1、search Api
ES支持两种基本方式检索；
通过REST request uri 发送搜索参数 （uri +检索参数）；
通过REST request body 来发送它们（uri+请求体）；
- 一切检索从_search开始
GET bank/_search?q=*&sort=account_number:asc
检索bank下所有信息，包括type和docs
GET bank/_search?q=*&sort=account_number:asc
请求参数方式检索
响应结果解释：
- took-Elasticsearch 执行搜索的时间（毫秒）
- time_out- 告诉我们搜索是否超时
- _shards- 告诉我们多少分配被搜索了，以及统计了成功/失败的搜索分片
- hits- 搜索结果
- hist.total- 搜集结果
- hist.hits- 实际搜集结果数组（默认为前10的文档）
- sort- 结果的排序 key（键）（没有则按score排序）
- score 和 max_score- 相关性得分和最高得分（全文检索用）
- uri+请求体进行检索
GET /bank/_search
{
"query": { "match_all": {} },
"sort": [
{ "account_number": "asc" },
{"balance":"desc"}
]
}HTTP客户端工具（POSTMAN）,get请求不能携带请求体，我们变为post也是一样的
我们POST一个JSON风格的查询请求体到_search API
需要了解，一旦搜索的结果被返回，Elasticsearch就完成了这次请求，并且不会维护任何服务端的资源或者结果的cursor（游标）
1）只有6条数据，这是因为存在分页查询；
2）详细的字段信息，参照： https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started-search.html
2、Query DSL
1、基本语法格式
Elasticsearch提供了一个可以执行查询的Json风格的DSL。这个被称为Query DSL，该查询语言非常全面。
一个查询语句的典型结构
QUERY_NAME:{
ARGUMENT:VALUE,
ARGUMENT:VALUE,...
}如果针对于某个字段，那么它的结构如下：
{
QUERY_NAME:{
FIELD_NAME:{
ARGUMENT:VALUE,
ARGUMENT:VALUE,...
}
}
}GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"sort": [
{
"account_number": {
"order": "desc"
}
}
]
}query定义如何查询；
- match_all查询类型【代表查询所有的所有】，es中可以在query中组合非常多的查询类型完成复杂查询；
- 除了query参数之外，我们可也传递其他的参数以改变查询结果，如sort，size；
- from+size限定，完成分页功能；
- sort排序，多字段排序，会在前序字段相等时后续字段内部排序，否则以前序为准；
2、返回部分字段
GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"sort": [
{
"account_number": {
"order": "desc"
}
}
],
"_source": ["balance","firstname"]
}查询结果：
3、match匹配查询
- 基本类型（非字符串），精确控制
GET bank/_search
{
"query": {
"match": {
"account_number": "20"
}
}
}match返回account_number=20的数据。
查询结果：
{
"took" : 11,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 1.0,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
}
]
}
}- 字符串，全文检索
GET bank/_search
{
"query": {
"match": {
"address": "kings"
}
}
}全文检索，最终会按照评分进行排序，会对检索条件进行分词匹配。
查询结果：
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 5.990829,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 5.990829,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "722",
"_score" : 5.990829,
"_source" : {
"account_number" : 722,
"balance" : 27256,
"firstname" : "Roberts",
"lastname" : "Beasley",
"age" : 34,
"gender" : "F",
"address" : "305 Kings Hwy",
"employer" : "Quintity",
"email" : "robertsbeasley@quintity.com",
"city" : "Hayden",
"state" : "PA"
}
}
]
}
}4、match_phrase [短句匹配]
将需要匹配的值当成一整个单词（不分词）进行检索
GET bank/_search
{
"query": {
"match_phrase": {
"address": "mill road"
}
}
}查处address中包含mill road的所有记录，并给出相关性得分
查看结果：
{
"took" : 76,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 8.926605,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 8.926605,
"_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"
}
}
]
}
}使用match的keyword
GET bank/_search
{
"query": {
"match": {
"address.keyword": "mill road"
}
}
}查询结果，一条也未匹配到
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}修改匹配条件为“990 Mill Road”
GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill Road"
}
}
}查询出一条数据
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 6.5032897,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.5032897,
"_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"
}
}
]
}
}总结
文本字段的匹配，使用keyword，匹配的条件就是要显示字段的全部值，要进行精确匹配的。
match_phrase是做短语匹配，只要文本中包含匹配条件，不区分大小写，就能匹配到。
5、multi_math【多字段匹配】
GET bank/_search
{
"query": {
"multi_match": {
"query": "mill movico",
"fields": ["city","address"]
}
}
}city或者address中包含mill或者movico，并且在查询过程中，会对于查询条件进行分词。
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 6.5059485,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 6.5059485,
"_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"
}
},
{
"_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"
}
}
]
}
}6、bool用来做复合查询
复合语句可以合并，任何其他查询语句，包括符合语句。这也就意味着，复合语句之间 可以互相嵌套，可以表达非常复杂的逻辑。
must：必须达到must所列举的所有条件
must_not，必须不匹配must_not所列举的所有条件。
should，应该满足should所列举的条件，不满足也可以，只影响得分
should：应该达到should列举的条件，如果到达会增加相关文档的评分，并不会改变查询的结果。如果query中只有should且只有一种匹配规则，那么should的条件就会被作为默认匹配条件二区改变查询结果。
- 实例：查询gender=m，并且address=mill的数据
GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"gender": "M"
}
},
{
"match": {
"address": "mill"
}
}
]
}
}
}- 实例：查询gender=m，并且address=mill的数据，但是age不等于38的
GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"gender": "M"
}
},
{
"match": {
"address": "mill"
}
}
],
"must_not": [
{
"match": {
"age": "38"
}
}
]
}
}- 实例：匹配lastName应该等于Wallace的数据
GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"gender": "M"
}
},
{
"match": {
"address": "mill"
}
}
],
"must_not": [
{
"match": {
"age": "18"
}
}
],
"should": [
{
"match": {
"lastname": "Wallace"
}
}
]
}
}
}能够看到相关度越高，得分也越高。
7、Filter【结果过滤】
并不是所有的查询都需要产生分数，特别是哪些仅用于filtering过滤的文档。为了不计算分数，elasticsearch会自动检查场景并且优化查询的执行。
filtering、must_not查询不会产生得分
- 查询所有匹配address=mill的文档，然后再根据10000<=balance<=20000进行过滤查询结果
GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"address": "mill"
}
}
],
"filter": {
"range": {
"balance": {
"gte": "10000",
"lte": "20000"
}
}
}
}
}
}官网文档：https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started-search.html
Each must, should, and must_not element in a Boolean query is referred to as a query clause. How well a document meets the criteria in each must or should clause contributes to the document’s relevance score. The higher the score, the better the document matches your search criteria. By default, Elasticsearch returns documents ranked by these relevance scores.
在boolean查询中，must, should 和must_not 元素都被称为查询子句 。 文档是否符合每个“must”或“should”子句中的标准，决定了文档的“相关性得分”。 得分越高，文档越符合您的搜索条件。 默认情况下，Elasticsearch返回根据这些相关性得分排序的文档。
The criteria in a must_not clause is treated as a filter. It affects whether or not the document is included in the results, but does not contribute to how documents are scored. You can also explicitly specify arbitrary filters to include or exclude documents based on structured data.
“must_not”子句中的条件被视为“过滤器”。 它影响文档是否包含在结果中， 但不影响文档的评分方式。 还可以显式地指定任意过滤器来包含或排除基于结构化数据的文档。
filter在使用过程中，并不会计算相关性得分：
GET bank/_search
{
"query": {
"bool": {
"filter": {
"range": {
"balance": {
"gte": "18611",
"lte": "18613"
}
}
}
}
}
}查询结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "37",
"_score" : 0.0,
"_source" : {
"account_number" : 37,
"balance" : 18612,
"firstname" : "Mcgee",
"lastname" : "Mooney",
"age" : 39,
"gender" : "M",
"address" : "826 Fillmore Place",
"employer" : "Reversus",
"email" : "mcgeemooney@reversus.com",
"city" : "Tooleville",
"state" : "OK"
}
}
]
}
}能看到所有文档的 “_score” : 0.0。
8、term
陌生语法，使用检索方式
和match一样，匹配某个属性的值。
总结：
全文检索字段用match，其他非text字段匹配用term。
Avoid using the term query for text fields.
避免对文本字段使用“term”查询
By default, Elasticsearch changes the values of text fields as part of analysis. This can make finding exact matches for text field values difficult.
默认情况下，Elasticsearch作为analysis的一部分更改' text '字段的值。这使得为“text”字段值寻找精确匹配变得困难。
To search text field values, use the match.
要搜索“text”字段值，请使用匹配。
https://www.elastic.co/guide/en/elasticsearch/reference/7.6/query-dsl-term-query.html9、Aggregation（执行聚合）
聚合提供了从数据中分组和提取数据的能力。最简单的聚合方法大致等于SQL Group by和SQL聚合函数。在elasticsearch中，执行搜索返回this（命中结果），并且同时返回聚合结果，把以响应中的所有hits（命中结果）分隔开的能力。这是非常强大且有效的，你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啦避免网络往返。
“size”:0
size:0不显示搜索数据 aggs：执行聚合。聚合语法如下：
"aggs":{
"aggs_name这次聚合的名字，方便展示在结果集中":{
"AGG_TYPE聚合的类型(avg,term,terms)":{}
}
}，- 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": {
"ageAgg": {
"terms": {
"field": "age",
"size": 10
}
},
"ageAvg": {
"avg": {
"field": "age"
}
},
"balanceAvg": {
"avg": {
"field": "balance"
}
}
},
"size": 0
}查询结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"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
}
]
},
"ageAvg" : {
"value" : 34.0
},
"balanceAvg" : {
"value" : 25208.0
}
}
}- 复杂： 按照年龄聚合，并且求这些年龄段的这些人的平均薪资
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 100
},
"aggs": {
"ageAvg": {
"avg": {
"field": "balance"
}
}
}
}
},
"size": 0
}输出结果：
{
"took" : 49,
"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,
"ageAvg" : {
"value" : 28312.918032786885
}
},
{
"key" : 39,
"doc_count" : 60,
"ageAvg" : {
"value" : 25269.583333333332
}
},
{
"key" : 26,
"doc_count" : 59,
"ageAvg" : {
"value" : 23194.813559322032
}
},
{
"key" : 32,
"doc_count" : 52,
"ageAvg" : {
"value" : 23951.346153846152
}
},
{
"key" : 35,
"doc_count" : 52,
"ageAvg" : {
"value" : 22136.69230769231
}
},
{
"key" : 36,
"doc_count" : 52,
"ageAvg" : {
"value" : 22174.71153846154
}
},
{
"key" : 22,
"doc_count" : 51,
"ageAvg" : {
"value" : 24731.07843137255
}
},
{
"key" : 28,
"doc_count" : 51,
"ageAvg" : {
"value" : 28273.882352941175
}
},
{
"key" : 33,
"doc_count" : 50,
"ageAvg" : {
"value" : 25093.94
}
},
{
"key" : 34,
"doc_count" : 49,
"ageAvg" : {
"value" : 26809.95918367347
}
},
{
"key" : 30,
"doc_count" : 47,
"ageAvg" : {
"value" : 22841.106382978724
}
},
{
"key" : 21,
"doc_count" : 46,
"ageAvg" : {
"value" : 26981.434782608696
}
},
{
"key" : 40,
"doc_count" : 45,
"ageAvg" : {
"value" : 27183.17777777778
}
},
{
"key" : 20,
"doc_count" : 44,
"ageAvg" : {
"value" : 27741.227272727272
}
},
{
"key" : 23,
"doc_count" : 42,
"ageAvg" : {
"value" : 27314.214285714286
}
},
{
"key" : 24,
"doc_count" : 42,
"ageAvg" : {
"value" : 28519.04761904762
}
},
{
"key" : 25,
"doc_count" : 42,
"ageAvg" : {
"value" : 27445.214285714286
}
},
{
"key" : 37,
"doc_count" : 42,
"ageAvg" : {
"value" : 27022.261904761905
}
},
{
"key" : 27,
"doc_count" : 39,
"ageAvg" : {
"value" : 21471.871794871793
}
},
{
"key" : 38,
"doc_count" : 39,
"ageAvg" : {
"value" : 26187.17948717949
}
},
{
"key" : 29,
"doc_count" : 35,
"ageAvg" : {
"value" : 29483.14285714286
}
}
]
}
}
}- 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的总体平均薪资
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 100
},
"aggs": {
"genderAgg": {
"terms": {
"field": "gender.keyword"
},
"aggs": {
"balanceAvg": {
"avg": {
"field": "balance"
}
}
}
},
"ageBalanceAvg": {
"avg": {
"field": "balance"
}
}
}
}
},
"size": 0
}输出结果：
{
"took" : 101,
"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,
"balanceAvg" : {
"value" : 29565.628571428573
}
},
{
"key" : "F",
"doc_count" : 26,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 26348.684210526317
}
},
{
"key" : "M",
"doc_count" : 22,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 25094.78125
}
},
{
"key" : "F",
"doc_count" : 27,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 22941.964285714286
}
},
{
"key" : "F",
"doc_count" : 24,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 24226.321428571428
}
},
{
"key" : "F",
"doc_count" : 24,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 20884.677419354837
}
},
{
"key" : "F",
"doc_count" : 21,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 22152.74074074074
}
},
{
"key" : "M",
"doc_count" : 24,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 27076.8064516129
}
},
{
"key" : "M",
"doc_count" : 20,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 26437.615384615383
}
},
{
"key" : "M",
"doc_count" : 24,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 26039.166666666668
}
},
{
"key" : "M",
"doc_count" : 19,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 25316.16
}
},
{
"key" : "M",
"doc_count" : 22,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 28210.916666666668
}
},
{
"key" : "M",
"doc_count" : 22,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 26474.958333333332
}
},
{
"key" : "F",
"doc_count" : 21,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 29047.444444444445
}
},
{
"key" : "F",
"doc_count" : 17,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 27730.75
}
},
{
"key" : "F",
"doc_count" : 18,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 29414.521739130436
}
},
{
"key" : "M",
"doc_count" : 19,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 29336.08695652174
}
},
{
"key" : "F",
"doc_count" : 19,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 25015.739130434784
}
},
{
"key" : "F",
"doc_count" : 19,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 21618.85714285714
}
},
{
"key" : "M",
"doc_count" : 18,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 27931.65
}
},
{
"key" : "M",
"doc_count" : 19,
"balanceAvg" : {
"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,
"balanceAvg" : {
"value" : 29943.17391304348
}
},
{
"key" : "F",
"doc_count" : 12,
"balanceAvg" : {
"value" : 28601.416666666668
}
}
]
},
"ageBalanceAvg" : {
"value" : 29483.14285714286
}
}
]
}
}
}
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