34. Databricks - Spark: Data Skew Optimization

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  • เผยแพร่เมื่อ 1 ธ.ค. 2024

ความคิดเห็น • 33

  • @Prashanth-os5he
    @Prashanth-os5he ปีที่แล้ว +4

    This is by far the best databricks and spark tutorial series on youtube... great job Raja

  • @srinubathina7191
    @srinubathina7191 ปีที่แล้ว +1

    Awesome content Thank You So much Sir

  • @joyo2122
    @joyo2122 2 ปีที่แล้ว +1

    You are the best Raja 🙌

  • @sumanmondal8836
    @sumanmondal8836 2 ปีที่แล้ว +3

    Thanks, Raja, your explanations are really good...can you please make a video on salting techniques with example? It will be very helpful.

  • @abhinavsingh1173
    @abhinavsingh1173 ปีที่แล้ว +2

    Your course it
    best. But problem with you course is that you are not attching the github link for your sample data and code. Irequest you as your audience please do this. Thanks

  • @skasifali4457
    @skasifali4457 2 ปีที่แล้ว +2

    Thanks Raja..Your video is really useful. Can you please create a video on debugging techniques and how we can use spark UI to debug and understand the bottleneck using use cases. Thanks a lot again

  • @iamkiri_
    @iamkiri_ ปีที่แล้ว +1

    Thanks for the video, I have a question.. Is salting technique applied while reading the data from source or during intermediate processing of the application..

  • @rajunaik8803
    @rajunaik8803 ปีที่แล้ว +1

    Hi Raja, QQ - Does AQE take care of salting and skew hint technique automatically in case of data skewness?
    Or do we have to explicitly apply them?

    • @rajasdataengineering7585
      @rajasdataengineering7585  ปีที่แล้ว +1

      Yes AQE handles data skewness automatically. In later spark versions after 3.0, it is enabled by default. For prior versions of spark, we just need to enable AQE through spark config settings

    • @rajunaik8803
      @rajunaik8803 ปีที่แล้ว

      @@rajasdataengineering7585 thanks alot for your response. Do you have any telegram channel? And may I know your LinkedIn id please

  • @naveenkumarsingh3829
    @naveenkumarsingh3829 5 หลายเดือนก่อน

    why cant we use set maxpartitionbytes to get equal size of partitions and handle data skewness?

  • @swapnilgosawi
    @swapnilgosawi 2 หลายเดือนก่อน

    Do you have a document with all these details ?if yes, that would be great to share on git., Really Great explanation. Thank you !!

  • @VishalSharma-hv6ks
    @VishalSharma-hv6ks 2 ปีที่แล้ว +2

    You mainly focus on theoretical. It would be great if you write the code for salting as well.

  • @SaurabhDestiny18
    @SaurabhDestiny18 ปีที่แล้ว

    Hi Tq for such useful videos, i have one question, i am still confused about executor boundary and cores/tasks boundary. In your first video you mentioned executor can have many cores/ram and then this video you mention executor runs in its own jvm process , which means all the cores/tasks are running under one jvm process? Or under than parent jvm process there are many more jvm process are running which are equal to number of cores/tasks?

  • @Personalcomments
    @Personalcomments 3 ปีที่แล้ว +1

    Your videos are very informative. Can you please post a video on Client mode vs Cluster mode vs local

  • @sravankumar1767
    @sravankumar1767 3 ปีที่แล้ว +1

    Superb

  • @sanskarsuman9340
    @sanskarsuman9340 2 ปีที่แล้ว +1

    i have doubt:
    when u say data is partitioned on country and there are five different countries, out of which lets say Germany has 80% of data, so how can I say that germany data is in single partition only? coz partition is determined on the size of the block and 1 parttion = 128mb size, so depending on its size, germany data could be splitted into multiple partitions automatically?

  • @balakrishna61
    @balakrishna61 7 หลายเดือนก่อน

    @rajasdataengineering7585 Please explain salting in detail.It's not clear how you parition the German-1,_2 and so on .Each record will become one partition correct in this case?

  • @prathapganesh7021
    @prathapganesh7021 8 หลายเดือนก่อน +1

    thank you

  • @tanushreenagar3116
    @tanushreenagar3116 2 ปีที่แล้ว +1

    nice