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From Kapil Garg <kapi...@flipkart.com.INVALID>
Subject Re: How to read multiple HDFS directories
Date Wed, 05 May 2021 18:37:11 GMT
PFB the stage metrics
[image: Screenshot 2021-05-06 at 12.05.19 AM.png]
These metrics are confusing in the way that shuffle remote reads are
uniformly distributed but shuffle read blocked time is not.

On Thu, May 6, 2021 at 12:00 AM Kapil Garg <kapil.g@flipkart.com> wrote:

> Hi,
> But the locality wait time is 3 seconds (default), It should not wait for
> minutes (all RDDs in the system are using the same paritioner)
>
> On Wed, May 5, 2021 at 11:37 PM joerg.strebel <joerg.strebel@gmail.com>
> wrote:
>
>> Hallo
>>
>> Apache Spark uses data locality for task scheduling. Maybe your data
>> happens to be on these executor nodes so Spark sends the work there?
>>
>> Jörg
>>
>> -------- Ursprüngliche Nachricht --------
>> Von: Kapil Garg <kapil.g@flipkart.com.INVALID>
>> Datum: 05.05.21 19:30 (GMT+01:00)
>> An: "Lalwani, Jayesh" <jlalwani@amazon.com.invalid>, Mich Talebzadeh <
>> mich.talebzadeh@gmail.com>
>> Cc: Kapil Garg <kapil.g@flipkart.com.invalid>, spark users <
>> user@spark.apache.org>
>> Betreff: Re: How to read multiple HDFS directories
>>
>> Also please see the below screenshot. What could have made the tasks wait
>> for 10-11 minutes to submit on the same executor ?
>> [image: Screenshot 2021-05-05 at 10.57.12 PM.png]
>>
>> On Wed, May 5, 2021 at 10:48 PM Kapil Garg <kapil.g@flipkart.com> wrote:
>>
>>> Hi Lalwani,
>>> But I need to augment the directory specific data to every record of
>>> that directory.
>>> Once I have read the data, there is no link back to the directory in the
>>> data which I can use to augment additional data
>>>
>>> On Wed, May 5, 2021 at 10:41 PM Lalwani, Jayesh
>>> <jlalwani@amazon.com.invalid> wrote:
>>>
>>>> You don’t have 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 <kapil.g@flipkart.com.INVALID>
>>>> *Date: *Wednesday, May 5, 2021 at 10:45 AM
>>>> *To: *spark users <user@spark.apache.org>
>>>> *Subject: *[EXTERNAL] How to read multiple HDFS directories
>>>>
>>>>
>>>>
>>>> *CAUTION*: This email originated from outside of the organization. Do
>>>> not click links or open attachments unless you can confirm the sender and
>>>> know the content is safe.
>>>>
>>>>
>>>>
>>>> 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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>>>> *-----------------------------------------------------------------------------------------*
>>>>
>>>>
>>>
>>> --
>>> Regards
>>> Kapil Garg
>>>
>>
>>
>> --
>> Regards
>> Kapil Garg
>>
>>
>> *-----------------------------------------------------------------------------------------*
>>
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>> *-----------------------------------------------------------------------------------------*
>>
>>
>
> --
> Regards
> Kapil Garg
>


-- 
Regards
Kapil Garg

-- 


*-----------------------------------------------------------------------------------------*

*This email and any files transmitted with it are confidential and 
intended solely for the use of the individual or entity to whom they are 
addressed. If you have received this email in error, please notify the 
system manager. This message contains confidential information and is 
intended only for the individual named. If you are not the named addressee, 
you should not disseminate, distribute or copy this email. Please notify 
the sender immediately by email if you have received this email by mistake 
and delete this email from your system. If you are not the intended 
recipient, you are notified that disclosing, copying, distributing or 
taking any action in reliance on the contents of this information is 
strictly prohibited.*****

 ****

*Any views or opinions presented in this 
email are solely those of the author and do not necessarily represent those 
of the organization. Any information on shares, debentures or similar 
instruments, recommended product pricing, valuations and the like are for 
information purposes only. It is not meant to be an instruction or 
recommendation, as the case may be, to buy or to sell securities, products, 
services nor an offer to buy or sell securities, products or services 
unless specifically stated to be so on behalf of the Flipkart group. 
Employees of the Flipkart group of companies are expressly required not to 
make defamatory statements and not to infringe or authorise any 
infringement of copyright or any other legal right by email communications. 
Any such communication is contrary to organizational policy and outside the 
scope of the employment of the individual concerned. The organization will 
not accept any liability in respect of such communication, and the employee 
responsible will be personally liable for any damages or other liability 
arising.*****

 ****

*Our organization accepts no liability for the 
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you are notified that disclosing, copying, distributing or taking any 
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