100. Databricks | Pyspark | Spark Architecture: Internals of Partition Creation Demystified

Azure Databricks Learning: Spark Architecture: Internals of Partition Creation Demystified
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How partitions are created within spark environment out of external storage system? How number of partitions are decided for given set of input files/folders?

Partition is key to any big data platform. It is important for every developer/architect to understand the internal working mechanism of partition creation. But it has always been mystery to understand that internals. I have invested huge amount of time in decoding the entire the process and explained it in easily understandable way in this video.

To get through understanding of this concept, please watch this video

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[utsavchanda]

This is brilliant, man. You took the pain to understand spark partitioning to such depths and then the effort to share that knowledge with others. And it just made a concept, which is otherwise difficult to master, so clear for us. Thank you again

[nikhilgupta]

more than 2.5 yrs of workex as pyspark dev....still remained alwz confused on dez things...

[amazhobner]

Updating the timestamps here for my future references:

4:15 How spark accesses files.
12:50 Input Format API.
15:50 Three Components of Input Format API.
17:08 FileInputFormat
18:18 InputSplit
23:15 RecordReader
26:12 Parameters defining partition count
29:18 bytesPerCore
31:20 MaxSplitBytes

[darbhakiran]

Worth watching. Never came across this detailed explanation. Thank you for you efforts in putting this together.

[MrZoomok]

It's a great and valuable explanation to the spark partition concepts. Really appreciate of your lesson and sharing, buddy

[vijayalakshmiv]

Excellent explanation.. what a dedication in explaining concepts end to end! thanks a lot for the efforts taken time spent in thsi channel is totally worth it!!!

[suryateja]

I got a great hike because of your Json Flattening video . ❤Thanks Sir.

[plearns]

Thanks sir it helps a lot.
Hope to see more from your side.
Ur channel will be hit one day

[MaheshReddyPeddaggari]

awesome explanation, even a beginner can understand it easily
Thanks for the wonderful content

[umashiva]

Wonderful explanation with simple examples Mr Raja. Thank you very much!!

[venkatnaresh]

Excellent buddy awesome explanation, I didn’t get this information any where

[pavankumarveesam]

Hi raja 49:59 in that case we shall change the maximumpartitionbytes to 135 mb inorder to merge the files right? and now which is optimized 30 partitions by using default maximumpartitionbytes or the one which i mentoned ?

[karthikeyana]

Wow, crystal clear explanation!! Thanks a lot

[dewakarprasad]

Nice explanation for partition creation in spark

[sailalithareddy]

This is excellent, thanks for sharing this knowledge and helping

[arnabchaudhury]

Your videos in the playlist have helped a lot. Thank you very much

[bharatpurohit]

Great content! really appreciate your work.
I always had this question and still confused.
When number of cores are responsible to execute each partition then why we need multiple executer within a node, why cant we use 1 node 1 executer with n number of cores?
Is it like 1 worker node can have limited number of cores?

[dsv]

Very good explanation . Its worth to watch

[venkatasai]

Great video Raja . Could you also explain how shuffle partitions created . Also how repartition and coalesce impact shuffle partitions?

[arnabbangal]

Hi sir, how can we use Spark parameters in databricks to involve all worker nodes and use their 100 % capacity ? I want to manually control these factors in my databricks script. We have 1-2GBs of multiple json files where are making some changes and saving it. We read the huge file in a dataframe and explode that json in multiple rows and take 200-300 rows of that dataframe at a time and apply some changes before saving it . Now if I have 9 worker nodes how 15 splitted files how I can divide among the worker nodes to process it parallelly