Hi, 这个是支持的哈。 你看到的现象是因为group by会产生retract结果，也就是会先发送-[old]，再发送+[new]. 如果是两层的话，就成了： 第一层-[old], 第二层-[cur], +[old] 第一层+[new], 第二层[-old], +[new]
[email protected] <[email protected]> 于2020年4月18日周六 上午2:11写道： > > Hi all: > > 我们有个streaming sql得到的结果不正确，现象是sink得到的数据一会大一会小，*我们想确认下，这是否是个bug， > 或者flink还不支持这种sql*。 > 具体场景是：先group by A, B两个维度计算UV，然后再group by A 把维度B的UV sum起来，对应的SQL如下：（A -> > dt, B -> pvareaid) > > SELECT dt, SUM(a.uv) AS uv > FROM ( > SELECT dt, pvareaid, COUNT(DISTINCT cuid) AS uv > FROM streaming_log_event > WHERE action IN ('action1') > AND pvareaid NOT IN ('pv1', 'pv2') > AND pvareaid IS NOT NULL > GROUP BY dt, pvareaid > ) a > GROUP BY dt; > > sink接收到的数据对应日志为： > > 2020-04-17 22:28:38,727 INFO groupBy xx -> to: Tuple2 -> Sink: Unnamed > (1/1) (GeneralRedisSinkFunction.invoke:169) - receive > data(false,0,86,20200417) > 2020-04-17 22:28:38,727 INFO groupBy xx -> to: Tuple2 -> Sink: Unnamed > (1/1) (GeneralRedisSinkFunction.invoke:169) - receive > data(true,0,130,20200417) > 2020-04-17 22:28:39,327 INFO groupBy xx -> to: Tuple2 -> Sink: Unnamed > (1/1) (GeneralRedisSinkFunction.invoke:169) - receive > data(false,0,130,20200417) > 2020-04-17 22:28:39,327 INFO groupBy xx -> to: Tuple2 -> Sink: Unnamed > (1/1) (GeneralRedisSinkFunction.invoke:169) - receive data(true,0,86,20200417) > 2020-04-17 22:28:39,327 INFO groupBy xx -> to: Tuple2 -> Sink: Unnamed > (1/1) (GeneralRedisSinkFunction.invoke:169) - receive > data(false,0,86,20200417) > 2020-04-17 22:28:39,328 INFO groupBy xx -> to: Tuple2 -> Sink: Unnamed > (1/1) (GeneralRedisSinkFunction.invoke:169) - receive > data(true,0,131,20200417) > > > 我们使用的是1.7.2， 测试作业的并行度为1。 > 这是对应的 issue: https://issues.apache.org/jira/browse/FLINK-17228 > > > ------------------------------ > [email protected] > > -- Benchao Li School of Electronics Engineering and Computer Science, Peking University Tel:+86-15650713730 Email: [email protected]; [email protected]
