一、搜索API
1. 搜索API 端點地址
從索引tweet裏面搜索字段user爲kimchy的記錄
GET /twitter/_search?q=user:kimchy
從索引tweet,user裏面搜索字段user爲kimchy的記錄
GET /twitter/tweet,user/_search?q=user:kimchy
GET /kimchy,elasticsearch/_search?q=tag:wow
從所有索引裏面搜索字段tag爲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。搜索的參數可作爲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。缺省爲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" } } }
包含什麼不包含什麼
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後返回
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:
View Code
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:
View Code
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 後置過濾:在查詢命中文檔、完成聚合後,再對命中的文檔進行過濾。
如:要在一次查詢中查詢品牌爲gucci且顏色爲紅色的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"} } } } }
往索引裏面放入文檔即類似數據庫裏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:
View Code
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 摺疊
用 collapse指定根據某個字段對命中結果進行摺疊
GET /bank/_search { "query": { "match_all": {} }, "collapse" : { "field" : "age" }, "sort": ["balance"] }
查詢結果:
View Code
高級摺疊
GET /bank/_search { "query": { "match_all": {} }, "collapse" : { "field" : "age" , <!--指定inner_hits來解釋摺疊 --> "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 <!-- 指定組查詢的併發數 --> }, "sort": ["balance"] }
查詢結果:
View Code
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,爲了不因分頁導致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,默認值爲 10,000。
需要深度分頁, 不受index.max_result_window 限制,怎麼辦?
Search after 在指定文檔後取文檔, 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"} ] }
後續頁的查詢
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 只能爲0、-1。
6.3.2 高亮
準備數據:
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
指定高亮標籤
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 爲了調試、優化
對於執行緩慢的查詢,我們很想知道它爲什麼慢,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
讓我們可以瞭解可執行查詢的索引分片節點情況
GET /twitter/_search_shards
查詢結果:
View Code
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
註冊一個模板
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": [] } }
詳細瞭解請參考官網:
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是什麼?
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 複合查詢字句
以邏輯方式組合多個葉子、複合查詢爲一個查詢
2. Query and filter context
一個查詢字句的行爲取決於它是用在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,最後兩個結果取交集
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,什麼都不查
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
全文查詢的標準查詢,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,最大編輯數爲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後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:
View Code
7. Common terms query
common 常用詞查詢
問1、什麼是停用詞?索引時做停用詞處理的目的是什麼?
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麼詞項?
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麼影響?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“世界盃”在文檔A中出現3次,那麼可以定義“世界盃”在文檔A中的詞頻爲3。請問在一篇3000字的文章中出現“世界盃”3次和一篇150字的文章中出現3詞,哪篇文章更是與“世界盃”有關的。也就是說,簡單用出現次數作爲頻率不夠準確。那就用佔比來表示:
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“世界盃”,文檔A中就出現了“世界盃”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範圍。
說明: +1 是爲了避免除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爲詞頻( 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爲高頻詞和低頻詞,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;然後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),和低頻詞累加作爲文檔得分。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表示佔比。 此處界定 文檔頻率大於 0.1%的詞爲高頻詞。
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中接到請求後,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,後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
範圍查詢示例1:
GET _search { "query": { "range" : { "age" : { "gte" : 10, "lte" : 20, "boost" : 2.0 } } } }
範圍查詢示例2:
GET _search { "query": { "range" : { "date" : { "gte" : "now-1d/d", "lt" : "now/d" } } } }
範圍查詢示例3:
GET _search { "query": { "range" : { "born" : { "gte": "01/01/2012", "lte": "2013", "format": "dd/MM/yyyy||yyyy" } } } }
範圍查詢參數說明:
範圍查詢時間舍入 ||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
查詢指定字段值不爲空的文檔。相當 SQL 中的 column is not null
GET /_search { "query": { "exists" : { "field" : "user" } } }
查詢指定字段值爲空的文檔
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 複合查詢
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爲一個常值。
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爲一個查詢。 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 } } }
說明:should滿足一個或者兩個或者都不滿足