Hi Yan,
SQL should not be the cause here. It is true that Flink removes the
timestamp from a record when entering the SQL API but this timestamp is
set again before time-based operations such as OVER windows. Watermarks
are not touched. I think your issue is related to [2]. One explanation
that I could image is that a watermark has to be broadcasted to all
workers, depending on the frequency of watermark generation, a higher
parallelism also causes a higher amout of watermarks to be broadcasted.
Which Flink version are you using? Can you run your tests on the latest
Flink RC, because there where some improvements to the buffer management
that might help. I will loop in Piotr for that.
Regards,
Timo
Am 24.05.18 um 01:02 schrieb Yan Zhou [FDS Science]:
The BoundedOutOfOrdernessTimestampExtractor is assigned to datastream
after kafka consumer. The graph is like:
KafkaSource-> map2Pojo -> BoundedOutOfOrdernessTimestampExtractor ->
Table -> ......
------------------------------------------------------------------------
*From:* Yan Zhou [FDS Science] <yz...@coupang.com>
*Sent:* Wednesday, May 23, 2018 3:21:24 PM
*To:* user@flink.apache.org
*Subject:* increasing parallelism increases the end2end latency in
flink sql
Hi,
My application assigned timestamp to kafka event
with BoundedOutOfOrdernessTimestampExtractor then converted them to a
table. Finally flink SQLover-window aggregation is run against the table.
When I double the parallelism of my flink application, the end2end
latency is doubled. What could be the cause? It seems to me that it's
because of slower advance of watermark in operator of operators
generated by sql.
In this email thread [1], it's said that flink sql remove the internal
DataStream timestamp and move it into the record. Does thequeryignore
the internal DataStream watermarks and re-generate themfrom the
record? Let say there are two operator instances for one task, do they
have same watermark?
There is a similar issue that i can find in the email thread [2] .
Best
Yan
[1]:
https://lists.apache.org/thread.html/c5182628272f018037ce832290f9b19976fe5c268aa72760635cf3cc@%3Cuser.flink.apache.org%3E
[2]:
https://lists.apache.org/thread.html/bf789df06e979f80caf23f6b2c8676aaf07b007ae0d450ae887b6a82@%3Cuser.flink.apache.org%3E
