Link Table API JAVA_STREAM_DEMO

<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-table-api-java-bridge_2.11</artifactId>
        <version>1.9.0</version>
        <!--<scope>provided</scope>-->
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-table-planner_2.11</artifactId>
        <version>1.9.0</version>
        <!--<scope>provided</scope>-->
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-scala_2.11</artifactId>
        <version>1.9.0</version>
        <!--<scope>provided</scope>-->
    </dependency>

</dependencies>

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;

/**
 * FLink Java Stream Table API DEMO
 *
 * @author: create by maoxiangyi
 * @version: v1.0
 */
public class WordCountSql_Stream {

   public static void main(String[] args) throws Exception {
      EnvironmentSettings fsSettings = EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build();
      StreamExecutionEnvironment fsEnv = StreamExecutionEnvironment.getExecutionEnvironment();
      StreamTableEnvironment fsTableEnv = StreamTableEnvironment.create(fsEnv, fsSettings);

      DataStreamSource<WC> myDataSource = fsEnv.addSource(new SourceFunction<WC>() {
         @Override
         public void run(SourceContext<WC> sourceContext) throws Exception {
            while (true) {
               Thread.sleep(1000);
               sourceContext.collect(new WC("Hello", 1));
            }
         }

         @Override
         public void cancel() {
            // nothing
         }
      });

      fsTableEnv.registerDataStream("WordCount", myDataSource);

      // run a SQL query on the Table and retrieve the result as a new Table
      Table table = fsTableEnv.sqlQuery(
            "SELECT word, SUM(frequency) as frequency FROM WordCount GROUP BY word");

      // 使用flinkSQL处理实时数据当我们把表转化成流的时候，需要使用toAppendStream与toRetractStream这两个方法。
      // 稍不注意可能直接选择了toAppendStream。
      //追加模式：只有在动态Table仅通过INSERT更改修改时才能使用此模式，即它仅附加，并且以前发出的结果永远不会更新。
      //           如果更新或删除操作使用追加模式会失败报错
      //缩进模式：始终可以使用此模式。返回值是boolean类型。
      //           它用true或false来标记数据的插入和撤回，返回true代表数据插入，false代表数据的撤回
      // 按照官网的理解如果数据只是不断添加，可以使用追加模式，
      // 其余方式则不可以使用追加模式，而缩进模式侧可以适用于更新，删除等场景
      //  https://blog.csdn.net/aa518189/article/details/87816139
      DataStream<Tuple2<Boolean, WC>> stream = fsTableEnv.toRetractStream(table, WC.class);

      stream.print();

      fsTableEnv.execute("flink stream  table job");
   }
}

10> (true,word='Hello', frequency=1)
10> (false,word='Hello', frequency=1)
10> (true,word='Hello', frequency=2)
10> (false,word='Hello', frequency=2)
10> (true,word='Hello', frequency=3)
10> (false,word='Hello', frequency=3)
10> (true,word='Hello', frequency=4)
10> (false,word='Hello', frequency=4)
10> (true,word='Hello', frequency=5)
10> (false,word='Hello', frequency=5)
10> (true,word='Hello', frequency=6)
10> (false,word='Hello', frequency=6)
10> (true,word='Hello', frequency=7)