一、查詢建議介紹
1. 查詢建議是什麼？
查詢建議，爲用戶提供良好的使用體驗。主要包括： 拼寫檢查； 自動建議查詢詞（自動補全）
拼寫檢查如圖：
自動建議查詢詞（自動補全）：
2. ES中查詢建議的API
查詢建議也是使用_search端點地址。在DSL中suggest節點來定義需要的建議查詢
示例1：定義單個建議查詢詞
POST twitter/_search
{
"query" : {
"match": {
"message": "tring out Elasticsearch"
}
},
"suggest" : { <!-- 定義建議查詢 -->
"my-suggestion" : { <!-- 一個建議查詢名 -->
"text" : "tring out Elasticsearch", <!-- 查詢文本 -->
"term" : { <!-- 使用詞項建議器 -->
"field" : "message" <!-- 指定在哪個字段上獲取建議詞 -->
}
}
}
}
示例2：定義多個建議查詢詞
POST _search
{
"suggest": {
"my-suggest-1" : {
"text" : "tring out Elasticsearch",
"term" : {
"field" : "message"
}
},
"my-suggest-2" : {
"text" : "kmichy",
"term" : {
"field" : "user"
}
}
}
}
示例3：多個建議查詢可以使用全局的查詢文本
POST _search
{
"suggest": {
"text" : "tring out Elasticsearch",
"my-suggest-1" : {
"term" : {
"field" : "message"
}
},
"my-suggest-2" : {
"term" : {
"field" : "user"
}
}
}
}
二、Suggester 介紹
1. Term suggester
term 詞項建議器，對給入的文本進行分詞，爲每個詞進行模糊查詢提供詞項建議。對於在索引中存在詞默認不提供建議詞，不存在的詞則根據模糊查詢結果進行排序後取一定數量的建議詞。
常用的建議選項：
示例1：
POST twitter/_search
{
"query" : {
"match": {
"message": "tring out Elasticsearch"
}
},
"suggest" : { <!-- 定義建議查詢 -->
"my-suggestion" : { <!-- 一個建議查詢名 -->
"text" : "tring out Elasticsearch", <!-- 查詢文本 -->
"term" : { <!-- 使用詞項建議器 -->
"field" : "message" <!-- 指定在哪個字段上獲取建議詞 -->
}
}
}
}
2. phrase suggester
phrase 短語建議，在term的基礎上，會考量多個term之間的關係，比如是否同時出現在索引的原文裏，相鄰程度，以及詞頻等
示例1：
POST /ftq/_search
{
"query": {
"match_all": {}
},
"suggest" : {
"myss":{
"text": "java sprin boot",
"phrase": {
"field": "title"
}
}
}
}
結果1：
{
"took": 177,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "ftq",
"_type": "_doc",
"_id": "2",
"_score": 1,
"_source": {
"title": "java spring boot",
"content": "lucene is writerd by java"
}
},
{
"_index": "ftq",
"_type": "_doc",
"_id": "1",
"_score": 1,
"_source": {
"title": "lucene solr and elasticsearch",
"content": "lucene solr and elasticsearch for search"
}
}
]
},
"suggest": {
"myss": [
{
"text": "java sprin boot",
"offset": 0,
"length": 15,
"options": [
{
"text": "java spring boot",
"score": 0.20745796
}
]
}
]
}
}
3. Completion suggester 自動補全
針對自動補全場景而設計的建議器。此場景下用戶每輸入一個字符的時候，就需要即時發送一次查詢請求到後端查找匹配項，在用戶輸入速度較高的情況下對後端響應速度要求比較苛刻。因此實現上它和前面兩個Suggester採用了不同的數據結構，索引並非通過倒排來完成，而是將analyze過的數據編碼成FST和索引一起存放。對於一個open狀態的索引，FST會被ES整個裝載到內存裏的，進行前綴查找速度極快。但是FST只能用於前綴查找，這也是Completion Suggester的侷限所在。
官網鏈接：
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-completion.html
爲了使用自動補全，索引中用來提供補全建議的字段需特殊設計，字段類型爲 completion。
PUT music
{
"mappings": {
"_doc" : {
"properties" : {
"suggest" : { <!-- 用於自動補全的字段 -->
"type" : "completion"
},
"title" : {
"type": "keyword"
}
}
}
}
}
Input 指定輸入詞 Weight 指定排序值（可選）
PUT music/_doc/1?refresh
{
"suggest" : {
"input": [ "Nevermind", "Nirvana" ],
"weight" : 34
}
}
指定不同的排序值：
PUT music/_doc/1?refresh
{
"suggest" : [
{
"input": "Nevermind",
"weight" : 10
},
{
"input": "Nirvana",
"weight" : 3
}
]}
放入一條重複數據
PUT music/_doc/2?refresh
{
"suggest" : {
"input": [ "Nevermind", "Nirvana" ],
"weight" : 20
}
}
示例1：查詢建議根據前綴查詢：
POST music/_search?pretty
{
"suggest": {
"song-suggest" : {
"prefix" : "nir",
"completion" : {
"field" : "suggest"
}
}
}
}
結果1：
{
"took": 25,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": 0,
"hits": []
},
"suggest": {
"song-suggest": [
{
"text": "nir",
"offset": 0,
"length": 3,
"options": [
{
"text": "Nirvana",
"_index": "music",
"_type": "_doc",
"_id": "2",
"_score": 20,
"_source": {
"suggest": {
"input": [
"Nevermind",
"Nirvana"
],
"weight": 20
}
}
},
{
"text": "Nirvana",
"_index": "music",
"_type": "_doc",
"_id": "1",
"_score": 1,
"_source": {
"suggest": [
"Nevermind",
"Nirvana"
]
}
}
]
}
]
}
}
示例2：對建議查詢結果去重
POST music/_search?pretty
{
"suggest": {
"song-suggest" : {
"prefix" : "nir",
"completion" : {
"field" : "suggest",
"skip_duplicates": true
}
} }}
結果2：
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": 0,
"hits": []
},
"suggest": {
"song-suggest": [
{
"text": "nir",
"offset": 0,
"length": 3,
"options": [
{
"text": "Nirvana",
"_index": "music",
"_type": "_doc",
"_id": "2",
"_score": 20,
"_source": {
"suggest": {
"input": [
"Nevermind",
"Nirvana"
],
"weight": 20
}
}
}
]
}
]
}
}
示例3：查詢建議文檔存儲短語
PUT music/_doc/3?refresh
{
"suggest" : {
"input": [ "lucene solr", "lucene so cool","lucene elasticsearch" ],
"weight" : 20
}
}
PUT music/_doc/4?refresh
{
"suggest" : {
"input": ["lucene solr cool","lucene elasticsearch" ],
"weight" : 10
}
}
查詢3：
POST music/_search?pretty
{
"suggest": {
"song-suggest" : {
"prefix" : "lucene s",
"completion" : {
"field" : "suggest" ,
"skip_duplicates": true
}
}
}
}
結果3：
{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": 0,
"hits": []
},
"suggest": {
"song-suggest": [
{
"text": "lucene s",
"offset": 0,
"length": 8,
"options": [
{
"text": "lucene so cool",
"_index": "music",
"_type": "_doc",
"_id": "3",
"_score": 20,
"_source": {
"suggest": {
"input": [
"lucene solr",
"lucene so cool",
"lucene elasticsearch"
],
"weight": 20
}
}
},
{
"text": "lucene solr cool",
"_index": "music",
"_type": "_doc",
"_id": "4",
"_score": 10,
"_source": {
"suggest": {
"input": [
"lucene solr cool",
"lucene elasticsearch"
],
"weight": 10
}
}
}
]
}
]
}
}