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Elasticsearch 查询 DSL

在 Elasticsearch 中，搜索是使用基于 JSON 的查询来进行的。查询由两个子句组成

叶子查询子句 - 这些子句是匹配项、术语或范围，它们在特定字段中查找特定值。
复合查询子句- 这些查询是叶查询子句和其他复合查询的组合，用于提取所需信息。

Elasticsearch 支持大量查询。查询以查询关键字开始，然后以 JSON 对象的形式在内部包含条件和过滤器。下面描述了不同类型的查询。

匹配所有查询

这是最基本的查询；它返回所有内容，每个对象的得分为 1.0。

POST /schools/_search
{
   "query":{
      "match_all":{}
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 7,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
         "value" : 2,
         "relation" : "eq"
      },
      "max_score" : 1.0,
      "hits" : [
         {
            "_index" : "schools",
            "_type" : "school",
            "_id" : "5",
            "_score" : 1.0,
            "_source" : {
               "name" : "Central School",
               "description" : "CBSE Affiliation",
               "street" : "Nagan",
               "city" : "paprola",
               "state" : "HP",
               "zip" : "176115",
               "location" : [
                  31.8955385,
                  76.8380405
               ],
               "fees" : 2200,
               "tags" : [
                  "Senior Secondary",
                  "beautiful campus"
               ],
               "rating" : "3.3"
            }
         },
         {
            "_index" : "schools",
            "_type" : "school",
            "_id" : "4",
            "_score" : 1.0,
            "_source" : {
               "name" : "City Best School",
               "description" : "ICSE",
               "street" : "West End",
               "city" : "Meerut",
               "state" : "UP",
               "zip" : "250002",
               "location" : [
                  28.9926174,
                  77.692485
               ],
               "fees" : 3500,
               "tags" : [
                  "fully computerized"
               ],
               "rating" : "4.5"
            }
         }
      ]
   }
}

全文查询

这些查询用于搜索完整的文本，如章节或新闻文章。该查询根据与该特定索引或文档关联的分析器工作。在本节中，我们将讨论不同类型的全文查询。

匹配查询

此查询将文本或短语与一个或多个字段的值相匹配。

POST /schools*/_search
{
   "query":{
      "match" : {
         "rating":"4.5"
      }
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 44,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
         "value" : 1,
         "relation" : "eq"
      },
      "max_score" : 0.47000363,
      "hits" : [
         {
            "_index" : "schools",
            "_type" : "school",
            "_id" : "4",
            "_score" : 0.47000363,
            "_source" : {
               "name" : "City Best School",
               "description" : "ICSE",
               "street" : "West End",
               "city" : "Meerut",
               "state" : "UP",
               "zip" : "250002",
               "location" : [
                  28.9926174,
                  77.692485
               ],
               "fees" : 3500,
               "tags" : [
                  "fully computerized"
               ],
               "rating" : "4.5"
            }
         }
      ]
   }
}

多匹配查询

此查询匹配具有多个字段的文本或短语。

POST /schools*/_search
{
   "query":{
      "multi_match" : {
         "query": "paprola",
         "fields": [ "city", "state" ]
      }
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 12,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
         "value" : 1,
         "relation" : "eq"
      },
      "max_score" : 0.9808292,
      "hits" : [
         {
            "_index" : "schools",
            "_type" : "school",
            "_id" : "5",
            "_score" : 0.9808292,
            "_source" : {
               "name" : "Central School",
               "description" : "CBSE Affiliation",
               "street" : "Nagan",
               "city" : "paprola",
               "state" : "HP",
               "zip" : "176115",
               "location" : [
                  31.8955385,
                  76.8380405
               ],
               "fees" : 2200,
               "tags" : [
                  "Senior Secondary",
                  "beautiful campus"
               ],
               "rating" : "3.3"
            }
         }
      ]
   }
}

Query String 查询

此查询使用查询解析器和 query_string 关键字。

POST /schools*/_search
{
   "query":{
      "query_string":{
         "query":"beautiful"
      }
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 60,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
      "value" : 1,
      "relation" : "eq"
   },
………………………………….

术语级别查询

这些查询主要处理结构化数据，如数字、日期和枚举。

POST /schools*/_search
{
   "query":{
      "term":{"zip":"176115"}
   }
}

运行上面的代码，我们得到以下结果

……………………………..
hits" : [
   {
      "_index" : "schools",
      "_type" : "school",
      "_id" : "5",
      "_score" : 0.9808292,
      "_source" : {
         "name" : "Central School",
         "description" : "CBSE Affiliation",
         "street" : "Nagan",
         "city" : "paprola",
         "state" : "HP",
         "zip" : "176115",
         "location" : [
            31.8955385,
            76.8380405
         ],
      }
   }
]   
…………………………………………..

范围查询

此查询用于查找具有给定值范围之间值的对象。为此，我们需要使用运算符，例如 -

gte - 大于等于
gt - 大于
lte - 小于等于
lt - 小于

例如，观察下面给出的代码

POST /schools*/_search
{
   "query":{
      "range":{
         "rating":{
            "gte":3.5
         }
      }
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 24,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
         "value" : 1,
         "relation" : "eq"
      },
      "max_score" : 1.0,
      "hits" : [
         {
            "_index" : "schools",
            "_type" : "school",
            "_id" : "4",
            "_score" : 1.0,
            "_source" : {
               "name" : "City Best School",
               "description" : "ICSE",
               "street" : "West End",
               "city" : "Meerut",
               "state" : "UP",
               "zip" : "250002",
               "location" : [
                  28.9926174,
                  77.692485
               ],
               "fees" : 3500,
               "tags" : [
                  "fully computerized"
               ],
               "rating" : "4.5"
            }
         }
      ]
   }
}

还存在其他类型的术语级别查询，例如

Exists 查询 - 如果某个字段具有非空值。
Missing 查询- 这与现有查询完全相反，此查询搜索没有特定字段或字段具有空值的对象。
通配符或正则表达式查询 - 此查询使用正则表达式来查找对象中的模式。

复合查询

这些查询是不同查询的集合，通过使用诸如 and 、or 、not or 之类的布尔运算符针对不同的索引或进行函数调用等相互合并。

POST /schools/_search
{
   "query": {
      "bool" : {
         "must" : {
            "term" : { "state" : "UP" }
         },
         "filter": {
            "term" : { "fees" : "2200" }
         },
         "minimum_should_match" : 1,
         "boost" : 1.0
      }
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 6,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
         "value" : 0,
         "relation" : "eq"
      },
      "max_score" : null,
      "hits" : [ ]
   }
}

Geo 查询

这些查询处理地理位置和地理点。这些查询有助于找出任何位置附近的学校或任何其他地理对象。我们需要使用地理点数据类型。

PUT /geo_example
{
   "mappings": {
      "properties": {
         "location": {
            "type": "geo_shape"
         }
      }
   }
}

运行上面的代码，我们得到以下结果

{  "acknowledged" : true,
   "shards_acknowledged" : true,
   "index" : "geo_example"
}

现在我们将数据发布到上面创建的索引中。

POST /geo_example/_doc?refresh
{
   "name": "Chapter One, London, UK",
   "location": {
      "type": "point",
      "coordinates": [11.660544, 57.800286]
   }
}

运行上面的代码，我们得到以下结果

{
   "took" : 1,
   "timed_out" : false,
   "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
   },
   "hits" : {
      "total" : {
         "value" : 2,
         "relation" : "eq"
      },
      "max_score" : 1.0,
      "hits" : [
         "_index" : "geo_example",
         "_type" : "_doc",
         "_id" : "hASWZ2oBbkdGzVfiXHKD",
         "_score" : 1.0,
         "_source" : {
            "name" : "Chapter One, London, UK",
            "location" : {
               "type" : "point",
               "coordinates" : [
                  11.660544,
                  57.800286
               ]
            }
         }
      }
   }

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Elasticsearch 查询 DSL

匹配所有查询

全文查询

匹配查询

多匹配查询

Query String 查询

术语级别查询

范围查询

复合查询

Geo 查询

查看笔记

Elasticsearch 查询 DSL

匹配所有查询

全文查询

匹配查询

多匹配查询

Query String 查询

术语级别查询

范围查询

复合查询

Geo 查询

 查看笔记

查看笔记