示例

该示例是从Kafka数据源中读取数据，并写入到Elasticsearch结果表中，其具体步骤如下：

1. 参考增强型跨源连接，在 DLI 上根据Elasticsearch和Kafka所在的虚拟私有云和子网分别创建相应的增强型跨源连接，并绑定所要使用的Flink弹性资源池。
2. 设置Elasticsearch和Kafka的安全组，添加入向规则使其对Flink的队列网段放通。参考测试地址连通性分别根据Elasticsearch和Kafka的地址测试队列连通性。若能连通，则表示跨源已经绑定成功，否则表示未成功。
3. 登录Elasticsearch集群的Kibana，并选择Dev Tools，输入下列语句并执行，以创建值为orders的index：

   PUT /orders
   {
     "settings": {
       "number_of_shards": 1
     },
   	"mappings": {
   	  "properties": {
   	    "order_id": {
   	      "type": "text"
   	    },
   	    "order_channel": {
   	      "type": "text"
   	    },
   	    "order_time": {
   	      "type": "text"
   	    },
   	    "pay_amount": {
   	      "type": "double"
   	    },
   	    "real_pay": {
   	      "type": "double"
   	    },
   	    "pay_time": {
   	      "type": "text"
   	    },
   	    "user_id": {
   	      "type": "text"
   	    },
   	    "user_name": {
   	      "type": "text"
   	    },
   	    "area_id": {
   	      "type": "text"
   	    }
   	  }
   	}
   }

4. 参考创建Flink OpenSource作业，创建flink opensource sql作业，输入以下作业运行脚本，提交运行作业。
   注意：创建作业时，在作业编辑界面的“运行参数”处，“Flink版本”选择“1.12”，勾选“保存作业日志”并设置保存作业日志的OBS桶，方便后续查看作业日志。如下脚本中的加粗参数请根据实际环境修改。

   CREATE TABLE kafkaSource (
     order_id string,
     order_channel string,
     order_time string, 
     pay_amount double,
     real_pay double,
     pay_time string,
     user_id string,
     user_name string,
     area_id string
   ) WITH (
     'connector' = 'kafka',
     'topic' = 'KafkaTopic',
     'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
     'properties.group.id' = 'GroupId',
     'scan.startup.mode' = 'latest-offset',
     "format" = "json"
   );
   
   CREATE TABLE elasticsearchSink (
     order_id string,
     order_channel string,
     order_time string, 
     pay_amount double,
     real_pay double,
     pay_time string,
     user_id string,
     user_name string,
     area_id string
   ) WITH (
     'connector' = 'elasticsearch-7',
     'hosts' = 'ElasticsearchAddress:ElasticsearchPort',
     'index' = 'orders'
   );
   
   insert into elasticsearchSink select * from kafkaSource;

5. 连接Kafka集群，向kafka中插入如下测试数据：

   {"order_id":"202103241000000001", "order_channel":"webShop", "order_time":"2021-03-24 10:00:00", "pay_amount":"100.00", "real_pay":"100.00", "pay_time":"2021-03-24 10:02:03", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}
   
   {"order_id":"202103241606060001", "order_channel":"appShop", "order_time":"2021-03-24 16:06:06", "pay_amount":"200.00", "real_pay":"180.00", "pay_time":"2021-03-24 16:10:06", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}

6. 在Elasticsearch集群的Kibana中输入下述语句并查看相应结果：

   GET orders/_search

   {
     "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" : "orders",
           "_type" : "_doc",
           "_id" : "ae7wpH4B1dV9conjpXeB",
           "_score" : 1.0,
           "_source" : {
             "order_id" : "202103241000000001",
             "order_channel" : "webShop",
             "order_time" : "2021-03-24 10:00:00",
             "pay_amount" : 100.0,
             "real_pay" : 100.0,
             "pay_time" : "2021-03-24 10:02:03",
             "user_id" : "0001",
             "user_name" : "Alice",
             "area_id" : "330106"
           }
         },
         {
           "_index" : "orders",
           "_type" : "_doc",
           "_id" : "au7xpH4B1dV9conjn3er",
           "_score" : 1.0,
           "_source" : {
             "order_id" : "202103241606060001",
             "order_channel" : "appShop",
             "order_time" : "2021-03-24 16:06:06",
             "pay_amount" : 200.0,
             "real_pay" : 180.0,
             "pay_time" : "2021-03-24 16:10:06",
             "user_id" : "0001",
             "user_name" : "Alice",
             "area_id" : "330106"
           }
         }
       ]
     }
   }