elasticSearch1-入门使用_elasticsearch 南京市长江大桥-优快云博客

本文链接：https://blog.youkuaiyun.com/growing_duck/article/details/121610640

本文详细介绍了Elasticsearch的基础索引操作，包括创建、修改映射、数据插入与搜索，以及高级查询技巧如多字段匹配、聚合、地理位置检索等。适合理解索引结构和进行高效信息检索。

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索引和映射：

//查看分词结果
POST _analyze 
{ 
  "analyzer": "ik_max_word", 
  "text": "南京市长江大桥" 
}

//创建索引
PUT /my-test
//查看索引信息
GET /my-test
//查看索引是否存在
HEAD /my-test
//打开关闭
POST /my-test/_close
POST /my-test/_open
//删除
DELETE /my-test

//创建索引和映射
PUT /my-test
{
  "settings": {
    "number_of_shards": 9,
    "number_of_replicas": 1
  }, 
 "mappings": {
   "properties": {
     "name":{
       "type": "text",
       "analyzer": "ik_max_word"
     },
     "logo":{
       "type": "keyword",
       "index": false
     },
    "payment":{
      "type": "float"
    }
   }
 } 
}


//修改索引映射（只能增加字段，其他修改只能删除索引重新建立）
PUT /my-test/_mapping
{
  "properties": {
     "address":{
       "type": "text",
       "analyzer": "ik_max_word"
     }
   }
}

//查看映射
GET /my-test/_mapping

映射类型：

1) type

String类型，又分两种：

        text：可分词，不可参与聚合

        keyword：不可分词，数据会作为完整字段进行匹配，可以参与聚合

Numerical：数值类型，分两类

        基本数据类型：long、interger、short、byte、double、flfloat、half_flfloat

        浮点数的高精度类型：scaled_flfloat

        需要指定一个精度因子，比如10或100。elasticsearch会把真实值乘以这个因子后存

储，取出时再还原。

Date：日期类型

        elasticsearch可以对日期格式化为字符串存储，但是建议我们存储为毫秒值，存储为long，节省空间。

Array：数组类型

        进行匹配时，任意一个元素满足，都认为满足

        排序时，如果升序则用数组中的最小值来排序，如果降序则用数组中的最大值来排序

2）index

        true：字段会被索引，则可以用来进行搜索。默认值就是true

false：字段不会被索引，不能用来搜索

3）store

        是否将数据进行独立存储。原始的文本会存储在 _source 里面，store:true来独立存储，要比从_source中解析快得多，但是也会占用更多的空间，所以要根据实际业务需求来设置，默认为false。

4）analyzer：指定分词器

        一般我们处理中文会选择ik分词器 ik_max_word ik_smart

文档：即数据

//插入数据
POST /my-test/_doc/1
{
  "name":"张三",
  "logo":"www.baidu.com",
  "payment":30000
}

POST /my-test/_doc/2
{
  "name":"李四",
  "logo":"www.baidu.com",
  "payment":30000
}

//查找
GET /my-test/_doc/1

//查找所有
GET /my-test/_search
POST /my-test/_search
POST /my-test/_search
{
  "query": {
    "match_all": {}
  }
}

//按id查找，返回指定字段
GET /my-test/_doc/1?_source=name,logo

//更新
POST /my-test/_update/1
{
 "doc": {
    "name":"张三"
 }
}

//删除
DELETE /my-test/_doc/1

//按条件删除
POST /my-test/_delete_by_query
{
  "query": {
    "match": {
      "name":"李四"
    }
  }
}

//全部删除
POST /my-test/_delete_by_query
{
  "query": {
    "match_all": {}
  }
}

Query DSL：

//分词后使用or查询
GET /my-test/_search
{
  "query": {
    "match": {
      "name": "小米电视4A"
    }
  }
}

//分词后使用and查询
GET /my-test/_search
{
  "query": {
    "match": {
      "name": {
        "query": "小米电视4A",
        "operator": "and"
      }
    }
  }
}

//短语查询，最小须满足此短语
GET /my-test/_search
{
  "query": {
    "match_phrase": {
      "name": "小米电视"
    }
  }
}


//query_string可以不指定字段查询，也可指定一个或多个字段
GET /my-test/_search
{
  "query": {
    "query_string": {
      "query": "小米电视"
    }
  }
}

GET /my-test/_search
{
  "query": {
    "query_string": {
      "default_field":"name",
      "query": "小米电视"
    }
  }
}

//注意： AND大写，否则不生效
GET /my-test/_search
{
  "query": {
    "query_string": {
      "default_field": "name",
      "query": "小米 AND 手机"
    }
  }
}

GET /my-test/_search
{
  "query": {
    "query_string": {
      "fields":["name","content"],
      "query": "小米电视"
    }
  }
}

//multi_match也可支持多个字段，效果和query_string，但是不支持AND查询，
GET /my-test/_search
{
  "query": {
    "multi_match": {
      "fields":["name","content"],
      "query": "小米手机"
    }
  }
}

//term：查询指定字段中，有指定value的分词
//和上面and查询不一样的是，and要求分词中，同时包含小米和手机，而term要求分词中一定有小米手机这个词
GET /my-test/_search
{
  "query": {
    "term": {
      "remark": {
        "value": "小米手机"
      }
    }
  }
}

//term也可指定多个value，分词中满足一个即可
GET /my-test/_search
{
  "query": {
    "terms": {
      "name": [
        "小米手机",
        "电视"
      ]
    }
  }
}


//range范围查询
GET /my-test/_search
{
  "query": {
    "range": {
      "payment": {
        "gte": 500,
        "lte": 10000
      }
    }
  }
}

GET /my-test/_search
{
  "query":{
    "range":{
      "create_time": {
        "gte": "2021-05-11 00:00:00",
        "lte":"2021-05-12 00:00:00",
        "format": "yyyy-MM-dd HH:mm:ss",
        "time_zone": "+08:00"
      }
    }
  }
}

//exists：指定字段有值
GET /my-test/_search
{
  "query": {
    "exists": 
      {
        "field": "logo"
      }
   }
}

//prefix: 指定的字段分词中，有以value开头的
GET /my-test/_search
{
  "query": {
    "prefix": {
      "name": {
        "value": "小米"
      }
    }
  }
}

//wildcard: 字段分词中有 电xx的
GET /my-test/_search
{
  "query": {
    "wildcard": {
      "name": {
        "value": "电*"
      }
    }
  }
}

//id集合查询
GET /my-test/_search
{
  "query": {
    "ids": {
      "values": [1,2,3]
    }
  }
}

//fuzzy: 分词中包含指定value为电圈的，允许value中有一个词是错的，也就是分词包含电或者圈的都能查出来
GET /my-test/_search
{
  "query": {
    "fuzzy": {
      "name": {
        "value": "电圈",
        "fuzziness": 1
      }
    }
  }
}

复合查询：

GET /my-test/_search
{
  "query": {
    "term": {
      "name": {
        "value": "华为"
      }
    }
  }
}

//constant_score: 可包装上面的term查询，并对结果给一个分值
GET /my-test/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "term": {
          "name": "华为"
        }
      },
      "boost": 1.2
    }
  }
}

//bool: 组装多个条件
    //must: 多个条件同时满足
    //should: 多个条件中满足n个即可，n由minimum_should_match指定，不指定默认为1
    //filter：同must，只是不对结果打分，可使用缓存，性能更好
GET /my-test/_search
{
  "query": {
    "bool": {
      "must": [
        {"match": {
          "name": "手机"
        }}
      ],
      "must_not": [
        {"match": {
          "name": "aa"
        }}
      ],
      "should": [
        {"term": {
          "logo": {
            "value": "小米手机"
          }
        }},
        {
          "term": {
            "logo": {
              "value": "华为手机"
            }
          }
        }
      ],
      "filter": [
        {"range": {
          "payment": {
            "gte": 10000,
            "lte": 40000
          }
        }}
      ],
      "minimum_should_match": 1
    }
  }
}

排序分页统计：

//分页排序
GET /my-test/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "payment": {
        "order": "asc"
      }
    },
    {
      "_id": {
        "order": "desc"
      }
    }
  ],
  "size": 5,
  "from": 0
}

//count: 统计条数
GET /my-test/_count
{
  "query": {
    "range": {
      "payment": {
        "gte": 20000
      }
    }
  }
}

//aggs： 桶。  将数据放到桶里，再进行统计如max,avg,group by 等

//最大值
GET /my-test/_search
{
  "size": 0,
  "aggs": {
    "maxPayment": {
      "max": {
        "field": "payment"
      }
    }
  }
}

//求和 sum
GET /edu_user_asset_trade_record/_search 
{
  "size": 0,
  "aggs": {
    "query_amount": {
      "sum": {
        "field": "trade_count"
      }
    }
  },
  "query": {
    "bool": {
      "must": [
        {
          "term": {
            "trade_type": {
              "value": 2
            }
          }
        },{
          "range": {
            "create_time": {
            "gte": "2022-06-01 00:00:00",
            "lt":"2022-07-01 00:00:00",
            "format": "yyyy-MM-dd HH:mm:ss",
            "time_zone": "+08:00"
          }
          }
        }
      ]
    }
  }
}

//value_count：统计payment字段不为空的条数
GET /my-test/_search
{
  "size": 0, 
  "aggs": {
    "paymentCount": {
      "value_count": {
        "field": "payment"
      }
    }
  }
}

//效果同上
GET /my-test/_count
{
  "query": {
    "exists": {
      "field": "payment"
    }
  }
}

//cardinality： 统计去重后的条数
GET /my-test/_search
{
  "size": 0, 
  "aggs": {
    "nameCount": {
      "cardinality": {
        "field": "payment"
      }
    }
  }
}

//collapse: 查询去重后的字段：select distinct(logo) from xxx where name = '小米'
GET /my-test/_search
{
  "query": {
    "term": {
      "name": {
        "value": "小米"
      }
    }
  },
  "collapse": {
    "field": "logo"
  }
}

//select count(1) from xx group by payment
GET /my-test/_search
{
  "size": 0, 
  "aggs": {
    "group_by_payment": {
      "terms": {
        "field": "payment"
      }
    }
  }
}



//不仅统计group by后的条数，还可以统计出group by后，对某个字段重后的条数
GET /my-test/_search
{
  "size": 0, 
  "aggs": {
    "group_by_payment": {
      "terms": {
        "field": "payment"
      },
      "aggs": {
        "my_distinct": {
          "cardinality": {
            "field": "logo"
          }
        }
      }
    }
  
  }
}

// select payment,avg(payment) my_by_payment,count(*) mycount from xxx group by payment having my_by_payment > 20000 and mycount  >=1;

GET /my-test/_search
{
  "size": 0,
  "aggs": {
    "group_by_payment": {
      "terms": {
        "field": "payment"
      },
      "aggs": {
        "my_avg_value": {
          "avg": {
            "field": "payment"
          }
        },
        "having":{
          "bucket_selector": {
            "buckets_path": {
              "mycount": "_count",
              "myAvgValue": "my_avg_value"
            },
            "script": {
              "source": "params.myAvgValue >20000 && params.mycount >= 1"
            }
          }
        }
      }
    }
  }
}


//stats: 同时统计count max min avg sum 5个值
GET /my-test/_search
{
  "size": 0, 
  "aggs": {
    "my_count": {
      "stats": {
        "field": "payment"
      }
    }
  }
}

//percentiles： 统计指定百分比的值，默认统计1,5,25,50,75,95,99这几个百分比阶段的值
GET /my-test/_search
{
  "size": 20,
  "aggs": {
    "my_percent": {
      "percentiles": {
        "field": "payment"
      }
    }
  }
}

//统计百分之二十和五十阶段的值
GET /my-test/_search
{
  "size": 20,
  "aggs": {
    "my_percent": {
      "percentiles": {
        "field": "payment",
        "percents": [
          20,50
        ]
      }
    }
  }
}

//统计小于15和小于1000000的数据的百分比
GET /my-test/_search
{
  "size": 0, 
  "aggs": {
    "my_percent": {
      "percentile_ranks": {
        "field": "payment",
        "values": [
            15,
            1000000     
         ]
      }
    }
  }
}

bulk批量增删改：

//删除索引id为1的，创建索引id为6的数据，修改索引id为2的数据的name和logo字段
POST /my-test/_bulk
{"delete":{"_id":1}}
{"create":{"_id":6}}
{"name":"三星手机","logo":"三星手机","content":"三星手机","remark":"三星手机","payment":60000}
{"update":{"_id":2}}
{"doc":{"name":"小米电视手机","logo":"小米电视手机"}}

bulk通常用于批量插入，，批量删除，批量更改。

格式：每个json不能换行。相邻json必须换行。

实际用法：bulk请求一次不要太大，否则一下积压到内存中，性能会下降。所以，一次请求几千个操作、大小在几M最好。

坐标：geo_point

将上面的my-test索引删掉，从新建立索引：
PUT /my-test/
{
  "mappings": {
    "properties": {
      "name":{
        "type": "text",
        "analyzer": "ik_max_word"
      },
      "remark":{
        "type": "keyword"
      },
      "location":{
        "type": "geo_point"
      },
      "age":{
        "type": "integer"
      }
    }
  }
}

//插入3条数据 注意：location使用[]传值，经纬度顺寻与其他两种不一样
POST /my-test/_doc/1
{
  "name":"我是大帅哥",
  "remark":"华为手机",
  "age":10,
  "location":[73.989,40.722]
}
POST /my-test/_doc/2
{
  "name":"我是大帅哥2",
  "remark":"华为手机",
  "age":10,
  "location":"40.722,73.989"
}
POST /my-test/_doc/3
{
  "name":"我是大帅哥3",
  "remark":"华为手机",
  "age":10,
  "location":{
    "lat":40.722,
    "lon":73.989
  }
}


//geo_bounding_box：指定两个点，在其为对角线的长方形中，包含的所有点
GET /my-test/_search
{
  "query": {
    "bool": {
      "must": [
        {"match_all": {}}
      ], 
      "filter": [
        {"geo_bounding_box": {
          "location": {
            "top_left": {
              "lat": 40.73,
              "lon": 71.12
            },
            "bottom_right": {
              "lat": 40.01,
              "lon": 74.0
            }
          }
        }}
      ]
    }
  }
}

//geo_distance： 距离指定点100km半径范围内所有的点
GET /my-test/_search
{
  "query": {
    "bool": {
      "must": [
        {"match_all": {}}
      ], 
      "filter": [
        {"geo_distance": {
          "distance": "100km",
          "location": {
            "lat": 40.722,
            "lon": 73.989
          }
        }}
      ]
    }
  }
}