34.DataStream API之Side Outputs

flink 1.9

 

除了从DataStream操作的结果中获取主数据流之外，你还可以产生任意数量额外的侧输出结果流。侧输出结果流的数据类型不需要与主数据流的类型一致，不同侧输出流的类型也可以不同。当您想要拆分数据流时，通常的做法是复制流,然后从每个流过滤出您不想拥有的数据，但是如果使用side output操作可以很好的解决这种问题。

在使用侧输出时，首先用户需要定义一个OutputTag，这个OutputTag将用来标识一个侧输出流side output stream:

// 这需要是一个匿名的内部类，以便我们可以分析类型
OutputTag outputTag = new OutputTag("side-output") {};

请注意OutputTag是如何根据侧输出流包含的元素类型进行类型化的。

可以通过以下类函数将数据发送到侧输出side output:

• ProcessFunction

• KeyedProcessFunction
• CoProcessFunction
• KeyedCoProcessFunction
• ProcessWindowFunction
• ProcessAllWindowFunction

您可以使用Context参数（在上述函数中向用户公开）将数据发送到OutputTag标识的侧输出。 以下是从ProcessFunction发出侧输出数据的示例：

public class DataSource1 extends RichParallelSourceFunction> {
   private volatile boolean running = true;

   @Override
   public void run(SourceContext> ctx) throws InterruptedException {

//    Tuple3[] elements = new Tuple3[]{
//       Tuple3.of("a", "1", 1551169050000L),
//       Tuple3.of("aa", "33", 1551169064000L),
//       Tuple3.of("a", "2", 1551169054000L),
//       Tuple3.of("a", "3", 1551169064000L),
//       Tuple3.of("b", "5", 1551169100000L),
//       Tuple3.of("a", "4", 1551169079999L),
//       Tuple3.of("aa1", "44", 1551169079000L),
//       Tuple3.of("b", "6", 1551169108000L)
//    };

      Tuple5[] elements1 = new Tuple5[]{

         Tuple5.of("a", 2, 1551169050002L, "w", "e"),
         Tuple5.of("a", 1, 1551169050002L, "2", "1"),
         Tuple5.of("a", 1, 1551169050001L, "3", "1"),
         Tuple5.of("aa", 33, 1551169064000L, "4", "1"),
         Tuple5.of("a", 2, 1551169054000L, "5", "1"),
         Tuple5.of("a", 1, 1551169050003L, "6", "1"),
         Tuple5.of("a", 3, 1551169064000L, "7", "1"),
         Tuple5.of("b", 5, 1551169100000L, "8", "1"),
         Tuple5.of("a", 4, 1551169079000L, "9", "1"),
         Tuple5.of("aa", 44, 1551169079000L, "10", "1"),
         Tuple5.of("b", 6, 1551169108000L, "11", "1")
      };

      int count = 0;
      while (running && count < elements1.length) {
         ctx.collect(new Tuple5<>((String)elements1[count].f0,(Integer)elements1[count].f1,(Long)elements1[count].f2,
            (String)elements1[count].f3,(String)elements1[count].f4));
         count++;
         Thread.sleep(300);
      }
   }

   @Override
   public void cancel() {
      running = false;
   }
}

public class SideOutputs {
   public static void main(String[] args) {
      Long delay = 5000L;
      final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
      env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
      env.setParallelism(2);

      // 设置数据源
      DataStream> source = env.addSource(new DataSource1()).setParallelism(1).name("Demo Source");
      final OutputTag outputTag = new OutputTag("side-output"){};

      SingleOutputStreamOperator mainDataStream = source
         .process(new ProcessFunction, Integer>() {
            @Override
            public void processElement(Tuple5 value, Context ctx, Collector out) throws Exception {
               out.collect(value.f1);
               ctx.output(outputTag,  "sideout-" + value.f4);
            }
         });
   }
}

你可以在DataStream操作的结果中使用getSideOutput(OutputTag)来获取侧输出流。这里为您提供一个DataStream类型，用于将输入结果发送到侧输出流side output stream中:

public class SideOutputs {
   public static void main(String[] args) {
      Long delay = 5000L;
      final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
      env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
      env.setParallelism(2);

      // 设置数据源
      DataStream> source = env.addSource(new DataSource1()).setParallelism(1).name("Demo Source");
      final OutputTag outputTag = new OutputTag("side-output"){};

      SingleOutputStreamOperator mainDataStream = source
         .process(new ProcessFunction, Integer>() {
            @Override
            public void processElement(Tuple5 value, Context ctx, Collector out) throws Exception {
               out.collect(value.f1);
               ctx.output(outputTag, "sideout-" + value.f4);
            }
         });
      DataStream sideOutput = mainDataStream.getSideOutput(outputTag);
   }
}

https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/stream/side_output.html

https://www.jianshu.com/p/0350cd9a38b5