Dynamic Partition Pruning: How It Works (And When It Doesn’t)

Dive deep into Dynamic Partition Pruning (DPP) in Apache Spark with this comprehensive tutorial. If you've already explored my previous video on partitioning, you're perfectly set up for this one. In this video, I explain the concept of static partition pruning and then transition into the more advanced and efficient technique of dynamic partition pruning.
You'll learn through practical examples, starting with a listening activity dataset partitioned by date, and then move to a complex scenario involving a join operation between listening activity and songs datasets. The video meticulously explains how DPP optimizes query performance by reducing unnecessary data scans, and the conditions necessary for its effective implementation. I also highlight the differences between static and dynamic partition pruning and the importance of having partitioned data for DPP to work effectively.
Whether you're a data engineering enthusiast or a professional working with Spark, this video will enhance your understanding of optimizing Spark queries using Dynamic Partition Pruning. Don't forget to like, share, and subscribe for more insightful content on Apache Spark and big data analytics!
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Chapters
00:00 Introduction
00:23 What is static pruning?
02:47 Dynamic partition pruning
12:07 Caveats when using dynamic partition pruning
14:29 Code to understand dynamic partition pruning
20:28 Thank you
#spark #dataengineering #apachespark #partition #partitioning #dynamicpartitionpruning #staticpruning #pruning #sparkperformancetuning #sparkoptimization #bigdataanalytics #sparktutorial #dataoptimization #sparkinterviewquestions

[@gopinathdhanasekar3286]

you deserve more subscribers !! thanks for explaining the concepts

[@afaqueahmad7117]

Those words mean a lot, thank you @gopinathdhanasekar328! If you wouldn't mind, a request to kindly share with your friends and colleagues, I would greatly appreciate your help in spreading the word

[@Wonderscope1]

Thanks for great video; you make these concept so simple. Thanks

[@iamexplorer6052]

Thank you sharing , new thing I learned from you

[@user-dx9qw3cl8w]

thanks for another indeapth video yes we need how spark uses it's memory executors and on what basis it split data to multiple executors

[@afaqueahmad7117]

Resource level optimisation videos upcoming in the next few weeks, stay tuned! :)

[@iamkiri_]

Loving ur videos Bro !

[@sathyamoorthy2362]

All videos are great and nicely explained , video clarity is bad even for 4k.

[@afaqueahmad7117]

Thanks, @sathyamoorthy2362, for the kind words. On the video quality, I was trying out a new tool and it didn't work out, but hope the other ones are good and you like them :)

[@anandchandrashekhar2933]

Thanks Afaque. Terminology wise, Is this the same as Filter pushdown which you explained during the Query Plan video?

[@afaqueahmad7117]

Hey @anandchandrashekhar2933 Appreciate it :)
On the question - DPP is different from "filter pushdown", although it uses filter pushdown to prune the large dataset based on the filters from the smaller dataset. It's effective when you have a large and a small dataset (which can be broadcasted) and want to use the small dataset to filter records from the large dataset at scan-time

[@plearns4551]

Hello, I think one correction, I think even if the dimension table(songs) don't have filter condition on release date still DPP would work right?? as it will forward the release date selected after the filter, irrespective of the filter condition. eg even if we apply filter on songID in songs table is there and after filter few record are selected in those records whatever the release dates are it will be forwarded.

[@roksig3823]

Can you make a video on how to decide driver/executor memory size, no of executor based file size like 100 GB in Spark ?

[@afaqueahmad7117]

Resource level optimisation videos upcoming in the next few weeks, stay tuned! :)

[@rohitshingare5352]

What if both datasets are too big , so in that case broadcast exchange is still happens?

[@afaqueahmad7117]

Hey @rohitshingare5352, Good question. DPP generally works best when one table is large and the other table is small enough to be broadcasted. The most significant reason for this if the two tables are large, the filters being moved will also be large (in the worst case) and this filter propagation mechanism over the network is the biggest bottleneck