elasticsearch系列五：搜索詳解（查詢建議介紹、Suggester 介紹）

時間  2019-11-11
標籤 elasticsearch 系列 搜索 詳解 查詢 建議 介紹 suggester 欄目 日誌分析 简体版
原文   原文鏈接

1、查詢建議介紹

 1. 查詢建議是什麼？

查詢建議，爲用戶提供良好的使用體驗。主要包括： 拼寫檢查； 自動建議查詢詞（自動補全）html

 拼寫檢查如圖：java

自動建議查詢詞（自動補全）：spring

 

2. ES中查詢建議的API

 查詢建議也是使用_search端點地址。在DSL中suggest節點來定義須要的建議查詢後端

 示例1：定義單個建議查詢詞數據結構

POST twitter/_search
{
  "query" : {
    "match": {
      "message": "tring out Elasticsearch"
    }
  },
  "suggest" : { <!-- 定義建議查詢 --> "my-suggestion" : { <!-- 一個建議查詢名 --> "text" : "tring out Elasticsearch", <!-- 查詢文本 --> "term" : { <!-- 使用詞項建議器 --> "field" : "message" <!-- 指定在哪一個字段上獲取建議詞 --> } } }
}

示例2：定義多個建議查詢詞app

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

示例3：多個建議查詢可使用全局的查詢文本elasticsearch

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

2、Suggester 介紹

1. Term suggester

term 詞項建議器，對給入的文本進行分詞，爲每一個詞進行模糊查詢提供詞項建議。對於在索引中存在詞默認不提供建議詞，不存在的詞則根據模糊查詢結果進行排序後取必定數量的建議詞。ide

經常使用的建議選項：ui

示例1：編碼

POST twitter/_search
{
  "query" : {
    "match": {
      "message": "tring out Elasticsearch"
    }
  },
  "suggest" : { <!-- 定義建議查詢 -->
    "my-suggestion" : { <!-- 一個建議查詢名 -->
      "text" : "tring out Elasticsearch", <!-- 查詢文本 -->
      "term" : { <!-- 使用詞項建議器 -->
        "field" : "message" <!-- 指定在哪一個字段上獲取建議詞 -->
      }
    }
  }
}

 2. phrase suggester

phrase 短語建議，在term的基礎上，會考量多個term之間的關係，好比是否同時出如今索引的原文裏，相鄰程度，以及詞頻等

 示例1：

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

 結果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   自動補全

針對自動補全場景而設計的建議器。此場景下用戶每輸入一個字符的時候，就須要即時發送一次查詢請求到後端查找匹配項，在用戶輸入速度較高的狀況下對後端響應速度要求比較苛刻。所以實現上它和前面兩個Suggester採用了不一樣的數據結構，索引並不是經過倒排來完成，而是將analyze過的數據編碼成FST和索引一塊兒存放。對於一個open狀態的索引，FST會被ES整個裝載到內存裏的，進行前綴查找速度極快。可是FST只能用於前綴查找，這也是Completion Suggester的侷限所在。

 官網連接：

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

 爲了使用自動補全，索引中用來提供補全建議的字段需特殊設計，字段類型爲 completion。

PUT music
{
    "mappings": {
        "_doc" : {
            "properties" : {
                "suggest" : { <!-- 用於自動補全的字段 --> "type" : "completion" },
                "title" : {
                    "type": "keyword"
                }
            }
        }
    }
}

Input 指定輸入詞 Weight 指定排序值（可選）

PUT music/_doc/1?refresh
{
    "suggest" : {
        "input": [ "Nevermind", "Nirvana" ],
        "weight" : 34
    }
}

 指定不一樣的排序值：

PUT music/_doc/1?refresh
{
    "suggest" : [
        {
            "input": "Nevermind",
            "weight" : 10
        },
        {
            "input": "Nirvana",
            "weight" : 3
        }
    ]}

 放入一條重複數據

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

 示例1：查詢建議根據前綴查詢：

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

結果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：對建議查詢結果去重

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

 結果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：查詢建議文檔存儲短語

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
    }
}

 查詢3：

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

結果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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