elasticsearch系列五：搜索详解（查询建议介绍、Suggester 介绍）

一、查询建议介绍

1. 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）：

2. ES中查询建议的API

查询建议也是使用_search端点地址。在DSL中suggest节点来定义需要的建议查询

示例1：定义单个建议查询词

POST twitter/_search
{"query" : {"match": {"message": "tring out Elasticsearch"}},"suggest" : { <!-- 定义建议查询 -->"my-suggestion" : { <!-- 一个建议查询名 -->"text" : "tring out Elasticsearch", <!-- 查询文本 -->"term" : { <!-- 使用词项建议器 -->"field" : "message" <!-- 指定在哪个字段上获取建议词 -->}}}
}

示例2：定义多个建议查询词

POST _search
{"suggest": {"my-suggest-1" : {"text" : "tring out Elasticsearch","term" : {"field" : "message"}},"my-suggest-2" : {"text" : "kmichy","term" : {"field" : "user"}}}
}

示例3：多个建议查询可以使用全局的查询文本

POST _search
{"suggest": {"text" : "tring out Elasticsearch","my-suggest-1" : {"term" : {"field" : "message"}},"my-suggest-2" : {"term" : {"field" : "user"}}}
}

二、Suggester 介绍

1. 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。

常用的建议选项：

示例1：

POST twitter/_search
{"query" : {"match": {"message": "tring out Elasticsearch"}},"suggest" : { <!-- 定义建议查询 -->"my-suggestion" : { <!-- 一个建议查询名 -->"text" : "tring out Elasticsearch", <!-- 查询文本 -->"term" : { <!-- 使用词项建议器 -->"field" : "message" <!-- 指定在哪个字段上获取建议词 -->}}}
}

2. 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

示例1：

POST /ftq/_search
{"query": {"match_all": {}},"suggest" : {"myss":{"text": "java sprin boot","phrase": {"field": "title"}}}
}

结果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 自动补全

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。

官网链接：

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

为了使用自动补全，索引中用来提供补全建议的字段需特殊设计，字段类型为 completion。

PUT music
{"mappings": {"_doc" : {"properties" : {"suggest" : {  <!-- 用于自动补全的字段 -->"type" : "completion"},"title" : {"type": "keyword"}}}}
}

Input 指定输入词 Weight 指定排序值（可选）

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}]}

放入一条重复数据

PUT music/_doc/2?refresh
{"suggest" : {"input": [ "Nevermind", "Nirvana" ],"weight" : 20}
}

示例1：查询建议根据前缀查询：

POST music/_search?pretty
{"suggest": {"song-suggest" : {"prefix" : "nir", "completion" : { "field" : "suggest" }}}
}

结果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：对建议查询结果去重

POST music/_search?pretty
{"suggest": {"song-suggest" : {"prefix" : "nir", "completion" : { "field" : "suggest","skip_duplicates": true }}    }}

结果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：查询建议文档存储短语

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}
}

查询3：

POST music/_search?pretty
{"suggest": {"song-suggest" : {"prefix" : "lucene s", "completion" : { "field" : "suggest" ,"skip_duplicates": true}}}
}

结果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}}}]}]}
}

转载于:https://www.cnblogs.com/leeSmall/p/9206646.html