Flink 实现无界流

package org.example.test;import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.api.java.operators.UnsortedGrouping;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;/*** DataSet API使用*/
public class WordCount2 {public static void main(String[] args) throws Exception {//该类主要是用于进行批处理final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();//读取文本DataSource<String> stringDataStreamSource = env.readTextFile("input/test.txt");//进行ETL处理,Tuple2 是二元数组的意思FlatMapOperator<String, Tuple2<String, Integer>> stringTuple2FlatMapOperator = stringDataStreamSource.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {@Overridepublic void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {String[] words = value.split(" ");for (String word : words) {Tuple2<String, Integer> oneTuple2 = Tuple2.of(word, 1);out.collect(oneTuple2);}}});//进行分组，分组字段取下标第0个UnsortedGrouping<Tuple2<String, Integer>> tuple2UnsortedGrouping =stringTuple2FlatMapOperator.groupBy(0);//进行sum操作AggregateOperator<Tuple2<String, Integer>> sum = tuple2UnsortedGrouping.sum(1);sum.print();}
}

ExecutionEnvironment 是批处理的方式，DataSource会慢慢被淘汰