1.前言

通过一文搞懂这一系列的文章，我们已经知道了，Flink 作业的提交过程：

这篇文章主要聚焦在

我们以简单的代码为例

/**
 * @author shengjk1
 * @date 2018/11/23
 */
public class FlinkJava8Demo {
	public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		DataStreamSource<Integer> source = env.fromElements(1, 2, 3, 4, 5);
		
		source.flatMap((Integer number, Collector<String> out)->{
			StringBuilder builder = new StringBuilder();
			for (int i = 0; i < number; i++) {
				builder.append("a");
				out.collect(builder.toString());
			}
		}).returns(Types.STRING).print();
		
		source.map(i-> Tuple2.of(i,i))
				.returns(Types.TUPLE(Types.INT,Types.INT))
				.print();
		
		env.execute("aa");
	}
}

2.FlatMap 的转化

public <R> SingleOutputStreamOperator<R> flatMap(FlatMapFunction<T, R> flatMapper, TypeInformation<R> outputType) {
		return transform("Flat Map", outputType, new StreamFlatMap<>(clean(flatMapper)));
	}

flatMap 是 DataStream 的一个方法或者就是我们常数的算子，而 StreamFlatMap 其实才是 StreamOperator

transform 方法

protected <R> SingleOutputStreamOperator<R> doTransform(
			String operatorName,
			TypeInformation<R> outTypeInfo,
			StreamOperatorFactory<R> operatorFactory) {
		// read the output type of the input Transform to coax out errors about MissingTypeInfo
		transformation.getOutputType();
		OneInputTransformation<T, R> resultTransform = new OneInputTransformation<>(
				this.transformation,
				operatorName,
				operatorFactory,
				outTypeInfo,
				environment.getParallelism());
		@SuppressWarnings({"unchecked", "rawtypes"})
		// DataStream 的一个子类
		SingleOutputStreamOperator<R> returnStream = new SingleOutputStreamOperator(environment, resultTransform);
		//添加 operator，成为 StreamGraph 的一个 operator
		getExecutionEnvironment().addOperator(resultTransform);
		// 返回 stream，供下游继续操作
		return returnStream;
	}

像 filter、map等都会进行类似的操作

一文搞懂Flink生成StreamGraph

1.前言

2.FlatMap 的转化