Elasticsearch使用系列-ES简介和环境搭建

Elasticsearch使用系列-ES增删查改基本操作+ik分词

Elasticsearch使用系列-基本查询和聚合查询+sql插件

一、基本查询

1.And查询must

GET user2/_search
{
  "query": {
    "bool":{
      "must": [
        {
          "match": {
            "name": "张三"
          }
        },
        {
          "match": {
            "hobby": "钓鱼"
          }
        }
        
      ]
    }
  },
  "_source": ["name","age","hobby"],
  "sort": [
    { "age": {"order": "desc"}},
     { "name2": {"order": "asc"}}
  ],
  "from": 0,
  "size": 20
}

• bool ：And查询属于bool查询，must里面带And的查询条件。
• _source：要查询的字段
• sort：对查询结果排序
• from：分页查询，跳过多少条
• size：分页查询，一页查多少条

2.or查询should

GET user2/_search
{
  "query": {
    "bool":{
      "should": [
        {
          "match": {
            "name": "张三"
          }
        },
        {
          "match": {
            "hobby": "钓鱼"
          }
        }
        
      ]
    }
  }
}

• bool：or查询属于bool查询
• should：里面放or的查询条件

3.排除查询 must_not

#查询名字不等于张三
GET user2/_search
{
  "query": {
    "bool":{
      "must_not": [
        {
          "match": {
            "name": "张三"
          }
        }
        
      ]
    }
  }
}

 4.过滤查询filter

#查询名字等于张三，年龄大于等于10小于等于20
GET user2/_search
{
  "query": {
    "bool":{
      "must": [
        {
          "match": {
            "name": "张三"
          }
        }
        
      ],
      "filter": [
        {"range": {
          "age": {
            "gte": 10,
            "lte": 20
          }
        }}
      ]
    }
    
  }
}

• filter：过滤条件，先过滤数据再查询结果
• range：范围查询，和term,match是同类的查询。
• gte：大于等于
• gt：大于
• lte：小于等于
• lt：小于

5.同字段多值查询

GET user2/_search
{
  "query": {
     "terms": {
           "name2": ["张三","李四"]
         }
  }
}

#text类型的多值查询，空格隔开
GET user2/_search
{
  "query": {
    "match": {
           "name": "张三 李四"
         }
  }
}

6.高亮查询highlight

高亮查询，就是平时搜索东西时，搜索结果会把你的关键词匹配到的显示颜色，像下图一样。

高亮展示的数据，本身就是文档中的一个field，单独将field以highlight的形式返回给你。
ES提供了一个highlight属性，和query同级别的。

• pre_tag：指定前缀标签，如 <font color="red">
• post_tags：指定后缀标签，如 </font>
• fields：指定那个字段为高亮字段

 查出来后，显示hobby字段的地方，就直接用高亮的hobby展示就行了。

二、聚合查询

bucket：分组后统计，类似于Mysql中的group by 

metric：对分组统计的结果，计算最大值，最小值，平均值等，类似于Mysql中的max()，min()，avg()函数的值。

1.准备数据

创建索引

PUT employee
{
  "mappings": {
    "properties": {
      "id": {
        "type": "integer"
      },
      "name": {
        "type": "keyword"
      },
      "job": {
        "type": "keyword"
      },
      "age": {
        "type": "integer"
      },
      "gender": {
        "type": "keyword"
      }
    }
  }
}

批量插入数据

PUT employee/_bulk
{"index": {"_id": 1}}
{"id": 1, "name": "Bob", "job": "java", "age": 21, "sal": 8000, "gender": "male"}
{"index": {"_id": 2}}
{"id": 2, "name": "Rod", "job": "html", "age": 31, "sal": 18000, "gender": "female"}
{"index": {"_id": 3}}
{"id": 3, "name": "Gaving", "job": "java", "age": 24, "sal": 12000, "gender": "male"}
{"index": {"_id": 4}}
{"id": 4, "name": "King", "job": "dba", "age": 26, "sal": 15000, "gender": "female"}
{"index": {"_id": 5}}
{"id": 5, "name": "Jonhson", "job": "dba", "age": 29, "sal": 16000, "gender": "male"}
{"index": {"_id": 6}}
{"id": 6, "name": "Douge", "job": "java", "age": 41, "sal": 20000, "gender": "female"}
{"index": {"_id": 7}}
{"id": 7, "name": "cutting", "job": "dba", "age": 27, "sal": 7000, "gender": "male"}
{"index": {"_id": 8}}
{"id": 8, "name": "Bona", "job": "html", "age": 22, "sal": 14000, "gender": "female"}
{"index": {"_id": 9}}
{"id": 9, "name": "Shyon", "job": "dba", "age": 20, "sal": 19000, "gender": "female"}
{"index": {"_id": 10}}
{"id": 10, "name": "James", "job": "html", "age": 18, "sal": 22000, "gender": "male"}
{"index": {"_id": 11}}
{"id": 11, "name": "Golsling", "job": "java", "age": 32, "sal": 23000, "gender": "female"}
{"index": {"_id": 12}}
{"id": 12, "name": "Lily", "job": "java", "age": 24, "sal": 2000, "gender": "male"}
{"index": {"_id": 13}}
{"id": 13, "name": "Jack", "job": "html", "age": 23, "sal": 3000, "gender": "female"}
{"index": {"_id": 14}}
{"id": 14, "name": "Rose", "job": "java", "age": 36, "sal": 6000, "gender": "female"}
{"index": {"_id": 15}}
{"id": 15, "name": "Will", "job": "dba", "age": 38, "sal": 4500, "gender": "male"}
{"index": {"_id": 16}}
{"id": 16, "name": "smith", "job": "java", "age": 32, "sal": 23000, "gender": "male"}

2.分组统计

查询员工各种语言数量，相当于group by

#查询员工各种语言数量
GET employee/_search
{
  "size": 0,
  "aggs": {
    "languge_count": {
      "terms": {
        "field": "job"
      }
    }
  }
}

•  size：0表示只要统计后的结果，原始数据不展现，如果是大于0，则会返回多少条原始数据
•  aggs:固定语法
•  languge_count：自定义的分组名称，可以随便写
•  terms：按什么字段进行分组
•  field：具体的字段名称

3.平均值，最大值，最小值，求和统计

GET employee/_search
{
  "size": 0,
  "aggs": {
    "language_count": {
      "terms": {
        "field": "job"
      },
      "aggs":{
        "age_avg":{
          "avg":{
            "field": "age"
          }
        }
      }
    }
  }
}

• aggs：固定写法
• age_avg：自定义统计名称，随便写
• avg：平均值，其他有 max：最大值，min：最小值，sum：求和
• fileld：要计算的字段

4.分段统计

#按年龄区间分段统计
GET employee/_search
{
  "size": 0,
  "aggs": {
    "language_count": {
      "histogram": {
        "field": "age",
        "interval": 10
      },
      "aggs":{
        "age_avg":{
          "sum":{
            "field": "age"
          }
        }
      }
    }
  }
}

• histogram：分段统计

• interval：分段间隔

5.日期分段统计

#按月份统计生日人数
GET employee/_search
{
  "size": 0,
  "aggs": {
    "language_count": {
      "date_histogram": {
        "field": "borthday",
        "interval": "month",
        "format": "yyyy-MM-dd",
        "min_doc_count": 0,
        "extended_bounds": {
          "min": "1970-10-01",
          "max": "2022-12-31"
        }
        
      }
    }
  }
}

• date_histogram：日期分段统计函数
• field：聚合分组的字段，类型需要为date
• interval：按什么时间聚合，interval字段支持多种关键字：year, quarter(季度), month, week, day, hour, minute, second，
• format：返回值格式化
• min_doc_count：0分组后没数据的也显示，最小有多少条才显示
• extended_bounds：强制规定最小值和最大值界限，ES默认把有数据的最小值开始做开始界限

6.同时统计多个集合

#分别统计年龄和性别
GET employee/_search
{
  "size": 0,
  "aggs": {
    "language_count": {
      "histogram": {
        "field": "age",
        "interval": 10
      },
      "aggs":{
        "age_avg":{
          "sum":{
            "field": "age"
          }
        }
      }
    },
    "gender_count":{
      "terms": {
        "field": "gender"
      }
    }
  }
}

三、sql插件

1.插件安装

上面的查询语句为DSL查询，sql插件可以编写sql语句，然后自动解析为DSL语句查询

sql插件github地址：https://github.com/NLPchina/elasticsearch-sql

 下载的对应es的版本。

解压后放到 plugins 文件夹并改名为sql，然后重启es

2.sql语句查询

2.1普通查询

GET /_sql?format=txt
{
  "query": "select * from employee where job='java'"
  
}

 2.2其他查询写法

#普通查询  
SELECT * FROM bank WHERE age >30 AND gender = 'm'

#聚合查询（分组统计）
select COUNT(*),SUM(age),MIN(age) as m, MAX(age),AVG(age)
  FROM bank GROUP BY gender ORDER BY SUM(age), m DESC

#删除 
DELETE FROM bank WHERE age >30 AND gender = 'm'

更多的查询看sql插件的github地址最下面的说明