elasticsearch系列四:搜索详解(搜索API、Query DSL)

一、搜索API

elasticsearch系列四:搜索详解(搜索API、Query DSL)

1. 搜索API 端点地址

从索引tweet里面搜索字段user为kimchy的记录

GET /twitter/_search?q=user:kimchy

从索引tweet,user里面搜索字段user为kimchy的记录

GET /twitter/tweet,user/_search?q=user:kimchy
GET /kimchy,elasticsearch/_search?q=tag:wow

从所有索引里面搜索字段tag为wow的记录

GET /_all/_search?q=tag:wow
GET /_search?q=tag:wow

说明:搜索的端点地址可以是多索引多mapping type的。搜索的参数可作为URI请求参数给出,也可用 request body 给出

2. URI Search

URI 搜索方式通过URI参数来指定查询相关参数。让我们可以快速做一个查询。

GET /twitter/_search?q=user:kimchy

可用的参数请参考: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-uri-request.html

3. 查询结果说明

elasticsearch系列四:搜索详解(搜索API、Query DSL)

5. 特殊的查询参数用法

如果我们只想知道有多少文档匹配某个查询,可以这样用参数:

GET /bank/_search?q=city:b*&size=0

elasticsearch系列四:搜索详解(搜索API、Query DSL)

如果我们只想知道有没有文档匹配某个查询,可以这样用参数:

GET /bank/_search?q=city:b*&size=0&terminate_after=1

elasticsearch系列四:搜索详解(搜索API、Query DSL)

比较两个查询的结果可以知道第一个查询返回所有的命中文档数,第二个查询由于只需要知道有没有文档,所以只要有文档就立即返回

6. Request body Search

Request body 搜索方式以JSON格式在请求体中定义查询 query。请求方式可以是 GET 、POST 。

GET /twitter/_search
{
"query" : {
"term" : { "user" : "kimchy" }
}
}

可用的参数:

timeout:请求超时时长,限定在指定时长内响应(即使没查完);
from: 分页的起始行,默认0;
size:分页大小;
request_cache:是否缓存请求结果,默认true。
terminate_after:限定每个分片取几个文档。如果设置,则响应将有一个布尔型字段terminated_early来指示查询执行是否实际已经terminate_early。缺省为no terminate_after;
search_type:查询的执行方式,可选值dfs_query_then_fetch or query_then_fetch ,默认: query_then_fetch ;
batched_reduce_size:一次在协调节点上应该减少的分片结果的数量。如果请求中的潜在分片数量可能很大,则应将此值用作保护机制以减少每个搜索请求的内存开销。

6.1 query 元素定义查询

query 元素用Query DSL 来定义查询。

GET /_search
{
"query" : {
"term" : { "user" : "kimchy" }
}
}

6.2 指定返回哪些内容

6.2.1 source filter  对_source字段进行选择

GET /_search
{
"_source": false,
"query" : {
"term" : { "user" : "kimchy" }
}
}

通配符查询

GET /_search
{
"_source": [ "obj1.*", "obj2.*" ],
"query" : {
"term" : { "user" : "kimchy" }
}
}
GET /_search
{
"_source": "obj.*",
"query" : {
"term" : { "user" : "kimchy" }
}
}

包含什么不包含什么

GET /_search
{
"_source": {
"includes": [ "obj1.*", "obj2.*" ],
"excludes": [ "*.description" ]
},
"query" : {
"term" : { "user" : "kimchy" }
}
}

6.2.2 stored_fields 来指定返回哪些stored字段

GET /_search
{
"stored_fields" : ["user", "postDate"],
"query" : {
"term" : { "user" : "kimchy" }
}
}

说明:* 可用来指定返回所有存储字段

6.2.3 docValue Field 返回存储了docValue的字段值

GET /_search
{
"query" : {
"match_all": {}
},
"docvalue_fields" : ["test1", "test2"]
}

6.2.4 version 来指定返回文档的版本字段

GET /_search
{
"version": true,
"query" : {
"term" : { "user" : "kimchy" }
}
}

6.2.5 explain 返回文档的评分解释

GET /_search
{
"explain": true,
"query" : {
"term" : { "user" : "kimchy" }
}
}

6.2.6 Script Field 用脚本来对命中的每个文档的字段进行运算后返回

GET /bank/_search
{
"query": {
"match_all": {}
},
"script_fields": {
"test1": {
"script": {
"lang": "painless",
"source": "doc['balance'].value * 2"
}
},
"test2": {
"script": {
"lang": "painless",
<!-- doc指文档-->
"source": "doc['age'].value * params.factor",
"params": {
"factor": 2
}
}
} }}

搜索结果:

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": 1,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "25",
"_score": 1,
"fields": {
"test1": [
81080
],
"test2": [
78
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "44",
"_score": 1,
"fields": {
"test1": [
68974
],
"test2": [
74
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "99",
"_score": 1,
"fields": {
"test1": [
94318
],
"test2": [
78
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "119",
"_score": 1,
"fields": {
"test1": [
98444
],
"test2": [
56
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "126",
"_score": 1,
"fields": {
"test1": [
7214
],
"test2": [
78
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "145",
"_score": 1,
"fields": {
"test1": [
94812
],
"test2": [
64
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "183",
"_score": 1,
"fields": {
"test1": [
28446
],
"test2": [
52
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "190",
"_score": 1,
"fields": {
"test1": [
6300
],
"test2": [
60
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "208",
"_score": 1,
"fields": {
"test1": [
81520
],
"test2": [
52
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "222",
"_score": 1,
"fields": {
"test1": [
29528
],
"test2": [
72
]
}
}
]
}
}
GET /bank/_search
{
"query": {
"match_all": {}
},
"script_fields": {
"ffx": {
"script": {
"lang": "painless",
"source": "doc['age'].value * doc['balance'].value"
}
},
"balance*2": {
"script": {
"lang": "painless",
"source": "params['_source'].balance*2"
}
}
}
}

说明:

params  _source 取 _source字段值

官方推荐使用doc,理由是用doc效率比取_source 高

搜索结果:

{
"took": 26,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": 1,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "25",
"_score": 1,
"fields": {
"balance*2": [
81080
],
"ffx": [
1581060
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "44",
"_score": 1,
"fields": {
"balance*2": [
68974
],
"ffx": [
1276019
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "99",
"_score": 1,
"fields": {
"balance*2": [
94318
],
"ffx": [
1839201
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "119",
"_score": 1,
"fields": {
"balance*2": [
98444
],
"ffx": [
1378216
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "126",
"_score": 1,
"fields": {
"balance*2": [
7214
],
"ffx": [
140673
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "145",
"_score": 1,
"fields": {
"balance*2": [
94812
],
"ffx": [
1516992
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "183",
"_score": 1,
"fields": {
"balance*2": [
28446
],
"ffx": [
369798
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "190",
"_score": 1,
"fields": {
"balance*2": [
6300
],
"ffx": [
94500
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "208",
"_score": 1,
"fields": {
"balance*2": [
81520
],
"ffx": [
1059760
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "222",
"_score": 1,
"fields": {
"balance*2": [
29528
],
"ffx": [
531504
]
}
}
]
}
}

6.2.7 min_score  限制最低评分得分

GET /_search
{
"min_score": 0.5,
"query" : {
"term" : { "user" : "kimchy" }
}
}

6.2.8 post_filter  后置过滤:在查询命中文档、完成聚合后,再对命中的文档进行过滤。

如:要在一次查询中查询品牌为gucci且颜色为红色的shirts,同时还要得到gucci品牌各颜色的shirts的分面统计。

创建索引并指定mappping:

PUT /shirts
{
"mappings": {
"_doc": {
"properties": {
"brand": { "type": "keyword"},
"color": { "type": "keyword"},
"model": { "type": "keyword"}
}
}
}
}

往索引里面放入文档即类似数据库里面的向表插入一行数据,并立即刷新

PUT /shirts/_doc/1?refresh
{
"brand": "gucci",
"color": "red",
"model": "slim"
}
PUT /shirts/_doc/2?refresh
{
"brand": "gucci",
"color": "green",
"model": "seec"
}

执行查询:

GET /shirts/_search
{
"query": {
"bool": {
"filter": {
"term": { "brand": "gucci" }
}
}
},
"aggs": {
"colors": {
"terms": { "field": "color" }
}
},
"post_filter": {
"term": { "color": "red" }
}
}

查询结果

{
"took": 109,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0,
"hits": [
{
"_index": "shirts",
"_type": "_doc",
"_id": "1",
"_score": 0,
"_source": {
"brand": "gucci",
"color": "red",
"model": "slim"
}
}
]
},
"aggregations": {
"colors": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "green",
"doc_count": 1
},
{
"key": "red",
"doc_count": 1
}
]
}
}
}

6.2.9 sort  排序

可以指定按一个或多个字段排序。也可通过_score指定按评分值排序,_doc 按索引顺序排序。默认是按相关性评分从高到低排序。

GET /bank/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"age": {
"order": "desc"
} },
{
"balance": {
"order": "asc"
} },
"_score"
]

}

说明:

order 值:asc、desc。如果不给定,默认是asc,_score默认是desc

查询结果:

{
"took": 181,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "549",
"_score": 1,
"_source": {
"account_number": 549,
"balance": 1932,
"firstname": "Jacqueline",
"lastname": "Maxwell",
"age": 40,
"gender": "M",
"address": "444 Schenck Place",
"employer": "Fuelworks",
"email": "jacquelinemaxwell@fuelworks.com",
"city": "Oretta",
"state": "OR"
},
"sort": [
40,
1932,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "306",
"_score": 1,
"_source": {
"account_number": 306,
"balance": 2171,
"firstname": "Hensley",
"lastname": "Hardin",
"age": 40,
"gender": "M",
"address": "196 Maujer Street",
"employer": "Neocent",
"email": "hensleyhardin@neocent.com",
"city": "Reinerton",
"state": "HI"
},
"sort": [
40,
2171,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "960",
"_score": 1,
"_source": {
"account_number": 960,
"balance": 2905,
"firstname": "Curry",
"lastname": "Vargas",
"age": 40,
"gender": "M",
"address": "242 Blake Avenue",
"employer": "Pearlesex",
"email": "curryvargas@pearlesex.com",
"city": "Henrietta",
"state": "NH"
},
"sort": [
40,
2905,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "584",
"_score": 1,
"_source": {
"account_number": 584,
"balance": 5346,
"firstname": "Pearson",
"lastname": "Bryant",
"age": 40,
"gender": "F",
"address": "971 Heyward Street",
"employer": "Anacho",
"email": "pearsonbryant@anacho.com",
"city": "Bluffview",
"state": "MN"
},
"sort": [
40,
5346,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "567",
"_score": 1,
"_source": {
"account_number": 567,
"balance": 6507,
"firstname": "Diana",
"lastname": "Dominguez",
"age": 40,
"gender": "M",
"address": "419 Albany Avenue",
"employer": "Ohmnet",
"email": "dianadominguez@ohmnet.com",
"city": "Wildwood",
"state": "TX"
},
"sort": [
40,
6507,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "938",
"_score": 1,
"_source": {
"account_number": 938,
"balance": 9597,
"firstname": "Sharron",
"lastname": "Santos",
"age": 40,
"gender": "F",
"address": "215 Matthews Place",
"employer": "Zenco",
"email": "sharronsantos@zenco.com",
"city": "Wattsville",
"state": "VT"
},
"sort": [
40,
9597,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "810",
"_score": 1,
"_source": {
"account_number": 810,
"balance": 10563,
"firstname": "Alyssa",
"lastname": "Ortega",
"age": 40,
"gender": "M",
"address": "977 Clymer Street",
"employer": "Eventage",
"email": "alyssaortega@eventage.com",
"city": "Convent",
"state": "SC"
},
"sort": [
40,
10563,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "302",
"_score": 1,
"_source": {
"account_number": 302,
"balance": 11298,
"firstname": "Isabella",
"lastname": "Hewitt",
"age": 40,
"gender": "M",
"address": "455 Bedford Avenue",
"employer": "Cincyr",
"email": "isabellahewitt@cincyr.com",
"city": "Blanford",
"state": "IN"
},
"sort": [
40,
11298,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "792",
"_score": 1,
"_source": {
"account_number": 792,
"balance": 13109,
"firstname": "Becky",
"lastname": "Jimenez",
"age": 40,
"gender": "F",
"address": "539 Front Street",
"employer": "Isologia",
"email": "beckyjimenez@isologia.com",
"city": "Summertown",
"state": "MI"
},
"sort": [
40,
13109,
1
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "495",
"_score": 1,
"_source": {
"account_number": 495,
"balance": 13478,
"firstname": "Abigail",
"lastname": "Nichols",
"age": 40,
"gender": "F",
"address": "887 President Street",
"employer": "Enquility",
"email": "abigailnichols@enquility.com",
"city": "Bagtown",
"state": "NM"
},
"sort": [
40,
13478,
1
]
}
]
}
}

结果中每个文档会有排序字段值给出

 "hits": {
"total": 1000,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "549",
"_score": 1,
"_source": {
"account_number": 549,
"balance": 1932, "age": 40, "state": "OR"
},
"sort": [
40,
1932,
1
]
}

多值字段排序

对于值是数组或多值的字段,也可进行排序,通过mode参数指定按多值的:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

PUT /my_index/_doc/1?refresh
{
"product": "chocolate",
"price": [20, 4]
} POST /_search
{
"query" : {
"term" : { "product" : "chocolate" }
},
"sort" : [
{"price" : {"order" : "asc", "mode" : "avg"}}
]
}

Missing values  缺失该字段的文档

missing 的值可以是 _last, _first

GET /_search
{
"sort" : [
{ "price" : {"missing" : "_last"} }
],
"query" : {
"term" : { "product" : "chocolate" }
}
}

 地理空间距离排序

官方文档:

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-sort.html#geo-sorting

GET /_search
{
"sort" : [
{
"_geo_distance" : {
"pin.location" : [-70, 40],
"order" : "asc",
"unit" : "km",
"mode" : "min",
"distance_type" : "arc"
}
}
]
,
"query" : {
"term" : { "user" : "kimchy" }
}
}

参数说明:

_geo_distance 距离排序关键字
pin.location是 geo_point 类型的字段
distance_type:距离计算方式 arc球面 、plane 平面。
unit: 距离单位 km 、m 默认m

Script Based Sorting 基于脚本计算的排序

GET /_search
{
"query" : {
"term" : { "user" : "kimchy" }
},
"sort" : {
"_script" : {
"type" : "number",
"script" : {
"lang": "painless",
"source": "doc['field_name'].value * params.factor",
"params" : {
"factor" : 1.1
}
},
"order" : "asc"
}
}
}

6.3.0 折叠

用 collapse指定根据某个字段对命中结果进行折叠

GET /bank/_search
{
"query": {
"match_all": {}
},
"collapse" : {
"field" : "age"
},

"sort": ["balance"]
}

查询结果:

{
"took": 56,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "820",
"_score": null,
"_source": {
"account_number": 820,
"balance": 1011,
"firstname": "Shepard",
"lastname": "Ramsey",
"age": 24,
"gender": "F",
"address": "806 Village Court",
"employer": "Mantro",
"email": "shepardramsey@mantro.com",
"city": "Tibbie",
"state": "NV"
},
"fields": {
"age": [
24
]
},
"sort": [
1011
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "894",
"_score": null,
"_source": {
"account_number": 894,
"balance": 1031,
"firstname": "Tyler",
"lastname": "Fitzgerald",
"age": 32,
"gender": "M",
"address": "787 Meserole Street",
"employer": "Jetsilk",
"email": "tylerfitzgerald@jetsilk.com",
"city": "Woodlands",
"state": "WV"
},
"fields": {
"age": [
32
]
},
"sort": [
1031
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "953",
"_score": null,
"_source": {
"account_number": 953,
"balance": 1110,
"firstname": "Baxter",
"lastname": "Black",
"age": 27,
"gender": "M",
"address": "720 Stillwell Avenue",
"employer": "Uplinx",
"email": "baxterblack@uplinx.com",
"city": "Drummond",
"state": "MN"
},
"fields": {
"age": [
27
]
},
"sort": [
1110
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "87",
"_score": null,
"_source": {
"account_number": 87,
"balance": 1133,
"firstname": "Hewitt",
"lastname": "Kidd",
"age": 22,
"gender": "M",
"address": "446 Halleck Street",
"employer": "Isologics",
"email": "hewittkidd@isologics.com",
"city": "Coalmont",
"state": "ME"
},
"fields": {
"age": [
22
]
},
"sort": [
1133
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "749",
"_score": null,
"_source": {
"account_number": 749,
"balance": 1249,
"firstname": "Rush",
"lastname": "Boyle",
"age": 36,
"gender": "M",
"address": "310 Argyle Road",
"employer": "Sportan",
"email": "rushboyle@sportan.com",
"city": "Brady",
"state": "WA"
},
"fields": {
"age": [
36
]
},
"sort": [
1249
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "315",
"_score": null,
"_source": {
"account_number": 315,
"balance": 1314,
"firstname": "Clare",
"lastname": "Morrow",
"age": 33,
"gender": "F",
"address": "728 Madeline Court",
"employer": "Gaptec",
"email": "claremorrow@gaptec.com",
"city": "Mapletown",
"state": "PA"
},
"fields": {
"age": [
33
]
},
"sort": [
1314
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "348",
"_score": null,
"_source": {
"account_number": 348,
"balance": 1360,
"firstname": "Karina",
"lastname": "Russell",
"age": 37,
"gender": "M",
"address": "797 Moffat Street",
"employer": "Limozen",
"email": "karinarussell@limozen.com",
"city": "Riegelwood",
"state": "RI"
},
"fields": {
"age": [
37
]
},
"sort": [
1360
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "490",
"_score": null,
"_source": {
"account_number": 490,
"balance": 1447,
"firstname": "Strong",
"lastname": "Hendrix",
"age": 26,
"gender": "F",
"address": "134 Beach Place",
"employer": "Duoflex",
"email": "stronghendrix@duoflex.com",
"city": "Allentown",
"state": "ND"
},
"fields": {
"age": [
26
]
},
"sort": [
1447
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "174",
"_score": null,
"_source": {
"account_number": 174,
"balance": 1464,
"firstname": "Gamble",
"lastname": "Pierce",
"age": 23,
"gender": "F",
"address": "650 Eagle Street",
"employer": "Matrixity",
"email": "gamblepierce@matrixity.com",
"city": "Abiquiu",
"state": "OR"
},
"fields": {
"age": [
23
]
},
"sort": [
1464
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "111",
"_score": null,
"_source": {
"account_number": 111,
"balance": 1481,
"firstname": "Traci",
"lastname": "Allison",
"age": 35,
"gender": "M",
"address": "922 Bryant Street",
"employer": "Enjola",
"email": "traciallison@enjola.com",
"city": "Robinette",
"state": "OR"
},
"fields": {
"age": [
35
]
},
"sort": [
1481
]
}
]
}
}

高级折叠

GET /bank/_search
{
"query": {
"match_all": {}
},
"collapse" : {
"field" : "age" ,
<!--指定inner_hits来解释折叠 -->
"inner_hits": {
"name": "details", <!-- 自命名 -->
"size": 5, <!-- 指定每组取几个文档 -->
"sort": [{ "balance": "asc" }] <!-- 组内排序 -->
},
"max_concurrent_group_searches": 4 <!-- 指定组查询的并发数 -->
},
"sort": ["balance"]
}

查询结果:

{
"took": 60,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "820",
"_score": null,
"_source": {
"account_number": 820,
"balance": 1011,
"firstname": "Shepard",
"lastname": "Ramsey",
"age": 24,
"gender": "F",
"address": "806 Village Court",
"employer": "Mantro",
"email": "shepardramsey@mantro.com",
"city": "Tibbie",
"state": "NV"
},
"fields": {
"age": [
24
]
},
"sort": [
1011
],
"inner_hits": {
"details": {
"hits": {
"total": 42,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "820",
"_score": null,
"_source": {
"account_number": 820,
"balance": 1011,
"firstname": "Shepard",
"lastname": "Ramsey",
"age": 24,
"gender": "F",
"address": "806 Village Court",
"employer": "Mantro",
"email": "shepardramsey@mantro.com",
"city": "Tibbie",
"state": "NV"
},
"sort": [
1011
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "924",
"_score": null,
"_source": {
"account_number": 924,
"balance": 3811,
"firstname": "Hilary",
"lastname": "Leonard",
"age": 24,
"gender": "M",
"address": "235 Hegeman Avenue",
"employer": "Metroz",
"email": "hilaryleonard@metroz.com",
"city": "Roosevelt",
"state": "ME"
},
"sort": [
3811
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "819",
"_score": null,
"_source": {
"account_number": 819,
"balance": 3971,
"firstname": "Karyn",
"lastname": "Medina",
"age": 24,
"gender": "F",
"address": "417 Utica Avenue",
"employer": "Qnekt",
"email": "karynmedina@qnekt.com",
"city": "Kerby",
"state": "WY"
},
"sort": [
3971
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "77",
"_score": null,
"_source": {
"account_number": 77,
"balance": 5724,
"firstname": "Byrd",
"lastname": "Conley",
"age": 24,
"gender": "F",
"address": "698 Belmont Avenue",
"employer": "Zidox",
"email": "byrdconley@zidox.com",
"city": "Rockbridge",
"state": "SC"
},
"sort": [
5724
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "493",
"_score": null,
"_source": {
"account_number": 493,
"balance": 5871,
"firstname": "Campbell",
"lastname": "Best",
"age": 24,
"gender": "M",
"address": "297 Friel Place",
"employer": "Fanfare",
"email": "campbellbest@fanfare.com",
"city": "Kidder",
"state": "GA"
},
"sort": [
5871
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "894",
"_score": null,
"_source": {
"account_number": 894,
"balance": 1031,
"firstname": "Tyler",
"lastname": "Fitzgerald",
"age": 32,
"gender": "M",
"address": "787 Meserole Street",
"employer": "Jetsilk",
"email": "tylerfitzgerald@jetsilk.com",
"city": "Woodlands",
"state": "WV"
},
"fields": {
"age": [
32
]
},
"sort": [
1031
],
"inner_hits": {
"details": {
"hits": {
"total": 52,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "894",
"_score": null,
"_source": {
"account_number": 894,
"balance": 1031,
"firstname": "Tyler",
"lastname": "Fitzgerald",
"age": 32,
"gender": "M",
"address": "787 Meserole Street",
"employer": "Jetsilk",
"email": "tylerfitzgerald@jetsilk.com",
"city": "Woodlands",
"state": "WV"
},
"sort": [
1031
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "402",
"_score": null,
"_source": {
"account_number": 402,
"balance": 1282,
"firstname": "Pacheco",
"lastname": "Rosales",
"age": 32,
"gender": "M",
"address": "538 Pershing Loop",
"employer": "Circum",
"email": "pachecorosales@circum.com",
"city": "Elbert",
"state": "ID"
},
"sort": [
1282
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "735",
"_score": null,
"_source": {
"account_number": 735,
"balance": 3984,
"firstname": "Loraine",
"lastname": "Willis",
"age": 32,
"gender": "F",
"address": "928 Grove Street",
"employer": "Gadtron",
"email": "lorainewillis@gadtron.com",
"city": "Lowgap",
"state": "NY"
},
"sort": [
3984
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "745",
"_score": null,
"_source": {
"account_number": 745,
"balance": 4572,
"firstname": "Jacobs",
"lastname": "Sweeney",
"age": 32,
"gender": "M",
"address": "189 Lott Place",
"employer": "Comtent",
"email": "jacobssweeney@comtent.com",
"city": "Advance",
"state": "NJ"
},
"sort": [
4572
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "173",
"_score": null,
"_source": {
"account_number": 173,
"balance": 5989,
"firstname": "Whitley",
"lastname": "Blevins",
"age": 32,
"gender": "M",
"address": "127 *lyn Avenue",
"employer": "Pawnagra",
"email": "whitleyblevins@pawnagra.com",
"city": "Rodanthe",
"state": "ND"
},
"sort": [
5989
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "953",
"_score": null,
"_source": {
"account_number": 953,
"balance": 1110,
"firstname": "Baxter",
"lastname": "Black",
"age": 27,
"gender": "M",
"address": "720 Stillwell Avenue",
"employer": "Uplinx",
"email": "baxterblack@uplinx.com",
"city": "Drummond",
"state": "MN"
},
"fields": {
"age": [
27
]
},
"sort": [
1110
],
"inner_hits": {
"details": {
"hits": {
"total": 39,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "953",
"_score": null,
"_source": {
"account_number": 953,
"balance": 1110,
"firstname": "Baxter",
"lastname": "Black",
"age": 27,
"gender": "M",
"address": "720 Stillwell Avenue",
"employer": "Uplinx",
"email": "baxterblack@uplinx.com",
"city": "Drummond",
"state": "MN"
},
"sort": [
1110
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "123",
"_score": null,
"_source": {
"account_number": 123,
"balance": 3079,
"firstname": "Cleo",
"lastname": "Beach",
"age": 27,
"gender": "F",
"address": "653 Haring Street",
"employer": "Proxsoft",
"email": "cleobeach@proxsoft.com",
"city": "Greensburg",
"state": "ME"
},
"sort": [
3079
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "637",
"_score": null,
"_source": {
"account_number": 637,
"balance": 3169,
"firstname": "Kathy",
"lastname": "Carter",
"age": 27,
"gender": "F",
"address": "410 Jamison Lane",
"employer": "Limage",
"email": "kathycarter@limage.com",
"city": "Ernstville",
"state": "WA"
},
"sort": [
3169
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "528",
"_score": null,
"_source": {
"account_number": 528,
"balance": 4071,
"firstname": "Thompson",
"lastname": "Hoover",
"age": 27,
"gender": "F",
"address": "580 Garden Street",
"employer": "Portalis",
"email": "thompsonhoover@portalis.com",
"city": "Knowlton",
"state": "AL"
},
"sort": [
4071
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "142",
"_score": null,
"_source": {
"account_number": 142,
"balance": 4544,
"firstname": "Vang",
"lastname": "Hughes",
"age": 27,
"gender": "M",
"address": "357 Landis Court",
"employer": "Bolax",
"email": "vanghughes@bolax.com",
"city": "Emerald",
"state": "WY"
},
"sort": [
4544
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "87",
"_score": null,
"_source": {
"account_number": 87,
"balance": 1133,
"firstname": "Hewitt",
"lastname": "Kidd",
"age": 22,
"gender": "M",
"address": "446 Halleck Street",
"employer": "Isologics",
"email": "hewittkidd@isologics.com",
"city": "Coalmont",
"state": "ME"
},
"fields": {
"age": [
22
]
},
"sort": [
1133
],
"inner_hits": {
"details": {
"hits": {
"total": 51,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "87",
"_score": null,
"_source": {
"account_number": 87,
"balance": 1133,
"firstname": "Hewitt",
"lastname": "Kidd",
"age": 22,
"gender": "M",
"address": "446 Halleck Street",
"employer": "Isologics",
"email": "hewittkidd@isologics.com",
"city": "Coalmont",
"state": "ME"
},
"sort": [
1133
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "411",
"_score": null,
"_source": {
"account_number": 411,
"balance": 1172,
"firstname": "Guzman",
"lastname": "Whitfield",
"age": 22,
"gender": "M",
"address": "181 Perry Terrace",
"employer": "Springbee",
"email": "guzmanwhitfield@springbee.com",
"city": "Balm",
"state": "IN"
},
"sort": [
1172
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "159",
"_score": null,
"_source": {
"account_number": 159,
"balance": 1696,
"firstname": "Alvarez",
"lastname": "Mack",
"age": 22,
"gender": "F",
"address": "897 Manor Court",
"employer": "Snorus",
"email": "alvarezmack@snorus.com",
"city": "Rosedale",
"state": "CA"
},
"sort": [
1696
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "220",
"_score": null,
"_source": {
"account_number": 220,
"balance": 3086,
"firstname": "Tania",
"lastname": "Middleton",
"age": 22,
"gender": "F",
"address": "541 Gunther Place",
"employer": "Zerology",
"email": "taniamiddleton@zerology.com",
"city": "Linwood",
"state": "IN"
},
"sort": [
3086
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "350",
"_score": null,
"_source": {
"account_number": 350,
"balance": 4267,
"firstname": "Wyatt",
"lastname": "Wise",
"age": 22,
"gender": "F",
"address": "896 Bleecker Street",
"employer": "Rockyard",
"email": "wyattwise@rockyard.com",
"city": "Joes",
"state": "MS"
},
"sort": [
4267
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "749",
"_score": null,
"_source": {
"account_number": 749,
"balance": 1249,
"firstname": "Rush",
"lastname": "Boyle",
"age": 36,
"gender": "M",
"address": "310 Argyle Road",
"employer": "Sportan",
"email": "rushboyle@sportan.com",
"city": "Brady",
"state": "WA"
},
"fields": {
"age": [
36
]
},
"sort": [
1249
],
"inner_hits": {
"details": {
"hits": {
"total": 52,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "749",
"_score": null,
"_source": {
"account_number": 749,
"balance": 1249,
"firstname": "Rush",
"lastname": "Boyle",
"age": 36,
"gender": "M",
"address": "310 Argyle Road",
"employer": "Sportan",
"email": "rushboyle@sportan.com",
"city": "Brady",
"state": "WA"
},
"sort": [
1249
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "427",
"_score": null,
"_source": {
"account_number": 427,
"balance": 1463,
"firstname": "Rebekah",
"lastname": "Garrison",
"age": 36,
"gender": "F",
"address": "837 Hampton Avenue",
"employer": "Niquent",
"email": "rebekahgarrison@niquent.com",
"city": "Zarephath",
"state": "NY"
},
"sort": [
1463
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "782",
"_score": null,
"_source": {
"account_number": 782,
"balance": 3960,
"firstname": "Maldonado",
"lastname": "Craig",
"age": 36,
"gender": "F",
"address": "345 Myrtle Avenue",
"employer": "Zilencio",
"email": "maldonadocraig@zilencio.com",
"city": "Yukon",
"state": "ID"
},
"sort": [
3960
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "6",
"_score": null,
"_source": {
"account_number": 6,
"balance": 5686,
"firstname": "Hattie",
"lastname": "Bond",
"age": 36,
"gender": "M",
"address": "671 Bristol Street",
"employer": "Netagy",
"email": "hattiebond@netagy.com",
"city": "Dante",
"state": "TN"
},
"sort": [
5686
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "170",
"_score": null,
"_source": {
"account_number": 170,
"balance": 6025,
"firstname": "Mann",
"lastname": "Madden",
"age": 36,
"gender": "F",
"address": "161 Radde Place",
"employer": "Farmex",
"email": "mannmadden@farmex.com",
"city": "Thermal",
"state": "LA"
},
"sort": [
6025
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "315",
"_score": null,
"_source": {
"account_number": 315,
"balance": 1314,
"firstname": "Clare",
"lastname": "Morrow",
"age": 33,
"gender": "F",
"address": "728 Madeline Court",
"employer": "Gaptec",
"email": "claremorrow@gaptec.com",
"city": "Mapletown",
"state": "PA"
},
"fields": {
"age": [
33
]
},
"sort": [
1314
],
"inner_hits": {
"details": {
"hits": {
"total": 50,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "315",
"_score": null,
"_source": {
"account_number": 315,
"balance": 1314,
"firstname": "Clare",
"lastname": "Morrow",
"age": 33,
"gender": "F",
"address": "728 Madeline Court",
"employer": "Gaptec",
"email": "claremorrow@gaptec.com",
"city": "Mapletown",
"state": "PA"
},
"sort": [
1314
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "118",
"_score": null,
"_source": {
"account_number": 118,
"balance": 2223,
"firstname": "Ballard",
"lastname": "Vasquez",
"age": 33,
"gender": "F",
"address": "101 Bush Street",
"employer": "Intergeek",
"email": "ballardvasquez@intergeek.com",
"city": "Century",
"state": "MN"
},
"sort": [
2223
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "786",
"_score": null,
"_source": {
"account_number": 786,
"balance": 3024,
"firstname": "Rene",
"lastname": "Vang",
"age": 33,
"gender": "M",
"address": "506 Randolph Street",
"employer": "Isopop",
"email": "renevang@isopop.com",
"city": "Vienna",
"state": "NJ"
},
"sort": [
3024
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "932",
"_score": null,
"_source": {
"account_number": 932,
"balance": 3111,
"firstname": "Summer",
"lastname": "Porter",
"age": 33,
"gender": "F",
"address": "949 Grand Avenue",
"employer": "Multiflex",
"email": "summerporter@multiflex.com",
"city": "Spokane",
"state": "OK"
},
"sort": [
3111
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "587",
"_score": null,
"_source": {
"account_number": 587,
"balance": 3468,
"firstname": "Carly",
"lastname": "Johns",
"age": 33,
"gender": "M",
"address": "390 Noll Street",
"employer": "Gallaxia",
"email": "carlyjohns@gallaxia.com",
"city": "Emison",
"state": "DC"
},
"sort": [
3468
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "348",
"_score": null,
"_source": {
"account_number": 348,
"balance": 1360,
"firstname": "Karina",
"lastname": "Russell",
"age": 37,
"gender": "M",
"address": "797 Moffat Street",
"employer": "Limozen",
"email": "karinarussell@limozen.com",
"city": "Riegelwood",
"state": "RI"
},
"fields": {
"age": [
37
]
},
"sort": [
1360
],
"inner_hits": {
"details": {
"hits": {
"total": 42,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "348",
"_score": null,
"_source": {
"account_number": 348,
"balance": 1360,
"firstname": "Karina",
"lastname": "Russell",
"age": 37,
"gender": "M",
"address": "797 Moffat Street",
"employer": "Limozen",
"email": "karinarussell@limozen.com",
"city": "Riegelwood",
"state": "RI"
},
"sort": [
1360
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "663",
"_score": null,
"_source": {
"account_number": 663,
"balance": 2456,
"firstname": "Rollins",
"lastname": "Richards",
"age": 37,
"gender": "M",
"address": "129 Sullivan Place",
"employer": "Geostele",
"email": "rollinsrichards@geostele.com",
"city": "Morgandale",
"state": "FL"
},
"sort": [
2456
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "699",
"_score": null,
"_source": {
"account_number": 699,
"balance": 4156,
"firstname": "Gallagher",
"lastname": "Marshall",
"age": 37,
"gender": "F",
"address": "648 Clifford Place",
"employer": "Exiand",
"email": "gallaghermarshall@exiand.com",
"city": "Belfair",
"state": "KY"
},
"sort": [
4156
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "161",
"_score": null,
"_source": {
"account_number": 161,
"balance": 4659,
"firstname": "Doreen",
"lastname": "Randall",
"age": 37,
"gender": "F",
"address": "178 Court Street",
"employer": "Calcula",
"email": "doreenrandall@calcula.com",
"city": "Belmont",
"state": "TX"
},
"sort": [
4659
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "258",
"_score": null,
"_source": {
"account_number": 258,
"balance": 5712,
"firstname": "Lindsey",
"lastname": "Hawkins",
"age": 37,
"gender": "M",
"address": "706 Frost Street",
"employer": "Enormo",
"email": "lindseyhawkins@enormo.com",
"city": "Gardners",
"state": "AK"
},
"sort": [
5712
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "490",
"_score": null,
"_source": {
"account_number": 490,
"balance": 1447,
"firstname": "Strong",
"lastname": "Hendrix",
"age": 26,
"gender": "F",
"address": "134 Beach Place",
"employer": "Duoflex",
"email": "stronghendrix@duoflex.com",
"city": "Allentown",
"state": "ND"
},
"fields": {
"age": [
26
]
},
"sort": [
1447
],
"inner_hits": {
"details": {
"hits": {
"total": 59,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "490",
"_score": null,
"_source": {
"account_number": 490,
"balance": 1447,
"firstname": "Strong",
"lastname": "Hendrix",
"age": 26,
"gender": "F",
"address": "134 Beach Place",
"employer": "Duoflex",
"email": "stronghendrix@duoflex.com",
"city": "Allentown",
"state": "ND"
},
"sort": [
1447
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "280",
"_score": null,
"_source": {
"account_number": 280,
"balance": 3380,
"firstname": "Vilma",
"lastname": "Shields",
"age": 26,
"gender": "F",
"address": "133 Berriman Street",
"employer": "Applidec",
"email": "vilmashields@applidec.com",
"city": "Adamstown",
"state": "ME"
},
"sort": [
3380
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "596",
"_score": null,
"_source": {
"account_number": 596,
"balance": 4063,
"firstname": "Letitia",
"lastname": "Walker",
"age": 26,
"gender": "F",
"address": "963 Vanderveer Place",
"employer": "Zizzle",
"email": "letitiawalker@zizzle.com",
"city": "Rossmore",
"state": "ID"
},
"sort": [
4063
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "780",
"_score": null,
"_source": {
"account_number": 780,
"balance": 4682,
"firstname": "Maryanne",
"lastname": "Hendricks",
"age": 26,
"gender": "F",
"address": "709 Wolcott Street",
"employer": "Sarasonic",
"email": "maryannehendricks@sarasonic.com",
"city": "Santel",
"state": "NH"
},
"sort": [
4682
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "405",
"_score": null,
"_source": {
"account_number": 405,
"balance": 5679,
"firstname": "Strickland",
"lastname": "Fuller",
"age": 26,
"gender": "M",
"address": "990 Concord Street",
"employer": "Digique",
"email": "stricklandfuller@digique.com",
"city": "Southmont",
"state": "NV"
},
"sort": [
5679
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "174",
"_score": null,
"_source": {
"account_number": 174,
"balance": 1464,
"firstname": "Gamble",
"lastname": "Pierce",
"age": 23,
"gender": "F",
"address": "650 Eagle Street",
"employer": "Matrixity",
"email": "gamblepierce@matrixity.com",
"city": "Abiquiu",
"state": "OR"
},
"fields": {
"age": [
23
]
},
"sort": [
1464
],
"inner_hits": {
"details": {
"hits": {
"total": 42,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "174",
"_score": null,
"_source": {
"account_number": 174,
"balance": 1464,
"firstname": "Gamble",
"lastname": "Pierce",
"age": 23,
"gender": "F",
"address": "650 Eagle Street",
"employer": "Matrixity",
"email": "gamblepierce@matrixity.com",
"city": "Abiquiu",
"state": "OR"
},
"sort": [
1464
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "110",
"_score": null,
"_source": {
"account_number": 110,
"balance": 4850,
"firstname": "Daphne",
"lastname": "Byrd",
"age": 23,
"gender": "F",
"address": "239 Conover Street",
"employer": "Freakin",
"email": "daphnebyrd@freakin.com",
"city": "Taft",
"state": "MN"
},
"sort": [
4850
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "900",
"_score": null,
"_source": {
"account_number": 900,
"balance": 6124,
"firstname": "Gonzalez",
"lastname": "Watson",
"age": 23,
"gender": "M",
"address": "624 Sullivan Street",
"employer": "Marvane",
"email": "gonzalezwatson@marvane.com",
"city": "Wikieup",
"state": "IL"
},
"sort": [
6124
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "443",
"_score": null,
"_source": {
"account_number": 443,
"balance": 7588,
"firstname": "Huff",
"lastname": "Thomas",
"age": 23,
"gender": "M",
"address": "538 Erskine Loop",
"employer": "Accufarm",
"email": "huffthomas@accufarm.com",
"city": "Corinne",
"state": "AL"
},
"sort": [
7588
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "643",
"_score": null,
"_source": {
"account_number": 643,
"balance": 8057,
"firstname": "Hendricks",
"lastname": "Stokes",
"age": 23,
"gender": "F",
"address": "142 Barbey Street",
"employer": "Remotion",
"email": "hendricksstokes@remotion.com",
"city": "Lewis",
"state": "MA"
},
"sort": [
8057
]
}
]
}
}
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "111",
"_score": null,
"_source": {
"account_number": 111,
"balance": 1481,
"firstname": "Traci",
"lastname": "Allison",
"age": 35,
"gender": "M",
"address": "922 Bryant Street",
"employer": "Enjola",
"email": "traciallison@enjola.com",
"city": "Robinette",
"state": "OR"
},
"fields": {
"age": [
35
]
},
"sort": [
1481
],
"inner_hits": {
"details": {
"hits": {
"total": 52,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "111",
"_score": null,
"_source": {
"account_number": 111,
"balance": 1481,
"firstname": "Traci",
"lastname": "Allison",
"age": 35,
"gender": "M",
"address": "922 Bryant Street",
"employer": "Enjola",
"email": "traciallison@enjola.com",
"city": "Robinette",
"state": "OR"
},
"sort": [
1481
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "417",
"_score": null,
"_source": {
"account_number": 417,
"balance": 1788,
"firstname": "Wheeler",
"lastname": "Ayers",
"age": 35,
"gender": "F",
"address": "677 Hope Street",
"employer": "Fortean",
"email": "wheelerayers@fortean.com",
"city": "Ironton",
"state": "PA"
},
"sort": [
1788
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "984",
"_score": null,
"_source": {
"account_number": 984,
"balance": 1904,
"firstname": "Viola",
"lastname": "Crawford",
"age": 35,
"gender": "F",
"address": "354 Linwood Street",
"employer": "Ginkle",
"email": "violacrawford@ginkle.com",
"city": "Witmer",
"state": "AR"
},
"sort": [
1904
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "527",
"_score": null,
"_source": {
"account_number": 527,
"balance": 2028,
"firstname": "Carver",
"lastname": "Peters",
"age": 35,
"gender": "M",
"address": "816 Victor Road",
"employer": "Housedown",
"email": "carverpeters@housedown.com",
"city": "Nadine",
"state": "MD"
},
"sort": [
2028
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "266",
"_score": null,
"_source": {
"account_number": 266,
"balance": 2777,
"firstname": "Monique",
"lastname": "Conner",
"age": 35,
"gender": "F",
"address": "489 Metrotech Courtr",
"employer": "Flotonic",
"email": "moniqueconner@flotonic.com",
"city": "Retsof",
"state": "MD"
},
"sort": [
2777
]
}
]
}
}
}
}
]
}
}

在inner_hits 中返回多个角度的组内topN

GET /twitter/_search
{
"query": {
"match": {
"message": "elasticsearch"
}
},
"collapse" : {
"field" : "user",
"inner_hits": [
{
"name": "most_liked",
"size": 3,
"sort": ["likes"]
},
{
"name": "most_recent",
"size": 3,
"sort": [{ "date": "asc" }]
}
]

},
"sort": ["likes"]
}

说明:

most_liked:最像

most_recent:最近一段时间的

 6.3.1 分页

from and size

GET /_search
{
"from" : 0, "size" : 10,
"query" : {
"term" : { "user" : "kimchy" }
}
}

注意:搜索请求耗用的堆内存和时间与 from + size 大小成正比。分页越深耗用越大,为了不因分页导致OOM或严重影响性能,ES中规定from + size 不能大于索引setting参数 index.max_result_window 的值,默认值为 10,000。

需要深度分页, 不受index.max_result_window 限制,怎么办?

Search after  在指定文档后取文档, 可用于深度分页

首次查询第一页

GET twitter/_search
{
"size": 10,
"query": {
"match" : {
"title" : "elasticsearch"
}
},
"sort": [
{"date": "asc"},
{"_id": "desc"}
]

}

后续页的查询

GET twitter/_search
{
"size": 10,
"query": {
"match" : {
"title" : "elasticsearch"
}
},
"search_after": [1463538857, "654323"],
"sort": [
{"date": "asc"},
{"_id": "desc"}
]
}

注意:使用search_after,要求查询必须指定排序,并且这个排序组合值每个文档唯一(最好排序中包含_id字段)。 search_after的值用的就是这个排序值。 用search_after时 from 只能为0、-1。

6.3.2 高亮

准备数据:

PUT /hl_test/_doc/1
{
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}

查询高亮数据

GET /hl_test/_search
{
"query": {
"match": {
"title": "lucene"
}
},
"highlight": {
"fields": {
"title": {},
"content": {}
}
}
}

查询结果:

{
"took": 113,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.2876821,
"hits": [
{
"_index": "hl_test",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
},
"highlight": {
"title": [
"<em>lucene</em> solr and elasticsearch"
]
}
}
]
}
}

多字段高亮

GET /hl_test/_search
{
"query": {
"match": {
"title": "lucene"
}
},
"highlight": {
"require_field_match": false,
"fields": {
"title": {},
"content": {}
}
}
}

查询结果:

{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.2876821,
"hits": [
{
"_index": "hl_test",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
},
"highlight": {
"title": [
"
<em>lucene</em> solr and elasticsearch"
],
"content": [
"<em>lucene</em>
solr and elasticsearch for search"
]

}
}
]
}
}

说明:

高亮结果在返回的每个文档中以hightlight节点给出

指定高亮标签

GET /hl_test/_search
{
"query": {
"match": {
"title": "lucene"
}
},
"highlight": {
"require_field_match": false,
"fields": {
"title": {
"pre_tags":["
<strong>"],
"post_tags": ["</strong>
"]
},

"content": {}
}
}
}

查询结果:

{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.2876821,
"hits": [
{
"_index": "hl_test",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
},
"highlight": {
"title": [
"<strong>lucene</strong> solr and elasticsearch"
],
"content": [
"<em>lucene</em> solr and elasticsearch for search"
]
}
}
]
}
}

高亮的详细设置请参考官网:https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-highlighting.html

6.3.3 Profile  为了调试、优化

对于执行缓慢的查询,我们很想知道它为什么慢,时间都耗在哪了,可以在查询上加入上 profile 来获得详细的执行步骤、耗时信息。

GET /twitter/_search
{
"profile": true,
"query" : {
"match" : { "message" : "some number" }
}
}

信息的说明请参考:

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-profile.html

7.  count api 查询数量

PUT /twitter/_doc/1?refresh
{
"user": "kimchy"
} GET /twitter/_doc/_count?q=user:kimchy GET /twitter/_doc/_count
{
"query" : {
"term" : { "user" : "kimchy" }
}
}

结果说明:

{
"count" : 1,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
}
}

8. validate api

用来检查我们的查询是否正确,以及查看底层生成查询是怎样的

GET twitter/_validate/query?q=user:foo

8.1 校验查询

GET twitter/_doc/_validate/query
{
"query": {
"query_string": {
"query": "post_date:foo",
"lenient": false
}
}
}

查询结果:

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
}
}

8.2 获得查询解释

GET twitter/_doc/_validate/query?explain=true
{
"query": {
"query_string": {
"query": "post_date:foo",
"lenient": false
}
}
}

查询结果

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"explanations": [
{
"index": "twitter",
"valid": true,
"explanation": """+MatchNoDocsQuery("unmapped field [post_date]") #MatchNoDocsQuery("Type list does not contain the index type")"""
}
]
}

8.3 用rewrite获得比explain 更详细的解释

GET twitter/_doc/_validate/query?rewrite=true
{
"query": {
"more_like_this": {
"like": {
"_id": "2"
},
"boost_terms": 1
}
}
}

查询结果:

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"explanations": [
{
"index": "twitter",
"valid": true,
"explanation": """+(MatchNoDocsQuery("empty BooleanQuery") -ConstantScore(MatchNoDocsQuery("empty BooleanQuery"))) #MatchNoDocsQuery("Type list does not contain the index type")"""
}
]
}

8.4 获得所有分片上的查询解释

GET twitter/_doc/_validate/query?rewrite=true&all_shards=true
{
"query": {
"match": {
"user": {
"query": "kimchy",
"fuzziness": "auto"
}
}
}
}

查询结果:

{
"valid": true,
"_shards": {
"total": 3,
"successful": 3,
"failed": 0
},
"explanations": [
{
"index": "twitter",
"shard": 0,
"valid": true,
"explanation": """MatchNoDocsQuery("unmapped field [user]")"""
},
{
"index": "twitter",
"shard": 1,
"valid": true,
"explanation": """MatchNoDocsQuery("unmapped field [user]")"""
},
{
"index": "twitter",
"shard": 2,
"valid": true,
"explanation": """MatchNoDocsQuery("unmapped field [user]")"""
}
]
}

官网链接:

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-validate.html

9. Explain api

获得某个查询的评分解释,及某个文档是否被这个查询命中

GET /twitter/_doc/0/_explain
{
"query" : {
"match" : { "message" : "elasticsearch" }
}
}

官网链接:

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-explain.html

10. Search Shards API

让我们可以了解可执行查询的索引分片节点情况

GET /twitter/_search_shards

查询结果:

{
"nodes": {
"qkmtovyLRPWjXcfDTryNwA": {
"name": "qkmtovy",
"ephemeral_id": "sxgsvzsORraAnN7PIlMYpg",
"transport_address": "127.0.0.1:9300",
"attributes": {}
}
},
"indices": {
"twitter": {}
},
"shards": [
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 0,
"index": "twitter",
"allocation_id": {
"id": "3Yf6lOjyQja_v4yP_gL8qA"
}
}
],
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 1,
"index": "twitter",
"allocation_id": {
"id": "8S88pnUkSSy8kiCcwBgb9Q"
}
}
],
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 2,
"index": "twitter",
"allocation_id": {
"id": "_uIup55LQZKaltUfuh5aFA"
}
}
]
]
}

想知道指定routing值的查询将在哪些分片节点上执行

GET /twitter/_search_shards?routing=foo,baz

查询结果:

{
"nodes": {
"qkmtovyLRPWjXcfDTryNwA": {
"name": "qkmtovy",
"ephemeral_id": "sxgsvzsORraAnN7PIlMYpg",
"transport_address": "127.0.0.1:9300",
"attributes": {}
}
},
"indices": {
"twitter": {}
},
"shards": [
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 1,
"index": "twitter",
"allocation_id": {
"id": "8S88pnUkSSy8kiCcwBgb9Q"
}
}
]
]
}

11. Search Template 查询模板

注册一个模板

POST _scripts/<templatename>
{
"script": {
"lang": "mustache",
"source": {
"query": {
"match": {
"title": "{{query_string}}"
}
}
}
}
}

使用模板进行查询

GET _search/template
{
"id": "<templateName>",
"params": {
"query_string": "search for these words"
}
}

查询结果:

{
"took": 11,
"timed_out": false,
"_shards": {
"total": 38,
"successful": 38,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}

详细了解请参考官网:

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-template.html

二、Query DSL

elasticsearch系列四:搜索详解(搜索API、Query DSL)

官网介绍链接:https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html

Query DSL 介绍

1. DSL是什么?

Domain Specific Language:领域特定语言

Elasticsearch基于JSON提供完整的查询DSL来定义查询。

一个查询可由两部分字句构成:

Leaf query clauses 叶子查询字句
Leaf query clauses 在指定的字段上查询指定的值, 如:match, term or range queries. 叶子字句可以单独使用.
Compound query clauses 复合查询字句
以逻辑方式组合多个叶子、复合查询为一个查询

2. Query and filter context

一个查询字句的行为取决于它是用在query context  还是 filter context 中 。

Query context 查询上下文
用在查询上下文中的字句回答“这个文档有多匹配这个查询?”。除了决定文档是否匹配,字句匹配的文档还会计算一个字句评分,来评定文档有多匹配。查询上下文由 query 元素表示。
Filter context 过滤上下文
过滤上下文由 filter 元素或 bool 中的 must not 表示。用在过滤上下文中的字句回答“这个文档是否匹配这个查询?”,不参与相关性评分
被频繁使用的过滤器将被ES自动缓存,来提高查询性能。

示例:

GET /_search
{
<!--查询 -->
"query": {
"bool": {
"must": [
{ "match": { "title": "Search" }},
{ "match": { "content": "Elasticsearch" }}
],
<!--过滤 -->
"filter": [
{ "term": { "status": "published" }},
{ "range": { "publish_date": { "gte": "2015-01-01" }}}
]
}
}
}

 说明:查询和过滤都是对所有文档进行查询,最后两个结果取交集

 提示:在查询上下文中使用查询子句来表示影响匹配文档得分的条件,并在过滤上下文中使用所有其他查询子句。

查询分类介绍

elasticsearch系列四:搜索详解(搜索API、Query DSL)

1. Match all query 查询所有

GET /_search
{
"query": {
"match_all": {}
}
}

相反,什么都不查

GET /_search
{
"query": {
"match_none": {}
}
}

2. Full text querys

全文查询,用于对分词的字段进行搜索。会用查询字段的分词器对查询的文本进行分词生成查询。可用于短语查询、模糊查询、前缀查询、临近查询等查询场景

官网链接:

https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html

3. match query

全文查询的标准查询,它可以对一个字段进行模糊、短语查询。 match queries 接收 text/numerics/dates, 对它们进行分词分析, 再组织成一个boolean查询。可通过operator 指定bool组合操作(or、and 默认是 or ), 以及minimum_should_match 指定至少需多少个should(or)字句需满足。还可用ananlyzer指定查询用的特殊分析器。

GET /_search
{
"query": {
"match" : {
"message" : "this is a test"
}
}
}

 说明:message是字段名

官网链接:https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html

示例:

构造索引和数据:

PUT /ftq/_doc/1
{
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
} PUT /ftq/_doc/2
{
"title": "java spring boot",
"content": "lucene is writerd by java"
}

执行查询1

GET ftq/_doc/_validate/query?rewrite=true
{
"query": {
"match": {
"title": "lucene java"
}
}
}

查询结果1:

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"explanations": [
{
"index": "ftq",
"valid": true,
"explanation": "title:lucene title:java"
}
]
}

执行查询2:

GET ftq/_search
{
"query": {
"match": {
"title": "lucene java"
}
}
}

查询结果2:

{
"took": 6,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.2876821,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.2876821,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
},
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}

执行查询3:指定操作符

GET ftq/_search
{
"query": {
"match": {
"title": {
"query": "lucene java",
"operator": "and"
}

}
}
}

查询结果3:

{
"took": 4,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}

模糊查询,最大编辑数为2

GET ftq/_search
{
"query": {
"match": {
"title": {
"query": "ucen elatic",
"fuzziness": 2
}
}
}
}

模糊查询结果:

{
"took": 280,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.14384104,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.14384104,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}

指定最少需满足两个词匹配

GET ftq/_search
{
"query": {
"match": {
"content": {
"query": "ucen elatic java",
"fuzziness": 2,
"minimum_should_match": 2
}
}
}
}

查询结果:

{
"took": 19,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.43152314,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.43152314,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
}
]
}
}

 可用max_expansions 指定模糊匹配的最大词项数,默认是50。比如:反向索引中有 100 个词项与 ucen 模糊匹配,只选用前50 个。

4. match  phrase  query

match_phrase 查询用来对一个字段进行短语查询,可以指定 analyzer、slop移动因子。

对字段进行短语查询1:

GET ftq/_search
{
"query": {
"match_phrase": {
"title": "lucene solr"
}
}
}

结果1:

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.5753642,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.5753642,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}

对字段进行短语查询2:

GET ftq/_search
{
"query": {
"match_phrase": {
"title": "lucene elasticsearch"
}
}
}

结果2:

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}

对查询指定移动因子:

GET ftq/_search
{
"query": {
"match_phrase": {
"title": {
"query": "lucene elasticsearch",
"slop": 2
}
}
}
}

查询结果:

{
"took": 2174,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.27517417,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.27517417,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}

5. match  phrase  prefix query

match_phrase_prefix 在 match_phrase 的基础上支持对短语的最后一个词进行前缀匹配

GET /_search
{
"query": {
"match_phrase_prefix" : {
"message" : "quick brown f"
}
}
}

指定前缀匹配选用的最大词项数量

GET /_search
{
"query": {
"match_phrase_prefix" : {
"message" : {
"query" : "quick brown f",
"max_expansions" : 10
}
}
}
}

6. Multi match query

如果你需要在多个字段上进行文本搜索,可用multi_match 。 multi_match在 match的基础上支持对多个字段进行文本查询。

查询1:

GET ftq/_search
{
"query": {
"multi_match" : {
"query": "lucene java",
"fields": [ "title", "content" ]
}
}
}

结果1:

{
"took": 1973,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.5753642,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.5753642,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
},
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}

查询2:字段通配符查询

GET ftq/_search
{
"query": {
"multi_match" : {
"query": "lucene java",
"fields": [ "title", "cont*" ]
}
}
}

结果2:

{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.5753642,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.5753642,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
},
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}

查询3:给字段的相关性评分加权重

GET ftq/_search?explain=true
{
"query": {
"multi_match" : {
"query": "lucene elastic",
"fields": [ "title^5", "content" ]
}
}
}

结果3:

{
"took": 6,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1.4384104,
"hits": [
{
"_shard": "[ftq][3]",
"_node": "qkmtovyLRPWjXcfDTryNwA",
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 1.4384104,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
},
"_explanation": {
"value": 1.4384104,
"description": "max of:",
"details": [
{
"value": 1.4384104,
"description": "sum of:",
"details": [
{
"value": 1.4384104,
"description": "weight(title:lucene in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 1.4384104,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 5,
"description": "boost",
"details": []
},
{
"value": 0.2876821,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 1,
"description": "docFreq",
"details": []
},
{
"value": 1,
"description": "docCount",
"details": []
}
]
},
{
"value": 1,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 4,
"description": "avgFieldLength",
"details": []
},
{
"value": 4,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
},
{
"value": 0.2876821,
"description": "sum of:",
"details": [
{
"value": 0.2876821,
"description": "weight(content:lucene in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.2876821,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.2876821,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 1,
"description": "docFreq",
"details": []
},
{
"value": 1,
"description": "docCount",
"details": []
}
]
},
{
"value": 1,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 6,
"description": "avgFieldLength",
"details": []
},
{
"value": 6,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
]
}
},
{
"_shard": "[ftq][2]",
"_node": "qkmtovyLRPWjXcfDTryNwA",
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.2876821,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
},
"_explanation": {
"value": 0.2876821,
"description": "max of:",
"details": [
{
"value": 0.2876821,
"description": "sum of:",
"details": [
{
"value": 0.2876821,
"description": "weight(content:lucene in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.2876821,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.2876821,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 1,
"description": "docFreq",
"details": []
},
{
"value": 1,
"description": "docCount",
"details": []
}
]
},
{
"value": 1,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 5,
"description": "avgFieldLength",
"details": []
},
{
"value": 5,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
]
}
}
]
}
}

7. Common terms query

common 常用词查询

问1、什么是停用词?索引时做停用词处理的目的是什么?

不再使用的词,做停用词处理的目的是提高索引的效率,去掉不需要的索引操作,即停用词不需要索引
问2、如果在索引时应用停用词处理,下面的两个查询会查询什么词项?
the brown fox—— brown fox
not happy——happy

问3、索引时应用停用词处理对搜索精度是否有影响?如果不做停用词处理又会有什么影响?如何协调这两个问题?如何保证搜索的精确度又兼顾搜索性能?

索引时应用停用词处理对搜索精度有影响,不做停用词处理又会影响索引的效率,要协调这两个问题就必须要使用tf-idf 相关性计算模型

7.1 tf-idf 相关性计算模型简介

tf:term frequency   词频 :指一个词在一篇文档中出现的频率。

如“世界杯”在文档A中出现3次,那么可以定义“世界杯”在文档A中的词频为3。请问在一篇3000字的文章中出现“世界杯”3次和一篇150字的文章中出现3词,哪篇文章更是与“世界杯”有关的。也就是说,简单用出现次数作为频率不够准确。那就用占比来表示:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

问:tf值越大是否就一定说明这个词更相关?

不是,出现太多了说明不重要

说明:tf的计算不一定非是这样的,可以定义不同的计算方式。

df:document frequency 词的文档频率 :指包含某个词的文档数(有多少文档中包含这个词)。 df越大的词越常见,哪些词会是高频词?

问1:词的df值越大说明这个词在这个文档集中是越重要还是越不重要?

越不重要

问2:词t的tf高,在文档集中的重要性也高,是否说明文档与该词越相关?举例:整个文档集中只有3篇文档中有“世界杯”,文档A中就出现了“世界杯”好几次。

不能说明文档与该词越相关

问3:如何用数值体现词t在文档集中的重要性?df可以吗?

不可以

 idf:inverse document frequency   词的逆文档频率 :用来表示词在文档集中的重要性。文档总数/ df ,df越小,词越重要,这个值会很大,那就对它取个自然对数,将值映射到一个较小的取值范围。

elasticsearch系列四:搜索详解(搜索API、Query DSL)

说明: +1 是为了避免除0(即词t在文档集中未出现的情况)

tf-idf 相关性性计算模型:tf-idf t = tf t,d * idf t

说明: tf-idf 相关性性计算模型的值为词频( tf t,d)乘以词的逆文档频率(idf t

7.2 Common terms query

common 区分常用(高频)词查询让我们可以通过cutoff_frequency来指定一个分界文档频率值,将搜索文本中的词分为高频词和低频词,低频词的重要性高于高频词,先对低频词进行搜索并计算所有匹配文档相关性得分;然后再搜索和高频词匹配的文档,这会搜到很多文档,但只对和低频词重叠的文档进行相关性得分计算(这可保证搜索精确度,同时大大提高搜索性能),和低频词累加作为文档得分。实际执行的搜索是 必须包含低频词 + 或包含高频词。

思考:这样处理下,如果用户输入的都是高频词如 “to be or not to be”结果会是怎样的?你希望是怎样的?

优化:如果都是高频词,那就对这些词进行and 查询。
进一步优化:让用户可以自己定对高频词做and/or 操作,自己定对低频词进行and/or 操作;或指定最少得多少个同时匹配

示例1:

GET /_search
{
"query": {
"common": {
"message": {
"query": "this is bonsai cool",
"cutoff_frequency": 0.001
}
}
}
}

说明:

cutoff_frequency : 值大于1表示文档数,0-1.0表示占比。 此处界定 文档频率大于 0.1%的词为高频词。

示例2:

GET /_search
{
"query": {
"common": {
"body": {
"query": "nelly the elephant as a cartoon",
"cutoff_frequency": 0.001,
"low_freq_operator": "and"
}
}
}
}
说明:low_freq_operator指定对低频词做与操作

可用参数:minimum_should_match (high_freq, low_freq), low_freq_operator (default “or”) and high_freq_operator (default “or”)、 boost and analyzer

示例3:

GET /_search
{
"query": {
"common": {
"body": {
"query": "nelly the elephant as a cartoon",
"cutoff_frequency": 0.001,
"minimum_should_match": 2
}
}
}
}

示例4:

GET /_search
{
"query": {
"common": {
"body": {
"query": "nelly the elephant not as a cartoon",
"cutoff_frequency": 0.001,
"minimum_should_match": {
"low_freq" : 2,
"high_freq" : 3
}

}
}
}
}

示例5:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

8. Query string query

query_string 查询,让我们可以直接用lucene查询语法写一个查询串进行查询,ES中接到请求后,通过查询解析器解析查询串生成对应的查询。使用它要求掌握lucene的查询语法。

示例1:指定单个字段查询

GET /_search
{
"query": {
"query_string" : {
"default_field" : "content",
"query" : "this AND that OR thus"
}
}
}

示例2:指定多字段通配符查询

GET /_search
{
"query": {
"query_string" : {
"fields" : ["content", "name.*^5"],
"query" : "this AND that OR thus"
}
}
}

可与query同用的参数,如 default_field、fields,及query 串的语法请参考:

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html

9. 查询描述规则语法(查询解析语法)

Term 词项:

单个词项的表示: 电脑
短语的表示: "联想笔记本电脑"

Field 字段:

字段名:
示例: name:“联想笔记本电脑” AND type:电脑
如果name是默认字段,则可写成: “联想笔记本电脑” AND type:电脑
如果查询串是:type:电脑 计算机 手机
注意:只有第一个是type的值,后两个则是使用默认字段。

 Term Modifiers 词项修饰符:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

elasticsearch系列四:搜索详解(搜索API、Query DSL)

elasticsearch系列四:搜索详解(搜索API、Query DSL)

elasticsearch系列四:搜索详解(搜索API、Query DSL)

10. Simple Query string query

simple_query_string 查同 query_string 查询一样用lucene查询语法写查询串,较query_string不同的地方:更小的语法集;查询串有错误,它会忽略错误的部分,不抛出错误。更适合给用户使用。

示例:

GET /_search
{
"query": {
"simple_query_string" : {
"query": "\"fried eggs\" +(eggplant | potato) -frittata",
"fields": ["title^5", "body"],
"default_operator": "and"
}
}
}

语法请参考:

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-simple-query-string-query.html

11. Term level querys

elasticsearch系列四:搜索详解(搜索API、Query DSL)

官网链接:

https://www.elastic.co/guide/en/elasticsearch/reference/current/term-level-queries.html

11.1 Term query

term 查询用于查询指定字段包含某个词项的文档。

示例1:

POST _search
{
"query": {
"term" : { "user" : "Kimchy" }
}
}

示例2:加权重

GET _search
{
"query": {
"bool": {
"should": [
{
"term": {
"status": {
"value": "urgent",
"boost": 2
}
}
},
{
"term": {
"status": "normal"
}
}
]
}
}
}

11.2 Terms query

terms 查询用于查询指定字段包含某些词项的文档

GET /_search
{
"query": {
"terms" : { "user" : ["kimchy", "elasticsearch"]}
}
}

Terms 查询支持嵌套查询的方式来获得查询词项,相当于 in (select term from other)

示例1:Terms query 嵌套查询示例

PUT /users/_doc/2
{
"followers" : ["1", "3"]
} PUT /tweets/_doc/1
{
"user" : "1"
} GET /tweets/_search
{
"query": {
"terms": {
"user": {
"index": "users",
"type": "_doc",
"id": "2",
"path": "followers"
}
}

}
}

查询结果:

{
"took": 14,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "tweets",
"_type": "_doc",
"_id": "1",
"_score": 1,
"_source": {
"user": "1"
}
}
]
}
}

嵌套查询可用参数说明:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

11.3 range query

范围查询示例1:

GET _search
{
"query": {
"range" : {
"age" : {
"gte" : 10,
"lte" : 20,
"boost" : 2.0
}
}
}
}

范围查询示例2:

GET _search
{
"query": {
"range" : {
"date" : {
"gte" : "now-1d/d",
"lt" : "now/d"

}
}
}
}

范围查询示例3:

GET _search
{
"query": {
"range" : {
"born" : {
"gte": "01/01/2012",
"lte": "2013",
"format": "dd/MM/yyyy||yyyy"
}
}
}
}

范围查询参数说明:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

范围查询时间舍入 ||说明:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

时间数学计算规则请参考:

https://www.elastic.co/guide/en/elasticsearch/reference/current/common-options.html#date-math

11.4 exists  query

查询指定字段值不为空的文档。相当 SQL 中的 column is not null

GET /_search
{
"query": {
"exists" : { "field" : "user" }
}
}

查询指定字段值为空的文档

GET /_search
{
"query": {
"bool": {
"must_not": {
"exists": {
"field": "user"
}
}
}
}
}

 11.5 prefix query 词项前缀查询

示例1:

GET /_search
{ "query": {
"prefix" : { "user" : "ki" }
}
}

示例2:加权

GET /_search
{ "query": {
"prefix" : { "user" : { "value" : "ki", "boost" : 2.0 } }
}
}

 11.6 wildcard query 通配符查询: ? *

示例1:

GET /_search
{
"query": {
"wildcard" : { "user" : "ki*y" }
}
}

示例2:加权

GET /_search
{
"query": {
"wildcard": {
"user": {
"value": "ki*y",
"boost": 2
}
}
}}

11.7  regexp query   正则查询

示例1:

GET /_search
{
"query": {
"regexp":{
"name.first": "s.*y"
}
}
}

示例2:加权

GET /_search
{
"query": {
"regexp":{
"name.first":{
"value":"s.*y",
"boost":1.2
}
}
}
}

正则语法请参考:

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-regexp-query.html#regexp-syntax

11.8 fuzzy query 模糊查询

示例1:

GET /_search
{
"query": {
"fuzzy" : { "user" : "ki" }
}
}

示例2:

GET /_search
{
"query": {
"fuzzy" : {
"user" : {
"value": "ki",
"boost": 1.0,
"fuzziness": 2,
"prefix_length": 0,
"max_expansions": 100

}
}
}
}

11.9 type query   mapping type 查询

GET /_search
{
"query": {
"type" : {
"value" : "_doc"
}
}
}

11.10 ids query   根据文档id查询

GET /_search
{
"query": {
"ids" : {
"type" : "_doc",
"values" : ["1", "4", "100"]
}
}
}

12. Compound querys 复合查询

elasticsearch系列四:搜索详解(搜索API、Query DSL)

官网链接:

https://www.elastic.co/guide/en/elasticsearch/reference/current/compound-queries.html

 12.1 Constant Score query

用来包装另一个查询,将查询匹配的文档的评分设为一个常值。

GET /_search
{
"query": {
"constant_score" : {
"filter" : {
"term" : { "user" : "kimchy"}
},
"boost" : 1.2
}
}
}

 12.2 Bool query

Bool 查询用bool操作来组合多个查询字句为一个查询。 可用的关键字:

elasticsearch系列四:搜索详解(搜索API、Query DSL)

示例:

POST _search
{
"query": {
"bool" : {
"must" : {
"term" : { "user" : "kimchy" }
},
"filter": {
"term" : { "tag" : "tech" }
},
"must_not" : {
"range" : {
"age" : { "gte" : 10, "lte" : 20 }
}
},
"should" : [
{ "term" : { "tag" : "wow" } },
{ "term" : { "tag" : "elasticsearch" } }
],
"minimum_should_match" : 1,
"boost" : 1.0
}
}
}

 说明:should满足一个或者两个或者都不满足

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