查询建议是什么？
查询建议，为用户提供良好的使用体验。主要包括： 拼写检查； 自动建议查询词（自动补全）
拼写检查如图：
自动建议查询词（自动补全）：
搜索建议
通过Suggester Api实现
原理是将输入的文本分解为Token，然后在词典中查找类似的Term返回
根据不同场景，ElasticSearch设计了4中类别的Suggesters。
Term Suggester
Phrase Suggester
Complete Suggester
Context Suggester
Term Suggester
term 词项建议器，对给入的文本进行分词，为每个词进行模糊查询提供词项建议。对于在索引中存在词默认不提供建议词，不存在的词则根据模糊查询结果进行排序后取一定数量的建议词。
类似Google搜索引擎，我给的是一个错误的单词elasticserch，但引擎友好地给出了搜索建议。
要实现这个功能，在ElasticSearch中很简单。
创建索引,并写入一些文档
POST articles/_bulk
{ "index" : { } }
{ "body": "lucene is very cool"}
{ "index" : { } }
{ "body": "Elasticsearch builds on top of lucene"}
{ "index" : { } }
{ "body": "Elasticsearch rocks"}
{ "index" : { } }
{ "body": "elastic is the company behind ELK stack"}
{ "index" : { } }
{ "body": "Elk stack rocks"}
{ "index" : {} }
{ "body": "elasticsearch is rock solid"}搜索文档，调用suggest api。有3种Suggestion Mode
1.missing 索引中已经存在，就不提供建议
2.popular 推荐出现频率更加高的词
3.always 无论是否存在，都提供建议
POST /articles/_search
{
"size": 1,
"query": {
"match": {
"body": "elasticserch"
}
},
"suggest": {
"term-suggestion": {
"text": "elasticserch",
"term": {
"suggest_mode": "missing",
"field": "body"
}
}
}
}返回结果
{
"took" : 130,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"suggest" : {
"term-suggestion" : [
{
"text" : "elasticserch",
"offset" : 0,
"length" : 12,
"options" : [
{
"text" : "elasticsearch",
"score" : 0.9166667,
"freq" : 3
}
]
}
]
}
}Phrase Suggester
phrase 短语建议，在term的基础上，会考量多个term之间的关系，比如是否同时出现在索引的原文里，相邻程度，以及词频等
其中一些参数
max_errors 最多可以拼错的terms
confidence 限制返回结果数，默认1
POST /articles/_search
{
"suggest": {
"my-suggestion": {
"text": "lucne and elasticsear rock hello world ",
"phrase": {
"field": "body",
"max_errors":2,
"confidence":2,
"direct_generator":[{
"field":"body",
"suggest_mode":"missing"
}],
"highlight": {
"pre_tag": "<em>",
"post_tag": "</em>"
}
}
}
}
}结果如下：
{
"took" : 288,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"suggest" : {
"my-suggestion" : [
{
"text" : "lucne and elasticsear rock hello world ",
"offset" : 0,
"length" : 39,
"options" : [
{
"text" : "lucene and elasticsearch rock hello world",
"highlighted" : "<em>lucene</em> and <em>elasticsearch</em> rock hello world",
"score" : 1.5788074E-4
}
]
}
]
}
}Completion Suggester
针对自动补全场景而设计的建议器。此场景下用户每输入一个字符的时候，就需要即时发送一次查询请求到后端查找匹配项，在用户输入速度较高的情况下对后端响应速度要求比较苛刻。因此实现上它和前面两个Suggester采用了不同的数据结构，索引并非通过倒排来完成，而是将analyze过的数据编码成FST和索引一起存放。对于一个open状态的索引，FST会被ES整个装载到内存里的，进行前缀查找速度极快。但是FST只能用于前缀查找，这也是Completion Suggester的局限所在。
类似百度这样的提示功能
在ElasticSearch要实现这样的功能也很简单。
1. 建立索引
PUT titles
{
"mappings": {
"properties": {
"title_completion": {
"type": "completion"
}
}
}
}2. 写入文档
POST titles/_bulk
{ "index" : { } }
{ "title_completion": "php是什么"}
{ "index" : { } }
{ "title_completion": "php是世界上最好的语言"}
{ "index" : { } }
{ "title_completion": "php货币"}
{ "index" : { } }
{ "title_completion": "php面试题2019"}3. 搜索数据
POST titles/_search?pretty
{
"size": 0,
"suggest": {
"article-suggester": {
"prefix": "php",
"completion": {
"field": "title_completion"
}
}
}
}3. 返回结果
{
"took" : 95,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"suggest" : {
"article-suggester" : [
{
"text" : "php",
"offset" : 0,
"length" : 3,
"options" : [
{
"text" : "php是世界上最好的语言",
"_index" : "titles",
"_type" : "_doc",
"_id" : "BhTVQXkBv4MlM6fI82Mo",
"_score" : 1.0,
"_source" : {
"title_completion" : "php是世界上最好的语言"
}
},
{
"text" : "php是什么",
"_index" : "titles",
"_type" : "_doc",
"_id" : "BRTVQXkBv4MlM6fI82Mo",
"_score" : 1.0,
"_source" : {
"title_completion" : "php是什么"
}
},
{
"text" : "php货币",
"_index" : "titles",
"_type" : "_doc",
"_id" : "BxTVQXkBv4MlM6fI82Mo",
"_score" : 1.0,
"_source" : {
"title_completion" : "php货币"
}
},
{
"text" : "php面试题2019",
"_index" : "titles",
"_type" : "_doc",
"_id" : "CBTVQXkBv4MlM6fI82Mo",
"_score" : 1.0,
"_source" : {
"title_completion" : "php面试题2019"
}
}
]
}
]
}
}Context Suggester
是Completion Suggester的扩展，加入了上下文信息场景。
例如：
你在电器商城，输入苹果，想要找到的苹果笔记本...
你在水果商城，输入苹果，想要找的是红苹果、绿苹果...
1. 建立索引，定制mapping
PUT comments
{
"mappings": {
"properties": {
"comment_autocomplete": {
"type": "completion",
"contexts": [{
"type": "category",
"name": "comment_category"
}]
}
}
}
}2. 并为每个文档加入Context信息
POST comments/_doc
{
"comment": "苹果电脑",
"comment_autocomplete": {
"input": ["苹果电脑"],
"contexts": {
"comment_category": "电器商城"
}
}
}
POST comments/_doc
{
"comment": "红红的冰糖心苹果",
"comment_autocomplete": {
"input": ["苹果"],
"contexts": {
"comment_category": "水果商城"
}
}
}3. 结合Context进行Suggestion查询
POST comments/_search
{
"suggest": {
"MY_SUGGESTION": {
"prefix": "苹",
"completion": {
"field": "comment_autocomplete",
"contexts": {
"comment_category": "电器商城"
}
}
}
}
}4. 返回结果
{
"took" : 586,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"suggest" : {
"MY_SUGGESTION" : [
{
"text" : "苹",
"offset" : 0,
"length" : 1,
"options" : [
{
"text" : "苹果电脑",
"_index" : "comments",
"_type" : "_doc",
"_id" : "CRTcQXkBv4MlM6fIDmPo",
"_score" : 1.0,
"_source" : {
"comment" : "苹果电脑",
"comment_autocomplete" : {
"input" : [
"苹果电脑"
],
"contexts" : {
"comment_category" : "电器商城"
}
}
},
"contexts" : {
"comment_category" : [
"电器商城"
]
}
}
]
}
]
}
}附录
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