",
  63.                     "from": 946684800000,
  64.                     "from_as_string": "2000",
  65.                     "doc_count": 0
  66.                 }
  67.             ]
  68.         }
  69.     }
  70. }

Historgram

直方图,以固定间隔的策略来分隔数据

  1. #表示间隔5000分隔工资的分布情况, 最小0,最大40000
  2. GET  test_search_index/_search
  3. {
  4. 	"size":0,
  5. 	"aggs":{
  6. 		"salary_hist":{
  7. 			"histogram":{
  8. 				"field":"salary",
  9. 				 "interval":5000,
  10. 				 "extended_bounds":{
  11. 				 	"min":0,
  12. 				 	"max":40000
  13. 				 }
  14. 			}
  15. 		}
  16. 	}
  17. }
  19. #返回
  20. {
  21.     "took": 2,
  22.     "timed_out": false,
  23.     "_shards": {
  24.         "total": 5,
  25.         "successful": 5,
  26.         "skipped": 0,
  27.         "failed": 0
  28.     },
  29.     "hits": {
  30.         "total": 6,
  31.         "max_score": 0,
  32.         "hits": []
  33.     },
  34.     "aggregations": {
  35.         "salary_hist": {
  36.             "buckets": [
  37.                 {
  38.                     "key": 0,
  39.                     "doc_count": 0
  40.                 },
  41.                 {
  42.                     "key": 5000,
  43.                     "doc_count": 2
  44.                 },
  45.                 {
  46.                     "key": 10000,
  47.                     "doc_count": 2
  48.                 },
  49.                 {
  50.                     "key": 15000,
  51.                     "doc_count": 1
  52.                 },
  53.                 {
  54.                     "key": 20000,
  55.                     "doc_count": 0
  56.                 },
  57.                 {
  58.                     "key": 25000,
  59.                     "doc_count": 0
  60.                 },
  61.                 {
  62.                     "key": 30000,
  63.                     "doc_count": 1
  64.                 },
  65.                 {
  66.                     "key": 35000,
  67.                     "doc_count": 0
  68.                 },
  69.                 {
  70.                     "key": 40000,
  71.                     "doc_count": 0
  72.                 }
  73.             ]
  74.         }
  75.     }
  76. }

Date Historgram

针对日期的直方图或者柱状图,是时序数据分析中常用的聚合分析类型

  1. GET  test_search_index/_search
  2. {
  3. 	"size":0,
  4. 	"aggs":{
  5. 		"salary_hist":{
  6. 			"date_histogram":{
  7. 				"field":"birth",
  8. 				 "interval":"year",
  9. 				 "format":"yyyy"
  10. 			}
  11. 		}
  12. 	}
  13. }
  15. #返回
  16. {
  17.     "took": 4,
  18.     "timed_out": false,
  19.     "_shards": {
  20.         "total": 5,
  21.         "successful": 5,
  22.         "skipped": 0,
  23.         "failed": 0
  24.     },
  25.     "hits": {
  26.         "total": 6,
  27.         "max_score": 0,
  28.         "hits": []
  29.     },
  30.     "aggregations": {
  31.         "salary_hist": {
  32.             "buckets": [
  33.                 {
  34.                     "key_as_string": "1980",
  35.                     "key": 315532800000,
  36.                     "doc_count": 1
  37.                 },
  38.                 {
  39.                     "key_as_string": "1981",
  40.                     "key": 347155200000,
  41.                     "doc_count": 0
  42.                 },
  43.                 {
  44.                     "key_as_string": "1982",
  45.                     "key": 378691200000,
  46.                     "doc_count": 0
  47.                 },
  48.                 {
  49.                     "key_as_string": "1983",
  50.                     "key": 410227200000,
  51.                     "doc_count": 0
  52.                 },
  53.                 {
  54.                     "key_as_string": "1984",
  55.                     "key": 441763200000,
  56.                     "doc_count": 0
  57.                 },
  58.                 {
  59.                     "key_as_string": "1985",
  60.                     "key": 473385600000,
  61.                     "doc_count": 1
  62.                 },
  63.                 {
  64.                     "key_as_string": "1986",
  65.                     "key": 504921600000,
  66.                     "doc_count": 0
  67.                 },
  68.                 {
  69.                     "key_as_string": "1987",
  70.                     "key": 536457600000,
  71.                     "doc_count": 1
  72.                 },
  73.                 {
  74.                     "key_as_string": "1988",
  75.                     "key": 567993600000,
  76.                     "doc_count": 0
  77.                 },
  78.                 {
  79.                     "key_as_string": "1989",
  80.                     "key": 599616000000,
  81.                     "doc_count": 1
  82.                 },
  83.                 {
  84.                     "key_as_string": "1990",
  85.                     "key": 631152000000,
  86.                     "doc_count": 1
  87.                 },
  88.                 {
  89.                     "key_as_string": "1991",
  90.                     "key": 662688000000,
  91.                     "doc_count": 0
  92.                 },
  93.                 {
  94.                     "key_as_string": "1992",
  95.                     "key": 694224000000,
  96.                     "doc_count": 0
  97.                 },
  98.                 {
  99.                     "key_as_string": "1993",
  100.                     "key": 725846400000,
  101.                     "doc_count": 0
  102.                 },
  103.                 {
  104.                     "key_as_string": "1994",
  105.                     "key": 757382400000,
  106.                     "doc_count": 1
  107.                 }
  108.             ]
  109.         }
  110.     }
  111. }
Bucket+Metric聚合分析

分桶后再分桶

  1. 1. GET  test_search_index/_search
  2. {
  3. 	"size":0,
  4. 	"aggs":{
  5. 		"jobs":{
  6. 			"terms":{
  7. 				"field":"job.keyword",
  8. 				"size":10
  9. 			},
  10. 			"aggs":{
  11. 				"age_range":{
  12. 					"range":{
  13. 						"field":"age",
  14. 						"ranges":[
  15. 						{"to":20},
  16. 						{"from":20,"to":30},
  17. 						{"from":30}
  18. 						]
  19. 					}
  20. 				}
  21. 			}
  22. 		}
  23. 	}
  24. }
  26. #返回
  27. {
  28.     "took": 2,
  29.     "timed_out": false,
  30.     "_shards": {
  31.         "total": 5,
  32.         "successful": 5,
  33.         "skipped": 0,
  34.         "failed": 0
  35.     },
  36.     "hits": {
  37.         "total": 6,
  38.         "max_score": 0,
  39.         "hits": []
  40.     },
  41.     "aggregations": {
  42.         "jobs": {
  43.             "doc_count_error_upper_bound": 0,
  44.             "sum_other_doc_count": 0,
  45.             "buckets": [
  46.                 {
  47.                     "key": "ruby engineer",
  48.                     "doc_count": 2,
  49.                     "age_range": {
  50.                         "buckets": [
  51.                             {
  52.                                 "key": "*-20.0",
  53.                                 "to": 20,
  54.                                 "doc_count": 0
  55.                             },
  56.                             {
  57.                                 "key": "20.0-30.0",
  58.                                 "from": 20,
  59.                                 "to": 30,
  60.                                 "doc_count": 2
  61.                             },
  62.                             {
  63.                                 "key": "30.0-*",
  64.                                 "from": 30,
  65.                                 "doc_count": 0
  66.                             }
  67.                         ]
  68.                     }
  69.                 },
  70.                 {
  71.                     "key": "web engineer",
  72.                     "doc_count": 2,
  73.                     "age_range": {
  74.                         "buckets": [
  75.                             {
  76.                                 "key": "*-20.0",
  77.                                 "to": 20,
  78.                                 "doc_count": 1
  79.                             },
  80.                             {
  81.                                 "key": "20.0-30.0",
  82.                                 "from": 20,
  83.                                 "to": 30,
  84.                                 "doc_count": 1
  85.                             },
  86.                             {
  87.                                 "key": "30.0-*",
  88.                                 "from": 30,
  89.                                 "doc_count": 0
  90.                             }
  91.                         ]
  92.                     }
  93.                 },
  94.                 {
  95.                     "key": "java engineer",
  96.                     "doc_count": 1,
  97.                     "age_range": {
  98.                         "buckets": [
  99.                             {
  100.                                 "key": "*-20.0",
  101.                                 "to": 20,
  102.                                 "doc_count": 1
  103.                             },
  104.                             {
  105.                                 "key": "20.0-30.0",
  106.                                 "from": 20,
  107.                                 "to": 30,
  108.                                 "doc_count": 0
  109.                             },
  110.                             {
  111.                                 "key": "30.0-*",
  112.                                 "from": 30,
  113.                                 "doc_count": 0
  114.                             }
  115.                         ]
  116.                     }
  117.                 },
  118.                 {
  119.                     "key": "java senior engineer",
  120.                     "doc_count": 1,
  121.                     "age_range": {
  122.                         "buckets": [
  123.                             {
  124.                                 "key": "*-20.0",
  125.                                 "to": 20,
  126.                                 "doc_count": 0
  127.                             },
  128.                             {
  129.                                 "key": "20.0-30.0",
  130.                                 "from": 20,
  131.                                 "to": 30,
  132.                                 "doc_count": 1
  133.                             },
  134.                             {
  135.                                 "key": "30.0-*",
  136.                                 "from": 30,
  137.                                 "doc_count": 0
  138.                             }
  139.                         ]
  140.                     }
  141.                 }
  142.             ]
  143.         }
  144.     }
  145. }
  147. 2.分桶后进行数据分析
  148. GET  test_search_index/_search
  150. {
  151. 	"size":0,
  152. 	"aggs":{
  153. 		"jobs":{
  154. 			"terms":{
  155. 				"field":"job.keyword",
  156. 				"size":10
  157. 			},
  158. 			"aggs":{
  159. 				"salary":{
  160. 					"stats":{
  161. 						"field":"salary"
  162. 					}
  163. 				}
  164. 			}
  165. 		}
  166. 	}
  167. }
  169. {
  170.     "took": 7,
  171.     "timed_out": false,
  172.     "_shards": {
  173.         "total": 5,
  174.         "successful": 5,
  175.         "skipped": 0,
  176.         "failed": 0
  177.     },
  178.     "hits": {
  179.         "total": 6,
  180.         "max_score": 0,
  181.         "hits": []
  182.     },
  183.     "aggregations": {
  184.         "jobs": {
  185.             "doc_count_error_upper_bound": 0,
  186.             "sum_other_doc_count": 0,
  187.             "buckets": [
  188.                 {
  189.                     "key": "ruby engineer",
  190.                     "doc_count": 2,
  191.                     "salary": {
  192.                         "count": 2,
  193.                         "min": 12000,
  194.                         "max": 15000,
  195.                         "avg": 13500,
  196.                         "sum": 27000
  197.                     }
  198.                 },
  199.                 {
  200.                     "key": "web engineer",
  201.                     "doc_count": 2,
  202.                     "salary": {
  203.                         "count": 2,
  204.                         "min": 5000,
  205.                         "max": 8000,
  206.                         "avg": 6500,
  207.                         "sum": 13000
  208.                     }
  209.                 },
  210.                 {
  211.                     "key": "java engineer",
  212.                     "doc_count": 1,
  213.                     "salary": {
  214.                         "count": 1,
  215.                         "min": 10000,
  216.                         "max": 10000,
  217.                         "avg": 10000,
  218.                         "sum": 10000
  219.                     }
  220.                 },
  221.                 {
  222.                     "key": "java senior engineer",
  223.                     "doc_count": 1,
  224.                     "salary": {
  225.                         "count": 1,
  226.                         "min": 30000,
  227.                         "max": 30000,
  228.                         "avg": 30000,
  229.                         "sum": 30000
  230.                     }
  231.                 }
  232.             ]
  233.         }
  234.     }
  235. }
Pipeline聚合分析

针对聚合分析的结果再次进行聚合分析,而且支持链式调用

Pipeline的分析结果会输出到原结果中,根据输出位置的不同,分为以下两类:

1.Parent结果内嵌到现有的聚合分析结果中

​ Derivative

​ Moving Average

​ Cumulative Sum

2.Sibling结果与现有聚合分析结果同级

​ Max/Min/Avg/Sum Bucket

​ Stats/Extended Stats Bucket

​ Percentitles Bucket

Sibling - Min Bucket

找出所有Bucket中值最小的Bucket名称和值

  1. 1.聚合分析求job的和
  2. 2.job里面内嵌套了一个求平均salary
  3. 3.然后用一个同级的 Min Bucket求上面平均工资里面最小的那个
  5. GET  test_search_index/_search
  6. {
  7. 	"size":0,
  8. 	"aggs":{
  9. 		"jobs":{
  10. 			"terms":{
  11. 				"field":"job.keyword",
  12. 				"size":10
  13. 			},
  14. 			"aggs":{
  15. 				"avg_salary":{
  16. 					"avg":{
  17. 						"field":"salary"
  18. 					}
  19. 				}
  20. 			}
  21. 		},
  22. 		"min_salary_by_job":{
  23. 			"min_bucket":{
  24. 				"buckets_path":"jobs>avg_salary"
  25. 			}
  26. 		}
  27. 	}
  28. }
  30. {
  31.     "took": 5,
  32.     "timed_out": false,
  33.     "_shards": {
  34.         "total": 5,
  35.         "successful": 5,
  36.         "skipped": 0,
  37.         "failed": 0
  38.     },
  39.     "hits": {
  40.         "total": 6,
  41.         "max_score": 0,
  42.         "hits": []
  43.     },
  44.     "aggregations": {
  45.         "jobs": {
  46.             "doc_count_error_upper_bound": 0,
  47.             "sum_other_doc_count": 0,
  48.             "buckets": [
  49.                 {
  50.                     "key": "ruby engineer",
  51.                     "doc_count": 2,
  52.                     "avg_salary": {
  53.                         "value": 13500
  54.                     }
  55.                 },
  56.                 {
  57.                     "key": "web engineer",
  58.                     "doc_count": 2,
  59.                     "avg_salary": {
  60.                         "value": 6500
  61.                     }
  62.                 },
  63.                 {
  64.                     "key": "java engineer",
  65.                     "doc_count": 1,
  66.                     "avg_salary": {
  67.                         "value": 10000
  68.                     }
  69.                 },
  70.                 {
  71.                     "key": "java senior engineer",
  72.                     "doc_count": 1,
  73.                     "avg_salary": {
  74.                         "value": 30000
  75.                     }
  76.                 }
  77.             ]
  78.         },
  79.         "min_salary_by_job": {
  80.             "value": 6500,
  81.             "keys": [
  82.                 "web engineer"
  83.             ]
  84.         }
  85.     }
  86. }
  88. 找出所有Bucket中值最大的Bucket名称和值
  89. GET  test_search_index/_search
  90. {
  91. 	"size":0,
  92. 	"aggs":{
  93. 		"jobs":{
  94. 			"terms":{
  95. 				"field":"job.keyword",
  96. 				"size":10
  97. 			},
  98. 			"aggs":{
  99. 				"avg_salary":{
  100. 					"avg":{
  101. 						"field":"salary"
  102. 					}
  103. 				}
  104. 			}
  105. 		},
  106. 		"max_salary_by_job":{
  107. 			"max_bucket":{
  108. 				"buckets_path":"jobs>avg_salary"
  109. 			}
  110. 		}
  111. 	}
  112. }
  114. 找出所有Bucket中值平均值
  115. GET  test_search_index/_search
  116. {
  117. 	"size":0,
  118. 	"aggs":{
  119. 		"jobs":{
  120. 			"terms":{
  121. 				"field":"job.keyword",
  122. 				"size":10
  123. 			},
  124. 			"aggs":{
  125. 				"avg_salary":{
  126. 					"avg":{
  127. 						"field":"salary"
  128. 					}
  129. 				}
  130. 			}
  131. 		},
  132. 		"avg_salary_by_job":{
  133. 			"avg_bucket":{
  134. 				"buckets_path":"jobs>avg_salary"
  135. 			}
  136. 		}
  137. 	}
  138. }
  140. 计算所有Bucket值的Stats分析
  141. GET  test_search_index/_search
  143. {
  144. 	"size":0,
  145. 	"aggs":{
  146. 		"jobs":{
  147. 			"terms":{
  148. 				"field":"job.keyword",
  149. 				"size":10
  150. 			},
  151. 			"aggs":{
  152. 				"avg_salary":{
  153. 					"avg":{
  154. 						"field":"salary"
  155. 					}
  156. 				}
  157. 			}
  158. 		},
  159. 		"stats_salary_by_job":{
  160. 			"stats_bucket":{
  161. 				"buckets_path":"jobs>avg_salary"
  162. 			}
  163. 		}
  164. 	}
  165. }
  167. #返回
  168. {
  169.     "took": 3,
  170.     "timed_out": false,
  171.     "_shards": {
  172.         "total": 5,
  173.         "successful": 5,
  174.         "skipped": 0,
  175.         "failed": 0
  176.     },
  177.     "hits": {
  178.         "total": 6,
  179.         "max_score": 0,
  180.         "hits": []
  181.     },
  182.     "aggregations": {
  183.         "jobs": {
  184.             "doc_count_error_upper_bound": 0,
  185.             "sum_other_doc_count": 0,
  186.             "buckets": [
  187.                 {
  188.                     "key": "ruby engineer",
  189.                     "doc_count": 2,
  190.                     "avg_salary": {
  191.                         "value": 13500
  192.                     }
  193.                 },
  194.                 {
  195.                     "key": "web engineer",
  196.                     "doc_count": 2,
  197.                     "avg_salary": {
  198.                         "value": 6500
  199.                     }
  200.                 },
  201.                 {
  202.                     "key": "java engineer",
  203.                     "doc_count": 1,
  204.                     "avg_salary": {
  205.                         "value": 10000
  206.                     }
  207.                 },
  208.                 {
  209.                     "key": "java senior engineer",
  210.                     "doc_count": 1,
  211.                     "avg_salary": {
  212.                         "value": 30000
  213.                     }
  214.                 }
  215.             ]
  216.         },
  217.         "stats_salary_by_job": {
  218.             "count": 4,
  219.             "min": 6500,
  220.             "max": 30000,
  221.             "avg": 15000,
  222.             "sum": 60000
  223.         }
  224.     }
  225. }
  227. 计算所有Bucket值的百分位数
  228. GET  test_search_index/_search
  230. {
  231. 	"size":0,
  232. 	"aggs":{
  233. 		"jobs":{
  234. 			"terms":{
  235. 				"field":"job.keyword",
  236. 				"size":10
  237. 			},
  238. 			"aggs":{
  239. 				"avg_salary":{
  240. 					"avg":{
  241. 						"field":"salary"
  242. 					}
  243. 				}
  244. 			}
  245. 		},
  246. 		"percentiles_salary_by_job":{
  247. 			"percentiles_bucket":{
  248. 				"buckets_path":"jobs>avg_salary"
  249. 			}
  250. 		}
  251. 	}
  252. }
  254. {
  255.     "took": 1,
  256.     "timed_out": false,
  257.     "_shards": {
  258.         "total": 5,
  259.         "successful": 5,
  260.         "skipped": 0,
  261.         "failed": 0
  262.     },
  263.     "hits": {
  264.         "total": 6,
  265.         "max_score": 0,
  266.         "hits": []
  267.     },
  268.     "aggregations": {
  269.         "jobs": {
  270.             "doc_count_error_upper_bound": 0,
  271.             "sum_other_doc_count": 0,
  272.             "buckets": [
  273.                 {
  274.                     "key": "ruby engineer",
  275.                     "doc_count": 2,
  276.                     "avg_salary": {
  277.                         "value": 13500
  278.                     }
  279.                 },
  280.                 {
  281.                     "key": "web engineer",
  282.                     "doc_count": 2,
  283.                     "avg_salary": {
  284.                         "value": 6500
  285.                     }
  286.                 },
  287.                 {
  288.                     "key": "java engineer",
  289.                     "doc_count": 1,
  290.                     "avg_salary": {
  291.                         "value": 10000
  292.                     }
  293.                 },
  294.                 {
  295.                     "key": "java senior engineer",
  296.                     "doc_count": 1,
  297.                     "avg_salary": {
  298.                         "value": 30000
  299.                     }
  300.                 }
  301.             ]
  302.         },
  303.         "percentiles_salary_by_job": {
  304.             "values": {
  305.                 "1.0": 6500,
  306.                 "5.0": 6500,
  307.                 "25.0": 10000,
  308.                 "50.0": 13500,
  309.                 "75.0": 13500,
  310.                 "95.0": 30000,
  311.                 "99.0": 30000
  312.             }
  313.         }
  314.     }
  315. }
Parent- Derivative

计算Bucket值的导数

  1. GET  test_search_index/_search
  3. {
  4. 	"size":0,
  5. 	"aggs":{
  6. 		"birth":{
  7. 			"date_histogram":{
  8. 				"field":"birth",
  9. 				"interval":"year",
  10. 				"min_doc_count":0
  11. 			},
  12. 			"aggs":{
  13. 				"avg_salary":{
  14. 					"avg":{
  15. 						"field":"salary"
  16. 					}
  17. 				},
  18. 				"derivative_avg_salary":{
  19. 					"derivative":{
  20. 						"buckets_path":"avg_salary"
  21. 					}
  22. 				}
  23. 			}
  24. 		}
  25. 	}
  26. }
  28. #返回
  29. {
  30.     "took": 2,
  31.     "timed_out": false,
  32.     "_shards": {
  33.         "total": 5,
  34.         "successful": 5,
  35.         "skipped": 0,
  36.         "failed": 0
  37.     },
  38.     "hits": {
  39.         "total": 6,
  40.         "max_score": 0,
  41.         "hits": []
  42.     },
  43.     "aggregations": {
  44.         "birth": {
  45.             "buckets": [
  46.                 {
  47.                     "key_as_string": "1980-01-01T00:00:00.000Z",
  48.                     "key": 315532800000,
  49.                     "doc_count": 1,
  50.                     "avg_salary": {
  51.                         "value": 30000
  52.                     }
  53.                 },
  54.                 {
  55.                     "key_as_string": "1981-01-01T00:00:00.000Z",
  56.                     "key": 347155200000,
  57.                     "doc_count": 0,
  58.                     "avg_salary": {
  59.                         "value": null
  60.                     },
  61.                     "derivative_avg_salary": {
  62.                         "value": null
  63.                     }
  64.                 },
  65.                 {
  66.                     "key_as_string": "1982-01-01T00:00:00.000Z",
  67.                     "key": 378691200000,
  68.                     "doc_count": 0,
  69.                     "avg_salary": {
  70.                         "value": null
  71.                     },
  72.                     "derivative_avg_salary": {
  73.                         "value": null
  74.                     }
  75.                 },
  76.                 {
  77.                     "key_as_string": "1983-01-01T00:00:00.000Z",
  78.                     "key": 410227200000,
  79.                     "doc_count": 0,
  80.                     "avg_salary": {
  81.                         "value": null
  82.                     },
  83.                     "derivative_avg_salary": {
  84.                         "value": null
  85.                     }
  86.                 },
  87.                 {
  88.                     "key_as_string": "1984-01-01T00:00:00.000Z",
  89.                     "key": 441763200000,
  90.                     "doc_count": 0,
  91.                     "avg_salary": {
  92.                         "value": null
  93.                     },
  94.                     "derivative_avg_salary": {
  95.                         "value": null
  96.                     }
  97.                 },
  98.                 {
  99.                     "key_as_string": "1985-01-01T00:00:00.000Z",
  100.                     "key": 473385600000,
  101.                     "doc_count": 1,
  102.                     "avg_salary": {
  103.                         "value": 15000
  104.                     },
  105.                     "derivative_avg_salary": {
  106.                         "value": null
  107.                     }
  108.                 },
  109.                 {
  110.                     "key_as_string": "1986-01-01T00:00:00.000Z",
  111.                     "key": 504921600000,
  112.                     "doc_count": 0,
  113.                     "avg_salary": {
  114.                         "value": null
  115.                     },
  116.                     "derivative_avg_salary": {
  117.                         "value": null
  118.                     }
  119.                 },
  120.                 {
  121.                     "key_as_string": "1987-01-01T00:00:00.000Z",
  122.                     "key": 536457600000,
  123.                     "doc_count": 1,
  124.                     "avg_salary": {
  125.                         "value": 12000
  126.                     },
  127.                     "derivative_avg_salary": {
  128.                         "value": null
  129.                     }
  130.                 },
  131.                 {
  132.                     "key_as_string": "1988-01-01T00:00:00.000Z",
  133.                     "key": 567993600000,
  134.                     "doc_count": 0,
  135.                     "avg_salary": {
  136.                         "value": null
  137.                     },
  138.                     "derivative_avg_salary": {
  139.                         "value": null
  140.                     }
  141.                 },
  142.                 {
  143.                     "key_as_string": "1989-01-01T00:00:00.000Z",
  144.                     "key": 599616000000,
  145.                     "doc_count": 1,
  146.                     "avg_salary": {
  147.                         "value": 8000
  148.                     },
  149.                     "derivative_avg_salary": {
  150.                         "value": null
  151.                     }
  152.                 },
  153.                 {
  154.                     "key_as_string": "1990-01-01T00:00:00.000Z",
  155.                     "key": 631152000000,
  156.                     "doc_count": 1,
  157.                     "avg_salary": {
  158.                         "value": 10000
  159.                     },
  160.                     "derivative_avg_salary": {
  161.                         "value": 2000
  162.                     }
  163.                 },
  164.                 {
  165.                     "key_as_string": "1991-01-01T00:00:00.000Z",
  166.                     "key": 662688000000,
  167.                     "doc_count": 0,
  168.                     "avg_salary": {
  169.                         "value": null
  170.                     },
  171.                     "derivative_avg_salary": {
  172.                         "value": null
  173.                     }
  174.                 },
  175.                 {
  176.                     "key_as_string": "1992-01-01T00:00:00.000Z",
  177.                     "key": 694224000000,
  178.                     "doc_count": 0,
  179.                     "avg_salary": {
  180.                         "value": null
  181.                     },
  182.                     "derivative_avg_salary": {
  183.                         "value": null
  184.                     }
  185.                 },
  186.                 {
  187.                     "key_as_string": "1993-01-01T00:00:00.000Z",
  188.                     "key": 725846400000,
  189.                     "doc_count": 0,
  190.                     "avg_salary": {
  191.                         "value": null
  192.                     },
  193.                     "derivative_avg_salary": {
  194.                         "value": null
  195.                     }
  196.                 },
  197.                 {
  198.                     "key_as_string": "1994-01-01T00:00:00.000Z",
  199.                     "key": 757382400000,
  200.                     "doc_count": 1,
  201.                     "avg_salary": {
  202.                         "value": 5000
  203.                     },
  204.                     "derivative_avg_salary": {
  205.                         "value": null
  206.                     }
  207.                 }
  208.             ]
  209.         }
  210.     }
  211. }

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