「Flink」事件时间与水印[通俗易懂]

/**
 * Method to get the window start for a timestamp.
 *
 * @param timestamp epoch millisecond to get the window start.
 * @param offset The offset which window start would be shifted by.
 * @param windowSize The size of the generated windows.
 * @return window start
 */

代码100分

代码100分public static long getWindowStartWithOffset(long timestamp, long offset, long windowSize) {
        return timestamp - (timestamp - offset + windowSize) % windowSize;
    }

public class WordCountWindow {
    public static void main(String[] args) throws Exception {
        // 1. 初始化流式运行环境
        Configuration conf = new Configuration();
        StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(conf);

        // 2. 设置时间处理类型，这里设置的方式处理时间
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        // 3. 定义数据源，每秒发送一个hadoop单词
        SingleOutputStreamOperator<Tuple2<String, Long>> wordDSWithWaterMark = env.addSource(new RichSourceFunction<Tuple2<String, Long>>() {

            private boolean isCanaled = false;
            private int TOTAL_NUM = 20;

            @Override
            public void run(SourceContext<Tuple2<String, Long>> ctx) throws Exception {
                while (!isCanaled) {
                    ctx.collect(Tuple2.of("hadooop", System.currentTimeMillis()));

                    // 打印窗口开始、结束时间
                    SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
                    System.out.println("事件发送时间:" + sdf.format(System.currentTimeMillis()));
                    Thread.sleep(1000);
                }
            }

            @Override
            public void cancel() {
                isCanaled = true;
            }
        }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Tuple2<String, Long>>(Time.seconds(5)) {
            @Override
            public long extractTimestamp(Tuple2<String, Long> element) {
                return element.f1;
            }
        });

        // 4. 每5秒进行一次，分组统计
        // 4.1 转换为元组
        wordDSWithWaterMark.map(word -> {
            return Tuple2.of(word.f0, 1);

        })
                // 指定返回类型
                .returns(Types.TUPLE(Types.STRING, Types.INT))
                // 按照单词进行分组
                .keyBy(t -> t.f0)
                // 滚动窗口，3秒计算一次
                .timeWindow(Time.seconds(3))
                .reduce(new ReduceFunction<Tuple2<String, Integer>>() {
                    @Override
                    public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1, Tuple2<String, Integer> value2) throws Exception {
                        return Tuple2.of(value1.f0, value1.f1 + value2.f1);
                    }
                }, new RichWindowFunction<Tuple2<String, Integer>, Tuple2<String, Integer>, String, TimeWindow>() {
                    @Override
                    public void apply(String word, TimeWindow window, Iterable<Tuple2<String, Integer>> input, Collector<Tuple2<String, Integer>> out) throws Exception {

                        // 打印窗口开始、结束时间
                        SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
                        System.out.println("窗口开始时间:" + sdf.format(window.getStart())
                                + " 窗口结束时间：" + sdf.format(window.getEnd())
                                + " 窗口计算时间:" + sdf.format(System.currentTimeMillis()));

                        int sum = 0;
                        Iterator<Tuple2<String, Integer>> iterator = input.iterator();
                        while(iterator.hasNext()) {
                            Integer count = iterator.next().f1;
                            sum += count;
                        }
                        out.collect(Tuple2.of(word, sum));
                    }
                }).print();

        env.execute("app");
    }
}