Partitioning

[@andys7596]

So many videos in other channel but this one after so many years still has best value content. Thank you !

[@EdouardTavinor]

This is such a brilliant series :) I can feel myself levelling up in spark while watching it :)

[@ajeepp06]

Awesome Explanation with every details in all aspects. Squared all corners nicely. Brilliant.

[@aneksingh4496]

absolutely great .... please add some more videos on spark real time use cases ... thanks

[@AbhishekSinghaABiS]

superb! explanation covering all possible scenarios, hats off..

[@bigdataenthusiast]

simply great

[@_sr]

The best explanation I have ever seen.

[@byrek1684]

Hey, that's pretty good!

[@jimmycheong7970]

This is incredibly explained

[@senthilkumara3151]

Well Explained !! Thanks

[@mrkrish501]

Excellent

[@MDTAMIM-lr3re]

so contentful!Thanks

[@debk4516]

excellent !!

[@mohammedzia1015]

Why the partition 0 is getting 5 keys , partition 1 - only 1 key and the other two partitions 2 & 3 got ZERO keys. What is the logic behind this or criteria for this kind of partition ?

[@MrAbhinavsunny]

It is Key%(number of partition). Example: key = 8 and partition = 4 , 8%4 = 0

[@premnathparthiban5128]

I remember that in other video u said groupBykey causes shuffling .. then how come its has a paritioner ?

[@kexinma7294]

That's my question too. I guess groupBykey still has a partitioner but it is a new one, there is some shuffling. But reduceBykey will keep the original partitions.

[@hadoopwithcp]

great

[@走刀口-r7g]

the only sorry that i cant get english