Apache Flink DataStream transformation

Operators transform one or more DataStreams into a new DataStream. 

Operators操作转换一个或多个DataStream到一个新的DataStream 。

filter function

Scala

object DataStreamTransformationApp {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    filterFunction(env)
    env.execute("DataStreamTransformationApp")
  }

  def filterFunction(env: StreamExecutionEnvironment): Unit = {
    val data=env.addSource(new CustomNonParallelSourceFunction)
    data.map(x=>{
      println("received:" + x)
      x
    }).filter(_%2 == 0).print().setParallelism(1)
  }

}

数据源选择之前的任意一个数据源即可。

这里的map中没有做任何实质性的操作，filter中将所有的数都对2取模操作，打印结果如下:

received:1
received:2
2
received:3
received:4
4
received:5
received:6
6
received:7
received:8
8

说明map中得到的所有的数据，而在filter中进行了过滤操作。

Java

public static void filterFunction(StreamExecutionEnvironment env) {
        DataStreamSource data = env.addSource(new JavaCustomParallelSourceFunction());
        data.setParallelism(1).map(new MapFunction() {
            @Override
            public Long map(Long value) throws Exception {
                System.out.println("received:"+value);
                return value;
            }
        }).filter(new FilterFunction() {
            @Override
            public boolean filter(Long value) throws Exception {
                return value % 2==0;
            }
        }).print().setParallelism(1);
    }

需要先使用data.setParallelism(1)然后再进行map操作，否则会输出多次。因为我们用的是JavaCustomParallelSourceFunction()，而当我们使用JavaCustomNonParallelSourceFunction时，默认就是并行度1，可以不用设置。

Union Function

Scala

def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment

//    filterFunction(env)
    unionFunction(env)
    env.execute("DataStreamTransformationApp")
  }

  def unionFunction(env: StreamExecutionEnvironment): Unit = {
    val data01 = env.addSource(new CustomNonParallelSourceFunction)
    val data02 = env.addSource(new CustomNonParallelSourceFunction)
    data01.union(data02).print().setParallelism(1)

  }

http://www.developcls.com/qa/d8c20a9e2ba34964a440f96b88730f2e.html

Union操作将两个数据集综合起来，可以一同处理，上面打印输出如下：

1
1
2
2
3
3
4
4

Java

public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
//        filterFunction(environment);
        unionFunction(environment);
        environment.execute("JavaDataStreamTransformationApp");
    }

    public static void unionFunction(StreamExecutionEnvironment env) {
        DataStreamSource data1 = env.addSource(new JavaCustomNonParallelSourceFunction());
        DataStreamSource data2 = env.addSource(new JavaCustomNonParallelSourceFunction());
        data1.union(data2).print().setParallelism(1);
    }

Split  Select  Function

Scala

split可以将一个流拆成多个流，select可以从多个流中进行选择处理的流。

def splitSelectFunction(env: StreamExecutionEnvironment): Unit = {
    val data = env.addSource(new CustomNonParallelSourceFunction)
    val split = data.split(new OutputSelector[Long] {
      override def select(value: Long): lang.Iterable[String] = {
        val list = new util.ArrayList[String]()
        if (value % 2 == 0) {
          list.add("even")
        } else {
          list.add("odd")
        }
        list
      }
    })
    split.select("odd","even").print().setParallelism(1)
  }

可以根据选择的名称来处理数据。

Java

public static void splitSelectFunction(StreamExecutionEnvironment env) {
        DataStreamSource data = env.addSource(new JavaCustomNonParallelSourceFunction());
        SplitStream split = data.split(new OutputSelector() {
            @Override
            public Iterable select(Long value) {
                List output = new ArrayList<>();
                if (value % 2 == 0) {
                    output.add("odd");
                } else {
                    output.add("even");
                }
                return output;
            }
        });
        split.select("odd").print().setParallelism(1);
    }