1.flink 的streaming api初步学习
有界数据流处理，文件数据处理。
package com.ycl; import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.java.functions.KeySelector; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.datastream.KeyedStream; import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.util.Collector; public class WordCountStreamDemo { public static void main(String[] args) throws Exception { //1.创建执行环境 StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); //2.读取数据 DataStreamSource<String> lineDS = env.readTextFile("input/word.txt"); //3.处理数据 切分，转换，分组，聚合。 SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOneDS = lineDS.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() { @Override public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception { //按照 空格切分 String[] words = value.split(" "); for (String word : words) { //转换成 二元组 （word,1） Tuple2<String, Integer> wordAndOne = Tuple2.of(word, 1); //通过采集器向下游发送数据 out.collect(wordAndOne); } } }); //3.2 分组 KeyedStream<Tuple2<String, Integer>, String> wordAndOneKS = wordAndOneDS.keyBy(new KeySelector<Tuple2<String, Integer>, String>() { @Override public String getKey(Tuple2<String, Integer> value) throws Exception { return value.f0; } }); //3.3聚合 SingleOutputStreamOperator<Tuple2<String, Integer>> sumDS = wordAndOneKS.sum(1); //4.输出数据 sumDS.print(); //5.执行:类似 sparkstreaming 最后，ssc.start(); env.execute(); } } /** 接口 A,里面有一个方法a(); 正常写法，定义个 class B,实现接口A,方法a() B b = new B(); 匿名实现类: new A(){ a(){} } */输出结果如下;
前面的编号是，并行度线程编号。
在 DataSet API 里面分组使用的groupBy ; 在streaming里面使用的分组函数是: keyBy;
执行环境: DataSet 是:ExecutionEnvironment, Streaming 是: StreamExecutionEnvironment
调用: DataStream里面 env.execute();是必须调用的。DataSet不用去调用。
