ave to union multiple RDDs. You can read files from multiple
> directories in a single read call. Spark will manage partitioning of the
> data across directories.
>
>
>
> *From: *Kapil Garg
> *Date: *Wednesday, May 5, 2021 at 10:45 AM
> *To: *spark users
> *Subject: *[EXTER
y monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Wed, 5 May 2021 at 17:03, Kapil Garg wrote:
>
>> Sorry but I didn't get the question. It is possible that 1 record is
>> present in multiple directories. That's why we do a reduceByKey a
struction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Wed, 5 May 20
own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or
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
> .option("startingOffsets",
> START_OFFSET).load() .selectExpr("CAST(value AS STRING)")
>
>
> query =
> df.writeStream.foreach(process_events).option("checkpointLo
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--
Regards
Kapil Garg
--
*--
ab during application start
>2. Executors Tab during application lifetime
>
>
>
> I need to tune my application , this Executor Info would be a great help
> for tuning the parameters. But currently it is blank shown.
>
> Regards
>
> Ranju
>
>
>
> *From:
, 2021 at 11:04 AM Ranju Jain
wrote:
> Hi ,
>
>
>
> I started using Spark 3.0.1 version recently and noticed the Executors Tab
> on Spark GUI appears as blank.
>
> Please suggest what could be the reason of this type of display?
>
>
>
> Regards
