Re: [Structured Streaming] Robust watermarking calculation with future timestamps

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Re: [Structured Streaming] Robust watermarking calculation with future timestamps

Jungtaek Lim-2
(dropping user@ as cross-posting mailing lists for mail threads would bother both lists, and it seems more appropriate to dev@)

AFAIK there's no API for custom watermark, and you're right picking max timestamp would introduce the issues you provided. Other streaming frameworks may pick min timestamp by default, which also has some tradeoff, slower advancing watermark or being stuck in skewed data.

As a workaround for now, you can adjust timestamp column before calling withWatermark so that future events can be adjusted, though that doesn't provide functionality like 95th percentile which requires aggregated calculation. I guess Spark community may consider adding the feature if the community sees more requests on this.

Jungtaek Lim (HeartSaVioR)

On Wed, Nov 13, 2019 at 6:58 PM Anastasios Zouzias <[hidden email]> wrote:
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,