一、DataStream转Table

通过TableEnvironment ，可 以 把 DataStream 或 者 DataSet 注 册 为 Table ， 这 样 就 可 以 使 用 Table API 和 SQL 查 询 了 。 通 过 TableEnvironment 也可以把Table对象转换为DataStream或者DataSet，这样就可以使用DataStream 或者DataSet中的相关API了

   1.1 使用DataStream创建view视图

    val ssENV = StreamExecutionEnvironment.getExecutionEnvironment
 
    val ssSettings = EnvironmentSettings.newInstance().useBlinkPlanner()
                    .inStreamingMode().build()
 
    val ssTableEnv = StreamTableEnvironment.create(ssENV, ssSettings)
    //获取DataStream
    import org.apache.flink.api.scala._
    val stream = ssENV.fromCollection(Array((1, "java"), (2, "tom"), (3, "mac")))
    import org.apache.flink.table.api._
    ssTableEnv.createTemporaryView("myTable",stream,fields = 'id,'name)
    ssTableEnv.sqlQuery("select * from myTable where id>1") .execute().print()

结果：

1.2 使用DataStream创建table对象

     val ssENV = StreamExecutionEnvironment.getExecutionEnvironment
 
     val ssSettings = EnvironmentSettings.newInstance().useBlinkPlanner()
     .inStreamingMode().build()
 
     val ssTableEnv = StreamTableEnvironment.create(ssENV, ssSettings)
 
     //获取DataStream
     import org.apache.flink.api.scala._
     val stream = ssENV.fromCollection(Array((1, "java"), (2, "tom"), (3, "mac")))
 
     import org.apache.flink.table.api._
     val table = ssTableEnv.fromDataStream(stream, fields = 'id,'name)
     table.select($"id", $"name").filter($"id" > 1).execute().print()

结果：

二、DataSet转Table

此时只能使用旧的执行引擎，新的blink执行引擎不支持和DataSet转换

  1.1 使用DataSet创建view视图

 def main(args: Array[String]): Unit = {
 
    val bbEnv = ExecutionEnvironment.getExecutionEnvironment
    val bbTable=   BatchTableEnvironment.create(bbEnv);
 
    import org.apache.flink.api.scala._
    val stream = bbEnv.fromCollection(Array((1, "java"), (2, "tom"), (3, "mac")))
    //第一种 将DataStream 转换成view试图
     import org.apache.flink.table.api._
     bbTable.createTemporaryView("myTable",stream,fields = 'id,'name)
     bbTable.sqlQuery("select * from myTable where id>1") .execute().print()
  }

1.2 使用DataSet创建table对象

 def main(args: Array[String]): Unit = {
 
    val bbEnv = ExecutionEnvironment.getExecutionEnvironment
    val bbTableEvc=   BatchTableEnvironment.create(bbEnv);
    import org.apache.flink.api.scala._
    val stream = bbEnv.fromCollection(Array((1, "java"), (2, "tom"), (3, "mac")))
    import org.apache.flink.table.api._
 
    val table = bbTableEvc.fromDataSet(stream, fields = 'id,'name)
    table.select($"id", $"name").filter($"id" > 1).execute().print()
  }

三、将table转换成 DataStream

流式查询的结果Table会被动态地更新，即每个新的记录到达输入流时结果就会发生变化。因此，转换此 动态查询的DataStream需要对表的更新进行编码。 有几种模式可以将Table转换为DataStream。

• Append Mode:这种模式只适用于当动态表仅由INSERT更改修改时(仅附加)，之前添加的数据不会被更新。
• Retract Mode:可以始终使用此模式，它使用一个Boolean标识来编码INSERT和DELETE更改。

  def main(args: Array[String]): Unit = {
    val ssEnv = StreamExecutionEnvironment.getExecutionEnvironment
    val ssSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build()
    val ssTableEnv = StreamTableEnvironment.create(ssEnv, ssSettings)
 
    ssTableEnv.executeSql("" +
      "create table myTable(\n" +
      "id int,\n" +
      "name string\n" +
      ") with (\n" +
      "'connector.type' = 'filesystem',\n" +
      "'connector.path' = 'D:\\bigdata\\source',\n" +
      "'format.type' = 'csv'\n" +
      ")")
 
    val table = ssTableEnv.from("myTable")
 
    import org.apache.flink.api.scala._
    //如果只有新增(追加)操作，可以使用toAppendStream
    val appStream = ssTableEnv.toAppendStream[Row](table)
    appStream.map(row=>(row.getField(0).toString.toInt,row.getField(1).toString)).print()
    
    //如果有增加操作，还有删除操作，则使用toRetractStream
    val retStream = ssTableEnv.toRetractStream[Row](table)
    retStream.map(tup=>{
      val flag = tup._1
      val row = tup._2
      val id = row.getField(0).toString.toInt
      val name = row.getField(1).toString
      (flag,id,name)
    }).print()
    //注意：将table对象转换为DataStream之后，就需要调用StreamExecutionEnvironment
    ssEnv.execute("TableToDataStreamScala")
 
  }

 四、将table转换成 DataSet

  def main(args: Array[String]): Unit = {
 
    val bbEnv = ExecutionEnvironment.getExecutionEnvironment
    val bbTableEnv = BatchTableEnvironment.create(bbEnv)
 
    bbTableEnv.executeSql("" +
      "create table myTable(\n" +
      "id int,\n" +
      "name string\n" +
      ") with (\n" +
      "'connector.type' = 'filesystem',\n" +
      "'connector.path' = 'D:\\bigdata\\source',\n" +
      "'format.type' = 'csv'\n" +
      ")")
    //获取table
    val table = bbTableEnv.from("myTable")
    //将table转换为DataSet
    import org.apache.flink.api.scala._
    val set = bbTableEnv.toDataSet[Row](table)
    set.map(row=>(row.getField(0).toString.toInt,row.getField(1).toString))
      .print()
 
  }