一、搜索 API
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
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
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
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": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "44",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "99",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "119",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "126",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "145",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "183",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "190",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "208",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "222",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
}
]
}
}
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": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "44",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "99",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "119",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "126",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "145",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "183",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "190",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "208",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "222",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
}
]
}
}
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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
},
{
"_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,
]
}
]
}
}
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：
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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
},
{
"_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": [
]
},
"sort": [
]
}
]
}
}
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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "173",
"_score": null,
"_source": {
"account_number": 173,
"balance": 5989,
"firstname": "Whitley",
"lastname": "Blevins",
"age": 32,
"gender": "M",
"address": "127 Brooklyn Avenue",
"employer": "Pawnagra",
"email": "whitleyblevins@pawnagra.com",
"city": "Rodanthe",
"state": "ND"
},
"sort": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
},
{
"_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": [
]
},
"sort": [
],
"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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
},
{
"_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": [
]
}
]
}
}
}
}
]
}
}
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
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
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：
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。
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：
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（and or），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：
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
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：
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：
范围查询时间舍入 || 说明：
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
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：
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
参考
Elasticsearch入常用RESTful API总结文章来源：https://www.toymoban.com/news/detail-761957.html
Elasticsearch之Search API文章来源地址https://www.toymoban.com/news/detail-761957.html
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