大概看了下。这个问题我业务中涉及到过。我是DataStream API做的。 不过我是在任务设计阶段就考虑了所有case，然后提前考虑了这些问题的。 watermark是可以重设的。其次我还更改了interval join的算子实现，默认1.10只支持inner join。不支持left/right join。 并且inner join后采用最大的timestamp。这个比较复杂，实际如果做left join，业务上可能更希望使用left的时间，right join则使用right的时间。out join则只能使用留下的那个的时间，inner join情况需要看业务。
你这个问题主要就是watermark重设就可以了。 Tianwang Li <[email protected]> 于2020年8月16日周日 上午10:45写道： > 展开讨论一些特点从场景。 > 1、inner join场景。有什么办法取两条流的的rowtime 的max吗？ > 使用SQL语句的场合，怎么实现？ > 例如： > SELECT if(left.rowtime > right.rowtime, left.rowtime, right.rowtime) as > rowtime, ... > > 如果支持了，那么这种场景我们还是可以在下游进行窗口计算和CEP之类的计算。 > > Tianwang Li <[email protected]> 于2020年8月16日周日 上午10:40写道： > > > 展开讨论一些特点场景。 > > > > Benchao Li <[email protected]> 于2020年7月6日周一 下午11:08写道： > > > >> 我们最开始发现这个现象的时候也有些惊讶，不过后来想了一下感觉也是合理的。 > >> > >> 因为双流Join的时间范围有可能会比较大，比如 A流 在 B流的[-10min, +10min]，那这样的话， > >> A流来一条数据，可能会join到几分钟之前的数据，而此时的watermark其实已经大于了那条数据的事件时间。 > >> > >> 我个人感觉，这应该就是在更实时的产生Join结果和导致数据时间晚于watermark之间，需要有一个balance。 > >> 现在默认实现是选择了更加实时的产生结果。当然还有另外一种实现思路，就是保证watermark不会超过数据时间， > >> 那样的话，Join结果的产生就会delay，或者需要修改watermark逻辑，让watermark一定要小于当前能join到的数据 > >> 的时间最早的那个。 > >> > >> 元始(Bob Hu) <[email protected]> 于2020年7月5日周日 下午8:48写道： > >> > >> > 谢谢您的解答。感觉flink这个机制有点奇怪呢 > >> > > >> > > >> > ------------------ 原始邮件 ------------------ > >> > *发件人:* "Benchao Li"<[email protected]>; > >> > *发送时间:* 2020年7月5日(星期天) 中午11:58 > >> > *收件人:* "元始(Bob Hu)"<[email protected]>; > >> > *抄送:* "user-zh"<[email protected]>; > >> > *主题:* Re: flink interval join后按窗口聚组问题 > >> > > >> > 回到你的问题，我觉得你的观察是正确的。Time interval join产生的结果的确是会有这种情况。 > >> > 所以如果用事件时间的time interval join，后面再接一个事件时间的window（或者其他的使用事件时间的算子，比如CEP等） > >> > 就会有些问题，很多数据被作为late数据直接丢掉了。 > >> > > >> > 元始(Bob Hu) <[email protected]> 于2020年7月3日周五 下午3:29写道： > >> > > >> >> 您好，我想请教一个问题： > >> >> flink双流表 interval join后再做window group是不是有问题呢，有些left join关联不上的数据会被丢掉。 > >> >> 比如关联条件是select * from a,b where a.id=b.id and b.rowtime between > >> a.rowtime > >> >> and a.rowtime + INTERVAL '1' HOUR > >> >> ，看源码leftRelativeSize=1小时，rightRelativeSize=0，左流cleanUpTime = rowTime > + > >> >> leftRelativeSize + (leftRelativeSize + rightRelativeSize) / 2 + > >> >> allowedLateness + > >> >> > >> 1，左表关联不上的数据会在1.5小时后输出（右表为null），而watermark的调整值是Math.max(leftRelativeSize, > >> >> rightRelativeSize) + > >> >> > >> > allowedLateness，也就是1小时，那这样等数据输出的时候watermark不是比左表rowtime还大0.5小时了吗,后面再有对连接流做group > >> >> by的时候这种右表数据为空的数据就丢掉了啊。 > >> >> flink版本 1.10.0。 > >> >> > >> >> 下面是我的一段测试代码： > >> >> > >> >> import org.apache.commons.net.ntp.TimeStamp; > >> >> import org.apache.flink.api.common.typeinfo.TypeInformation; > >> >> import org.apache.flink.api.common.typeinfo.Types; > >> >> import org.apache.flink.api.java.typeutils.RowTypeInfo; > >> >> import org.apache.flink.streaming.api.TimeCharacteristic; > >> >> import org.apache.flink.streaming.api.datastream.DataStream; > >> >> import > >> org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; > >> >> import > >> org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarks; > >> >> import org.apache.flink.streaming.api.functions.ProcessFunction; > >> >> import > org.apache.flink.streaming.api.functions.source.SourceFunction; > >> >> import org.apache.flink.streaming.api.watermark.Watermark; > >> >> import org.apache.flink.table.api.EnvironmentSettings; > >> >> import org.apache.flink.table.api.Table; > >> >> import org.apache.flink.table.api.java.StreamTableEnvironment; > >> >> import org.apache.flink.table.functions.ScalarFunction; > >> >> import org.apache.flink.types.Row; > >> >> import org.apache.flink.util.Collector; > >> >> import org.apache.flink.util.IOUtils; > >> >> > >> >> import java.io.BufferedReader; > >> >> import java.io.InputStreamReader; > >> >> import java.io.Serializable; > >> >> import java.net.InetSocketAddress; > >> >> import java.net.Socket; > >> >> import java.sql.Timestamp; > >> >> import java.text.SimpleDateFormat; > >> >> import java.util.ArrayList; > >> >> import java.util.Date; > >> >> import java.util.List; > >> >> > >> >> public class TimeBoundedJoin { > >> >> > >> >> public static AssignerWithPeriodicWatermarks<Row> > >> getWatermark(Integer maxIdleTime, long finalMaxOutOfOrderness) { > >> >> AssignerWithPeriodicWatermarks<Row> timestampExtractor = new > >> AssignerWithPeriodicWatermarks<Row>() { > >> >> private long currentMaxTimestamp = 0; > >> >> private long lastMaxTimestamp = 0; > >> >> private long lastUpdateTime = 0; > >> >> boolean firstWatermark = true; > >> >> // Integer maxIdleTime = 30; > >> >> > >> >> @Override > >> >> public Watermark getCurrentWatermark() { > >> >> if(firstWatermark) { > >> >> lastUpdateTime = System.currentTimeMillis(); > >> >> firstWatermark = false; > >> >> } > >> >> if(currentMaxTimestamp != lastMaxTimestamp) { > >> >> lastMaxTimestamp = currentMaxTimestamp; > >> >> lastUpdateTime = System.currentTimeMillis(); > >> >> } > >> >> if(maxIdleTime != null && System.currentTimeMillis() > - > >> lastUpdateTime > maxIdleTime * 1000) { > >> >> return new Watermark(new Date().getTime() - > >> finalMaxOutOfOrderness * 1000); > >> >> } > >> >> return new Watermark(currentMaxTimestamp - > >> finalMaxOutOfOrderness * 1000); > >> >> > >> >> } > >> >> > >> >> @Override > >> >> public long extractTimestamp(Row row, long > >> previousElementTimestamp) { > >> >> Object value = row.getField(1); > >> >> long timestamp; > >> >> try { > >> >> timestamp = (long)value; > >> >> } catch (Exception e) { > >> >> timestamp = ((Timestamp)value).getTime(); > >> >> } > >> >> if(timestamp > currentMaxTimestamp) { > >> >> currentMaxTimestamp = timestamp; > >> >> } > >> >> return timestamp; > >> >> } > >> >> }; > >> >> return timestampExtractor; > >> >> } > >> >> > >> >> public static void main(String[] args) throws Exception { > >> >> StreamExecutionEnvironment bsEnv = > >> StreamExecutionEnvironment.getExecutionEnvironment(); > >> >> EnvironmentSettings bsSettings = > >> > EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build(); > >> >> StreamTableEnvironment bsTableEnv = > >> StreamTableEnvironment.create(bsEnv, bsSettings); > >> >> bsEnv.setParallelism(1); > >> >> > >> bsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); > >> >> > >> >> > >> >> // DataStream<Row> ds1 = > bsEnv.addSource(sourceFunction(9000)); > >> >> SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd > >> HH:mm:ss"); > >> >> List<Row> list = new ArrayList<>(); > >> >> list.add(Row.of("001",new Timestamp(sdf.parse("2020-05-13 > >> 00:00:00").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 00:20:00").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 00:40:00").getTime()), 100)); > >> >> list.add(Row.of("002",new Timestamp(sdf.parse("2020-05-13 > >> 01:00:01").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 02:20:00").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 02:30:00").getTime()), 100)); > >> >> list.add(Row.of("003",new Timestamp(sdf.parse("2020-05-13 > >> 02:00:02").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 02:20:00").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 02:40:00").getTime()), 100)); > >> >> list.add(Row.of("004",new Timestamp(sdf.parse("2020-05-13 > >> 03:00:03").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 03:20:00").getTime()), 100)); > >> >> list.add(Row.of("000",new Timestamp(sdf.parse("2020-05-13 > >> 03:40:00").getTime()), 100)); > >> >> list.add(Row.of("005",new Timestamp(sdf.parse("2020-05-13 > >> 04:00:04").getTime()), 100)); > >> >> DataStream<Row> ds1 = bsEnv.addSource(new > >> SourceFunction<Row>() { > >> >> @Override > >> >> public void run(SourceContext<Row> ctx) throws Exception > { > >> >> for(Row row : list) { > >> >> ctx.collect(row); > >> >> Thread.sleep(1000); > >> >> } > >> >> > >> >> } > >> >> > >> >> @Override > >> >> public void cancel() { > >> >> > >> >> } > >> >> }); > >> >> ds1 = ds1.assignTimestampsAndWatermarks(getWatermark(null, > 0)); > >> >> ds1.getTransformation().setOutputType((new > >> RowTypeInfo(Types.STRING, Types.SQL_TIMESTAMP, Types.INT))); > >> >> bsTableEnv.createTemporaryView("order_info", ds1, "order_id, > >> order_time, fee, rowtime.rowtime"); > >> >> > >> >> List<Row> list2 = new ArrayList<>(); > >> >> list2.add(Row.of("001",new Timestamp(sdf.parse("2020-05-13 > >> 01:00:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 01:20:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 01:30:00").getTime()))); > >> >> list2.add(Row.of("002",new Timestamp(sdf.parse("2020-05-13 > >> 02:00:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 02:20:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 02:40:00").getTime()))); > >> >> // list2.add(Row.of("003",new Timestamp(sdf.parse("2020-05-13 > >> 03:00:03").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 03:20:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 03:40:00").getTime()))); > >> >> list2.add(Row.of("004",new Timestamp(sdf.parse("2020-05-13 > >> 04:00:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 04:20:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 04:40:00").getTime()))); > >> >> list2.add(Row.of("005",new Timestamp(sdf.parse("2020-05-13 > >> 05:00:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 05:20:00").getTime()))); > >> >> list2.add(Row.of("111",new Timestamp(sdf.parse("2020-05-13 > >> 05:40:00").getTime()))); > >> >> DataStream<Row> ds2 = bsEnv.addSource(new > >> SourceFunction<Row>() { > >> >> @Override > >> >> public void run(SourceContext<Row> ctx) throws Exception > { > >> >> for(Row row : list2) { > >> >> ctx.collect(row); > >> >> Thread.sleep(1000); > >> >> } > >> >> > >> >> } > >> >> > >> >> @Override > >> >> public void cancel() { > >> >> > >> >> } > >> >> }); > >> >> ds2 = ds2.assignTimestampsAndWatermarks(getWatermark(null, > 0)); > >> >> ds2.getTransformation().setOutputType((new > >> RowTypeInfo(Types.STRING, Types.SQL_TIMESTAMP))); > >> >> bsTableEnv.createTemporaryView("pay", ds2, "order_id, > >> pay_time, rowtime.rowtime"); > >> >> > >> >> Table joinTable = bsTableEnv.sqlQuery("SELECT a.*,b.order_id > >> from order_info a left join pay b on a.order_id=b.order_id and b.rowtime > >> between a.rowtime and a.rowtime + INTERVAL '1' HOUR where a.order_id > >> <>'000' "); > >> >> > >> >> bsTableEnv.toAppendStream(joinTable, Row.class).process(new > >> ProcessFunction<Row, Object>() { > >> >> @Override > >> >> public void processElement(Row value, Context ctx, > >> Collector<Object> out) throws Exception { > >> >> SimpleDateFormat sdf = new > >> SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS"); > >> >> System.err.println("row:" + value + ",rowtime:" + > >> value.getField(3) + ",watermark:" + > >> sdf.format(ctx.timerService().currentWatermark())); > >> >> } > >> >> }); > >> >> > >> >> bsTableEnv.execute("job"); > >> >> } > >> >> } > >> >> > >> >> > >> > > >> > -- > >> > > >> > Best, > >> > Benchao Li > >> > > >> > >> > >> -- > >> > >> Best, > >> Benchao Li > >> > > > > > > -- > > ************************************** > > tivanli > > ************************************** > > > > > -- > ************************************** > tivanli > ************************************** >
