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 additionally they lead to important value financial savings for our knowledge processing duties

In our earlier implementation with out using 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.

In consequence, we will preserve excessive efficiency whereas minimizing the price and complexity of our knowledge processing duties.

Warning: Utilizing SideInput for Giant Datasets

Please bear in mind that utilizing SideInput in Apache Beam is advisable just for small datasets that may match into the employee’s reminiscence. The entire quantity of knowledge that needs to be processed utilizing SideInput shouldn’t exceed 1 GB.

Bigger datasets could cause important efficiency degradation and will even lead to your pipeline failing as a consequence of reminiscence constraints. If it’s essential course of a dataset bigger than 1 GB, take into account different 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 capabilities, we have been in a position to considerably enhance the effectivity of our knowledge processing pipeline. The brand new method allowed us to distribute the small dataset throughout all employees and course of hundreds of thousands of occasions a lot sooner. In consequence, we diminished the processing time for one circulation from 1 days to simply half-hour. This optimization additionally had a optimistic impression on our CPU utilization, guaranteeing that our sources have been used extra successfully.

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

Thanks for studying this weblog. In case you have any additional questions or if there’s the rest we will help you with, be at liberty to ask.

On behalf of Crew 77, Hazal and Eyyub

Some helpful hyperlinks:

** Google Dataflow

** Apache Beam

** Stateful processing

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