Apache Flink 零基础入门（十六）Flink DataStream transformation

Operators transform one or more DataStreams into a new DataStream. 

Operators操做转换一个或多个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)
  }

}

数据源选择以前的任意一个数据源便可。

这里的map中没有作任何实质性的操做，filter中将全部的数都对2取模操做，打印结果以下:

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

说明map中获得的全部的数据，而在filter中进行了过滤操做。

Java

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

须要先使用data.setParallelism(1)而后再进行map操做，不然会输出屡次。由于咱们用的是JavaCustomParallelSourceFunction()，而当咱们使用JavaCustomNonParallelSourceFunction时，默认就是并行度1，能够不用设置。

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)

  }

Union操做将两个数据集综合起来，能够一同处理，上面打印输出以下：

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<Long> data1 = env.addSource(new JavaCustomNonParallelSourceFunction());
        DataStreamSource<Long> data2 = env.addSource(new JavaCustomNonParallelSourceFunction());
        data1.union(data2).print().setParallelism(1);
    }

Split  Select  Function

Scala

split能够将一个流拆成多个流，select能够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)
  }

能够根据选择的名称来处理数据。

Java

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