elasticsearch 笔记二:搜索DSL 语法(搜索API、Query DSL)

文章目录

  • 一、搜索 API
    • 1. 搜索 API 端点地址
    • 2. URI Search
    • 3. 查询结果说明
    • 5. 特殊的查询参数用法
    • 6. Request body Search
      • 6.1 query 元素定义查询
      • 6.2 指定返回哪些内容
        • 6.2.1 source filter 对_source 字段进行选择
        • 6.2.2 stored_fields 来指定返回哪些 stored 字段
        • 6.2.3 docValue Field 返回存储了 docValue 的字段值
        • 6.2.4 version 来指定返回文档的版本字段
        • 6.2.5 explain 返回文档的评分解释
        • 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
        • 6.2.7 min_score 限制最低评分得分
        • 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。
        • 6.2.9 sort 排序
        • 6.3.0 折叠
        • 6.3.1 [分页](https://andyoung.blog.csdn.net/article/details/104329603)
        • 6.3.2 高亮
        • 6.3.3 Profile 为了调试、优化
    • 7. count api 查询数量
    • 8. validate api
      • 8.1 校验查询
      • 8.2 获得查询解释
      • 8.3 用 rewrite 获得比 explain 更详细的解释
      • 8.4 获得所有分片上的查询解释
      • 9. Explain api
    • 10. Search Shards API
    • 11. Search Template 查询模板
  • 二、Query DSL
    • Query DSL 介绍
      • 1. DSL 是什么?
      • 2. Query and filter context
    • 查询分类介绍
      • 1. Match all query 查询所有
      • 2. Full text querys
      • 3. match query
      • 4. match phrase query
      • 5. match phrase prefix query
      • 6. Multi match query
      • 7. Common terms query
        • 7.1 tf-idf 相关性计算模型简介
        • 7.2 Common terms query
      • 8. Query string query
      • 9. 查询描述规则语法(查询解析语法)
      • 10. Simple Query string query
      • 11. Term level querys
        • 11.1 Term query
        • 11.2 Terms query
      • 12. Compound querys 复合查询
  • 参考

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:kimchyGET /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

Elasticsearch之Search API

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