flink-reduce
程序员文章站
2022-06-07 23:02:49
...
一.背景
有时候我们需要过滤数据,有些中间数据是不需要的,比如场景:
binlog 数据更新的时候,我们仅仅需要最新数据。会根据ID 分组,然后取version 最大的一条,存储
二.简单实例
@Data @ToString public class Order { // 主键id private Integer id; // 版本 private Integer version; private Timestamp mdTime; public Order(int id, Integer version) { this.id = id; this.version = version; this.mdTime = new Timestamp(System.currentTimeMillis()); } public Order() { } }
public class OrderSource implements SourceFunction<Order> { Random random = new Random(); @Override public void run(SourceContext<Order> ctx) throws Exception { while (true) { TimeUnit.MILLISECONDS.sleep(100); // 为了区分,我们简单生0~2的id, 和版本0~99 int id = random.nextInt(3); Order o = new Order(id, random.nextInt(100)); ctx.collect(o); } } @Override public void cancel() { } }
public class ReduceApp { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setParallelism(1); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); DataStream<Order> userInfoDataStream = env.addSource(new OrderSource()); DataStream<Order> timedData = userInfoDataStream.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<Order>() { @Override public long extractAscendingTimestamp(Order element) { return element.getMdTime().getTime(); } }); SingleOutputStreamOperator<Order> reduce = timedData .keyBy("id") .timeWindow(Time.seconds(10), Time.seconds(5)) .reduce((ReduceFunction<Order>) (v1, v2) -> v1.getVersion() >= v2.getVersion() ? v1 : v2); reduce.print(); env.execute("test"); } }
结果:
Order(id=2, version=97, mdTime=2019-03-11 17:39:34.052)
Order(id=0, version=99, mdTime=2019-03-11 17:39:32.913)
Order(id=1, version=96, mdTime=2019-03-11 17:39:34.155)
Order(id=2, version=97, mdTime=2019-03-11 17:39:34.052)
Order(id=1, version=96, mdTime=2019-03-11 17:39:34.155)
Order(id=0, version=99, mdTime=2019-03-11 17:39:32.913)
这个会对同一个窗口做过滤,比如同步到另一个mysql,hdfs,就能减少数据量
上一篇: 能让新站优化三个月上首页的秘诀
推荐阅读