25. Databricks | Spark | Broadcast Variable| Interview Question | Performance Tuning

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24 июл 2021

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Комментарии : 39

@ririraman7 2 года назад

You should come in the top RU-vidrs for Apache Spark PySpark tutorials. Awesome sir, brilliant. Thank You Thank You Thank You....

@rajasdataengineering7585 2 года назад

Thanks Ramandeep!

@kartikjaiswal8923 5 дней назад

insightful and precise

@rajasdataengineering7585 5 дней назад

Glad it is helpful! Thanks for your comment

@deepjyotimitra1340 2 года назад

Thank you for your detailed video.

@gulsahtanay2341 4 месяца назад

Good to know!

@swethakulkarni3563 6 месяцев назад

you are absolutely great!

@rajasdataengineering7585 6 месяцев назад

Thank you!

@irannamented9296 11 месяцев назад

Very useful nice explanations.

@rajasdataengineering7585 11 месяцев назад

Glad it was helpful!

@sivagssri 2 года назад

Good job... Keep posting interview questions on Databricks and Spark... I have shared your channel in my group.

@rajasdataengineering7585 2 года назад

Thanks Siva...will post interview questions

@vigneshgaming6286 2 года назад

Hi sir,will you training on pyspark

@roshankumargupta46 2 года назад

Very useful..keep going!

@rajasdataengineering7585 2 года назад

Thank you Roshan

@chessforevery1 6 месяцев назад

Great explained

@rajasdataengineering7585 6 месяцев назад

Glad it was helpful!

@prathapganesh7021 4 месяца назад

Thank you

@rajasdataengineering7585 4 месяца назад

You're welcome

@himanshuchourasia8936 Год назад

Hi Raja, Could you please also make video on accumulator variable.

@rajasdataengineering7585 Год назад

Hi Himanshu, sure will make a video on accumulator

@vishalaaa1 Год назад

excellent

@rajasdataengineering7585 Год назад

Thank you! Cheers!

@user-dx9pj6bp3w Месяц назад

Hi Raja, it covers only broadcast join part not the broadcast variables part. Please include that part also.

@AmericaMuchatlu86 Месяц назад

Thank you for your wonderful playlist on Apache Spark. Can you please help on the difference between broadcast variable's and broadcast joins. Both are same?

@rajasdataengineering7585 Месяц назад

Yes both are same

@ElhamMirshekari 2 года назад

Hi, thanks for the videos, can you explain about the checkpoints, what are they ? how they are useful in optimizations?

@rajasdataengineering7585 2 года назад

Checkpoint is mainly used in 2 places in spark. One is Spark optimization and another is Spark streaming. Your question is related to spark optimization. It is quite similar to persist which stores the dataframe in disk. Only difference is persist would retain the lineage but checkpoint would remove the lineage once data is saved to disk

@ElhamMirshekari 2 года назад

@@rajasdataengineering7585 Thank you ! Please go ahead and explain the checkpoint in streaming as well, I really appreciate it!

@rajasdataengineering7585 2 года назад

Checkpoint is a location in streaming where spark maintains the metadata about processed data such as offset etc. So when there is a failure in streaming execution, spark can understand till which data it has already processed and from where it needs to resume

@rahamanabdul6388 2 года назад

Good Stuff. Can you please share or create a copy code in git so that we can use for our learning.

@rajasdataengineering7585 2 года назад

Sure, will do.

@ADFTrainer 8 месяцев назад

it would be great if u provide script

@sohelsayyad5572 Год назад

Hiii Raja, Good content !! table is broadcasted nd stored on all nodes, but at what part of memory, is it on heap memory or off heap memory managed by OS ? thank you

@rajasdataengineering7585 Год назад

Thanks Sohel! Its stored within on-heap memory

@sohelsayyad5572 Год назад

@@rajasdataengineering7585 thanks Raja 👍

@sohelsayyad5572 Год назад

@@rajasdataengineering7585 IF we persist with storage level MEMORY_AND_DISK and offHeap.use enabled true. then data will spill to offHeap or directly to disk ? Also that Data structure can't be split when its spilling somewhere. what does it mean. I appreciate your response. thank you :)

@chidellasrinivas Месяц назад

Hi Raja, i have few doubts. 1st Doubt - once data is cached in all worker nodes if there is any new records added to dim table. then do we need to broadcast again ? 2nd doubt - Once joining is completed can we clear data from each executors