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From Anastasios Zouzias <zouz...@gmail.com>
Subject [Structured Streaming] Robust watermarking calculation with future timestamps
Date Wed, 13 Nov 2019 09:57:47 GMT
Hi all,

We currently have the following issue with a Spark Structured Streaming
(SS) application. The application reads messages from thousands of source
systems, stores them in Kafka and Spark aggregates them using SS and
watermarking (15 minutes).

The root problem is that a few of the source systems have a wrong timezone
setup that makes them emit messages from the future, i.e., +1 hour ahead of
current time (mis-configuration or winter/summer timezone change (yeah!) ).
Since watermarking is calculated as

(most latest timestamp value of all messages) - (watermarking threshold
value, 15 mins),

most of the messages are dropped due to the fact that are delayed by more
than 45 minutes. To an even more extreme scenario, even a single "future" /
adversarial message can make the structured streaming application to report
zero messages (per mini-batch).

Is there any user exposed SS API that allows a more robust calculation of
watermarking, i.e., 95th percentile of timestamps instead of max timestamp?
I understand that such calculation will be more expensive, but it will make
the application more robust.

Any suggestions/ideas?

PS. Of course the best approach would be to fix the issue on all source
systems but this might take time to do so (or perhaps drop future messages
programmatically (yikes) ).

Best regards,
Anastasios

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