flink SourceFunction SinkFunction timeWindowAll reduce

1、实现SourceFunction接口生成数据源

/**
 * @Description: 产生数据 traceid,userid,timestamp,status,response time
 */
public class SourceData implements SourceFunction<String> {
    private volatile boolean Running = true;
    static int status[] = {200, 404, 500, 501, 301};

    @Override
    public void run(SourceContext<String> ctx) throws Exception {
        while (Running) {
            Thread.sleep((int) (Math.random() * 10));

            StringBuffer stringBuffer = new StringBuffer();
            stringBuffer.append(UUID.randomUUID().toString());
            stringBuffer.append(",");
            stringBuffer.append((int) (Math.random() * 100));
            stringBuffer.append(",");
            stringBuffer.append(System.currentTimeMillis());
            stringBuffer.append(",");
            stringBuffer.append(status[(int) (Math.random() * 4)]);
            stringBuffer.append(",");
            stringBuffer.append((int)(Math.random()*200));

            ctx.collect(stringBuffer.toString());
        }
    }

    @Override
    public void cancel() {

    }
}

2、实现SinkFunction接口，实现数据下沉存储及使用

public class TraceSourceData {
    public static void main(String args[]) throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<Tuple5<String, Integer, Long, Integer, Integer>> ds =
                env.addSource(new SourceData())
                        .flatMap(new FlatMapFunction<String, Tuple5<String, Integer, Long, Integer, Integer>>() {
                            @Override
                            public void flatMap(String value, Collector<Tuple5<String, Integer, Long, Integer, Integer>> out) throws Exception {
                                String ss[] = value.split(",");
                                out.collect(Tuple5.of(ss[0], Integer.parseInt(ss[1]), Long.parseLong(ss[2]), Integer.parseInt(ss[3]), Integer.parseInt(ss[4])));
                            }
                        });

        //5秒窗口统计各状态的次数
        DataStream<Tuple2<Integer, Integer>> statusData = ds
                .flatMap(new FlatMapFunction<Tuple5<String, Integer, Long, Integer, Integer>, Tuple2<Integer, Integer>>() {
                    @Override
                    public void flatMap(Tuple5<String, Integer, Long, Integer, Integer> value, Collector<Tuple2<Integer, Integer>> out) throws Exception {

                        out.collect(Tuple2.of(value.f3, 1));
                    }
                })
                .keyBy(0)
                .timeWindow(Time.seconds(5))
                .sum(1);

        statusData.print().setParallelism(1);

        //5秒窗口统计响应时间大于50的用户访问次数在整个响应中的占比
        //大于50，小于等于50，所有次数
        DataStream<Tuple3<Integer, Integer, Integer>> greater100UserPer = ds
                .flatMap(new FlatMapFunction<Tuple5<String, Integer, Long, Integer, Integer>, Tuple3<Integer, Integer, Integer>>() {
                    @Override
                    public void flatMap(Tuple5<String, Integer, Long, Integer, Integer> value, Collector<Tuple3<Integer, Integer, Integer>> out) throws Exception {
                        if (value.f4 > 50)
                            out.collect(Tuple3.of(1, 0, 1));
                        else
                            out.collect(Tuple3.of(0, 1, 1));
                    }
                })//注意这里，没有使用keyBy
                .timeWindowAll(Time.seconds(5))
                .reduce(new ReduceFunction<Tuple3<Integer, Integer, Integer>>() {
                    @Override
                    public Tuple3<Integer, Integer, Integer> reduce(Tuple3<Integer, Integer, Integer> value1, Tuple3<Integer, Integer, Integer> value2) throws Exception {
                        return Tuple3.of(value1.f0 + value2.f0, value1.f1 + value2.f1, value1.f2 + value2.f2);
                    }
                })//正常情况下应该重新起一个Double的数据类型，这里懒得麻烦，直接就做map转换了
                .map(new MapFunction<Tuple3<Integer, Integer, Integer>, Tuple3<Integer, Integer, Integer>>() {
                    @Override
                    public Tuple3<Integer, Integer, Integer> map(Tuple3<Integer, Integer, Integer> value) throws Exception {
                        Double rate1 = (value.f0.doubleValue() / value.f2.doubleValue()) * 100;
                        Double rate2 = (value.f1.doubleValue() / value.f2.doubleValue()) * 100;

                        return Tuple3.of(rate1.intValue(), rate2.intValue(), 1);
                    }
                });

        //SinkFunction，实现接口后，可以随意处理数据
        greater100UserPer.addSink(new SinkFunction<Tuple3<Integer, Integer, Integer>>() {
            @Override
            public void invoke(Tuple3<Integer, Integer, Integer> value, Context context) throws Exception {
                System.out.println(LocalDateTime.ofInstant(Instant.ofEpochMilli(context.timestamp()), ZoneId.systemDefault()) + " " + value);
            }
        });

        env.execute("TraceSourceData");
    }

}