• ElasticSearch中全文搜索（单词搜索、多次搜索、组合搜索和权重搜索）

    全文搜索两个最重要的方面是：

    • 相关性（Relevance） 它是评价查询与其结果间的相关程度，并根据这种相关程度对结果排名的一种能力，这种计算方式可以是 TF/IDF 方法、地理位置邻近、模糊相似，或其他的某些算法。
    • 分词（Analysis） 它是将文本块转换为有区别的、规范化的 token 的一个过程，目的是为了创建倒排索引以及 查询倒排索引。

    一、构造数据

    1、数据库中当前数据

     2、构建索引

    1. PUT http://127.0.0.1:9200/study
    2.  
    3. # 请求数据
    4. {
    5.     "settings": {
    6.         "index": {
    7.             "number_of_shards": "1",
    8.             "number_of_replicas": "0"
    9.         }
    10.     },
    11.     "mappings": {
    12.         "properties": {
    13.             "name": {
    14.                 "type": "text"
    15.             },
    16.             "age": {
    17.                 "type": "integer"
    18.             },
    19.             "mail": {
    20.                 "type": "keyword"
    21.             },
    22.             "hobby": {
    23.                 "type": "text",
    24.                 "analyzer": "ik_max_word"
    25.             }
    26.         }
    27.     }
    28. }
    29.  
    30. # 响应数据
    31. {
    32.     "acknowledged": true,
    33.     "shards_acknowledged": true,
    34.     "index": "study"
    35. }

    3、添加数据

    1. POST http://127.0.0.1:9200/study/_bulk
    2.  
    3. # 请求数据
    4. {"index":{"_index":"study"}} 
    5. {"name":"张三","age": 20,"mail": "111@qq.com","hobby":"羽毛球、乒乓球、足球"} 
    6. {"index":{"_index":"study"}} 
    7. {"name":"李四","age": 21,"mail": "222@qq.com","hobby":"羽毛球、乒乓球、足球、篮球"} 
    8. {"index":{"_index":"study"}} 
    9. {"name":"王五","age": 22,"mail": "333@qq.com","hobby":"羽毛球、篮球、游泳、听音乐"} 
    10. {"index":{"_index":"study"}} 
    11. {"name":"赵六","age": 23,"mail": "444@qq.com","hobby":"跑步、游泳"} 
    12. {"index":{"_index":"study"}} 
    13. {"name":"孙七","age": 24,"mail": "555@qq.com","hobby":"听音乐、看电影"}
    14.  
    15. # 响应数据
    16. {
    17.     "took": 16,
    18.     "errors": false,
    19.     "items": [
    20.         {
    21.             "index": {
    22.                 "_index": "study",
    23.                 "_type": "_doc",
    24.                 "_id": "i6jJdoIBU4c5cKp3GGKx",
    25.                 "_version": 1,
    26.                 "result": "created",
    27.                 "_shards": {
    28.                     "total": 1,
    29.                     "successful": 1,
    30.                     "failed": 0
    31.                 },
    32.                 "_seq_no": 0,
    33.                 "_primary_term": 1,
    34.                 "status": 201
    35.             }
    36.         },
    37.         {
    38.             "index": {
    39.                 "_index": "study",
    40.                 "_type": "_doc",
    41.                 "_id": "jKjJdoIBU4c5cKp3GGKx",
    42.                 "_version": 1,
    43.                 "result": "created",
    44.                 "_shards": {
    45.                     "total": 1,
    46.                     "successful": 1,
    47.                     "failed": 0
    48.                 },
    49.                 "_seq_no": 1,
    50.                 "_primary_term": 1,
    51.                 "status": 201
    52.             }
    53.         },
    54.         {
    55.             "index": {
    56.                 "_index": "study",
    57.                 "_type": "_doc",
    58.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    59.                 "_version": 1,
    60.                 "result": "created",
    61.                 "_shards": {
    62.                     "total": 1,
    63.                     "successful": 1,
    64.                     "failed": 0
    65.                 },
    66.                 "_seq_no": 2,
    67.                 "_primary_term": 1,
    68.                 "status": 201
    69.             }
    70.         },
    71.         {
    72.             "index": {
    73.                 "_index": "study",
    74.                 "_type": "_doc",
    75.                 "_id": "jqjJdoIBU4c5cKp3GGKx",
    76.                 "_version": 1,
    77.                 "result": "created",
    78.                 "_shards": {
    79.                     "total": 1,
    80.                     "successful": 1,
    81.                     "failed": 0
    82.                 },
    83.                 "_seq_no": 3,
    84.                 "_primary_term": 1,
    85.                 "status": 201
    86.             }
    87.         },
    88.         {
    89.             "index": {
    90.                 "_index": "study",
    91.                 "_type": "_doc",
    92.                 "_id": "j6jJdoIBU4c5cKp3GGKx",
    93.                 "_version": 1,
    94.                 "result": "created",
    95.                 "_shards": {
    96.                     "total": 1,
    97.                     "successful": 1,
    98.                     "failed": 0
    99.                 },
    100.                 "_seq_no": 4,
    101.                 "_primary_term": 1,
    102.                 "status": 201
    103.             }
    104.         }
    105.     ]
    106. }

    二、全文搜索

    2.1、单词搜索

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "match": {
    7.             "hobby": "音乐"
    8.         }
    9.     },
    10.     "highlight": {
    11.         "fields": {
    12.             "hobby": {}
    13.         }
    14.     }
    15. }
    16.  
    17. # 响应数据
    18. {
    19.     "took": 67,
    20.     "timed_out": false,
    21.     "_shards": {
    22.         "total": 1,
    23.         "successful": 1,
    24.         "skipped": 0,
    25.         "failed": 0
    26.     },
    27.     "hits": {
    28.         "total": {
    29.             "value": 2,
    30.             "relation": "eq"
    31.         },
    32.         "max_score": 0.9395274,
    33.         "hits": [
    34.             {
    35.                 "_index": "study",
    36.                 "_type": "_doc",
    37.                 "_id": "j6jJdoIBU4c5cKp3GGKx",
    38.                 "_score": 0.9395274,
    39.                 "_source": {
    40.                     "name": "孙七",
    41.                     "age": 24,
    42.                     "mail": "555@qq.com",
    43.                     "hobby": "听音乐、看电影"
    44.                 },
    45.                 "highlight": {
    46.                     "hobby": [
    47.                         "听音乐、看电影"
    48.                     ]
    49.                 }
    50.             },
    51.             {
    52.                 "_index": "study",
    53.                 "_type": "_doc",
    54.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    55.                 "_score": 0.77041256,
    56.                 "_source": {
    57.                     "name": "王五",
    58.                     "age": 22,
    59.                     "mail": "333@qq.com",
    60.                     "hobby": "羽毛球、篮球、游泳、听音乐"
    61.                 },
    62.                 "highlight": {
    63.                     "hobby": [
    64.                         "羽毛球、篮球、游泳、听音乐"
    65.                     ]
    66.                 }
    67.             }
    68.         ]
    69.     }
    70. }

    过程说明：

    1. 检查字段类型

            爱好 hobby 字段是一个 text 类型（ 指定了IK分词器），这意味着查询字符串本身也应该被分词。

    2. 分析查询字符串 。

            将查询的字符串 “音乐” 传入IK分词器中，输出的结果是单个项 音乐。因为只有一个单词项，所以 match 查询执 行的是单个底层 term 查询。

    3. 查找匹配文档 。

            用 term 查询在倒排索引中查找 “音乐” 然后获取一组包含该项的文档，本例的结果是文档：3 、5 。

    4. 为每个文档评分 。

            用 term 查询计算每个文档相关度评分 _score ，这是种将 词频（term frequency，即词 “音乐” 在相关文档的 hobby 字段中出现的频率）和 反向文档频率（inverse document frequency，即词 “音乐” 在所有文档的 hobby 字段中出现的频率），以及字段的长度（即字段越短相关度越高）相结合的计算方式。

    2.2、单词搜索

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "match": {
    7.             "hobby": "音乐 篮球"
    8.         }
    9.     },
    10.     "highlight": {
    11.         "fields": {
    12.             "hobby": {}
    13.         }
    14.     }
    15. }
    16.  
    17. # 响应数据
    18. {
    19.     "took": 22,
    20.     "timed_out": false,
    21.     "_shards": {
    22.         "total": 1,
    23.         "successful": 1,
    24.         "skipped": 0,
    25.         "failed": 0
    26.     },
    27.     "hits": {
    28.         "total": {
    29.             "value": 3,
    30.             "relation": "eq"
    31.         },
    32.         "max_score": 1.5408251,
    33.         "hits": [
    34.             {
    35.                 "_index": "study",
    36.                 "_type": "_doc",
    37.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    38.                 "_score": 1.5408251,
    39.                 "_source": {
    40.                     "name": "王五",
    41.                     "age": 22,
    42.                     "mail": "333@qq.com",
    43.                     "hobby": "羽毛球、篮球、游泳、听音乐"
    44.                 },
    45.                 "highlight": {
    46.                     "hobby": [
    47.                         "羽毛球、篮球、游泳、听音乐"
    48.                     ]
    49.                 }
    50.             },
    51.             {
    52.                 "_index": "study",
    53.                 "_type": "_doc",
    54.                 "_id": "j6jJdoIBU4c5cKp3GGKx",
    55.                 "_score": 0.9395274,
    56.                 "_source": {
    57.                     "name": "孙七",
    58.                     "age": 24,
    59.                     "mail": "555@qq.com",
    60.                     "hobby": "听音乐、看电影"
    61.                 },
    62.                 "highlight": {
    63.                     "hobby": [
    64.                         "听音乐、看电影"
    65.                     ]
    66.                 }
    67.             },
    68.             {
    69.                 "_index": "study",
    70.                 "_type": "_doc",
    71.                 "_id": "jKjJdoIBU4c5cKp3GGKx",
    72.                 "_score": 0.77041256,
    73.                 "_source": {
    74.                     "name": "李四",
    75.                     "age": 21,
    76.                     "mail": "222@qq.com",
    77.                     "hobby": "羽毛球、乒乓球、足球、篮球"
    78.                 },
    79.                 "highlight": {
    80.                     "hobby": [
    81.                         "羽毛球、乒乓球、足球、篮球"
    82.                     ]
    83.                 }
    84.             }
    85.         ]
    86.     }
    87. }

            上面查询中只要是包含篮球和音乐的都被查询出来了。但是这有时候不能达到我们的要求，我们大部分时候都是希望两个词是同时包含的。这时候可以使用elasticsearch中指定词之间逻辑关系operator："and"

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "match": {
    7.             "hobby": {
    8.                 "query": "音乐 篮球",
    9.                 "operator": "and"
    10.             }
    11.         }
    12.     },
    13.     "highlight": {
    14.         "fields": {
    15.             "hobby": {}
    16.         }
    17.     }
    18. }
    19.  
    20. # 响应结果
    21. {
    22.     "took": 6,
    23.     "timed_out": false,
    24.     "_shards": {
    25.         "total": 1,
    26.         "successful": 1,
    27.         "skipped": 0,
    28.         "failed": 0
    29.     },
    30.     "hits": {
    31.         "total": {
    32.             "value": 1,
    33.             "relation": "eq"
    34.         },
    35.         "max_score": 1.5408251,
    36.         "hits": [
    37.             {
    38.                 "_index": "study",
    39.                 "_type": "_doc",
    40.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    41.                 "_score": 1.5408251,
    42.                 "_source": {
    43.                     "name": "王五",
    44.                     "age": 22,
    45.                     "mail": "333@qq.com",
    46.                     "hobby": "羽毛球、篮球、游泳、听音乐"
    47.                 },
    48.                 "highlight": {
    49.                     "hobby": [
    50.                         "羽毛球、篮球、游泳、听音乐"
    51.                     ]
    52.                 }
    53.             }
    54.         ]
    55.     }
    56. }

            上面的测试结果都是选择了"and"和"or"两个极端情况下，但是在真正搜索中，我们不会使用这两个极端情况的，这样就需要另外一种查询方式，即为只需要符合一定的相似度就可以查询到的数据，在elasticsearch中就支持这种查询方式，如使用minimum_should_match来指定匹配度，如60%。

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "match": {
    7.             "hobby": {
    8.                 "query": "游泳 羽毛球",
    9.                 "minimum_should_match": "80%"
    10.             }
    11.         }
    12.     },
    13.     "highlight": {
    14.         "fields": {
    15.             "hobby": {}
    16.         }
    17.     }
    18. }
    19.  
    20. # 响应数据
    21. {
    22.     "took": 6,
    23.     "timed_out": false,
    24.     "_shards": {
    25.         "total": 1,
    26.         "successful": 1,
    27.         "skipped": 0,
    28.         "failed": 0
    29.     },
    30.     "hits": {
    31.         "total": {
    32.             "value": 3,
    33.             "relation": "eq"
    34.         },
    35.         "max_score": 2.1933634,
    36.         "hits": [
    37.             {
    38.                 "_index": "study",
    39.                 "_type": "_doc",
    40.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    41.                 "_score": 2.1933634,
    42.                 "_source": {
    43.                     "name": "王五",
    44.                     "age": 22,
    45.                     "mail": "333@qq.com",
    46.                     "hobby": "羽毛球、篮球、游泳、听音乐"
    47.                 },
    48.                 "highlight": {
    49.                     "hobby": [
    50.                         "羽毛球、篮球、游泳、听音乐"
    51.                     ]
    52.                 }
    53.             },
    54.             {
    55.                 "_index": "study",
    56.                 "_type": "_doc",
    57.                 "_id": "i6jJdoIBU4c5cKp3GGKx",
    58.                 "_score": 1.7171206,
    59.                 "_source": {
    60.                     "name": "张三",
    61.                     "age": 20,
    62.                     "mail": "111@qq.com",
    63.                     "hobby": "羽毛球、乒乓球、足球"
    64.                 },
    65.                 "highlight": {
    66.                     "hobby": [
    67.                         "羽毛球、乒乓球、足球"
    68.                     ]
    69.                 }
    70.             },
    71.             {
    72.                 "_index": "study",
    73.                 "_type": "_doc",
    74.                 "_id": "jKjJdoIBU4c5cKp3GGKx",
    75.                 "_score": 1.6262295,
    76.                 "_source": {
    77.                     "name": "李四",
    78.                     "age": 21,
    79.                     "mail": "222@qq.com",
    80.                     "hobby": "羽毛球、乒乓球、足球、篮球"
    81.                 },
    82.                 "highlight": {
    83.                     "hobby": [
    84.                         "羽毛球、乒乓球、足球、篮球"
    85.                     ]
    86.                 }
    87.             }
    88.         ]
    89.     }
    90. }

    2.3、组合搜索

            在搜索时除了上面的方法外，还可以使用过滤器中的bool组合搜索。

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "bool": {
    7.             "must": {
    8.                 "match": {
    9.                     "hobby": "篮球"
    10.                 }
    11.             },
    12.             "must_not": {
    13.                 "match": {
    14.                     "hobby": "音乐"
    15.                 }
    16.             },
    17.             "should": [
    18.                 {
    19.                     "match": {
    20.                         "hobby": "游泳"
    21.                     }
    22.                 }
    23.             ]
    24.         }
    25.     },
    26.     "highlight": {
    27.         "fields": {
    28.             "hobby": {}
    29.         }
    30.     }
    31. }
    32.  
    33. # 响应数据
    34. {
    35.     "took": 6,
    36.     "timed_out": false,
    37.     "_shards": {
    38.         "total": 1,
    39.         "successful": 1,
    40.         "skipped": 0,
    41.         "failed": 0
    42.     },
    43.     "hits": {
    44.         "total": {
    45.             "value": 1,
    46.             "relation": "eq"
    47.         },
    48.         "max_score": 0.77041256,
    49.         "hits": [
    50.             {
    51.                 "_index": "study",
    52.                 "_type": "_doc",
    53.                 "_id": "jKjJdoIBU4c5cKp3GGKx",
    54.                 "_score": 0.77041256,
    55.                 "_source": {
    56.                     "name": "李四",
    57.                     "age": 21,
    58.                     "mail": "222@qq.com",
    59.                     "hobby": "羽毛球、乒乓球、足球、篮球"
    60.                 },
    61.                 "highlight": {
    62.                     "hobby": [
    63.                         "羽毛球、乒乓球、足球、篮球"
    64.                     ]
    65.                 }
    66.             }
    67.         ]
    68.     }
    69. }

    注意：上面示例中在搜索结果中必须包含篮球，不能包含音乐，如果包含了游泳，那么它的相似度更高。

    评分的计算规则

    • bool 查询会为每个文档计算相关度评分 _score ， 再将所有匹配的 must 和 should 语句的分数 _score 求和， 最后除以 must 和 should 语句的总数。
    • must_not 语句不会影响评分； 它的作用只是将不相关的文档排除。

    注意：默认情况下，should中的内容不是必须匹配的，如果查询语句中没有must，那么就会至少匹配其中一个。当然了， 也可以通过minimum_should_match参数进行控制，该值可以是数字也可以的百分比。

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "bool": {
    7.             "should": [
    8.                 {
    9.                     "match": {
    10.                         "hobby": "游泳"
    11.                     }
    12.                 },
    13.                 {
    14.                     "match": {
    15.                         "hobby": "篮球"
    16.                     }
    17.                 },
    18.                 {
    19.                     "match": {
    20.                         "hobby": "音乐"
    21.                     }
    22.                 }
    23.             ],
    24.             "minimum_should_match": 2
    25.         }
    26.     },
    27.     "highlight": {
    28.         "fields": {
    29.             "hobby": {}
    30.         }
    31.     }
    32. }
    33.  
    34. # 响应数据
    35. {
    36.     "took": 4,
    37.     "timed_out": false,
    38.     "_shards": {
    39.         "total": 1,
    40.         "successful": 1,
    41.         "skipped": 0,
    42.         "failed": 0
    43.     },
    44.     "hits": {
    45.         "total": {
    46.             "value": 1,
    47.             "relation": "eq"
    48.         },
    49.         "max_score": 2.3112378,
    50.         "hits": [
    51.             {
    52.                 "_index": "study",
    53.                 "_type": "_doc",
    54.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    55.                 "_score": 2.3112378,
    56.                 "_source": {
    57.                     "name": "王五",
    58.                     "age": 22,
    59.                     "mail": "333@qq.com",
    60.                     "hobby": "羽毛球、篮球、游泳、听音乐"
    61.                 },
    62.                 "highlight": {
    63.                     "hobby": [
    64.                         "羽毛球、篮球、游泳、听音乐"
    65.                     ]
    66.                 }
    67.             }
    68.         ]
    69.     }
    70. }

    2.4、权重搜索

            在一些情况下，可能会对某些词增加权重来影响该条数据的得分。

    1. POST  http://127.0.0.1:9200/study/_search
    2.  
    3. # 请求数据
    4. {
    5.     "query": {
    6.         "bool": {
    7.             "must": {
    8.                 "match": {
    9.                     "hobby": {
    10.                         "query": "游泳篮球",
    11.                         "operator": "and"
    12.                     }
    13.                 }
    14.             },
    15.             "should": [
    16.                 {
    17.                     "match": {
    18.                         "hobby": {
    19.                             "query": "音乐",
    20.                             "boost": 10
    21.                         }
    22.                     }
    23.                 },
    24.                 {
    25.                     "match": {
    26.                         "hobby": {
    27.                             "query": "跑步",
    28.                             "boost": 2
    29.                         }
    30.                     }
    31.                 }
    32.             ]
    33.         }
    34.     },
    35.     "highlight": {
    36.         "fields": {
    37.             "hobby": {}
    38.         }
    39.     }
    40. }
    41.  
    42. # 响应数据
    43. {
    44.     "took": 5,
    45.     "timed_out": false,
    46.     "_shards": {
    47.         "total": 1,
    48.         "successful": 1,
    49.         "skipped": 0,
    50.         "failed": 0
    51.     },
    52.     "hits": {
    53.         "total": {
    54.             "value": 1,
    55.             "relation": "eq"
    56.         },
    57.         "max_score": 9.24495,
    58.         "hits": [
    59.             {
    60.                 "_index": "study",
    61.                 "_type": "_doc",
    62.                 "_id": "jajJdoIBU4c5cKp3GGKx",
    63.                 "_score": 9.24495,
    64.                 "_source": {
    65.                     "name": "王五",
    66.                     "age": 22,
    67.                     "mail": "333@qq.com",
    68.                     "hobby": "羽毛球、篮球、游泳、听音乐"
    69.                 },
    70.                 "highlight": {
    71.                     "hobby": [
    72.                         "羽毛球、篮球、游泳、听音乐"
    73.                     ]
    74.                 }
    75.             }
    76.         ]
    77.     }
    78. }

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  • 原文地址：https://blog.csdn.net/weixin_44799217/article/details/126208398