elasticsearch最全詳細使用教程：搜索詳解

一、搜索API
 

1. 搜索API 端點地址
從索引tweet里面搜索字段user為kimchy的記錄

GET /twitter/_search?q=user:kimchy
從索引tweet,user里面搜索字段user為kimchy的記錄

GET /twitter/tweet,user/_search?q=user:kimchy
GET /kimchy,elasticsearch/_search?q=tag:wow
從所有索引里面搜索字段tag為wow的記錄

GET /_all/_search?q=tag:wow
GET /_search?q=tag:wow
說明：搜索的端點地址可以是多索引多mapping type的。搜索的參數可作為URI請求參數給出，也可用 request body 給出

2. URI Search
URI 搜索方式通過URI參數來指定查詢相關參數。讓我們可以快速做一個查詢。

GET /twitter/_search?q=user:kimchy
可用的參數請參考： https://www.elastic.co/guide/en/elasticsearch/reference/current/search-uri-request.html

3. 查詢結果說明

5. 特殊的查詢參數用法
 如果我們只想知道有多少文檔匹配某個查詢，可以這樣用參數：

GET /bank/_search?q=city:b*&size=0
 

 

 

 如果我們只想知道有沒有文檔匹配某個查詢，可以這樣用參數：

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

 

 

 比較兩個查詢的結果可以知道第一個查詢返回所有的命中文檔數，第二個查詢由於只需要知道有沒有文檔，所以只要有文檔就立即返回

 6. Request body Search
 Request body 搜索方式以JSON格式在請求體中定義查詢 query。請求方式可以是 GET 、POST 。

GET /twitter/_search
{
"query" : {
"term" : { "user" : "kimchy" }
}
}
可用的參數:

timeout：請求超時時長，限定在指定時長內響應（即使沒查完）；
from： 分頁的起始行，默認0；
size：分頁大小；
request_cache：是否緩存請求結果，默認true。
terminate_after：限定每個分片取幾個文檔。如果設置，則響應將有一個布爾型字段terminated_early來指示查詢執行是否實際已經terminate_early。缺省為no terminate_after；
search_type：查詢的執行方式，可選值dfs_query_then_fetch or query_then_fetch ，默認： query_then_fetch ；
batched_reduce_size：一次在協調節點上應該減少的分片結果的數量。如果請求中的潛在分片數量可能很大，則應將此值用作保護機制以減少每個搜索請求的內存開銷。

6.1 query 元素定義查詢

query 元素用Query DSL 來定義查詢。

GET /_search
{
"query" : {
"term" : { "user" : "kimchy" }
}
}
6.2 指定返回哪些內容

6.2.1 source filter  對_source字段進行選擇

GET /_search
{
"_source": false,
"query" : {
"term" : { "user" : "kimchy" }
}
}
通配符查詢

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

GET /_search
{
"_source": "obj.*",
"query" : {
"term" : { "user" : "kimchy" }
}
}
包含什么不包含什么

GET /_search
{
"_source": {
"includes": [ "obj1.*", "obj2.*" ],
"excludes": [ "*.description" ]
},
"query" : {
"term" : { "user" : "kimchy" }
}
}
6.2.2 stored_fields 來指定返回哪些stored字段

GET /_search
{
"stored_fields" : ["user", "postDate"],
"query" : {
"term" : { "user" : "kimchy" }
}
}
說明：* 可用來指定返回所有存儲字段

6.2.3 docValue Field 返回存儲了docValue的字段值

GET /_search
{
"query" : {
"match_all": {}
},
"docvalue_fields" : ["test1", "test2"]
}
6.2.4 version 來指定返回文檔的版本字段

GET /_search
{
"version": true,
"query" : {
"term" : { "user" : "kimchy" }
}
}
6.2.5 explain 返回文檔的評分解釋

GET /_search
{
"explain": true,
"query" : {
"term" : { "user" : "kimchy" }
}
}
6.2.6 Script Field 用腳本來對命中的每個文檔的字段進行運算后返回

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

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": 1,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "25",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "44",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "99",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "119",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "126",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "145",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "183",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "190",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "208",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "222",
"_score": 1,
"fields": {
"test1": [
],
"test2": [
]
}
}
]
}
}

GET /bank/_search
{
"query": {
"match_all": {}
},
"script_fields": {
"ffx": {
"script": {
"lang": "painless",
"source": "doc['age'].value * doc['balance'].value"
}
},
"balance*2": {
"script": {
"lang": "painless",
"source": "params['_source'].balance*2"
}
}
}
}
說明：

params  _source 取 _source字段值

官方推薦使用doc，理由是用doc效率比取_source 高

搜索結果：

{
"took": 26,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": 1,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "25",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "44",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "99",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "119",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "126",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "145",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "183",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "190",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "208",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
},
{
"_index": "bank",
"_type": "_doc",
"_id": "222",
"_score": 1,
"fields": {
"balance*2": [
],
"ffx": [
]
}
}
]
}
}
6.2.7 min_score  限制最低評分得分

GET /_search
{
"min_score": 0.5,
"query" : {
"term" : { "user" : "kimchy" }
}
}
6.2.8 post_filter  后置過濾：在查詢命中文檔、完成聚合后，再對命中的文檔進行過濾。

如：要在一次查詢中查詢品牌為gucci且顏色為紅色的shirts，同時還要得到gucci品牌各顏色的shirts的分面統計。

創建索引並指定mappping：

PUT /shirts
{
"mappings": {
"_doc": {
"properties": {
"brand": { "type": "keyword"},
"color": { "type": "keyword"},
"model": { "type": "keyword"}
}
}
}
}
往索引里面放入文檔即類似數據庫里面的向表插入一行數據，並立即刷新

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

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

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

可以指定按一個或多個字段排序。也可通過_score指定按評分值排序，_doc 按索引順序排序。默認是按相關性評分從高到低排序。

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

order 值：asc、desc。如果不給定，默認是asc，_score默認是desc

查詢結果：

{
"took": 181,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": null,
"hits": [
{
"_index": "bank",
"_type": "_doc",
"_id": "549",
"_score": 1,
"_source": {
"account_number": 549,
"balance": 1932,
"firstname": "Jacqueline",
"lastname": "Maxwell",
"age": 40,
"gender": "M",
"address": "444 Schenck Place",
"employer": "Fuelworks",
"email": "[e-mail]",
"city": "Oretta",
"state": "OR"
},
"sort": [
40,
1932,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "306",
"_score": 1,
"_source": {
"account_number": 306,
"balance": 2171,
"firstname": "Hensley",
"lastname": "Hardin",
"age": 40,
"gender": "M",
"address": "196 Maujer Street",
"employer": "Neocent",
"email": "[e-mail]",
"city": "Reinerton",
"state": "HI"
},
"sort": [
40,
2171,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "960",
"_score": 1,
"_source": {
"account_number": 960,
"balance": 2905,
"firstname": "Curry",
"lastname": "Vargas",
"age": 40,
"gender": "M",
"address": "242 Blake Avenue",
"employer": "Pearlesex",
"email": "[e-mail]",
"city": "Henrietta",
"state": "NH"
},
"sort": [
40,
2905,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "584",
"_score": 1,
"_source": {
"account_number": 584,
"balance": 5346,
"firstname": "Pearson",
"lastname": "Bryant",
"age": 40,
"gender": "F",
"address": "971 Heyward Street",
"employer": "Anacho",
"email": "[e-mail]",
"city": "Bluffview",
"state": "MN"
},
"sort": [
40,
5346,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "567",
"_score": 1,
"_source": {
"account_number": 567,
"balance": 6507,
"firstname": "Diana",
"lastname": "Dominguez",
"age": 40,
"gender": "M",
"address": "419 Albany Avenue",
"employer": "Ohmnet",
"email": "[e-mail]",
"city": "Wildwood",
"state": "TX"
},
"sort": [
40,
6507,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "938",
"_score": 1,
"_source": {
"account_number": 938,
"balance": 9597,
"firstname": "Sharron",
"lastname": "Santos",
"age": 40,
"gender": "F",
"address": "215 Matthews Place",
"employer": "Zenco",
"email": "[e-mail]",
"city": "Wattsville",
"state": "VT"
},
"sort": [
40,
9597,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "810",
"_score": 1,
"_source": {
"account_number": 810,
"balance": 10563,
"firstname": "Alyssa",
"lastname": "Ortega",
"age": 40,
"gender": "M",
"address": "977 Clymer Street",
"employer": "Eventage",
"email": "[e-mail]",
"city": "Convent",
"state": "SC"
},
"sort": [
40,
10563,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "302",
"_score": 1,
"_source": {
"account_number": 302,
"balance": 11298,
"firstname": "Isabella",
"lastname": "Hewitt",
"age": 40,
"gender": "M",
"address": "455 Bedford Avenue",
"employer": "Cincyr",
"email": "[e-mail]",
"city": "Blanford",
"state": "IN"
},
"sort": [
40,
11298,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "792",
"_score": 1,
"_source": {
"account_number": 792,
"balance": 13109,
"firstname": "Becky",
"lastname": "Jimenez",
"age": 40,
"gender": "F",
"address": "539 Front Street",
"employer": "Isologia",
"email": "[e-mail]",
"city": "Summertown",
"state": "MI"
},
"sort": [
40,
13109,
]
},
{
"_index": "bank",
"_type": "_doc",
"_id": "495",
"_score": 1,
"_source": {
"account_number": 495,
"balance": 13478,
"firstname": "Abigail",
"lastname": "Nichols",
"age": 40,
"gender": "F",
"address": "887 President Street",
"employer": "Enquility",
"email": "[e-mail]",
"city": "Bagtown",
"state": "NM"
},
"sort": [
40,
13478,
]
}
]
}
}
結果中每個文檔會有排序字段值給出

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

多值字段排序

對於值是數組或多值的字段，也可進行排序，通過mode參數指定按多值的：

 

PUT /my_index/_doc/1?refresh
{
"product": "chocolate",
"price": [20, 4]
}

POST /_search
{
"query" : {
"term" : { "product" : "chocolate" }
},
"sort" : [
{"price" : {"order" : "asc", "mode" : "avg"}}
]
}
 Missing values  缺失該字段的文檔

missing 的值可以是 _last, _first

GET /_search
{
"sort" : [
{ "price" : {"missing" : "_last"} }
],
"query" : {
"term" : { "product" : "chocolate" }
}
}
 地理空間距離排序

官方文檔：

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

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

_geo_distance 距離排序關鍵字
pin.location是 geo_point 類型的字段
distance_type：距離計算方式 arc球面 、plane 平面。
unit: 距離單位 km 、m 默認m

Script Based Sorting 基於腳本計算的排序

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


 6.3.0 折疊用 collapse指定根據某個字段對命中結果進行折疊

GET /bank/_search
{
"query": {
"match_all": {}
},
"collapse" : {
"field" : "age"
},
"sort": ["balance"]
}
 查詢結果：

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

GET /bank/_search
{
"query": {
"match_all": {}
},
"collapse" : {
"field" : "age" ,
<!--指定inner_hits來解釋折疊 -->
"inner_hits": {
"name": "details", <!-- 自命名 -->
"size": 5, <!-- 指定每組取幾個文檔 -->
"sort": [{ "balance": "asc" }] <!-- 組內排序 -->
},
"max_concurrent_group_searches": 4 <!-- 指定組查詢的並發數 -->
},
"sort": ["balance"]
}
 查詢結果：

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

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

most_liked：最像

most_recent：最近一段時間的

 6.3.1 分頁

 from and size

GET /_search
{
"from" : 0, "size" : 10,
"query" : {
"term" : { "user" : "kimchy" }
}
}
注意：搜索請求耗用的堆內存和時間與 from + size 大小成正比。分頁越深耗用越大，為了不因分頁導致OOM或嚴重影響性能，ES中規定from + size 不能大於索引setting參數 index.max_result_window 的值，默認值為 10,000。

需要深度分頁， 不受index.max_result_window 限制，怎么辦？ 

Search after  在指定文檔后取文檔， 可用於深度分頁

 首次查詢第一頁

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

GET twitter/_search
{
"size": 10,
"query": {
"match" : {
"title" : "elasticsearch"
}
},
"search_after": [1463538857, "654323"],
"sort": [
{"date": "asc"},
{"_id": "desc"}
]
}
注意：使用search_after，要求查詢必須指定排序，並且這個排序組合值每個文檔唯一（最好排序中包含_id字段）。 search_after的值用的就是這個排序值。 用search_after時 from 只能為0、-1。

6.3.2 高亮

准備數據：

PUT /hl_test/_doc/1
{
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
查詢高亮數據

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

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

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

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

高亮結果在返回的每個文檔中以hightlight節點給出

指定高亮標簽

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

{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.2876821,
"hits": [
{
"_index": "hl_test",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
},
"highlight": {
"title": [
"<strong>lucene</strong> solr and elasticsearch"
],
"content": [
"<em>lucene</em> solr and elasticsearch for search"
]
}
}
]
}
}
高亮的詳細設置請參考官網：https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-highlighting.html

6.3.3 Profile  為了調試、優化

對於執行緩慢的查詢，我們很想知道它為什么慢，時間都耗在哪了，可以在查詢上加入上 profile 來獲得詳細的執行步驟、耗時信息。

GET /twitter/_search
{
"profile": true,
"query" : {
"match" : { "message" : "some number" }
}
}
信息的說明請參考：

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

7.  count api 查詢數量

PUT /twitter/_doc/1?refresh
{
"user": "kimchy"
}

GET /twitter/_doc/_count?q=user:kimchy

GET /twitter/_doc/_count
{
"query" : {
"term" : { "user" : "kimchy" }
}
}
結果說明：

{
"count" : 1,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
}
}
8. validate api  
用來檢查我們的查詢是否正確，以及查看底層生成查詢是怎樣的

GET twitter/_validate/query?q=user:foo
8.1 校驗查詢

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

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
}
}
8.2 獲得查詢解釋

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

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"explanations": [
{
"index": "twitter",
"valid": true,
"explanation": """+MatchNoDocsQuery("unmapped field [post_date]") #MatchNoDocsQuery("Type list does not contain the index type")"""
}
]
}
8.3 用rewrite獲得比explain 更詳細的解釋

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

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"explanations": [
{
"index": "twitter",
"valid": true,
"explanation": """+(MatchNoDocsQuery("empty BooleanQuery") -ConstantScore(MatchNoDocsQuery("empty BooleanQuery"))) #MatchNoDocsQuery("Type list does not contain the index type")"""
}
]
}
8.4 獲得所有分片上的查詢解釋

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

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

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

9. Explain api  
獲得某個查詢的評分解釋,及某個文檔是否被這個查詢命中

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

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

10. Search Shards API
讓我們可以了解可執行查詢的索引分片節點情況

GET /twitter/_search_shards
查詢結果：

{
"nodes": {
"qkmtovyLRPWjXcfDTryNwA": {
"name": "qkmtovy",
"ephemeral_id": "sxgsvzsORraAnN7PIlMYpg",
"transport_address": "127.0.0.1:9300",
"attributes": {}
}
},
"indices": {
"twitter": {}
},
"shards": [
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 0,
"index": "twitter",
"allocation_id": {
"id": "3Yf6lOjyQja_v4yP_gL8qA"
}
}
],
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 1,
"index": "twitter",
"allocation_id": {
"id": "8S88pnUkSSy8kiCcwBgb9Q"
}
}
],
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 2,
"index": "twitter",
"allocation_id": {
"id": "_uIup55LQZKaltUfuh5aFA"
}
}
]
]
}
想知道指定routing值的查詢將在哪些分片節點上執行

GET /twitter/_search_shards?routing=foo,baz
查詢結果：

{
"nodes": {
"qkmtovyLRPWjXcfDTryNwA": {
"name": "qkmtovy",
"ephemeral_id": "sxgsvzsORraAnN7PIlMYpg",
"transport_address": "127.0.0.1:9300",
"attributes": {}
}
},
"indices": {
"twitter": {}
},
"shards": [
[
{
"state": "STARTED",
"primary": true,
"node": "qkmtovyLRPWjXcfDTryNwA",
"relocating_node": null,
"shard": 1,
"index": "twitter",
"allocation_id": {
"id": "8S88pnUkSSy8kiCcwBgb9Q"
}
}
]
]
}
11. Search Template 查詢模板
注冊一個模板

POST _scripts/<templatename>
{
"script": {
"lang": "mustache",
"source": {
"query": {
"match": {
"title": "{{query_string}}"
}
}
}
}
}
使用模板進行查詢

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

{
"took": 11,
"timed_out": false,
"_shards": {
"total": 38,
"successful": 38,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}
詳細了解請參考官網：

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

二、Query DSL
 

官網介紹鏈接：https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html

 Query DSL 介紹
 1. DSL是什么？
Domain Specific Language：領域特定語言

Elasticsearch基於JSON提供完整的查詢DSL來定義查詢。

一個查詢可由兩部分字句構成：

Leaf query clauses 葉子查詢字句
Leaf query clauses 在指定的字段上查詢指定的值, 如：match, term or range queries. 葉子字句可以單獨使用. 
Compound query clauses 復合查詢字句
以邏輯方式組合多個葉子、復合查詢為一個查詢

 2. Query and filter context
 一個查詢字句的行為取決於它是用在query context  還是 filter context 中 。

Query context 查詢上下文
用在查詢上下文中的字句回答“這個文檔有多匹配這個查詢?”。除了決定文檔是否匹配，字句匹配的文檔還會計算一個字句評分，來評定文檔有多匹配。查詢上下文由 query 元素表示。
Filter context 過濾上下文
過濾上下文由 filter 元素或 bool 中的 must not 表示。用在過濾上下文中的字句回答“這個文檔是否匹配這個查詢？”，不參與相關性評分。
被頻繁使用的過濾器將被ES自動緩存，來提高查詢性能。

 示例：

GET /_search
{
<!--查詢 -->
"query": {
"bool": {
"must": [
{ "match": { "title": "Search" }},
{ "match": { "content": "Elasticsearch" }}
],
<!--過濾 -->
"filter": [
{ "term": { "status": "published" }},
{ "range": { "publish_date": { "gte": "2015-01-01" }}}
]
}
}
}
 說明：查詢和過濾都是對所有文檔進行查詢，最后兩個結果取交集

 提示：在查詢上下文中使用查詢子句來表示影響匹配文檔得分的條件，並在過濾上下文中使用所有其他查詢子句。

 查詢分類介紹
 

1. Match all query 查詢所有
GET /_search
{
"query": {
"match_all": {}
}
}
 相反，什么都不查

GET /_search
{
"query": {
"match_none": {}
}
}
 2. Full text querys
全文查詢，用於對分詞的字段進行搜索。會用查詢字段的分詞器對查詢的文本進行分詞生成查詢。可用於短語查詢、模糊查詢、前綴查詢、臨近查詢等查詢場景

 官網鏈接：

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

 3. match query
全文查詢的標准查詢，它可以對一個字段進行模糊、短語查詢。 match queries 接收 text/numerics/dates, 對它們進行分詞分析, 再組織成一個boolean查詢。可通過operator 指定bool組合操作（or、and 默認是 or ）， 以及minimum_should_match 指定至少需多少個should(or)字句需滿足。還可用ananlyzer指定查詢用的特殊分析器。

GET /_search
{
"query": {
"match" : {
"message" : "this is a test"
}
}
}
 說明：message是字段名

 官網鏈接：https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html

 示例：

構造索引和數據：

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

PUT /ftq/_doc/2
{
"title": "java spring boot",
"content": "lucene is writerd by java"
}
 執行查詢1

GET ftq/_doc/_validate/query?rewrite=true
{
"query": {
"match": {
"title": "lucene java"
}
}
}
 查詢結果1：

{
"valid": true,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"explanations": [
{
"index": "ftq",
"valid": true,
"explanation": "title:lucene title:java"
}
]
}
 執行查詢2：

GET ftq/_search
{
"query": {
"match": {
"title": "lucene java"
}
}
}
 查詢結果2：

{
"took": 6,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.2876821,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.2876821,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
},
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}
 執行查詢3：指定操作符

GET ftq/_search
{
"query": {
"match": {
"title": {
"query": "lucene java",
"operator": "and"
}
}
}
}
 查詢結果3：

{
"took": 4,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}
模糊查詢，最大編輯數為2

GET ftq/_search
{
"query": {
"match": {
"title": {
"query": "ucen elatic",
"fuzziness": 2
}
}
}
}
模糊查詢結果：

{
"took": 280,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.14384104,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.14384104,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}
指定最少需滿足兩個詞匹配

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

{
"took": 19,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.43152314,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.43152314,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
}
]
}
}
 可用max_expansions 指定模糊匹配的最大詞項數，默認是50。比如：反向索引中有 100 個詞項與 ucen 模糊匹配，只選用前50 個。

 4. match  phrase  query
match_phrase 查詢用來對一個字段進行短語查詢，可以指定 analyzer、slop移動因子。

 對字段進行短語查詢1：

 

GET ftq/_search
{
"query": {
"match_phrase": {
"title": "lucene solr"
}
}
}
 結果1：

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.5753642,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.5753642,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}
 對字段進行短語查詢2：

 

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

結果2：

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}
對查詢指定移動因子：

 

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

{
"took": 2174,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.27517417,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.27517417,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}
 5. match  phrase  prefix query
match_phrase_prefix 在 match_phrase 的基礎上支持對短語的最后一個詞進行前綴匹配

 

GET /_search
{
"query": {
"match_phrase_prefix" : {
"message" : "quick brown f"
}
}
}
 指定前綴匹配選用的最大詞項數量

GET /_search
{
"query": {
"match_phrase_prefix" : {
"message" : {
"query" : "quick brown f",
"max_expansions" : 10
}
}
}
}
 6. Multi match query
如果你需要在多個字段上進行文本搜索，可用multi_match 。 multi_match在 match的基礎上支持對多個字段進行文本查詢。

查詢1：

GET ftq/_search
{
"query": {
"multi_match" : {
"query": "lucene java",
"fields": [ "title", "content" ]
}
}
}
結果1：

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

GET ftq/_search
{
"query": {
"multi_match" : {
"query": "lucene java",
"fields": [ "title", "cont*" ]
}
}
}
結果2：

{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.5753642,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 0.5753642,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
},
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
}
}
查詢3：給字段的相關性評分加權重

 

GET ftq/_search?explain=true
{
"query": {
"multi_match" : {
"query": "lucene elastic",
"fields": [ "title^5", "content" ]
}
}
}
結果3：

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

問1、什么是停用詞？索引時做停用詞處理的目的是什么？

    不再使用的詞，做停用詞處理的目的是提高索引的效率，去掉不需要的索引操作，即停用詞不需要索引
問2、如果在索引時應用停用詞處理，下面的兩個查詢會查詢什么詞項？
the brown fox—— brown fox
not happy——happy

問3、索引時應用停用詞處理對搜索精度是否有影響？如果不做停用詞處理又會有什么影響？如何協調這兩個問題？如何保證搜索的精確度又兼顧搜索性能？

索引時應用停用詞處理對搜索精度有影響，不做停用詞處理又會影響索引的效率，要協調這兩個問題就必須要使用tf-idf 相關性計算模型

7.1 tf-idf 相關性計算模型簡介

tf：term frequency   詞頻 ：指一個詞在一篇文檔中出現的頻率。

如“世界杯”在文檔A中出現3次，那么可以定義“世界杯”在文檔A中的詞頻為3。請問在一篇3000字的文章中出現“世界杯”3次和一篇150字的文章中出現3詞，哪篇文章更是與“世界杯”有關的。也就是說，簡單用出現次數作為頻率不夠准確。那就用占比來表示：

 

問：tf值越大是否就一定說明這個詞更相關？

 不是，出現太多了說明不重要

 說明：tf的計算不一定非是這樣的，可以定義不同的計算方式。

df：document frequency 詞的文檔頻率 ：指包含某個詞的文檔數（有多少文檔中包含這個詞）。 df越大的詞越常見，哪些詞會是高頻詞？

問1：詞的df值越大說明這個詞在這個文檔集中是越重要還是越不重要？

 越不重要

問2：詞t的tf高，在文檔集中的重要性也高，是否說明文檔與該詞越相關？舉例：整個文檔集中只有3篇文檔中有“世界杯”，文檔A中就出現了“世界杯”好幾次。 

 不能說明文檔與該詞越相關

問3：如何用數值體現詞t在文檔集中的重要性？df可以嗎？

 不可以

 idf：inverse document frequency   詞的逆文檔頻率 ：用來表示詞在文檔集中的重要性。文檔總數/ df ，df越小，詞越重要，這個值會很大，那就對它取個自然對數，將值映射到一個較小的取值范圍。

 

說明： +1 是為了避免除0（即詞t在文檔集中未出現的情況）

tf-idf 相關性性計算模型：tf-idf t = tf t,d * idf t

 說明： tf-idf 相關性性計算模型的值為詞頻（ tf t,d）乘以詞的逆文檔頻率（idf t）

7.2 Common terms query

common 區分常用（高頻）詞查詢讓我們可以通過cutoff_frequency來指定一個分界文檔頻率值，將搜索文本中的詞分為高頻詞和低頻詞，低頻詞的重要性高於高頻詞，先對低頻詞進行搜索並計算所有匹配文檔相關性得分；然后再搜索和高頻詞匹配的文檔，這會搜到很多文檔，但只對和低頻詞重疊的文檔進行相關性得分計算（這可保證搜索精確度，同時大大提高搜索性能），和低頻詞累加作為文檔得分。實際執行的搜索是 必須包含低頻詞 + 或包含高頻詞。

思考：這樣處理下，如果用戶輸入的都是高頻詞如 “to be or not to be”結果會是怎樣的？你希望是怎樣的？

優化：如果都是高頻詞，那就對這些詞進行and 查詢。
進一步優化：讓用戶可以自己定對高頻詞做and/or 操作，自己定對低頻詞進行and/or 操作；或指定最少得多少個同時匹配

示例1:

 

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

說明：

cutoff_frequency : 值大於1表示文檔數，0-1.0表示占比。 此處界定 文檔頻率大於 0.1%的詞為高頻詞。

示例2：

 

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

說明：low_freq_operator指定對低頻詞做與操作
可用參數：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 查詢，讓我們可以直接用lucene查詢語法寫一個查詢串進行查詢，ES中接到請求后，通過查詢解析器解析查詢串生成對應的查詢。使用它要求掌握lucene的查詢語法。

 示例1：指定單個字段查詢

GET /_search
{
"query": {
"query_string" : {
"default_field" : "content",
"query" : "this AND that OR thus"
}
}
}
 示例2：指定多字段通配符查詢

GET /_search
{
"query": {
"query_string" : {
"fields" : ["content", "name.*^5"],
"query" : "this AND that OR thus"
}
}
}
 可與query同用的參數，如 default_field、fields，及query 串的語法請參考：

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

 9. 查詢描述規則語法（查詢解析語法）
Term 詞項：

單個詞項的表示： 電腦
短語的表示： "聯想筆記本電腦"

Field 字段：

字段名:
示例： name:“聯想筆記本電腦” AND type:電腦
如果name是默認字段，則可寫成： “聯想筆記本電腦” AND type:電腦
如果查詢串是：type:電腦 計算機 手機
注意：只有第一個是type的值，后兩個則是使用默認字段。

 Term Modifiers 詞項修飾符：

 

 

 

 

10. Simple Query string query
simple_query_string 查同 query_string 查詢一樣用lucene查詢語法寫查詢串，較query_string不同的地方：更小的語法集；查詢串有錯誤，它會忽略錯誤的部分，不拋出錯誤。更適合給用戶使用。

 示例：

 

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

 語法請參考：

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

 11. Term level querys
 

官網鏈接：

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

 11.1 Term query

term 查詢用於查詢指定字段包含某個詞項的文檔。

 示例1：

POST _search
{
"query": {
"term" : { "user" : "Kimchy" }
}
}
 示例2：加權重

 

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

 11.2 Terms query

 terms 查詢用於查詢指定字段包含某些詞項的文檔。

GET /_search
{
"query": {
"terms" : { "user" : ["kimchy", "elasticsearch"]}
}
}
Terms 查詢支持嵌套查詢的方式來獲得查詢詞項，相當於 in (select term from other)

示例1：Terms query 嵌套查詢示例

 

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"
}
}
}
}
 

查詢結果：

{
"took": 14,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "tweets",
"_type": "_doc",
"_id": "1",
"_score": 1,
"_source": {
"user": "1"
}
}
]
}
}
嵌套查詢可用參數說明：

 

11.3 range query

 范圍查詢示例1：

GET _search
{
"query": {
"range" : {
"age" : {
"gte" : 10,
"lte" : 20,
"boost" : 2.0
}
}
}
}
  范圍查詢示例2：

 

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

  范圍查詢示例3：

 

GET _search
{
"query": {
"range" : {
"born" : {
"gte": "01/01/2012",
"lte": "2013",
"format": "dd/MM/yyyy||yyyy"
}
}
}
}
 范圍查詢參數說明：

 

范圍查詢時間舍入 ||說明：

 

時間數學計算規則請參考：

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

11.4 exists  query

查詢指定字段值不為空的文檔。相當 SQL 中的 column is not null

GET /_search
{
"query": {
"exists" : { "field" : "user" }
}
}
查詢指定字段值為空的文檔

 

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

 11.5 prefix query 詞項前綴查詢

 示例1：

GET /_search
{ "query": {
"prefix" : { "user" : "ki" }
}
}
 示例2：加權

GET /_search
{ "query": {
"prefix" : { "user" : { "value" : "ki", "boost" : 2.0 } }
}
}
 11.6 wildcard query 通配符查詢： ？ *

 示例1：

GET /_search
{
"query": {
"wildcard" : { "user" : "ki*y" }
}
}
 示例2：加權

 

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

11.7  regexp query   正則查詢

示例1：

 

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

示例2：加權

 

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

正則語法請參考：

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

11.8 fuzzy query 模糊查詢

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 查詢

 

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

11.10 ids query   根據文檔id查詢

 

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

12. Compound querys 復合查詢

 官網鏈接：

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

 12.1 Constant Score query

 用來包裝另一個查詢，將查詢匹配的文檔的評分設為一個常值。

 

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

 12.2 Bool query

 Bool 查詢用bool操作來組合多個查詢字句為一個查詢。 可用的關鍵字：

 

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
}
}
}
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