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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: How to read multiple HDFS directories
Date Wed, 05 May 2021 15:50:10 GMT
When you are doing union on these RDDs, (each RDD has one to one
correspondence with an HDFS directory), do you have a common key across all?


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On Wed, 5 May 2021 at 16:23, Kapil Garg <kapil.g@flipkart.com> wrote:

> Hi Mich,
> I went through the thread and it doesn't relate to the problem statement I
> shared above.
>
> In my problem statement, there is a simple ETL job which doesn't use any
> external library (such as pandas)
> This is the flow
>
> *hdfsDirs := List<String>(); //contains N directories*
>
> *rddList := List<RDD>();*
> *for each directory in hdfsDirs:*
> *    rdd = spark.read(directory)*
> *    rdd.map() //augment the data with additional directory related data*
> *    rddList.add(rdd)*
>
> *finalRdd = spark.union(rddList) // number of tasks = N*K // Files are
> distributed unevenly on executors here*
>
> *finalRdd.partionBy(hashpartitioner); // here tasks take uneven time*
>
> Is it possible to make the union step read the directory evenly on each
> executor, that way, each executor will have roughly the same amount of data
>
>
>
> On Wed, May 5, 2021 at 8:35 PM Mich Talebzadeh <mich.talebzadeh@gmail.com>
> wrote:
>
>> 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
>>
>>
>>
>>    view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* Use it at your 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 destruction.
>>
>>
>>
>>
>> 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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>
> --
> Regards
> Kapil Garg
>
>
> *-----------------------------------------------------------------------------------------*
>
> *This email and any files transmitted with it are confidential and
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>
> *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
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