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。对于在索引中存在的词，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，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
    }
}

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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