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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: How to read multiple HDFS directories
Date Wed, 05 May 2021 15:04:36 GMT
Hi,

Have a look at this thread called

Tasks are skewed to one executor

and see if it helps and we can take it from there.

HTH



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On Wed, 5 May 2021 at 15:46, Kapil Garg <kapil.g@flipkart.com.invalid>
wrote:

> Hi,
> I am facing issues while reading multiple HDFS directories. Please read
> the problem statement and current approach below
>
> *Problem Statement*
> There are N HDFS directories each having K files. We want to read data
> from all directories such that when we read data from directory D, we map
> all the data and augment it with additional information specific to that
> directory.
>
> *Current Approach*
> In current approach, we are iterating over the directories, reading it in
> RDD, mapping the RDD and the putting the RDD into a list.
> After all N directories have been read, we have a list of N RDDs
> We call spark Union on the list to merge them together.
>
> This approach is causing data skewness because there is 1 directory of
> size 12 GBs whereas other RDDs are less than 1 GB. So when the large RDD's
> turn comes, spark submits its task on available executors causing the RDD
> to present on few executors instead of spreading on all.
>
> Is there a way to avoid this data skewness ? I couldn't find any RDD API,
> spark config which could enforce the data reading tasks evenly on all
> executors.
>
> --
> Regards
> Kapil Garg
>
>
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