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              }            }          }        ]      }    ]  }}