Boosting Dataflow Effectivity: How We Diminished Processing Time from 1 Day to 30 Minutes in Dataflow | Weblog |

The substantial enhancements in these key metrics spotlight the effectiveness of utilizing the Apache Beam SideInput function in our Google DataFlow jobs. Not solely do these optimizations result in extra environment friendly processing, however in addition they lead to vital price financial savings for our knowledge processing duties

In our earlier implementation with out the usage of SideInput, the job took greater than roughly 24 hours to finish, however the brand new job with SideInput was accomplished in about half-hour, so the algorithm has resulted in a 97.92% discount within the execution interval.

Because of this, we are able to preserve excessive efficiency whereas minimizing the associated fee and complexity of our knowledge processing duties.

Warning: Utilizing SideInput for Massive Datasets

Please remember that utilizing SideInput in Apache Beam is really helpful just for small datasets that may match into the employee’s reminiscence. The overall quantity of information that must be processed utilizing SideInput mustn’t exceed 1 GB.

Bigger datasets could cause vital efficiency degradation and should even lead to your pipeline failing as a result of reminiscence constraints. If it is advisable to course of a dataset bigger than 1 GB, contemplate various approaches like utilizing CoGroupByKey, partitioning your knowledge, or utilizing a distributed database to carry out the mandatory be part of operations. At all times consider the scale of your dataset earlier than deciding on utilizing SideInput to make sure environment friendly and profitable processing of your knowledge.


By switching from CoGroupByKey to SideInput and utilizing DoFn features, we have been capable of considerably enhance the effectivity of our knowledge processing pipeline. The brand new method allowed us to distribute the small dataset throughout all staff and course of tens of millions of occasions a lot quicker. Because of this, we decreased the processing time for one circulation from 1 days to simply half-hour. This optimization additionally had a constructive affect on our CPU utilization, making certain that our assets have been used extra successfully.

In the event you’re experiencing comparable efficiency bottlenecks in your Apache Beam dataflow jobs, contemplate re-evaluating your enrichment strategies and exploring choices akin to SideInput and DoFn to spice up your processing effectivity.

Thanks for studying this weblog. You probably have any additional questions or if there’s the rest we are able to help you with, be happy to ask.

On behalf of Group 77, Hazal and Eyyub

Some helpful hyperlinks:

** Google Dataflow

** Apache Beam

** Stateful processing

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