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

2024-06-16 23:23| 来源: 网络整理| 查看: 265

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" : { "my-suggestion" : { "text" : "tring out Elasticsearch", "term" : { "field" : "message" } } } }

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" : { "my-suggestion" : { "text" : "tring out Elasticsearch", "term" : { "field" : "message" } } } } 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" : { "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 } } } ] } ] } }

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