Spark Runtime Architecture (Cluster Mode) |

#pyspark #azuredataengineer #databricks
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Пікірлер: 7

  • @2412_Sujoy_Das
    @2412_Sujoy_Das4 ай бұрын

    Needed this one badly... Thanks Naresh

  • @PavanKumar-vi7hd
    @PavanKumar-vi7hd4 ай бұрын

    Hi Naresh your way of explanation is excellent. this is first time i understand spark architeecture is very easy way in Cluster Mode

  • @cleverstudies

    @cleverstudies

    4 ай бұрын

    Thank you Pavan.❤

  • @pankajchikhalwale8769
    @pankajchikhalwale87694 ай бұрын

    Please make a short video on the relationship between stages, node, executor, dataframe/dataset/RDD, and core, partition, and task. Want to know what consists what ? And what contains what.

  • @ranjithg7598
    @ranjithg75982 ай бұрын

    What about the node manager do in this architecture

  • @user-tl7sh4tm8x
    @user-tl7sh4tm8x3 ай бұрын

    How cluster manager can create any of the nearest worker as application master because the configuration of the master can be different. So will it not go and create master to the machine that is configured for master role. With memory allocated to master depending on the type of task.

  • @vivekmerugu6711
    @vivekmerugu67114 ай бұрын

    Hi Naresh, Thank you so much, This help me a lot. ❤ Naresh, I've execute my spark application in cluster mode (yarn) in emr cluster, My spark application is failing with an exception saying application master container failed 2 times, exists with 137 code. This exceptional is occuring for only one dataset which I'm processing with spark application. For other datasets, my spark application is working fine. The dataset for which spark application is failing having large input payload, ( one record with 25000+ characters ). I tried increasing the driver memory and executor memory, now this time , I'm getting an exception while deserialization of input payload. Any suggestions how to resolve this issue. It will be helpful, please

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