Elasticsearch-aggregations聚合分析
# 前言
前面学习了如何通过Query DSL检索数据，接下学习如何将数据进行聚合分析。
# 简介
聚合提供了从数据中分组和提取数据的能力。最简单的绝活方法大致等于SQL GROUP BY和SQL聚合函数。
在
Elasticsearch中，您有执行搜索会犯hits（命中结果），并且同时返回聚合结果，把一个响应中的所有hits（命中结果）分隔开的能力。这是非常强大且有效的，您可以执行查询和多个聚合，并且在一次使用中得到各自的（任何一个的）返回结果，使用一次简洁和简化的API来避免网络往返。
# 聚合语法
"aggregations" : {
"<aggregation_name>" : {
"<aggregation_type>" : {
<aggregation_body>
}
[,"meta" : { [<meta_data_body>] } ]?
[,"aggregations" : { [<sub_aggregation>]+ } ]?
}
[,"<aggregation_name_2>" : { ... } ]*
}
1
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
不但可以多次聚合，还可以进行子聚合，也就是在聚合的基础上，再进行一次聚合。
# Terms Aggregation 统计
# 语法
{
"aggs" : {
"genres" : {
"terms" : { "field" : "genre" }
}
}
}
1
2
3
4
5
6
7
2
3
4
5
6
7
# 实例
搜索address中包含mill的所有人的年龄分布。
- 请求
GET bank/_search
{
"query": {
"match": {
"address": "mill"
}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 10
}
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
- 返回
{
"took" : 7,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 5.4032025,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 5.4032025,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 5.4032025,
"_source" : {
"account_number" : 472,
"balance" : 25571,
"firstname" : "Lee",
"lastname" : "Long",
"age" : 32,
"gender" : "F",
"address" : "288 Mill Street",
"employer" : "Comverges",
"email" : "leelong@comverges.com",
"city" : "Movico",
"state" : "MT"
}
}
]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 38,
"doc_count" : 2
},
{
"key" : 28,
"doc_count" : 1
},
{
"key" : 32,
"doc_count" : 1
}
]
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
总共有4条记录，年龄为38的有2条记录，年龄为28和32的各一条记录。
# Avg Aggregation 平均
# 语法
POST /exams/_search?size=0
{
"aggs" : {
"avg_grade" : { "avg" : { "field" : "grade" } }
}
}
1
2
3
4
5
6
2
3
4
5
6
# 实例1
搜索address中包含mill的所有人的平均年龄。
- 请求
GET bank/_search
{
"query": {
"match": {
"address": "mill"
}
},
"aggs": {
"aggAvg": {
"avg": {
"field": "age"
}
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
- 返回
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 5.4032025,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 5.4032025,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 5.4032025,
"_source" : {
"account_number" : 472,
"balance" : 25571,
"firstname" : "Lee",
"lastname" : "Long",
"age" : 32,
"gender" : "F",
"address" : "288 Mill Street",
"employer" : "Comverges",
"email" : "leelong@comverges.com",
"city" : "Movico",
"state" : "MT"
}
}
]
},
"aggregations" : {
"aggAvg" : {
"value" : 34.0
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
得出平均年龄是34。
# 实例2
搜索address中包含mill的所有人的平均薪资。
- 请求
GET bank/_search
{
"query": {
"match": {
"address": "mill"
}
},
"aggs": {
"balanceAvg": {
"avg": {
"field": "balance"
}
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
2
3
4
5
6
7
8
9
10
11
12
13
14
15
- 返回
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 5.4032025,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 5.4032025,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 5.4032025,
"_source" : {
"account_number" : 472,
"balance" : 25571,
"firstname" : "Lee",
"lastname" : "Long",
"age" : 32,
"gender" : "F",
"address" : "288 Mill Street",
"employer" : "Comverges",
"email" : "leelong@comverges.com",
"city" : "Movico",
"state" : "MT"
}
}
]
},
"aggregations" : {
"balanceAvg" : {
"value" : 25208.0
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
可以看出，平均薪资是25208.0
# 复杂聚合分析实例
搜索address中包含mill的所有人的年龄分布以及平均年龄，但不显示这些人的详情。
GET bank/_search
{
"query": {
"match": {
"address": "mill"
}
},
"aggs": {
"aggCount": {
"terms": {
"field": "age",
"size": 10
}
},
"aggAvg": {
"avg": {
"field": "age"
}
}
},
"size": 0
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
- 返回
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"aggAvg" : {
"value" : 34.0
},
"aggCount" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 38,
"doc_count" : 2
},
{
"key" : 28,
"doc_count" : 1
},
{
"key" : 32,
"doc_count" : 1
}
]
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
# 子聚合
不但可以多次聚合，还可以进行子聚合，也就是在聚合的基础上，再进行一次聚合。
# 子聚合实例
按照年龄聚合，并且请求这些年龄段的这些人的平均薪资
- 请求
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 100
},
"aggs": {
"balanceAvg": {
"avg": {
"field": "balance"
}
}
}
}
},
"size": 0
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
- 返回
{
"took" : 11,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 31,
"doc_count" : 61,
"balanceAvg" : {
"value" : 28312.918032786885
}
},
{
"key" : 39,
"doc_count" : 60,
"balanceAvg" : {
"value" : 25269.583333333332
}
},
{
"key" : 26,
"doc_count" : 59,
"balanceAvg" : {
"value" : 23194.813559322032
}
},
{
"key" : 32,
"doc_count" : 52,
"balanceAvg" : {
"value" : 23951.346153846152
}
},
{
"key" : 35,
"doc_count" : 52,
"balanceAvg" : {
"value" : 22136.69230769231
}
},
{
"key" : 36,
"doc_count" : 52,
"balanceAvg" : {
"value" : 22174.71153846154
}
},
{
"key" : 22,
"doc_count" : 51,
"balanceAvg" : {
"value" : 24731.07843137255
}
},
{
"key" : 28,
"doc_count" : 51,
"balanceAvg" : {
"value" : 28273.882352941175
}
},
{
"key" : 33,
"doc_count" : 50,
"balanceAvg" : {
"value" : 25093.94
}
},
{
"key" : 34,
"doc_count" : 49,
"balanceAvg" : {
"value" : 26809.95918367347
}
},
{
"key" : 30,
"doc_count" : 47,
"balanceAvg" : {
"value" : 22841.106382978724
}
},
{
"key" : 21,
"doc_count" : 46,
"balanceAvg" : {
"value" : 26981.434782608696
}
},
{
"key" : 40,
"doc_count" : 45,
"balanceAvg" : {
"value" : 27183.17777777778
}
},
{
"key" : 20,
"doc_count" : 44,
"balanceAvg" : {
"value" : 27741.227272727272
}
},
{
"key" : 23,
"doc_count" : 42,
"balanceAvg" : {
"value" : 27314.214285714286
}
},
{
"key" : 24,
"doc_count" : 42,
"balanceAvg" : {
"value" : 28519.04761904762
}
},
{
"key" : 25,
"doc_count" : 42,
"balanceAvg" : {
"value" : 27445.214285714286
}
},
{
"key" : 37,
"doc_count" : 42,
"balanceAvg" : {
"value" : 27022.261904761905
}
},
{
"key" : 27,
"doc_count" : 39,
"balanceAvg" : {
"value" : 21471.871794871793
}
},
{
"key" : 38,
"doc_count" : 39,
"balanceAvg" : {
"value" : 26187.17948717949
}
},
{
"key" : 29,
"doc_count" : 35,
"balanceAvg" : {
"value" : 29483.14285714286
}
}
]
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
# 复杂实例
查出所有年龄分布，并且这些年龄段中M的平均薪资和F的平均薪资以及这个年龄段总体平均薪资
- 请求
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 100
},
"aggs": {
"ageBalanceAvg": {
"avg": {
"field": "balance"
}
},
"genderAgg": {
"terms": {
"field": "gender.keyword",
"size": 10
},
"aggs": {
"genderBlanceAvg": {
"avg": {
"field": "balance"
}
}
}
}
}
}
},
"size": 0
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
- 返回
{
"took" : 82,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 31,
"doc_count" : 61,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 35,
"genderBlanceAvg" : {
"value" : 29565.628571428573
}
},
{
"key" : "F",
"doc_count" : 26,
"genderBlanceAvg" : {
"value" : 26626.576923076922
}
}
]
},
"ageBalanceAvg" : {
"value" : 28312.918032786885
}
},
{
"key" : 39,
"doc_count" : 60,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 38,
"genderBlanceAvg" : {
"value" : 26348.684210526317
}
},
{
"key" : "M",
"doc_count" : 22,
"genderBlanceAvg" : {
"value" : 23405.68181818182
}
}
]
},
"ageBalanceAvg" : {
"value" : 25269.583333333332
}
},
{
"key" : 26,
"doc_count" : 59,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 32,
"genderBlanceAvg" : {
"value" : 25094.78125
}
},
{
"key" : "F",
"doc_count" : 27,
"genderBlanceAvg" : {
"value" : 20943.0
}
}
]
},
"ageBalanceAvg" : {
"value" : 23194.813559322032
}
},
{
"key" : 32,
"doc_count" : 52,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 28,
"genderBlanceAvg" : {
"value" : 22941.964285714286
}
},
{
"key" : "F",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 25128.958333333332
}
}
]
},
"ageBalanceAvg" : {
"value" : 23951.346153846152
}
},
{
"key" : 35,
"doc_count" : 52,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 28,
"genderBlanceAvg" : {
"value" : 24226.321428571428
}
},
{
"key" : "F",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 19698.791666666668
}
}
]
},
"ageBalanceAvg" : {
"value" : 22136.69230769231
}
},
{
"key" : 36,
"doc_count" : 52,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 31,
"genderBlanceAvg" : {
"value" : 20884.677419354837
}
},
{
"key" : "F",
"doc_count" : 21,
"genderBlanceAvg" : {
"value" : 24079.04761904762
}
}
]
},
"ageBalanceAvg" : {
"value" : 22174.71153846154
}
},
{
"key" : 22,
"doc_count" : 51,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 27,
"genderBlanceAvg" : {
"value" : 22152.74074074074
}
},
{
"key" : "M",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 27631.708333333332
}
}
]
},
"ageBalanceAvg" : {
"value" : 24731.07843137255
}
},
{
"key" : 28,
"doc_count" : 51,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 31,
"genderBlanceAvg" : {
"value" : 27076.8064516129
}
},
{
"key" : "M",
"doc_count" : 20,
"genderBlanceAvg" : {
"value" : 30129.35
}
}
]
},
"ageBalanceAvg" : {
"value" : 28273.882352941175
}
},
{
"key" : 33,
"doc_count" : 50,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 26,
"genderBlanceAvg" : {
"value" : 26437.615384615383
}
},
{
"key" : "M",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 23638.291666666668
}
}
]
},
"ageBalanceAvg" : {
"value" : 25093.94
}
},
{
"key" : 34,
"doc_count" : 49,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 30,
"genderBlanceAvg" : {
"value" : 26039.166666666668
}
},
{
"key" : "M",
"doc_count" : 19,
"genderBlanceAvg" : {
"value" : 28027.0
}
}
]
},
"ageBalanceAvg" : {
"value" : 26809.95918367347
}
},
{
"key" : 30,
"doc_count" : 47,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 25,
"genderBlanceAvg" : {
"value" : 25316.16
}
},
{
"key" : "M",
"doc_count" : 22,
"genderBlanceAvg" : {
"value" : 20028.545454545456
}
}
]
},
"ageBalanceAvg" : {
"value" : 22841.106382978724
}
},
{
"key" : 21,
"doc_count" : 46,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 28210.916666666668
}
},
{
"key" : "M",
"doc_count" : 22,
"genderBlanceAvg" : {
"value" : 25640.18181818182
}
}
]
},
"ageBalanceAvg" : {
"value" : 26981.434782608696
}
},
{
"key" : 40,
"doc_count" : 45,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 26474.958333333332
}
},
{
"key" : "F",
"doc_count" : 21,
"genderBlanceAvg" : {
"value" : 27992.571428571428
}
}
]
},
"ageBalanceAvg" : {
"value" : 27183.17777777778
}
},
{
"key" : 20,
"doc_count" : 44,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 27,
"genderBlanceAvg" : {
"value" : 29047.444444444445
}
},
{
"key" : "F",
"doc_count" : 17,
"genderBlanceAvg" : {
"value" : 25666.647058823528
}
}
]
},
"ageBalanceAvg" : {
"value" : 27741.227272727272
}
},
{
"key" : 23,
"doc_count" : 42,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 24,
"genderBlanceAvg" : {
"value" : 27730.75
}
},
{
"key" : "F",
"doc_count" : 18,
"genderBlanceAvg" : {
"value" : 26758.833333333332
}
}
]
},
"ageBalanceAvg" : {
"value" : 27314.214285714286
}
},
{
"key" : 24,
"doc_count" : 42,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 23,
"genderBlanceAvg" : {
"value" : 29414.521739130436
}
},
{
"key" : "M",
"doc_count" : 19,
"genderBlanceAvg" : {
"value" : 27435.052631578947
}
}
]
},
"ageBalanceAvg" : {
"value" : 28519.04761904762
}
},
{
"key" : 25,
"doc_count" : 42,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 23,
"genderBlanceAvg" : {
"value" : 29336.08695652174
}
},
{
"key" : "F",
"doc_count" : 19,
"genderBlanceAvg" : {
"value" : 25156.263157894737
}
}
]
},
"ageBalanceAvg" : {
"value" : 27445.214285714286
}
},
{
"key" : 37,
"doc_count" : 42,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 23,
"genderBlanceAvg" : {
"value" : 25015.739130434784
}
},
{
"key" : "F",
"doc_count" : 19,
"genderBlanceAvg" : {
"value" : 29451.21052631579
}
}
]
},
"ageBalanceAvg" : {
"value" : 27022.261904761905
}
},
{
"key" : 27,
"doc_count" : 39,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 21,
"genderBlanceAvg" : {
"value" : 21618.85714285714
}
},
{
"key" : "M",
"doc_count" : 18,
"genderBlanceAvg" : {
"value" : 21300.38888888889
}
}
]
},
"ageBalanceAvg" : {
"value" : 21471.871794871793
}
},
{
"key" : 38,
"doc_count" : 39,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "F",
"doc_count" : 20,
"genderBlanceAvg" : {
"value" : 27931.65
}
},
{
"key" : "M",
"doc_count" : 19,
"genderBlanceAvg" : {
"value" : 24350.894736842107
}
}
]
},
"ageBalanceAvg" : {
"value" : 26187.17948717949
}
},
{
"key" : 29,
"doc_count" : 35,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 23,
"genderBlanceAvg" : {
"value" : 29943.17391304348
}
},
{
"key" : "F",
"doc_count" : 12,
"genderBlanceAvg" : {
"value" : 28601.416666666668
}
}
]
},
"ageBalanceAvg" : {
"value" : 29483.14285714286
}
}
]
}
}
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
至此，我们已经体验到ES聚合分析能力的强大之处，非常高效，很短时间就能查出我们想要的结果，至于其他聚合API后面实际发开中，可以参考官方文档。
帮我改善此页面 (opens new window)
上次更新: 2020/12/17, 08:38:35