elasticsearch 的基本操作多维度分享

news/2024/11/20 23:27:38/

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IdLong 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updatepublic 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IdLong 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UpdateRequestupdateRequest.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queryupdateByQueryRequest.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querytry {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！


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