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elasticsearch最全详细使用教程：搜索详解_es搜索

作者：小丑西瓜9 | 2024-05-06 08:51:11

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es搜索

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

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

从所有索引里面搜索字段tag为wow的记录

  1. GET /_all/_search?q=tag:wow
  2. 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 。

  1. GET /twitter/_search
  2. {
  3.     "query" : {
  4.         "term" : { "user" : "kimchy" }
  5.     }
  6. }

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。

  1. GET /_search
  2. {
  3.     "query" : {
  4.         "term" : { "user" : "kimchy" }
  5.     }
  6. }

6.2 指定返回哪些内容

6.2.1 source filter  对_source字段进行选择

  1.  
  2. GET /_search
  3. {
  4.     "_source": false,
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }

通配符查询

  1.  
  2. GET /_search
  3. {
  4.     "_source": [ "obj1.*", "obj2.*" ],
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }
  9.  
  10. GET /_search
  11. {
  12.     "_source": "obj.*",
  13.     "query" : {
  14.         "term" : { "user" : "kimchy" }
  15.     }
  16. }

包含什么不包含什么

  1.  
  2. GET /_search
  3. {
  4.     "_source": {
  5.         "includes": [ "obj1.*", "obj2.*" ],
  6.         "excludes": [ "*.description" ]
  7.     },
  8.     "query" : {
  9.         "term" : { "user" : "kimchy" }
  10.     }
  11. }

6.2.2 stored_fields 来指定返回哪些stored字段

  1.  
  2. GET /_search
  3. {
  4.     "stored_fields" : ["user", "postDate"],
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }

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

  1.  
  2. GET /_search
  3. {
  4.     "query" : {
  5.         "match_all": {}
  6.     },
  7.     "docvalue_fields" : ["test1", "test2"]
  8. }

6.2.4 version 来指定返回文档的版本字段

  1.  
  2. GET /_search
  3. {
  4.     "version": true,
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }

6.2.5 explain 返回文档的评分解释

  1.  
  2. GET /_search
  3. {
  4.     "explain": true,
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }

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

  1.  
  2. GET /bank/_search
  3. {
  4.   "query": {
  5.     "match_all": {}
  6.   },
  7.   "script_fields": {
  8.     "test1": {
  9.       "script": {
  10.         "lang": "painless",
  11.         "source": "doc['balance'].value * 2"
  12.       }
  13.     },
  14.     "test2": {
  15.       "script": {
  16.         "lang": "painless",
  17.         <!--  doc指文档-->
  18.         "source": "doc['age'].value * params.factor",
  19.         "params": {
  20.           "factor": 2
  21.         }
  22.       }
  23.     } }}

搜索结果：

  1. {
  2.   "took": 3,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 1,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "25",
  18.         "_score": 1,
  19.         "fields": {
  20.           "test1": [
  21.           ],
  22.           "test2": [
  23.           ]
  24.         }
  25.       },
  26.       {
  27.         "_index": "bank",
  28.         "_type": "_doc",
  29.         "_id": "44",
  30.         "_score": 1,
  31.         "fields": {
  32.           "test1": [
  33.           ],
  34.           "test2": [
  35.           ]
  36.         }
  37.       },
  38.       {
  39.         "_index": "bank",
  40.         "_type": "_doc",
  41.         "_id": "99",
  42.         "_score": 1,
  43.         "fields": {
  44.           "test1": [
  45.           ],
  46.           "test2": [
  47.           ]
  48.         }
  49.       },
  50.       {
  51.         "_index": "bank",
  52.         "_type": "_doc",
  53.         "_id": "119",
  54.         "_score": 1,
  55.         "fields": {
  56.           "test1": [
  57.           ],
  58.           "test2": [
  59.           ]
  60.         }
  61.       },
  62.       {
  63.         "_index": "bank",
  64.         "_type": "_doc",
  65.         "_id": "126",
  66.         "_score": 1,
  67.         "fields": {
  68.           "test1": [
  69.           ],
  70.           "test2": [
  71.           ]
  72.         }
  73.       },
  74.       {
  75.         "_index": "bank",
  76.         "_type": "_doc",
  77.         "_id": "145",
  78.         "_score": 1,
  79.         "fields": {
  80.           "test1": [
  81.           ],
  82.           "test2": [
  83.           ]
  84.         }
  85.       },
  86.       {
  87.         "_index": "bank",
  88.         "_type": "_doc",
  89.         "_id": "183",
  90.         "_score": 1,
  91.         "fields": {
  92.           "test1": [
  93.           ],
  94.           "test2": [
  95.           ]
  96.         }
  97.       },
  98.       {
  99.         "_index": "bank",
  100.         "_type": "_doc",
  101.         "_id": "190",
  102.         "_score": 1,
  103.         "fields": {
  104.           "test1": [
  105.           ],
  106.           "test2": [
  107.           ]
  108.         }
  109.       },
  110.       {
  111.         "_index": "bank",
  112.         "_type": "_doc",
  113.         "_id": "208",
  114.         "_score": 1,
  115.         "fields": {
  116.           "test1": [
  117.           ],
  118.           "test2": [
  119.           ]
  120.         }
  121.       },
  122.       {
  123.         "_index": "bank",
  124.         "_type": "_doc",
  125.         "_id": "222",
  126.         "_score": 1,
  127.         "fields": {
  128.           "test1": [
  129.           ],
  130.           "test2": [
  131.           ]
  132.         }
  133.       }
  134.     ]
  135.   }
  136. }
  1.  
  2. GET /bank/_search
  3. {
  4.   "query": {
  5.     "match_all": {}
  6.   },
  7.   "script_fields": {
  8.     "ffx": {
  9.       "script": {
  10.         "lang": "painless",
  11.         "source": "doc['age'].value * doc['balance'].value"
  12.       }
  13.     },
  14.     "balance*2": {
  15.       "script": {
  16.         "lang": "painless",
  17.         "source": "params['_source'].balance*2"
  18.       }
  19.     }
  20.   }
  21. }

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：

  1. {
  2.   "took": 26,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": 1,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "25",
  18.         "_score": 1,
  19.         "fields": {
  20.           "balance*2": [
  21.           ],
  22.           "ffx": [
  23.           ]
  24.         }
  25.       },
  26.       {
  27.         "_index": "bank",
  28.         "_type": "_doc",
  29.         "_id": "44",
  30.         "_score": 1,
  31.         "fields": {
  32.           "balance*2": [
  33.           ],
  34.           "ffx": [
  35.           ]
  36.         }
  37.       },
  38.       {
  39.         "_index": "bank",
  40.         "_type": "_doc",
  41.         "_id": "99",
  42.         "_score": 1,
  43.         "fields": {
  44.           "balance*2": [
  45.           ],
  46.           "ffx": [
  47.           ]
  48.         }
  49.       },
  50.       {
  51.         "_index": "bank",
  52.         "_type": "_doc",
  53.         "_id": "119",
  54.         "_score": 1,
  55.         "fields": {
  56.           "balance*2": [
  57.           ],
  58.           "ffx": [
  59.           ]
  60.         }
  61.       },
  62.       {
  63.         "_index": "bank",
  64.         "_type": "_doc",
  65.         "_id": "126",
  66.         "_score": 1,
  67.         "fields": {
  68.           "balance*2": [
  69.           ],
  70.           "ffx": [
  71.           ]
  72.         }
  73.       },
  74.       {
  75.         "_index": "bank",
  76.         "_type": "_doc",
  77.         "_id": "145",
  78.         "_score": 1,
  79.         "fields": {
  80.           "balance*2": [
  81.           ],
  82.           "ffx": [
  83.           ]
  84.         }
  85.       },
  86.       {
  87.         "_index": "bank",
  88.         "_type": "_doc",
  89.         "_id": "183",
  90.         "_score": 1,
  91.         "fields": {
  92.           "balance*2": [
  93.           ],
  94.           "ffx": [
  95.           ]
  96.         }
  97.       },
  98.       {
  99.         "_index": "bank",
  100.         "_type": "_doc",
  101.         "_id": "190",
  102.         "_score": 1,
  103.         "fields": {
  104.           "balance*2": [
  105.           ],
  106.           "ffx": [
  107.           ]
  108.         }
  109.       },
  110.       {
  111.         "_index": "bank",
  112.         "_type": "_doc",
  113.         "_id": "208",
  114.         "_score": 1,
  115.         "fields": {
  116.           "balance*2": [
  117.           ],
  118.           "ffx": [
  119.           ]
  120.         }
  121.       },
  122.       {
  123.         "_index": "bank",
  124.         "_type": "_doc",
  125.         "_id": "222",
  126.         "_score": 1,
  127.         "fields": {
  128.           "balance*2": [
  129.           ],
  130.           "ffx": [
  131.           ]
  132.         }
  133.       }
  134.     ]
  135.   }
  136. }

6.2.7 min_score  限制最低评分得分

  1.  
  2. GET /_search
  3. {
  4.     "min_score": 0.5,
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }

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：

  1.  
  2. PUT /shirts
  3. {
  4.     "mappings": {
  5.         "_doc": {
  6.             "properties": {
  7.                 "brand": { "type": "keyword"},
  8.                 "color": { "type": "keyword"},
  9.                 "model": { "type": "keyword"}
  10.             }
  11.         }
  12.     }
  13. }

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

  1.  
  2. PUT /shirts/_doc/1?refresh
  3. {
  4.     "brand": "gucci",
  5.     "color": "red",
  6.     "model": "slim"
  7. }
  8. PUT /shirts/_doc/2?refresh
  9. {
  10.     "brand": "gucci",
  11.     "color": "green",
  12.     "model": "seec"
  13. }

执行查询:

  1.  
  2. GET /shirts/_search
  3. {
  4.   "query": {
  5.     "bool": {
  6.       "filter": {
  7.         "term": { "brand": "gucci" } 
  8.       }
  9.     }
  10.   },
  11.   "aggs": {
  12.     "colors": {
  13.       "terms": { "field": "color" } 
  14.     }
  15.   },
  16.   "post_filter": { 
  17.     "term": { "color": "red" }
  18.   }
  19. }

查询结果

  1.  
  2. {
  3.   "took": 109,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0,
  14.     "hits": [
  15.       {
  16.         "_index": "shirts",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0,
  20.         "_source": {
  21.           "brand": "gucci",
  22.           "color": "red",
  23.           "model": "slim"
  24.         }
  25.       }
  26.     ]
  27.   },
  28.   "aggregations": {
  29.     "colors": {
  30.       "doc_count_error_upper_bound": 0,
  31.       "sum_other_doc_count": 0,
  32.       "buckets": [
  33.         {
  34.           "key": "green",
  35.           "doc_count": 1
  36.         },
  37.         {
  38.           "key": "red",
  39.           "doc_count": 1
  40.         }
  41.       ]
  42.     }
  43.   }
  44. }

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。

  1.  
  2. GET /bank/_search
  3. {
  4.   "query": {
  5.     "match_all": {}
  6.   },
  7.   "sort": [
  8.     {
  9.       "age": {
  10.         "order": "desc"
  11.       }    },
  12.     {
  13.       "balance": {
  14.         "order": "asc"
  15.       }    },
  16.     "_score"
  17.   ]
  18. }

说明：

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

查询结果：

  1. {
  2.   "took": 181,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": null,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "549",
  18.         "_score": 1,
  19.         "_source": {
  20.           "account_number": 549,
  21.           "balance": 1932,
  22.           "firstname": "Jacqueline",
  23.           "lastname": "Maxwell",
  24.           "age": 40,
  25.           "gender": "M",
  26.           "address": "444 Schenck Place",
  27.           "employer": "Fuelworks",
  28.           "email": "jacquelinemaxwell@fuelworks.com",
  29.           "city": "Oretta",
  30.           "state": "OR"
  31.         },
  32.         "sort": [
  33.           40,
  34.           1932,
  35.         ]
  36.       },
  37.       {
  38.         "_index": "bank",
  39.         "_type": "_doc",
  40.         "_id": "306",
  41.         "_score": 1,
  42.         "_source": {
  43.           "account_number": 306,
  44.           "balance": 2171,
  45.           "firstname": "Hensley",
  46.           "lastname": "Hardin",
  47.           "age": 40,
  48.           "gender": "M",
  49.           "address": "196 Maujer Street",
  50.           "employer": "Neocent",
  51.           "email": "hensleyhardin@neocent.com",
  52.           "city": "Reinerton",
  53.           "state": "HI"
  54.         },
  55.         "sort": [
  56.           40,
  57.           2171,
  58.         ]
  59.       },
  60.       {
  61.         "_index": "bank",
  62.         "_type": "_doc",
  63.         "_id": "960",
  64.         "_score": 1,
  65.         "_source": {
  66.           "account_number": 960,
  67.           "balance": 2905,
  68.           "firstname": "Curry",
  69.           "lastname": "Vargas",
  70.           "age": 40,
  71.           "gender": "M",
  72.           "address": "242 Blake Avenue",
  73.           "employer": "Pearlesex",
  74.           "email": "curryvargas@pearlesex.com",
  75.           "city": "Henrietta",
  76.           "state": "NH"
  77.         },
  78.         "sort": [
  79.           40,
  80.           2905,
  81.         ]
  82.       },
  83.       {
  84.         "_index": "bank",
  85.         "_type": "_doc",
  86.         "_id": "584",
  87.         "_score": 1,
  88.         "_source": {
  89.           "account_number": 584,
  90.           "balance": 5346,
  91.           "firstname": "Pearson",
  92.           "lastname": "Bryant",
  93.           "age": 40,
  94.           "gender": "F",
  95.           "address": "971 Heyward Street",
  96.           "employer": "Anacho",
  97.           "email": "pearsonbryant@anacho.com",
  98.           "city": "Bluffview",
  99.           "state": "MN"
  100.         },
  101.         "sort": [
  102.           40,
  103.           5346,
  104.         ]
  105.       },
  106.       {
  107.         "_index": "bank",
  108.         "_type": "_doc",
  109.         "_id": "567",
  110.         "_score": 1,
  111.         "_source": {
  112.           "account_number": 567,
  113.           "balance": 6507,
  114.           "firstname": "Diana",
  115.           "lastname": "Dominguez",
  116.           "age": 40,
  117.           "gender": "M",
  118.           "address": "419 Albany Avenue",
  119.           "employer": "Ohmnet",
  120.           "email": "dianadominguez@ohmnet.com",
  121.           "city": "Wildwood",
  122.           "state": "TX"
  123.         },
  124.         "sort": [
  125.           40,
  126.           6507,
  127.         ]
  128.       },
  129.       {
  130.         "_index": "bank",
  131.         "_type": "_doc",
  132.         "_id": "938",
  133.         "_score": 1,
  134.         "_source": {
  135.           "account_number": 938,
  136.           "balance": 9597,
  137.           "firstname": "Sharron",
  138.           "lastname": "Santos",
  139.           "age": 40,
  140.           "gender": "F",
  141.           "address": "215 Matthews Place",
  142.           "employer": "Zenco",
  143.           "email": "sharronsantos@zenco.com",
  144.           "city": "Wattsville",
  145.           "state": "VT"
  146.         },
  147.         "sort": [
  148.           40,
  149.           9597,
  150.         ]
  151.       },
  152.       {
  153.         "_index": "bank",
  154.         "_type": "_doc",
  155.         "_id": "810",
  156.         "_score": 1,
  157.         "_source": {
  158.           "account_number": 810,
  159.           "balance": 10563,
  160.           "firstname": "Alyssa",
  161.           "lastname": "Ortega",
  162.           "age": 40,
  163.           "gender": "M",
  164.           "address": "977 Clymer Street",
  165.           "employer": "Eventage",
  166.           "email": "alyssaortega@eventage.com",
  167.           "city": "Convent",
  168.           "state": "SC"
  169.         },
  170.         "sort": [
  171.           40,
  172.           10563,
  173.         ]
  174.       },
  175.       {
  176.         "_index": "bank",
  177.         "_type": "_doc",
  178.         "_id": "302",
  179.         "_score": 1,
  180.         "_source": {
  181.           "account_number": 302,
  182.           "balance": 11298,
  183.           "firstname": "Isabella",
  184.           "lastname": "Hewitt",
  185.           "age": 40,
  186.           "gender": "M",
  187.           "address": "455 Bedford Avenue",
  188.           "employer": "Cincyr",
  189.           "email": "isabellahewitt@cincyr.com",
  190.           "city": "Blanford",
  191.           "state": "IN"
  192.         },
  193.         "sort": [
  194.           40,
  195.           11298,
  196.         ]
  197.       },
  198.       {
  199.         "_index": "bank",
  200.         "_type": "_doc",
  201.         "_id": "792",
  202.         "_score": 1,
  203.         "_source": {
  204.           "account_number": 792,
  205.           "balance": 13109,
  206.           "firstname": "Becky",
  207.           "lastname": "Jimenez",
  208.           "age": 40,
  209.           "gender": "F",
  210.           "address": "539 Front Street",
  211.           "employer": "Isologia",
  212.           "email": "beckyjimenez@isologia.com",
  213.           "city": "Summertown",
  214.           "state": "MI"
  215.         },
  216.         "sort": [
  217.           40,
  218.           13109,
  219.         ]
  220.       },
  221.       {
  222.         "_index": "bank",
  223.         "_type": "_doc",
  224.         "_id": "495",
  225.         "_score": 1,
  226.         "_source": {
  227.           "account_number": 495,
  228.           "balance": 13478,
  229.           "firstname": "Abigail",
  230.           "lastname": "Nichols",
  231.           "age": 40,
  232.           "gender": "F",
  233.           "address": "887 President Street",
  234.           "employer": "Enquility",
  235.           "email": "abigailnichols@enquility.com",
  236.           "city": "Bagtown",
  237.           "state": "NM"
  238.         },
  239.         "sort": [
  240.           40,
  241.           13478,
  242.         ]
  243.       }
  244.     ]
  245.   }
  246. }

结果中每个文档会有排序字段值给出

  1.  
  2.  "hits": {
  3.     "total": 1000,
  4.     "max_score": null,
  5.     "hits": [
  6.       {
  7.         "_index": "bank",
  8.         "_type": "_doc",
  9.         "_id": "549",
  10.         "_score": 1,
  11.         "_source": {
  12.           "account_number": 549,
  13.           "balance": 1932, "age": 40, "state": "OR"
  14.         },
  15.         "sort": [
  16.           40,
  17.           1932,
  18.           1
  19.         ]    
  20. }

 

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：

  1.  
  2. PUT /my_index/_doc/1?refresh
  3. {
  4.    "product": "chocolate",
  5.    "price": [20, 4]
  6. }
  7.  
  8. POST /_search
  9. {
  10.    "query" : {
  11.       "term" : { "product" : "chocolate" }
  12.    },
  13.    "sort" : [
  14.       {"price" : {"order" : "asc", "mode" : "avg"}}
  15.    ]
  16. }

 Missing values  缺失该字段的文档

missing 的值可以是 _last, _first

  1.  
  2. GET /_search
  3. {
  4.     "sort" : [
  5.         { "price" : {"missing" : "_last"} }
  6.     ],
  7.     "query" : {
  8.         "term" : { "product" : "chocolate" }
  9.     }
  10. }

 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

  1.  
  2. GET /_search
  3. {
  4.     "sort" : [
  5.         {
  6.             "_geo_distance" : {
  7.                 "pin.location" : [-70, 40],
  8.                 "order" : "asc",
  9.                 "unit" : "km",
  10.                 "mode" : "min",
  11.                 "distance_type" : "arc"
  12.             }
  13.         }
  14.     ],
  15.     "query" : {
  16.         "term" : { "user" : "kimchy" }
  17.     }
  18. }

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

  1.  
  2. GET /_search
  3. {
  4.     "query" : {
  5.         "term" : { "user" : "kimchy" }
  6.     },
  7.     "sort" : {
  8.         "_script" : {
  9.             "type" : "number",
  10.             "script" : {
  11.                 "lang": "painless",
  12.                 "source": "doc['field_name'].value * params.factor",
  13.                 "params" : {
  14.                     "factor" : 1.1
  15.                 }
  16.             },
  17.             "order" : "asc"
  18.         }
  19.     }
  20. }
  21.  
  22.

 6.3.0 折叠用 collapse指定根据某个字段对命中结果进行折叠

  1.  
  2. GET /bank/_search
  3. {
  4.     "query": {
  5.         "match_all": {}
  6.     },
  7.     "collapse" : {
  8.         "field" : "age" 
  9.     },
  10.     "sort": ["balance"] 
  11. }

 查询结果：

  1. {
  2.   "took": 56,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": null,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "820",
  18.         "_score": null,
  19.         "_source": {
  20.           "account_number": 820,
  21.           "balance": 1011,
  22.           "firstname": "Shepard",
  23.           "lastname": "Ramsey",
  24.           "age": 24,
  25.           "gender": "F",
  26.           "address": "806 Village Court",
  27.           "employer": "Mantro",
  28.           "email": "shepardramsey@mantro.com",
  29.           "city": "Tibbie",
  30.           "state": "NV"
  31.         },
  32.         "fields": {
  33.           "age": [
  34.           ]
  35.         },
  36.         "sort": [
  37.         ]
  38.       },
  39.       {
  40.         "_index": "bank",
  41.         "_type": "_doc",
  42.         "_id": "894",
  43.         "_score": null,
  44.         "_source": {
  45.           "account_number": 894,
  46.           "balance": 1031,
  47.           "firstname": "Tyler",
  48.           "lastname": "Fitzgerald",
  49.           "age": 32,
  50.           "gender": "M",
  51.           "address": "787 Meserole Street",
  52.           "employer": "Jetsilk",
  53.           "email": "tylerfitzgerald@jetsilk.com",
  54.           "city": "Woodlands",
  55.           "state": "WV"
  56.         },
  57.         "fields": {
  58.           "age": [
  59.           ]
  60.         },
  61.         "sort": [
  62.         ]
  63.       },
  64.       {
  65.         "_index": "bank",
  66.         "_type": "_doc",
  67.         "_id": "953",
  68.         "_score": null,
  69.         "_source": {
  70.           "account_number": 953,
  71.           "balance": 1110,
  72.           "firstname": "Baxter",
  73.           "lastname": "Black",
  74.           "age": 27,
  75.           "gender": "M",
  76.           "address": "720 Stillwell Avenue",
  77.           "employer": "Uplinx",
  78.           "email": "baxterblack@uplinx.com",
  79.           "city": "Drummond",
  80.           "state": "MN"
  81.         },
  82.         "fields": {
  83.           "age": [
  84.           ]
  85.         },
  86.         "sort": [
  87.         ]
  88.       },
  89.       {
  90.         "_index": "bank",
  91.         "_type": "_doc",
  92.         "_id": "87",
  93.         "_score": null,
  94.         "_source": {
  95.           "account_number": 87,
  96.           "balance": 1133,
  97.           "firstname": "Hewitt",
  98.           "lastname": "Kidd",
  99.           "age": 22,
  100.           "gender": "M",
  101.           "address": "446 Halleck Street",
  102.           "employer": "Isologics",
  103.           "email": "hewittkidd@isologics.com",
  104.           "city": "Coalmont",
  105.           "state": "ME"
  106.         },
  107.         "fields": {
  108.           "age": [
  109.           ]
  110.         },
  111.         "sort": [
  112.         ]
  113.       },
  114.       {
  115.         "_index": "bank",
  116.         "_type": "_doc",
  117.         "_id": "749",
  118.         "_score": null,
  119.         "_source": {
  120.           "account_number": 749,
  121.           "balance": 1249,
  122.           "firstname": "Rush",
  123.           "lastname": "Boyle",
  124.           "age": 36,
  125.           "gender": "M",
  126.           "address": "310 Argyle Road",
  127.           "employer": "Sportan",
  128.           "email": "rushboyle@sportan.com",
  129.           "city": "Brady",
  130.           "state": "WA"
  131.         },
  132.         "fields": {
  133.           "age": [
  134.           ]
  135.         },
  136.         "sort": [
  137.         ]
  138.       },
  139.       {
  140.         "_index": "bank",
  141.         "_type": "_doc",
  142.         "_id": "315",
  143.         "_score": null,
  144.         "_source": {
  145.           "account_number": 315,
  146.           "balance": 1314,
  147.           "firstname": "Clare",
  148.           "lastname": "Morrow",
  149.           "age": 33,
  150.           "gender": "F",
  151.           "address": "728 Madeline Court",
  152.           "employer": "Gaptec",
  153.           "email": "claremorrow@gaptec.com",
  154.           "city": "Mapletown",
  155.           "state": "PA"
  156.         },
  157.         "fields": {
  158.           "age": [
  159.           ]
  160.         },
  161.         "sort": [
  162.         ]
  163.       },
  164.       {
  165.         "_index": "bank",
  166.         "_type": "_doc",
  167.         "_id": "348",
  168.         "_score": null,
  169.         "_source": {
  170.           "account_number": 348,
  171.           "balance": 1360,
  172.           "firstname": "Karina",
  173.           "lastname": "Russell",
  174.           "age": 37,
  175.           "gender": "M",
  176.           "address": "797 Moffat Street",
  177.           "employer": "Limozen",
  178.           "email": "karinarussell@limozen.com",
  179.           "city": "Riegelwood",
  180.           "state": "RI"
  181.         },
  182.         "fields": {
  183.           "age": [
  184.           ]
  185.         },
  186.         "sort": [
  187.         ]
  188.       },
  189.       {
  190.         "_index": "bank",
  191.         "_type": "_doc",
  192.         "_id": "490",
  193.         "_score": null,
  194.         "_source": {
  195.           "account_number": 490,
  196.           "balance": 1447,
  197.           "firstname": "Strong",
  198.           "lastname": "Hendrix",
  199.           "age": 26,
  200.           "gender": "F",
  201.           "address": "134 Beach Place",
  202.           "employer": "Duoflex",
  203.           "email": "stronghendrix@duoflex.com",
  204.           "city": "Allentown",
  205.           "state": "ND"
  206.         },
  207.         "fields": {
  208.           "age": [
  209.           ]
  210.         },
  211.         "sort": [
  212.         ]
  213.       },
  214.       {
  215.         "_index": "bank",
  216.         "_type": "_doc",
  217.         "_id": "174",
  218.         "_score": null,
  219.         "_source": {
  220.           "account_number": 174,
  221.           "balance": 1464,
  222.           "firstname": "Gamble",
  223.           "lastname": "Pierce",
  224.           "age": 23,
  225.           "gender": "F",
  226.           "address": "650 Eagle Street",
  227.           "employer": "Matrixity",
  228.           "email": "gamblepierce@matrixity.com",
  229.           "city": "Abiquiu",
  230.           "state": "OR"
  231.         },
  232.         "fields": {
  233.           "age": [
  234.           ]
  235.         },
  236.         "sort": [
  237.         ]
  238.       },
  239.       {
  240.         "_index": "bank",
  241.         "_type": "_doc",
  242.         "_id": "111",
  243.         "_score": null,
  244.         "_source": {
  245.           "account_number": 111,
  246.           "balance": 1481,
  247.           "firstname": "Traci",
  248.           "lastname": "Allison",
  249.           "age": 35,
  250.           "gender": "M",
  251.           "address": "922 Bryant Street",
  252.           "employer": "Enjola",
  253.           "email": "traciallison@enjola.com",
  254.           "city": "Robinette",
  255.           "state": "OR"
  256.         },
  257.         "fields": {
  258.           "age": [
  259.           ]
  260.         },
  261.         "sort": [
  262.         ]
  263.       }
  264.     ]
  265.   }
  266. }

 高级折叠

  1.  
  2. GET /bank/_search
  3. {
  4.     "query": {
  5.         "match_all": {}
  6.     },
  7.     "collapse" : {
  8.         "field" : "age" ,
  9.         <!--指定inner_hits来解释折叠 -->
  10.         "inner_hits": {
  11.             "name": "details", <!-- 自命名 -->
  12.             "size": 5,   <!-- 指定每组取几个文档 -->
  13.             "sort": [{ "balance": "asc" }] <!-- 组内排序 -->
  14.         },
  15.         "max_concurrent_group_searches": 4 <!-- 指定组查询的并发数 -->
  16.     },
  17.     "sort": ["balance"] 
  18. }

 查询结果：

  1. {
  2.   "took": 60,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 1000,
  12.     "max_score": null,
  13.     "hits": [
  14.       {
  15.         "_index": "bank",
  16.         "_type": "_doc",
  17.         "_id": "820",
  18.         "_score": null,
  19.         "_source": {
  20.           "account_number": 820,
  21.           "balance": 1011,
  22.           "firstname": "Shepard",
  23.           "lastname": "Ramsey",
  24.           "age": 24,
  25.           "gender": "F",
  26.           "address": "806 Village Court",
  27.           "employer": "Mantro",
  28.           "email": "shepardramsey@mantro.com",
  29.           "city": "Tibbie",
  30.           "state": "NV"
  31.         },
  32.         "fields": {
  33.           "age": [
  34.           ]
  35.         },
  36.         "sort": [
  37.         ],
  38.         "inner_hits": {
  39.           "details": {
  40.             "hits": {
  41.               "total": 42,
  42.               "max_score": null,
  43.               "hits": [
  44.                 {
  45.                   "_index": "bank",
  46.                   "_type": "_doc",
  47.                   "_id": "820",
  48.                   "_score": null,
  49.                   "_source": {
  50.                     "account_number": 820,
  51.                     "balance": 1011,
  52.                     "firstname": "Shepard",
  53.                     "lastname": "Ramsey",
  54.                     "age": 24,
  55.                     "gender": "F",
  56.                     "address": "806 Village Court",
  57.                     "employer": "Mantro",
  58.                     "email": "shepardramsey@mantro.com",
  59.                     "city": "Tibbie",
  60.                     "state": "NV"
  61.                   },
  62.                   "sort": [
  63.                   ]
  64.                 },
  65.                 {
  66.                   "_index": "bank",
  67.                   "_type": "_doc",
  68.                   "_id": "924",
  69.                   "_score": null,
  70.                   "_source": {
  71.                     "account_number": 924,
  72.                     "balance": 3811,
  73.                     "firstname": "Hilary",
  74.                     "lastname": "Leonard",
  75.                     "age": 24,
  76.                     "gender": "M",
  77.                     "address": "235 Hegeman Avenue",
  78.                     "employer": "Metroz",
  79.                     "email": "hilaryleonard@metroz.com",
  80.                     "city": "Roosevelt",
  81.                     "state": "ME"
  82.                   },
  83.                   "sort": [
  84.                   ]
  85.                 },
  86.                 {
  87.                   "_index": "bank",
  88.                   "_type": "_doc",
  89.                   "_id": "819",
  90.                   "_score": null,
  91.                   "_source": {
  92.                     "account_number": 819,
  93.                     "balance": 3971,
  94.                     "firstname": "Karyn",
  95.                     "lastname": "Medina",
  96.                     "age": 24,
  97.                     "gender": "F",
  98.                     "address": "417 Utica Avenue",
  99.                     "employer": "Qnekt",
  100.                     "email": "karynmedina@qnekt.com",
  101.                     "city": "Kerby",
  102.                     "state": "WY"
  103.                   },
  104.                   "sort": [
  105.                   ]
  106.                 },
  107.                 {
  108.                   "_index": "bank",
  109.                   "_type": "_doc",
  110.                   "_id": "77",
  111.                   "_score": null,
  112.                   "_source": {
  113.                     "account_number": 77,
  114.                     "balance": 5724,
  115.                     "firstname": "Byrd",
  116.                     "lastname": "Conley",
  117.                     "age": 24,
  118.                     "gender": "F",
  119.                     "address": "698 Belmont Avenue",
  120.                     "employer": "Zidox",
  121.                     "email": "byrdconley@zidox.com",
  122.                     "city": "Rockbridge",
  123.                     "state": "SC"
  124.                   },
  125.                   "sort": [
  126.                   ]
  127.                 },
  128.                 {
  129.                   "_index": "bank",
  130.                   "_type": "_doc",
  131.                   "_id": "493",
  132.                   "_score": null,
  133.                   "_source": {
  134.                     "account_number": 493,
  135.                     "balance": 5871,
  136.                     "firstname": "Campbell",
  137.                     "lastname": "Best",
  138.                     "age": 24,
  139.                     "gender": "M",
  140.                     "address": "297 Friel Place",
  141.                     "employer": "Fanfare",
  142.                     "email": "campbellbest@fanfare.com",
  143.                     "city": "Kidder",
  144.                     "state": "GA"
  145.                   },
  146.                   "sort": [
  147.                   ]
  148.                 }
  149.               ]
  150.             }
  151.           }
  152.         }
  153.       },
  154.       {
  155.         "_index": "bank",
  156.         "_type": "_doc",
  157.         "_id": "894",
  158.         "_score": null,
  159.         "_source": {
  160.           "account_number": 894,
  161.           "balance": 1031,
  162.           "firstname": "Tyler",
  163.           "lastname": "Fitzgerald",
  164.           "age": 32,
  165.           "gender": "M",
  166.           "address": "787 Meserole Street",
  167.           "employer": "Jetsilk",
  168.           "email": "tylerfitzgerald@jetsilk.com",
  169.           "city": "Woodlands",
  170.           "state": "WV"
  171.         },
  172.         "fields": {
  173.           "age": [
  174.           ]
  175.         },
  176.         "sort": [
  177.         ],
  178.         "inner_hits": {
  179.           "details": {
  180.             "hits": {
  181.               "total": 52,
  182.               "max_score": null,
  183.               "hits": [
  184.                 {
  185.                   "_index": "bank",
  186.                   "_type": "_doc",
  187.                   "_id": "894",
  188.                   "_score": null,
  189.                   "_source": {
  190.                     "account_number": 894,
  191.                     "balance": 1031,
  192.                     "firstname": "Tyler",
  193.                     "lastname": "Fitzgerald",
  194.                     "age": 32,
  195.                     "gender": "M",
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  895.                     "gender": "M",
  896.                     "address": "797 Moffat Street",
  897.                     "employer": "Limozen",
  898.                     "email": "karinarussell@limozen.com",
  899.                     "city": "Riegelwood",
  900.                     "state": "RI"
  901.                   },
  902.                   "sort": [
  903.                   ]
  904.                 },
  905.                 {
  906.                   "_index": "bank",
  907.                   "_type": "_doc",
  908.                   "_id": "663",
  909.                   "_score": null,
  910.                   "_source": {
  911.                     "account_number": 663,
  912.                     "balance": 2456,
  913.                     "firstname": "Rollins",
  914.                     "lastname": "Richards",
  915.                     "age": 37,
  916.                     "gender": "M",
  917.                     "address": "129 Sullivan Place",
  918.                     "employer": "Geostele",
  919.                     "email": "rollinsrichards@geostele.com",
  920.                     "city": "Morgandale",
  921.                     "state": "FL"
  922.                   },
  923.                   "sort": [
  924.                   ]
  925.                 },
  926.                 {
  927.                   "_index": "bank",
  928.                   "_type": "_doc",
  929.                   "_id": "699",
  930.                   "_score": null,
  931.                   "_source": {
  932.                     "account_number": 699,
  933.                     "balance": 4156,
  934.                     "firstname": "Gallagher",
  935.                     "lastname": "Marshall",
  936.                     "age": 37,
  937.                     "gender": "F",
  938.                     "address": "648 Clifford Place",
  939.                     "employer": "Exiand",
  940.                     "email": "gallaghermarshall@exiand.com",
  941.                     "city": "Belfair",
  942.                     "state": "KY"
  943.                   },
  944.                   "sort": [
  945.                   ]
  946.                 },
  947.                 {
  948.                   "_index": "bank",
  949.                   "_type": "_doc",
  950.                   "_id": "161",
  951.                   "_score": null,
  952.                   "_source": {
  953.                     "account_number": 161,
  954.                     "balance": 4659,
  955.                     "firstname": "Doreen",
  956.                     "lastname": "Randall",
  957.                     "age": 37,
  958.                     "gender": "F",
  959.                     "address": "178 Court Street",
  960.                     "employer": "Calcula",
  961.                     "email": "doreenrandall@calcula.com",
  962.                     "city": "Belmont",
  963.                     "state": "TX"
  964.                   },
  965.                   "sort": [
  966.                   ]
  967.                 },
  968.                 {
  969.                   "_index": "bank",
  970.                   "_type": "_doc",
  971.                   "_id": "258",
  972.                   "_score": null,
  973.                   "_source": {
  974.                     "account_number": 258,
  975.                     "balance": 5712,
  976.                     "firstname": "Lindsey",
  977.                     "lastname": "Hawkins",
  978.                     "age": 37,
  979.                     "gender": "M",
  980.                     "address": "706 Frost Street",
  981.                     "employer": "Enormo",
  982.                     "email": "lindseyhawkins@enormo.com",
  983.                     "city": "Gardners",
  984.                     "state": "AK"
  985.                   },
  986.                   "sort": [
  987.                   ]
  988.                 }
  989.               ]
  990.             }
  991.           }
  992.         }
  993.       },
  994.       {
  995.         "_index": "bank",
  996.         "_type": "_doc",
  997.         "_id": "490",
  998.         "_score": null,
  999.         "_source": {
  1000.           "account_number": 490,
  1001.           "balance": 1447,
  1002.           "firstname": "Strong",
  1003.           "lastname": "Hendrix",
  1004.           "age": 26,
  1005.           "gender": "F",
  1006.           "address": "134 Beach Place",
  1007.           "employer": "Duoflex",
  1008.           "email": "stronghendrix@duoflex.com",
  1009.           "city": "Allentown",
  1010.           "state": "ND"
  1011.         },
  1012.         "fields": {
  1013.           "age": [
  1014.           ]
  1015.         },
  1016.         "sort": [
  1017.         ],
  1018.         "inner_hits": {
  1019.           "details": {
  1020.             "hits": {
  1021.               "total": 59,
  1022.               "max_score": null,
  1023.               "hits": [
  1024.                 {
  1025.                   "_index": "bank",
  1026.                   "_type": "_doc",
  1027.                   "_id": "490",
  1028.                   "_score": null,
  1029.                   "_source": {
  1030.                     "account_number": 490,
  1031.                     "balance": 1447,
  1032.                     "firstname": "Strong",
  1033.                     "lastname": "Hendrix",
  1034.                     "age": 26,
  1035.                     "gender": "F",
  1036.                     "address": "134 Beach Place",
  1037.                     "employer": "Duoflex",
  1038.                     "email": "stronghendrix@duoflex.com",
  1039.                     "city": "Allentown",
  1040.                     "state": "ND"
  1041.                   },
  1042.                   "sort": [
  1043.                   ]
  1044.                 },
  1045.                 {
  1046.                   "_index": "bank",
  1047.                   "_type": "_doc",
  1048.                   "_id": "280",
  1049.                   "_score": null,
  1050.                   "_source": {
  1051.                     "account_number": 280,
  1052.                     "balance": 3380,
  1053.                     "firstname": "Vilma",
  1054.                     "lastname": "Shields",
  1055.                     "age": 26,
  1056.                     "gender": "F",
  1057.                     "address": "133 Berriman Street",
  1058.                     "employer": "Applidec",
  1059.                     "email": "vilmashields@applidec.com",
  1060.                     "city": "Adamstown",
  1061.                     "state": "ME"
  1062.                   },
  1063.                   "sort": [
  1064.                   ]
  1065.                 },
  1066.                 {
  1067.                   "_index": "bank",
  1068.                   "_type": "_doc",
  1069.                   "_id": "596",
  1070.                   "_score": null,
  1071.                   "_source": {
  1072.                     "account_number": 596,
  1073.                     "balance": 4063,
  1074.                     "firstname": "Letitia",
  1075.                     "lastname": "Walker",
  1076.                     "age": 26,
  1077.                     "gender": "F",
  1078.                     "address": "963 Vanderveer Place",
  1079.                     "employer": "Zizzle",
  1080.                     "email": "letitiawalker@zizzle.com",
  1081.                     "city": "Rossmore",
  1082.                     "state": "ID"
  1083.                   },
  1084.                   "sort": [
  1085.                   ]
  1086.                 },
  1087.                 {
  1088.                   "_index": "bank",
  1089.                   "_type": "_doc",
  1090.                   "_id": "780",
  1091.                   "_score": null,
  1092.                   "_source": {
  1093.                     "account_number": 780,
  1094.                     "balance": 4682,
  1095.                     "firstname": "Maryanne",
  1096.                     "lastname": "Hendricks",
  1097.                     "age": 26,
  1098.                     "gender": "F",
  1099.                     "address": "709 Wolcott Street",
  1100.                     "employer": "Sarasonic",
  1101.                     "email": "maryannehendricks@sarasonic.com",
  1102.                     "city": "Santel",
  1103.                     "state": "NH"
  1104.                   },
  1105.                   "sort": [
  1106.                   ]
  1107.                 },
  1108.                 {
  1109.                   "_index": "bank",
  1110.                   "_type": "_doc",
  1111.                   "_id": "405",
  1112.                   "_score": null,
  1113.                   "_source": {
  1114.                     "account_number": 405,
  1115.                     "balance": 5679,
  1116.                     "firstname": "Strickland",
  1117.                     "lastname": "Fuller",
  1118.                     "age": 26,
  1119.                     "gender": "M",
  1120.                     "address": "990 Concord Street",
  1121.                     "employer": "Digique",
  1122.                     "email": "stricklandfuller@digique.com",
  1123.                     "city": "Southmont",
  1124.                     "state": "NV"
  1125.                   },
  1126.                   "sort": [
  1127.                   ]
  1128.                 }
  1129.               ]
  1130.             }
  1131.           }
  1132.         }
  1133.       },
  1134.       {
  1135.         "_index": "bank",
  1136.         "_type": "_doc",
  1137.         "_id": "174",
  1138.         "_score": null,
  1139.         "_source": {
  1140.           "account_number": 174,
  1141.           "balance": 1464,
  1142.           "firstname": "Gamble",
  1143.           "lastname": "Pierce",
  1144.           "age": 23,
  1145.           "gender": "F",
  1146.           "address": "650 Eagle Street",
  1147.           "employer": "Matrixity",
  1148.           "email": "gamblepierce@matrixity.com",
  1149.           "city": "Abiquiu",
  1150.           "state": "OR"
  1151.         },
  1152.         "fields": {
  1153.           "age": [
  1154.           ]
  1155.         },
  1156.         "sort": [
  1157.         ],
  1158.         "inner_hits": {
  1159.           "details": {
  1160.             "hits": {
  1161.               "total": 42,
  1162.               "max_score": null,
  1163.               "hits": [
  1164.                 {
  1165.                   "_index": "bank",
  1166.                   "_type": "_doc",
  1167.                   "_id": "174",
  1168.                   "_score": null,
  1169.                   "_source": {
  1170.                     "account_number": 174,
  1171.                     "balance": 1464,
  1172.                     "firstname": "Gamble",
  1173.                     "lastname": "Pierce",
  1174.                     "age": 23,
  1175.                     "gender": "F",
  1176.                     "address": "650 Eagle Street",
  1177.                     "employer": "Matrixity",
  1178.                     "email": "gamblepierce@matrixity.com",
  1179.                     "city": "Abiquiu",
  1180.                     "state": "OR"
  1181.                   },
  1182.                   "sort": [
  1183.                   ]
  1184.                 },
  1185.                 {
  1186.                   "_index": "bank",
  1187.                   "_type": "_doc",
  1188.                   "_id": "110",
  1189.                   "_score": null,
  1190.                   "_source": {
  1191.                     "account_number": 110,
  1192.                     "balance": 4850,
  1193.                     "firstname": "Daphne",
  1194.                     "lastname": "Byrd",
  1195.                     "age": 23,
  1196.                     "gender": "F",
  1197.                     "address": "239 Conover Street",
  1198.                     "employer": "Freakin",
  1199.                     "email": "daphnebyrd@freakin.com",
  1200.                     "city": "Taft",
  1201.                     "state": "MN"
  1202.                   },
  1203.                   "sort": [
  1204.                   ]
  1205.                 },
  1206.                 {
  1207.                   "_index": "bank",
  1208.                   "_type": "_doc",
  1209.                   "_id": "900",
  1210.                   "_score": null,
  1211.                   "_source": {
  1212.                     "account_number": 900,
  1213.                     "balance": 6124,
  1214.                     "firstname": "Gonzalez",
  1215.                     "lastname": "Watson",
  1216.                     "age": 23,
  1217.                     "gender": "M",
  1218.                     "address": "624 Sullivan Street",
  1219.                     "employer": "Marvane",
  1220.                     "email": "gonzalezwatson@marvane.com",
  1221.                     "city": "Wikieup",
  1222.                     "state": "IL"
  1223.                   },
  1224.                   "sort": [
  1225.                   ]
  1226.                 },
  1227.                 {
  1228.                   "_index": "bank",
  1229.                   "_type": "_doc",
  1230.                   "_id": "443",
  1231.                   "_score": null,
  1232.                   "_source": {
  1233.                     "account_number": 443,
  1234.                     "balance": 7588,
  1235.                     "firstname": "Huff",
  1236.                     "lastname": "Thomas",
  1237.                     "age": 23,
  1238.                     "gender": "M",
  1239.                     "address": "538 Erskine Loop",
  1240.                     "employer": "Accufarm",
  1241.                     "email": "huffthomas@accufarm.com",
  1242.                     "city": "Corinne",
  1243.                     "state": "AL"
  1244.                   },
  1245.                   "sort": [
  1246.                   ]
  1247.                 },
  1248.                 {
  1249.                   "_index": "bank",
  1250.                   "_type": "_doc",
  1251.                   "_id": "643",
  1252.                   "_score": null,
  1253.                   "_source": {
  1254.                     "account_number": 643,
  1255.                     "balance": 8057,
  1256.                     "firstname": "Hendricks",
  1257.                     "lastname": "Stokes",
  1258.                     "age": 23,
  1259.                     "gender": "F",
  1260.                     "address": "142 Barbey Street",
  1261.                     "employer": "Remotion",
  1262.                     "email": "hendricksstokes@remotion.com",
  1263.                     "city": "Lewis",
  1264.                     "state": "MA"
  1265.                   },
  1266.                   "sort": [
  1267.                   ]
  1268.                 }
  1269.               ]
  1270.             }
  1271.           }
  1272.         }
  1273.       },
  1274.       {
  1275.         "_index": "bank",
  1276.         "_type": "_doc",
  1277.         "_id": "111",
  1278.         "_score": null,
  1279.         "_source": {
  1280.           "account_number": 111,
  1281.           "balance": 1481,
  1282.           "firstname": "Traci",
  1283.           "lastname": "Allison",
  1284.           "age": 35,
  1285.           "gender": "M",
  1286.           "address": "922 Bryant Street",
  1287.           "employer": "Enjola",
  1288.           "email": "traciallison@enjola.com",
  1289.           "city": "Robinette",
  1290.           "state": "OR"
  1291.         },
  1292.         "fields": {
  1293.           "age": [
  1294.           ]
  1295.         },
  1296.         "sort": [
  1297.         ],
  1298.         "inner_hits": {
  1299.           "details": {
  1300.             "hits": {
  1301.               "total": 52,
  1302.               "max_score": null,
  1303.               "hits": [
  1304.                 {
  1305.                   "_index": "bank",
  1306.                   "_type": "_doc",
  1307.                   "_id": "111",
  1308.                   "_score": null,
  1309.                   "_source": {
  1310.                     "account_number": 111,
  1311.                     "balance": 1481,
  1312.                     "firstname": "Traci",
  1313.                     "lastname": "Allison",
  1314.                     "age": 35,
  1315.                     "gender": "M",
  1316.                     "address": "922 Bryant Street",
  1317.                     "employer": "Enjola",
  1318.                     "email": "traciallison@enjola.com",
  1319.                     "city": "Robinette",
  1320.                     "state": "OR"
  1321.                   },
  1322.                   "sort": [
  1323.                   ]
  1324.                 },
  1325.                 {
  1326.                   "_index": "bank",
  1327.                   "_type": "_doc",
  1328.                   "_id": "417",
  1329.                   "_score": null,
  1330.                   "_source": {
  1331.                     "account_number": 417,
  1332.                     "balance": 1788,
  1333.                     "firstname": "Wheeler",
  1334.                     "lastname": "Ayers",
  1335.                     "age": 35,
  1336.                     "gender": "F",
  1337.                     "address": "677 Hope Street",
  1338.                     "employer": "Fortean",
  1339.                     "email": "wheelerayers@fortean.com",
  1340.                     "city": "Ironton",
  1341.                     "state": "PA"
  1342.                   },
  1343.                   "sort": [
  1344.                   ]
  1345.                 },
  1346.                 {
  1347.                   "_index": "bank",
  1348.                   "_type": "_doc",
  1349.                   "_id": "984",
  1350.                   "_score": null,
  1351.                   "_source": {
  1352.                     "account_number": 984,
  1353.                     "balance": 1904,
  1354.                     "firstname": "Viola",
  1355.                     "lastname": "Crawford",
  1356.                     "age": 35,
  1357.                     "gender": "F",
  1358.                     "address": "354 Linwood Street",
  1359.                     "employer": "Ginkle",
  1360.                     "email": "violacrawford@ginkle.com",
  1361.                     "city": "Witmer",
  1362.                     "state": "AR"
  1363.                   },
  1364.                   "sort": [
  1365.                   ]
  1366.                 },
  1367.                 {
  1368.                   "_index": "bank",
  1369.                   "_type": "_doc",
  1370.                   "_id": "527",
  1371.                   "_score": null,
  1372.                   "_source": {
  1373.                     "account_number": 527,
  1374.                     "balance": 2028,
  1375.                     "firstname": "Carver",
  1376.                     "lastname": "Peters",
  1377.                     "age": 35,
  1378.                     "gender": "M",
  1379.                     "address": "816 Victor Road",
  1380.                     "employer": "Housedown",
  1381.                     "email": "carverpeters@housedown.com",
  1382.                     "city": "Nadine",
  1383.                     "state": "MD"
  1384.                   },
  1385.                   "sort": [
  1386.                   ]
  1387.                 },
  1388.                 {
  1389.                   "_index": "bank",
  1390.                   "_type": "_doc",
  1391.                   "_id": "266",
  1392.                   "_score": null,
  1393.                   "_source": {
  1394.                     "account_number": 266,
  1395.                     "balance": 2777,
  1396.                     "firstname": "Monique",
  1397.                     "lastname": "Conner",
  1398.                     "age": 35,
  1399.                     "gender": "F",
  1400.                     "address": "489 Metrotech Courtr",
  1401.                     "employer": "Flotonic",
  1402.                     "email": "moniqueconner@flotonic.com",
  1403.                     "city": "Retsof",
  1404.                     "state": "MD"
  1405.                   },
  1406.                   "sort": [
  1407.                   ]
  1408.                 }
  1409.               ]
  1410.             }
  1411.           }
  1412.         }
  1413.       }
  1414.     ]
  1415.   }
  1416. }

在inner_hits 中返回多个角度的组内topN

  1.  
  2. GET /twitter/_search
  3. {
  4.     "query": {
  5.         "match": {
  6.             "message": "elasticsearch"
  7.         }
  8.     },
  9.     "collapse" : {
  10.         "field" : "user", 
  11.         "inner_hits": [
  12.             {
  13.                 "name": "most_liked",  
  14.                 "size": 3,
  15.                 "sort": ["likes"]
  16.             },
  17.             {
  18.                 "name": "most_recent", 
  19.                 "size": 3,
  20.                 "sort": [{ "date": "asc" }]
  21.             }
  22.         ]
  23.     },
  24.     "sort": ["likes"]
  25. }

 说明：

most_liked：最像

most_recent：最近一段时间的

 6.3.1 分页

 from and size

  1.  
  2. GET /_search
  3. {
  4.     "from" : 0, "size" : 10,
  5.     "query" : {
  6.         "term" : { "user" : "kimchy" }
  7.     }
  8. }

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

  1.  
  2. GET twitter/_search
  3. {
  4.     "size": 10,
  5.     "query": {
  6.         "match" : {
  7.             "title" : "elasticsearch"
  8.         }
  9.     },
  10.     "sort": [
  11.         {"date": "asc"},
  12.         {"_id": "desc"}
  13.     ]
  14. }

后续页的查询

  1.  
  2. GET twitter/_search
  3. {
  4.     "size": 10,
  5.     "query": {
  6.         "match" : {
  7.             "title" : "elasticsearch"
  8.         }
  9.     },
  10.     "search_after": [1463538857, "654323"],
  11.     "sort": [
  12.         {"date": "asc"},
  13.         {"_id": "desc"}
  14.     ]
  15. }

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：

  1. PUT /hl_test/_doc/1
  2. {
  3.   "title": "lucene solr and elasticsearch",
  4.   "content": "lucene solr and elasticsearch for search"
  5. }

查询高亮数据

  1.  
  2. GET /hl_test/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": "lucene"
  7.     }
  8.   },
  9.   "highlight": {
  10.     "fields": {
  11.       "title": {},
  12.       "content": {}
  13.     }
  14.   }
  15. }

查询结果：

  1.  
  2. {
  3.   "took": 113,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.2876821,
  14.     "hits": [
  15.       {
  16.         "_index": "hl_test",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0.2876821,
  20.         "_source": {
  21.           "title": "lucene solr and elasticsearch",
  22.           "content": "lucene solr and elasticsearch for search"
  23.         },
  24.         "highlight": {
  25.           "title": [
  26.             "<em>lucene</em> solr and elasticsearch"
  27.           ]
  28.         }
  29.       }
  30.     ]
  31.   }
  32. }

多字段高亮

  1.  
  2. GET /hl_test/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": "lucene"
  7.     }
  8.   },
  9.   "highlight": {
  10.     "require_field_match": false,
  11.     "fields": {
  12.       "title": {},
  13.       "content": {}
  14.     }
  15.   }
  16. }

查询结果：

  1.  
  2. {
  3.   "took": 5,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.2876821,
  14.     "hits": [
  15.       {
  16.         "_index": "hl_test",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0.2876821,
  20.         "_source": {
  21.           "title": "lucene solr and elasticsearch",
  22.           "content": "lucene solr and elasticsearch for search"
  23.         },
  24.         "highlight": {
  25.           "title": [
  26.             "<em>lucene</em> solr and elasticsearch"
  27.           ],
  28.           "content": [
  29.             "<em>lucene</em> solr and elasticsearch for search"
  30.           ]
  31.         }
  32.       }
  33.     ]
  34.   }
  35. }

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

  1.  
  2. GET /hl_test/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": "lucene"
  7.     }
  8.   },
  9.   "highlight": {
  10.     "require_field_match": false,
  11.     "fields": {
  12.       "title": {
  13.         "pre_tags":["<strong>"],
  14.         "post_tags": ["</strong>"]
  15.       },
  16.       "content": {}
  17.     }
  18.   }
  19. }

查询结果：

  1.  
  2. {
  3.   "took": 5,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.2876821,
  14.     "hits": [
  15.       {
  16.         "_index": "hl_test",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0.2876821,
  20.         "_source": {
  21.           "title": "lucene solr and elasticsearch",
  22.           "content": "lucene solr and elasticsearch for search"
  23.         },
  24.         "highlight": {
  25.           "title": [
  26.             "<strong>lucene</strong> solr and elasticsearch"
  27.           ],
  28.           "content": [
  29.             "<em>lucene</em> solr and elasticsearch for search"
  30.           ]
  31.         }
  32.       }
  33.     ]
  34.   }
  35. }

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。

  1.  
  2. GET /twitter/_search
  3. {
  4.   "profile": true,
  5.   "query" : {
  6.     "match" : { "message" : "some number" }
  7.   }
  8. }

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

  1.  
  2. PUT /twitter/_doc/1?refresh
  3. {
  4.     "user": "kimchy"
  5. }
  6.  
  7. GET /twitter/_doc/_count?q=user:kimchy
  8.  
  9. GET /twitter/_doc/_count
  10. {
  11.     "query" : {
  12.         "term" : { "user" : "kimchy" }
  13.     }
  14. }

结果说明：

  1.  
  2. {
  3.     "count" : 1,
  4.     "_shards" : {
  5.         "total" : 5,
  6.         "successful" : 5,
  7.         "skipped" : 0,
  8.         "failed" : 0
  9.     }
  10. }

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

  1.  
  2. GET twitter/_doc/_validate/query
  3. {
  4.   "query": {
  5.     "query_string": {
  6.       "query": "post_date:foo",
  7.       "lenient": false
  8.     }
  9.   }
  10. }

查询结果：

  1.  
  2. {
  3.   "valid": true,
  4.   "_shards": {
  5.     "total": 1,
  6.     "successful": 1,
  7.     "failed": 0
  8.   }
  9. }

8.2 获得查询解释

  1.  
  2. GET twitter/_doc/_validate/query?explain=true
  3. {
  4.   "query": {
  5.     "query_string": {
  6.       "query": "post_date:foo",
  7.       "lenient": false
  8.     }
  9.   }
  10. }

查询结果

  1.  
  2. {
  3.   "valid": true,
  4.   "_shards": {
  5.     "total": 1,
  6.     "successful": 1,
  7.     "failed": 0
  8.   },
  9.   "explanations": [
  10.     {
  11.       "index": "twitter",
  12.       "valid": true,
  13.       "explanation": """+MatchNoDocsQuery("unmapped field [post_date]") #MatchNoDocsQuery("Type list does not contain the index type")"""
  14.     }
  15.   ]
  16. }

8.3 用rewrite获得比explain 更详细的解释

  1.  
  2. GET twitter/_doc/_validate/query?rewrite=true
  3. {
  4.   "query": {
  5.     "more_like_this": {
  6.       "like": {
  7.         "_id": "2"
  8.       },
  9.       "boost_terms": 1
  10.     }
  11.   }
  12. }

查询结果：

  1.  
  2. {
  3.   "valid": true,
  4.   "_shards": {
  5.     "total": 1,
  6.     "successful": 1,
  7.     "failed": 0
  8.   },
  9.   "explanations": [
  10.     {
  11.       "index": "twitter",
  12.       "valid": true,
  13.       "explanation": """+(MatchNoDocsQuery("empty BooleanQuery") -ConstantScore(MatchNoDocsQuery("empty BooleanQuery"))) #MatchNoDocsQuery("Type list does not contain the index type")"""
  14.     }
  15.   ]
  16. }

8.4 获得所有分片上的查询解释

  1.  
  2. GET twitter/_doc/_validate/query?rewrite=true&all_shards=true
  3. {
  4.   "query": {
  5.     "match": {
  6.       "user": {
  7.         "query": "kimchy",
  8.         "fuzziness": "auto"
  9.       }
  10.     }
  11.   }
  12. }

查询结果：

  1.  
  2. {
  3.   "valid": true,
  4.   "_shards": {
  5.     "total": 3,
  6.     "successful": 3,
  7.     "failed": 0
  8.   },
  9.   "explanations": [
  10.     {
  11.       "index": "twitter",
  12.       "shard": 0,
  13.       "valid": true,
  14.       "explanation": """MatchNoDocsQuery("unmapped field [user]")"""
  15.     },
  16.     {
  17.       "index": "twitter",
  18.       "shard": 1,
  19.       "valid": true,
  20.       "explanation": """MatchNoDocsQuery("unmapped field [user]")"""
  21.     },
  22.     {
  23.       "index": "twitter",
  24.       "shard": 2,
  25.       "valid": true,
  26.       "explanation": """MatchNoDocsQuery("unmapped field [user]")"""
  27.     }
  28.   ]
  29. }

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

  1. GET /twitter/_doc/0/_explain
  2. {
  3.       "query" : {
  4.         "match" : { "message" : "elasticsearch" }
  5.       }
  6. }

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：

  1. {
  2.   "nodes": {
  3.     "qkmtovyLRPWjXcfDTryNwA": {
  4.       "name": "qkmtovy",
  5.       "ephemeral_id": "sxgsvzsORraAnN7PIlMYpg",
  6.       "transport_address": "127.0.0.1:9300",
  7.       "attributes": {}
  8.     }
  9.   },
  10.   "indices": {
  11.     "twitter": {}
  12.   },
  13.   "shards": [
  14.     [
  15.       {
  16.         "state": "STARTED",
  17.         "primary": true,
  18.         "node": "qkmtovyLRPWjXcfDTryNwA",
  19.         "relocating_node": null,
  20.         "shard": 0,
  21.         "index": "twitter",
  22.         "allocation_id": {
  23.           "id": "3Yf6lOjyQja_v4yP_gL8qA"
  24.         }
  25.       }
  26.     ],
  27.     [
  28.       {
  29.         "state": "STARTED",
  30.         "primary": true,
  31.         "node": "qkmtovyLRPWjXcfDTryNwA",
  32.         "relocating_node": null,
  33.         "shard": 1,
  34.         "index": "twitter",
  35.         "allocation_id": {
  36.           "id": "8S88pnUkSSy8kiCcwBgb9Q"
  37.         }
  38.       }
  39.     ],
  40.     [
  41.       {
  42.         "state": "STARTED",
  43.         "primary": true,
  44.         "node": "qkmtovyLRPWjXcfDTryNwA",
  45.         "relocating_node": null,
  46.         "shard": 2,
  47.         "index": "twitter",
  48.         "allocation_id": {
  49.           "id": "_uIup55LQZKaltUfuh5aFA"
  50.         }
  51.       }
  52.     ]
  53.   ]
  54. }

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：

  1.  
  2. {
  3.   "nodes": {
  4.     "qkmtovyLRPWjXcfDTryNwA": {
  5.       "name": "qkmtovy",
  6.       "ephemeral_id": "sxgsvzsORraAnN7PIlMYpg",
  7.       "transport_address": "127.0.0.1:9300",
  8.       "attributes": {}
  9.     }
  10.   },
  11.   "indices": {
  12.     "twitter": {}
  13.   },
  14.   "shards": [
  15.     [
  16.       {
  17.         "state": "STARTED",
  18.         "primary": true,
  19.         "node": "qkmtovyLRPWjXcfDTryNwA",
  20.         "relocating_node": null,
  21.         "shard": 1,
  22.         "index": "twitter",
  23.         "allocation_id": {
  24.           "id": "8S88pnUkSSy8kiCcwBgb9Q"
  25.         }
  26.       }
  27.     ]
  28.   ]
  29. }

11. Search Template 查询模板

注册一个模板

  1.  
  2. POST _scripts/<templatename>
  3. {
  4.     "script": {
  5.         "lang": "mustache",
  6.         "source": {
  7.             "query": {
  8.                 "match": {
  9.                     "title": "{{query_string}}"
  10.                 }
  11.             }
  12.         }
  13.     }
  14. }

使用模板进行查询

  1.  
  2. GET _search/template
  3. {
  4.     "id": "<templateName>", 
  5.     "params": {
  6.         "query_string": "search for these words"
  7.     }
  8. }

查询结果：

  1.  
  2. {
  3.   "took": 11,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 38,
  7.     "successful": 38,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 0,
  13.     "max_score": null,
  14.     "hits": []
  15.   }
  16. }

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：

  1.  
  2. GET /_search
  3. {
  4.   <!--查询 -->
  5.   "query": { 
  6.     "bool": { 
  7.       "must": [
  8.         { "match": { "title":   "Search"        }}, 
  9.         { "match": { "content": "Elasticsearch" }}  
  10.       ],
  11.       <!--过滤 -->
  12.       "filter": [ 
  13.         { "term":  { "status": "published" }}, 
  14.         { "range": { "publish_date": { "gte": "2015-01-01" }}} 
  15.       ]
  16.     }
  17.   }
  18. }

 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

  1. GET /_search
  2. {
  3.     "query": {
  4.         "match_all": {}
  5.     }
  6. }

 相反，什么都不查

  1. GET /_search
  2. {
  3.     "query": {
  4.         "match_none": {}
  5.     }
  6. }

 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。

  1.  
  2. GET /_search
  3. {
  4.     "query": {
  5.         "match" : {
  6.             "message" : "this is a test"
  7.         }
  8.     }
  9. }

 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：

  1.  
  2. PUT /ftq/_doc/1
  3. {
  4.   "title": "lucene solr and elasticsearch",
  5.   "content": "lucene solr and elasticsearch for search"
  6. }
  7.  
  8. PUT /ftq/_doc/2
  9. {
  10.   "title": "java spring boot",
  11.   "content": "lucene is writerd by java"
  12. }

 执行查询1

  1.  
  2. GET ftq/_doc/_validate/query?rewrite=true
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": "lucene java"
  7.     }
  8.   }
  9. }

 查询结果1：

  1.  
  2. {
  3.   "valid": true,
  4.   "_shards": {
  5.     "total": 1,
  6.     "successful": 1,
  7.     "failed": 0
  8.   },
  9.   "explanations": [
  10.     {
  11.       "index": "ftq",
  12.       "valid": true,
  13.       "explanation": "title:lucene title:java"
  14.     }
  15.   ]
  16. }

 执行查询2：

  1.  
  2. GET ftq/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": "lucene java"
  7.     }
  8.   }
  9. }

 查询结果2：

  1.  
  2. {
  3.   "took": 6,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 2,
  13.     "max_score": 0.2876821,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "2",
  19.         "_score": 0.2876821,
  20.         "_source": {
  21.           "title": "java spring boot",
  22.           "content": "lucene is writerd by java"
  23.         }
  24.       },
  25.       {
  26.         "_index": "ftq",
  27.         "_type": "_doc",
  28.         "_id": "1",
  29.         "_score": 0.2876821,
  30.         "_source": {
  31.           "title": "lucene solr and elasticsearch",
  32.           "content": "lucene solr and elasticsearch for search"
  33.         }
  34.       }
  35.     ]
  36.   }
  37. }

 执行查询3：指定操作符

  1.  
  2. GET ftq/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": {
  7.         "query": "lucene java",
  8.         "operator": "and"
  9.       }
  10.     }
  11.   }
  12. }

 查询结果3：

  1.  
  2. {
  3.   "took": 4,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 0,
  13.     "max_score": null,
  14.     "hits": []
  15.   }
  16. }

模糊查询，最大编辑数为2

  1.  
  2. GET ftq/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "title": {
  7.         "query": "ucen elatic",
  8.         "fuzziness": 2
  9.       }
  10.     }
  11.   }
  12. }

模糊查询结果：

  1.  
  2. {
  3.   "took": 280,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.14384104,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0.14384104,
  20.         "_source": {
  21.           "title": "lucene solr and elasticsearch",
  22.           "content": "lucene solr and elasticsearch for search"
  23.         }
  24.       }
  25.     ]
  26.   }
  27. }

指定最少需满足两个词匹配

  1.  
  2. GET ftq/_search
  3. {
  4.   "query": {
  5.     "match": {
  6.       "content": {
  7.         "query": "ucen elatic java",
  8.         "fuzziness": 2,
  9.         "minimum_should_match": 2
  10.       }
  11.     }
  12.   }
  13. }

 查询结果：

  1.  
  2. {
  3.   "took": 19,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.43152314,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "2",
  19.         "_score": 0.43152314,
  20.         "_source": {
  21.           "title": "java spring boot",
  22.           "content": "lucene is writerd by java"
  23.         }
  24.       }
  25.     ]
  26.   }
  27. }

 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：

  1.  
  2. {
  3.   "took": 3,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.5753642,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0.5753642,
  20.         "_source": {
  21.           "title": "lucene solr and elasticsearch",
  22.           "content": "lucene solr and elasticsearch for search"
  23.         }
  24.       }
  25.     ]
  26.   }
  27. }

 对字段进行短语查询2：

 

  1. GET ftq/_search
  2. {
  3.   "query": {
  4.     "match_phrase": {
  5.       "title": "lucene elasticsearch"
  6.     }
  7.   }
  8. }

 

结果2：

  1.  
  2. {
  3.   "took": 3,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 0,
  13.     "max_score": null,
  14.     "hits": []
  15.   }
  16. }

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：

  1.  
  2. {
  3.   "took": 2174,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 0.27517417,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 0.27517417,
  20.         "_source": {
  21.           "title": "lucene solr and elasticsearch",
  22.           "content": "lucene solr and elasticsearch for search"
  23.         }
  24.       }
  25.     ]
  26.   }
  27. }

 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

  1.  
  2. GET /_search
  3. {
  4.     "query": {
  5.         "match_phrase_prefix" : {
  6.             "message" : {
  7.                 "query" : "quick brown f",
  8.                 "max_expansions" : 10
  9.             }
  10.         }
  11.     }
  12. }

 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：

  1.  
  2. GET ftq/_search
  3. {
  4.   "query": {
  5.     "multi_match" : {
  6.       "query":    "lucene java", 
  7.       "fields": [ "title", "content" ] 
  8.     }
  9.   }
  10. }

结果1：

  1.  
  2. {
  3.   "took": 1973,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 2,
  13.     "max_score": 0.5753642,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "2",
  19.         "_score": 0.5753642,
  20.         "_source": {
  21.           "title": "java spring boot",
  22.           "content": "lucene is writerd by java"
  23.         }
  24.       },
  25.       {
  26.         "_index": "ftq",
  27.         "_type": "_doc",
  28.         "_id": "1",
  29.         "_score": 0.2876821,
  30.         "_source": {
  31.           "title": "lucene solr and elasticsearch",
  32.           "content": "lucene solr and elasticsearch for search"
  33.         }
  34.       }
  35.     ]
  36.   }
  37. }

查询2：字段通配符查询

  1.  
  2. GET ftq/_search
  3. {
  4.   "query": {
  5.     "multi_match" : {
  6.       "query":    "lucene java", 
  7.       "fields": [ "title", "cont*" ] 
  8.     }
  9.   }
  10. }

结果2：

  1.  
  2. {
  3.   "took": 5,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 2,
  13.     "max_score": 0.5753642,
  14.     "hits": [
  15.       {
  16.         "_index": "ftq",
  17.         "_type": "_doc",
  18.         "_id": "2",
  19.         "_score": 0.5753642,
  20.         "_source": {
  21.           "title": "java spring boot",
  22.           "content": "lucene is writerd by java"
  23.         }
  24.       },
  25.       {
  26.         "_index": "ftq",
  27.         "_type": "_doc",
  28.         "_id": "1",
  29.         "_score": 0.2876821,
  30.         "_source": {
  31.           "title": "lucene solr and elasticsearch",
  32.           "content": "lucene solr and elasticsearch for search"
  33.         }
  34.       }
  35.     ]
  36.   }
  37. }

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：

  1. {
  2.   "took": 6,
  3.   "timed_out": false,
  4.   "_shards": {
  5.     "total": 5,
  6.     "successful": 5,
  7.     "skipped": 0,
  8.     "failed": 0
  9.   },
  10.   "hits": {
  11.     "total": 2,
  12.     "max_score": 1.4384104,
  13.     "hits": [
  14.       {
  15.         "_shard": "[ftq][3]",
  16.         "_node": "qkmtovyLRPWjXcfDTryNwA",
  17.         "_index": "ftq",
  18.         "_type": "_doc",
  19.         "_id": "1",
  20.         "_score": 1.4384104,
  21.         "_source": {
  22.           "title": "lucene solr and elasticsearch",
  23.           "content": "lucene solr and elasticsearch for search"
  24.         },
  25.         "_explanation": {
  26.           "value": 1.4384104,
  27.           "description": "max of:",
  28.           "details": [
  29.             {
  30.               "value": 1.4384104,
  31.               "description": "sum of:",
  32.               "details": [
  33.                 {
  34.                   "value": 1.4384104,
  35.                   "description": "weight(title:lucene in 0) [PerFieldSimilarity], result of:",
  36.                   "details": [
  37.                     {
  38.                       "value": 1.4384104,
  39.                       "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
  40.                       "details": [
  41.                         {
  42.                           "value": 5,
  43.                           "description": "boost",
  44.                           "details": []
  45.                         },
  46.                         {
  47.                           "value": 0.2876821,
  48.                           "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
  49.                           "details": [
  50.                             {
  51.                               "value": 1,
  52.                               "description": "docFreq",
  53.                               "details": []
  54.                             },
  55.                             {
  56.                               "value": 1,
  57.                               "description": "docCount",
  58.                               "details": []
  59.                             }
  60.                           ]
  61.                         },
  62.                         {
  63.                           "value": 1,
  64.                           "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
  65.                           "details": [
  66.                             {
  67.                               "value": 1,
  68.                               "description": "termFreq=1.0",
  69.                               "details": []
  70.                             },
  71.                             {
  72.                               "value": 1.2,
  73.                               "description": "parameter k1",
  74.                               "details": []
  75.                             },
  76.                             {
  77.                               "value": 0.75,
  78.                               "description": "parameter b",
  79.                               "details": []
  80.                             },
  81.                             {
  82.                               "value": 4,
  83.                               "description": "avgFieldLength",
  84.                               "details": []
  85.                             },
  86.                             {
  87.                               "value": 4,
  88.                               "description": "fieldLength",
  89.                               "details": []
  90.                             }
  91.                           ]
  92.                         }
  93.                       ]
  94.                     }
  95.                   ]
  96.                 }
  97.               ]
  98.             },
  99.             {
  100.               "value": 0.2876821,
  101.               "description": "sum of:",
  102.               "details": [
  103.                 {
  104.                   "value": 0.2876821,
  105.                   "description": "weight(content:lucene in 0) [PerFieldSimilarity], result of:",
  106.                   "details": [
  107.                     {
  108.                       "value": 0.2876821,
  109.                       "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
  110.                       "details": [
  111.                         {
  112.                           "value": 0.2876821,
  113.                           "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
  114.                           "details": [
  115.                             {
  116.                               "value": 1,
  117.                               "description": "docFreq",
  118.                               "details": []
  119.                             },
  120.                             {
  121.                               "value": 1,
  122.                               "description": "docCount",
  123.                               "details": []
  124.                             }
  125.                           ]
  126.                         },
  127.                         {
  128.                           "value": 1,
  129.                           "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
  130.                           "details": [
  131.                             {
  132.                               "value": 1,
  133.                               "description": "termFreq=1.0",
  134.                               "details": []
  135.                             },
  136.                             {
  137.                               "value": 1.2,
  138.                               "description": "parameter k1",
  139.                               "details": []
  140.                             },
  141.                             {
  142.                               "value": 0.75,
  143.                               "description": "parameter b",
  144.                               "details": []
  145.                             },
  146.                             {
  147.                               "value": 6,
  148.                               "description": "avgFieldLength",
  149.                               "details": []
  150.                             },
  151.                             {
  152.                               "value": 6,
  153.                               "description": "fieldLength",
  154.                               "details": []
  155.                             }
  156.                           ]
  157.                         }
  158.                       ]
  159.                     }
  160.                   ]
  161.                 }
  162.               ]
  163.             }
  164.           ]
  165.         }
  166.       },
  167.       {
  168.         "_shard": "[ftq][2]",
  169.         "_node": "qkmtovyLRPWjXcfDTryNwA",
  170.         "_index": "ftq",
  171.         "_type": "_doc",
  172.         "_id": "2",
  173.         "_score": 0.2876821,
  174.         "_source": {
  175.           "title": "java spring boot",
  176.           "content": "lucene is writerd by java"
  177.         },
  178.         "_explanation": {
  179.           "value": 0.2876821,
  180.           "description": "max of:",
  181.           "details": [
  182.             {
  183.               "value": 0.2876821,
  184.               "description": "sum of:",
  185.               "details": [
  186.                 {
  187.                   "value": 0.2876821,
  188.                   "description": "weight(content:lucene in 0) [PerFieldSimilarity], result of:",
  189.                   "details": [
  190.                     {
  191.                       "value": 0.2876821,
  192.                       "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
  193.                       "details": [
  194.                         {
  195.                           "value": 0.2876821,
  196.                           "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
  197.                           "details": [
  198.                             {
  199.                               "value": 1,
  200.                               "description": "docFreq",
  201.                               "details": []
  202.                             },
  203.                             {
  204.                               "value": 1,
  205.                               "description": "docCount",
  206.                               "details": []
  207.                             }
  208.                           ]
  209.                         },
  210.                         {
  211.                           "value": 1,
  212.                           "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
  213.                           "details": [
  214.                             {
  215.                               "value": 1,
  216.                               "description": "termFreq=1.0",
  217.                               "details": []
  218.                             },
  219.                             {
  220.                               "value": 1.2,
  221.                               "description": "parameter k1",
  222.                               "details": []
  223.                             },
  224.                             {
  225.                               "value": 0.75,
  226.                               "description": "parameter b",
  227.                               "details": []
  228.                             },
  229.                             {
  230.                               "value": 5,
  231.                               "description": "avgFieldLength",
  232.                               "details": []
  233.                             },
  234.                             {
  235.                               "value": 5,
  236.                               "description": "fieldLength",
  237.                               "details": []
  238.                             }
  239.                           ]
  240.                         }
  241.                       ]
  242.                     }
  243.                   ]
  244.                 }
  245.               ]
  246.             }
  247.           ]
  248.         }
  249.       }
  250.     ]
  251.   }
  252. }

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，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

  1.  
  2. GET /_search
  3. {
  4.     "query": {
  5.         "query_string" : {
  6.             "default_field" : "content",
  7.             "query" : "this AND that OR thus"
  8.         }
  9.     }
  10. }

 示例2：指定多字段通配符查询

  1.  
  2. GET /_search
  3. {
  4.     "query": {
  5.         "query_string" : {
  6.             "fields" : ["content", "name.*^5"],
  7.             "query" : "this AND that OR thus"
  8.         }
  9.     }
  10. }

 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：

  1.  
  2. {
  3.   "took": 14,
  4.   "timed_out": false,
  5.   "_shards": {
  6.     "total": 5,
  7.     "successful": 5,
  8.     "skipped": 0,
  9.     "failed": 0
  10.   },
  11.   "hits": {
  12.     "total": 1,
  13.     "max_score": 1,
  14.     "hits": [
  15.       {
  16.         "_index": "tweets",
  17.         "_type": "_doc",
  18.         "_id": "1",
  19.         "_score": 1,
  20.         "_source": {
  21.           "user": "1"
  22.         }
  23.       }
  24.     ]
  25.   }
  26. }

嵌套查询可用参数说明：

11.3 range query

 范围查询示例1：

  1.  
  2. GET _search
  3. {
  4.     "query": {
  5.         "range" : {
  6.             "age" : {
  7.                 "gte" : 10,
  8.                 "lte" : 20,
  9.                 "boost" : 2.0
  10.             }
  11.         }
  12.     }
  13. }

  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

 

转自：推荐博客地址

https://www.cnblogs.com/leeSmall/p/9206641.html 

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