Spark Performance Optimization | Join | UNION vs OR

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#Apache #Spark #Performance #Optimization
In this particular video, we have discussed spark join performance Optimization in the scenario where 'OR' operator is used within the Joins.
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Great video and greate explanation, thanks for your work:)

vanessaserna

Thank you sir. Clean format, contents, explanation. I think nothing best than this

Ashish

Thanks buddy.. Your efforts are valuable to many, including me.

sandeepverma

Viresh this is a very useful tip. Can you extend this video bit to cover on what does union do under the hood. In terms of partitions and joins

shobhaiyer

What was the time of execution?...I can see when you call action it tooks 0.96 sec in 1st case(broadcastnestedloopjoin) and 3.99 sec in 2nd case(sortmergejoin)

raksadi

Can I use this condition in Join ?

[ (df1.title == df2.title) | (df1.title == df2.original_title), df1.description == df2.description]

louishudson

Wonderful videos . This is real good content on Apache Spark :)

harpalsingh

I found all your videos are excellent . Could you please explain how kryo serialization is 10x faster than the Java io serialization???

rameshthamizhselvan

sir please make a video on data skewness in spark and how to handle it using rdd and using data frame. Thanks for tutorial

wkxvxzc

Hi @TechWithViresh, thanks for these spark series videos.
I have some requests, please make a video on spark internals like executors, cores, how partitions will be computed,
Different between shuffle write, shuffle spill(memory), shuffle spill(disk) ? In which scenario this will come into picture?
For every shuffle there might be aggregation transformation or join transformation. What happen in side each partition data when these 2 types of shuffle happens? Please

vamshi

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