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Boosting Dataflow Efficiency: How We Reduced Processing Time from 1 Day to 30 Minutes in Dataflow | Blog | bol.com


The substantial improvements in these key metrics highlight the effectiveness of using the Apache Beam SideInput feature in our Google DataFlow jobs. Not only do these optimizations lead to more efficient processing, but they also result in significant cost savings for our data processing tasks

In our previous implementation without the use of SideInput, the job took more than approximately 24 hours to complete, but the new job with SideInput was completed in about 30 minutes, so the algorithm has resulted in a 97.92% reduction in the execution period.

As a result, we can maintain high performance while minimizing the cost and complexity of our data processing tasks.

Warning: Using SideInput for Large Datasets

Please be aware that using SideInput in Apache Beam is recommended only for small datasets that can fit into the worker’s memory. The total amount of data that should be processed using SideInput should not exceed 1 GB.

Larger datasets can cause significant performance degradation and may even result in your pipeline failing due to memory constraints. If you need to process a dataset larger than 1 GB, consider alternative approaches like using CoGroupByKey, partitioning your data, or using a distributed database to perform the necessary join operations. Always evaluate the size of your dataset before deciding on using SideInput to ensure efficient and successful processing of your data.

Conclusion

By switching from CoGroupByKey to SideInput and using DoFn functions, we were able to significantly improve the efficiency of our data processing pipeline. The new approach allowed us to distribute the small dataset across all workers and process millions of events much faster. As a result, we reduced the processing time for one flow from 1 days to just 30 minutes. This optimization also had a positive impact on our CPU utilization, ensuring that our resources were used more effectively.

If you’re experiencing similar performance bottlenecks in your Apache Beam dataflow jobs, consider re-evaluating your enrichment methods and exploring options such as SideInput and DoFn to boost your processing efficiency.

Thank you for reading this blog. If you have any further questions or if there’s anything else we can assist you with, feel free to ask.

On behalf of Team 77, Hazal and Eyyub

Some useful links:

** Google Dataflow

** Apache Beam

** Stateful processing