Hadoop的未来发展趋势与挑战分析

    import org.apache.flink.api.common.functions.FlatMapFunction;
    import org.apache.flink.api.java.tuple.Tuple2;
    import org.apache.flink.streaming.api.datastream.DataStream;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    import org.apache.flink.util.Collector;
    
    public class RealTimeWordCount {
    public static void main(String[] args) throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        
        DataStream<String> text = env.socketTextStream("localhost", 9999); // 从socket端口接收数据
        
        DataStream<Tuple2<String, Integer>> counts = text
                .flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
                    @Override
                    public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
                        // normalize and split the line into words
                        String[] words = value.toLowerCase().split("\ W+");
                        
                        // emit the words
                        for (String word : words) {
                            if (word.length() > 0) {
                                out.collect(new Tuple2<>(word, 1));
                            }
                        }
                    }
                })
                .keyBy(0) // 按照第一个元素（单词）分组
                .sum(1);  // 对第二个元素（计数）求和
        
        counts.print(); // 打印结果到stdout
        
        env.execute("Real-time Word Count"); // 执行任务
    }
    }