目录

一、索引操作

二、映射操作

三、文档操作

---

elasticsearch 的基本操作多维度分享此篇正式分享，具体包括索引、映射、文档的相关处理，模拟生成环境，通过DSL语句和java的高级REST形式全方位展示给大家；

一、索引操作

1、创建索引：设置分片和副分片

PUT /nandao_scenic
{
  "settings": {
    "number_of_shards": 5,
    "number_of_replicas": 2
  } ,
  "mappings": {
    "properties": {
      "title":{
        "type": "text"
      },
      "city":{
        "type": "keyword"
      },
      "price":{
        "type": "double"
      }
    }
  }
}

2、删除索引

DELETE /nandao_scenic

3、关闭索引

POST /nandao_scenic/_close

4、打开索引

POST /nandao_scenic/_open

还有很多类似的DSL语句，具体可以参考：

 5、索引别名：

每个月一个索引，我要查1、2月份的数据，只能建立别名索引，即通过一个索引去查数据

5.1、一月份的索引和数据

PUT /nandao_one_log
{
  "mappings": {
    "properties": {
      "scenic_id":{
        "type": "keyword"
      },
      "uid":{
        "type": "keyword"
      },
      "check_in_date":{
        "type": "keyword"
      }
    }
  }
}


POST /nandao_one_log/_doc/01
{
  "uid":"01",
   "scenic_id":"1212",
  "check_in_date":"2023-01-23"
}

5.2、二月份的索引和数据

PUT /nandao_two_log
{
  "mappings": {
    "properties": {
      "scenic_id":{
        "type": "keyword"
      },
      "uid":{
        "type": "keyword"
      },
      "check_in_date":{
        "type": "keyword"
      }
    }
  }
}

POST /nandao_two_log/_doc/01
{
  "uid":"01",
   "scenic_id":"1552",
  "check_in_date":"2023-02-23"
}

5.3、构建别名索引

POST /_aliases
{
  "actions": [
    {
      "add": {
        "index": "nandao_one_log",
        "alias": "last_two_month"
      }
    },
    {
      "add": {
        "index": "nandao_two_log",
        "alias": "last_two_month"
      }
    }
  ]
}

5.4、通过别名索引查询数据

GET /last_two_month/_search
{
  "query": {
    "term": {
        "uid": "01"
    }
  }
}

#查询结果，1、2月份的数据
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 0.6931471,
    "hits" : [
      {
        "_index" : "nandao_one_log",
        "_type" : "_doc",
        "_id" : "01",
        "_score" : 0.6931471,
        "_source" : {
          "uid" : "01",
          "scenic_id" : "1212",
          "check_in_date" : "2023-01-23"
        }
      },
      {
        "_index" : "nandao_two_log",
        "_type" : "_doc",
        "_id" : "01",
        "_score" : 0.2876821,
        "_source" : {
          "uid" : "01",
          "scenic_id" : "1552",
          "check_in_date" : "2023-02-23"
        }
      }
    ]
  }
}

 6、向别名索引中写数据-错误示例

POST /last_two_month/_doc/02
{
  "uid":"02",
   "scenic_id":"12232",
  "check_in_date":"2023-02-23"
}

此时是报错的，不知道往哪个目标索引写数据，设置目标索引：

POST /_aliases
{
  "actions": [
    {
      "add": {
        "index": "nandao_one_log",
        "alias": "last_two_month",
        "is_write_index":true
      }
    } 
  ]
}

 设置完成后再通过别名索引写数据就可以成功了！

7、间接添加数据分片

数据分片不能改变，但是随着业务数据的急剧增长，需要增加分片，这时候我们只能通过别名索引间接添加分片

7.1、最初的索引

PUT /nandao_scenic_1
{
  "settings": {
    "number_of_shards": 5,
    "number_of_replicas": 2
  } ,
  "mappings": {
    "properties": {
      "title":{
        "type": "text"
      },
      "city":{
        "type": "keyword"
      },
      "price":{
        "type": "double"
      }
    }
  }
}

7.2、最初的别名索引

POST /_aliases
{
  "actions": [
    {
      "add": {
        "index": "nandao_scenic_1",
        "alias": "nandao_scenic_a"
        
      }
    } 
  ]
}

 7.3、新建更多分片的索引：

PUT /nandao_scenic_2
{
  "settings": {
    "number_of_shards": 10,
    "number_of_replicas": 2
  } ,
  "mappings": {
    "properties": {
      "title":{
        "type": "text"
      },
      "city":{
        "type": "keyword"
      },
      "price":{
        "type": "double"
      }
    }
  }
}

当数据向nandao_scenic_2索引写完后，就可以正式切换索引了！ 

7.4、最终的别名索引

POST /_aliases
{
  "actions": [
    {
      "remove": {
        "index": "nandao_scenic_1",
        "alias": "nandao_scenic_a"
        
      }
    } ,
    {
      "add": {
        "index": "nandao_scenic_2",
        "alias": "nandao_scenic_a"
        
      }
    } 
  ]
}

二、映射操作

1、查看索引映射

GET /nandao_scenic/_mapping

#结果
{
  "nandao_scenic" : {
    "mappings" : {
      "properties" : {
        "city" : {
          "type" : "keyword"
        },
        "price" : {
          "type" : "double"
        },
        "title" : {
          "type" : "text"
        }
      }
    }
  }
}

2、扩展映射：

POST /nandao_scenic/_mapping
{
  "properties":{
    "tag":{
      "type":"keyword"
    }
  }
}

3、基本数据类型

3.1、keyword 类型是不进行切分的字符串类型，一般用来描述姓名、类型、用户id、URL和状态码等

3.2、text 类型是可进行切分的字符串类型，一般用match匹配搜索，如果用term查询，会查不到数据的。

3.3、数值类型包括long、integer、short、byte、double、float等常用类型，一般用gte\lte等参数查询，即范围查询。

3.4、布尔类型，是或否的意思，比较容易理解。

3.5、日期类型,date，可以使用范围查询，20221212类型，尽量不要format类型，处理历史数据容易出错。

添加扩展映射：

#继续扩展映射
POST /nandao_scenic/_mapping
{
  "properties":{
    "full":{
      "type":"boolean"
    },
      "creata_time":{
        "type":"date"
      }
  }
}

查询看到所有常用的基本数据类型：

{
  "nandao_scenic" : {
    "mappings" : {
      "properties" : {
        "city" : {
          "type" : "keyword"
        },
        "creata_time" : {
          "type" : "date"
        },
        "full" : {
          "type" : "boolean"
        },
        "price" : {
          "type" : "double"
        },
        "tag" : {
          "type" : "keyword"
        },
        "title" : {
          "type" : "text"
        }
      }
    }
  }
}

 4、复杂的数据类型

4.1、数组类型

比如上面的tag字段时keyword类型，但是可以存数组，后期搜索时对应原来的term，精准完全匹配原则，也是用相应的数组数据去查询，如果需要的话：

POST /nandao_scenic/_doc/002
{
  "title":"五台山",
  "city":"山西",
  "price":"88.8",
  "tag":["有车位","人多"]
}

查询语句

GET /nandao_scenic/_search
{
  "query": {
    "term": {
      "tag": {
        "value": "人多"
      }
    }
  }
}

 4.2、对象类型

对象和数组一样不用事先定义，写入时自动转化成对象类型，同时对应的索引映射中也有相应的变化，比如：

POST /nandao_scenic/_doc/001
{
  "title":"五山",
  "city":"江西",
  "price":"88.8",
  "comment":{  //对象数据
    "properties":{
      "good":88,  //好评
      "bad":7    //差评
    }
  } 
}

查询方式：比如好评大于10的景区

GET /nandao_scenic/_search
{
  "query": {
    "range": {
      "comment.properties.good": {
        "gte": 10
      }
    }
  }
}

 4.3、地理类型

互联网时代位置定位越来越多，es顺势也支持地理类型，扩展相应索引

POST /nandao_scenic/_mapping
{
  "properties":{
    "location":{
      "type":"geo_point"
    }
  }
}

添加包含地理类型数据

POST /nandao_scenic/_doc/003
{
  "title":"雍和宫",
  "city":"北京",
  "price":"88.8",
  "location":{
      "lat":80.01234,
      "lon":118.98756
    }
}

 5、动态映射

5.1、多字段处理：

对于一个相同的字段有时需要不同的数据类型，即不同的业务选择不同的查询方式，比如在用户查询过程，既要根据用户查询，又要进行模糊查询或者姓氏查询，此时我们在mapping映射过程建立text和keyword两份索引，其中keyword作为子字段类型：

PUT /nandao_scenic_order
{
  "mappings": {
    "properties": {
      "order_id":{
        "type": "keyword"
      },
      "user_id":{
        "type": "keyword"
      },
        "user_name":{
        "type": "text",
        "fields": {
          "user_name_keyword":{
             "type": "keyword"
          }
        }
      },
      "scenic_id":{
        "type": "keyword"
      }
    }
  }
}

写入多字段数据

#写入多字段数据
POST /nandao_scenic_order/_doc/001
{
  "order_id":"009",
  "user_id":"u001",
  "user_name":"nandao",
    "scenic_id": "s001"
}

POST /nandao_scenic_order/_doc/002
{
  "order_id":"008",
  "user_id":"u002",
  "user_name":"lisi",
    "scenic_id": "s002"
}

POST /nandao_scenic_order/_doc/003
{
  "order_id":"0011",
  "user_id":"u003",
  "user_name":"wang wu",
    "scenic_id": "s003"
}

POST /nandao_scenic_order/_doc/004
{
  "order_id":"0010",
  "user_id":"u004",
  "user_name":"su wu",
    "scenic_id": "s004"
}

 查询并排序

GET /nandao_scenic_order/_search
{
  "query": {
    "match": {
      "user_name": "wu"
    }
  },
  "sort": 
    {
      "user_name.user_name_keyword": "desc"
    }
}

查询结果：

{
  "took" : 369,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [
      {
        "_index" : "nandao_scenic_order",
        "_type" : "_doc",
        "_id" : "003",
        "_score" : null,
        "_source" : {
          "order_id" : "0011",
          "user_id" : "u003",
          "user_name" : "wang wu",
          "scenic_id" : "s003"
        },
        "sort" : [
          "wang wu"
        ]
      },
      {
        "_index" : "nandao_scenic_order",
        "_type" : "_doc",
        "_id" : "004",
        "_score" : null,
        "_source" : {
          "order_id" : "0010",
          "user_id" : "u004",
          "user_name" : "su wu",
          "scenic_id" : "s004"
        },
        "sort" : [
          "su wu"
        ]
      }
    ]
  }
}

倒序、正序均可以排列查询。

三、文档操作

1、单条文档写入：上面已经演示，此处演示不指定文档id，即_doc后面不加id即可

POST /nandao_scenic_order/_doc
{
  "order_id":"0010",
  "user_id":"u004",
  "user_name":"su wu",
    "scenic_id": "s004"
}

REST客户端java语言展示

 public void singleIndexDoc(Map<String, Object> dataMap, String indexName, String indexId) {
        IndexRequest indexRequest = new IndexRequest(indexName).id(indexId).source(dataMap);//构建IndexRequest对象并设置对应的索引和_id字段名称
        try {
            IndexResponse indexResponse = client.index(indexRequest, RequestOptions.DEFAULT);//执行写入
            String index = indexResponse.getIndex();//通过IndexResponse获取索引名称
            String id = indexResponse.getId();//通过IndexResponse获取文档Id
            Long version = indexResponse.getVersion();//通过IndexResponse获取文档版本
            System.out.println("index=" + index + ",id=" + id + ",version=" + version );
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

 2、批量写入

DSL语言：

 POST /_bulk
{"index":{"_index":"nandao_scenic","_id":"7"}}
{"title":"少林寺","city":"河南","price":"88.8","tag":["有位","人少"]}
{"index":{ "_index":"nandao_scenic", "_id":"8"}}
 {"title":"动物园","city":"河南","price":"80.8","tag":["有位","人少"]}

注意：一行一行的写，不能结构化，否则报错。 

java语言：

    //批量写入索引
    public void bulkIndexDoc(String indexName, String docIdKey, List<Map<String, Object>> recordMapList) {
        BulkRequest bulkRequest = new BulkRequest(indexName);//构建批量操作BulkRequest对象
        for (Map<String, Object> dataMap : recordMapList) {//遍历数据
            String docId = dataMap.get(docIdKey).toString();//获取主键作为Elasticsearch索引的主键
            IndexRequest indexRequest = new IndexRequest().id(docId).source(dataMap);//构建IndexRequest对象
            bulkRequest.add(indexRequest);//添加IndexRequest
        }
        bulkRequest.timeout(TimeValue.timeValueSeconds(5));//设置超时时间
        try {
            BulkResponse bulkResponse = client.bulk(bulkRequest, RequestOptions.DEFAULT);//执行批量写入
            if (bulkResponse.hasFailures()) {//判断执行状态
                System.out.println("bulk fail,message:" + bulkResponse.buildFailureMessage());
            }
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

3、单条更新

DSL语句

  #更新单条数据
 POST /nandao_scenic/_update/001
 {
   "doc": {
     "title":"海洋馆",
    "city":"河南",
    "price":"100.8",
   "tag":["有车位","好玩"]
   }
 }

 #如果存在就更新，否则插入数据
  POST /nandao_scenic/_update/001
 {
   "doc": {
     "title":"海洋馆",
    "city":"河南",
    "price":"100.8",
   "tag":["有车位","好玩"]
   },
    "upsert": {
     "title":"海洋馆",
    "city":"河南",
    "price":"100.8",
   "tag":["有车位","好玩"]
   }
 }

java客户端语句

    //单条update
    public void singleUpdate(String indexName, String docIdKey, Map<String, Object> recordMap) {
        UpdateRequest updateRequest = new UpdateRequest(indexName, docIdKey);
        updateRequest.doc(recordMap);
        try {
            UpdateResponse updateResponse=client.update(updateRequest, RequestOptions.DEFAULT);
            String index = updateResponse.getIndex();//通过IndexResponse获取索引名称
            String id = updateResponse.getId();//通过IndexResponse获取文档Id
            Long version = updateResponse.getVersion();//通过IndexResponse获取文档版本
            System.out.println("index=" + index + ",id=" + id + ",version="+version);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }


    //单条upsert文档
    public void singleUpsert(String index, String docIdKey, Map<String, Object> recordMap,Map<String, Object> upRecordMap) {
        UpdateRequest updateRequest = new UpdateRequest(index, docIdKey);//构建UpdateRequest
        updateRequest.doc(recordMap);//设置写更新辑
        updateRequest.upsert(upRecordMap);//设置插入逻辑
        try {
            client.update(updateRequest, RequestOptions.DEFAULT);//执行upsert
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

4、批量更新文档

DSL语句

 POST /_bulk
{"update":{"_index":"nandao_scenic","_id":"7"}}
{"doc":{"title":"少林寺1","city":"河南","price":"88.8","tag":["有位","人少"]}}
{"update":{ "_index":"nandao_scenic", "_id":"8"}}
 {"doc":{"title":"动物园1","city":"河南","price":"80.8","tag":["有位","人少"]}}

java语句

public void bulkUpsert(String indexName, String docIdKey, List<Map<String, Object>> recordMapList) {
        BulkRequest bulkRequest = new BulkRequest();//新建请求
        //遍历所有的提示词和提示词对应的拼音形式数据
        for (Map<String, Object> dataMap : recordMapList) {
            String docId = dataMap.get(docIdKey).toString();//获取主键作为Elasticsearch索引的主键
            UpdateRequest updateRequest = new UpdateRequest(indexName, docId).doc(dataMap, XContentType.JSON).upsert(dataMap, XContentType.JSON);//构建写入请求
            bulkRequest.add(updateRequest);//批量加入写入请求
        }
        try {
            BulkResponse bulkResponse = client.bulk(bulkRequest, RequestOptions.DEFAULT);//获取批量写入的返回结果
            if (bulkResponse.hasFailures()) {//判断是否写入失败
                System.out.println("bulk fail,message:" + bulkResponse.buildFailureMessage());
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

5、根据条件更新文档

DSL语句

 POST /nandao_scenic/_update_by_query
 {
   "query": {
     "term": {
       "city": {
         "value": "北京"
       }
     }
   },
   "script": {
     "source": "ctx._source['city']='北京市'",
     "lang": "painless"
   }
 }

java语句

  public void updateCityByQuery(String index,String oldCity,String newCity) {
        UpdateByQueryRequest updateByQueryRequest=new UpdateByQueryRequest(index);//构建UpdateByQueryRequest对象
        updateByQueryRequest.setQuery(new TermQueryBuilder("city",oldCity));//设置按照城市查找文档的query
        updateByQueryRequest.setScript(new Script("ctx._source['city']='"+newCity+"';"));//设置更新城市字段的脚本逻辑
        try {
            client.updateByQuery(updateByQueryRequest,RequestOptions.DEFAULT);//执行更新
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

6、删除单条

 DELETE /nandao_scenic/_doc/001

java语言

  public void singleDelete(String index, String docId) {
        DeleteRequest deleteRequest=new DeleteRequest(index,docId);//构建删除请求
        try {
            client.delete(deleteRequest, RequestOptions.DEFAULT);//执行删除
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

7、批量删除

  POST /_bulk
{"delete":{"_index":"nandao_scenic","_id":"7"}}
{"delete":{ "_index":"nandao_scenic", "_id":"8"}}

java客户端语言

public void bulkDelete(String index, String docIdKey, List<String> docIdList) {
        BulkRequest bulkRequest = new BulkRequest();//构建BulkRequest对象
        for (String docId : docIdList) {//遍历文档_d列表
            DeleteRequest deleteRequest=new DeleteRequest(index,docId);//构建删除请求
            bulkRequest.add(deleteRequest);//创建UpdateRequest对象
        }
        try {
            BulkResponse bulkResponse = client.bulk(bulkRequest,RequestOptions.DEFAULT);//执行批量删除
            if (bulkResponse.hasFailures()) {//判断状态
                System.out.println("bulk fail,message:" + bulkResponse.buildFailureMessage());
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

8、根据条件删除

 POST /nandao_scenic/_delete_by_query
 {
   "query": {
     "term": {
       "city": {
         "value": "北京"
       }
     }
   }
 }

java语言客户端

public void deleteByQuery(String index,String city) {
        DeleteByQueryRequest deleteByQueryRequest=new DeleteByQueryRequest(index);//构建DeleteByQueryRequest对象
        deleteByQueryRequest.setQuery(new TermQueryBuilder("city",city));//设置按照城市查找文档的query
        try {
            client.deleteByQuery(deleteByQueryRequest,RequestOptions.DEFAULT);//执行删除
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

 到此、es基本操作分享完毕，下篇我们分享es丰富的搜索功能，敬请期待！