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From ayan guha <guha.a...@gmail.com>
Subject Re: RDD Partitions on HDFS file in Hive on Spark Query
Date Tue, 22 Nov 2016 12:37:19 GMT
Because snappy is not splittable, so single task makes sense.

Are sure about rack topology? Ie 225 is in a different rack than 227 or
228? What does your topology file says?
On 22 Nov 2016 10:14, "yeshwanth kumar" <yeshwanth43@gmail.com> wrote:

> Thanks for your reply,
>
> i can definitely change the underlying compression format.
> but i am trying to understand the Locality Level,
> why executor ran on a different node, where the blocks are not present,
> when Locality Level is RACK_LOCAL
>
> can you shed some light on this.
>
>
> Thanks,
> Yesh
>
>
> -Yeshwanth
> Can you Imagine what I would do if I could do all I can - Art of War
>
> On Mon, Nov 21, 2016 at 4:59 PM, Jörn Franke <jornfranke@gmail.com> wrote:
>
>> Use as a format orc, parquet or avro because they support any compression
>> type with parallel processing. Alternatively split your file in several
>> smaller ones. Another alternative would be bzip2 (but slower in general) or
>> Lzo (usually it is not included by default in many distributions).
>>
>> On 21 Nov 2016, at 23:17, yeshwanth kumar <yeshwanth43@gmail.com> wrote:
>>
>> Hi,
>>
>> we are running Hive on Spark, we have an external table over snappy
>> compressed csv file of size 917.4 M
>> HDFS block size is set to 256 MB
>>
>> as per my Understanding, if i run a query over that external table , it
>> should launch 4 tasks. one for each block.
>> but i am seeing one executor and one task processing all the file.
>>
>> trying to understand the reason behind,
>>
>> i went one step further to understand the block locality
>> when i get the block locations for that file, i found
>>
>> [DatanodeInfoWithStorage[10.11.0.226:50010,DS-bf39d33d-48e1-
>> 4a8f-be48-b0953fdaad37,DISK],
>>  DatanodeInfoWithStorage[10.11.0.227:50010,DS-a760c1c8-ce0c-
>> 4eb8-8183-8d8ff5f24115,DISK],
>>  DatanodeInfoWithStorage[10.11.0.228:50010,DS-0e5427e2-b030-
>> 43f8-91c9-d8517e68414a,DISK]]
>>
>> DatanodeInfoWithStorage[10.11.0.226:50010,DS-f50ddf2f-b827-4
>> 845-b043-8b91ae4017c0,DISK],
>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-e8c9785f-c352-4
>> 89b-8209-4307f3296211,DISK],
>> DatanodeInfoWithStorage[10.11.0.225:50010,DS-6f6a3ffd-334b-4
>> 5fd-ae0f-cc6eb268b0d2,DISK]]
>>
>> DatanodeInfoWithStorage[10.11.0.226:50010,DS-f8bea6a8-a433-4
>> 601-8070-f6c5da840e09,DISK],
>> DatanodeInfoWithStorage[10.11.0.227:50010,DS-8aa3f249-790e-4
>> 94d-87ee-bcfff2182a96,DISK],
>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-d06714f4-2fbb-4
>> 8d3-b858-a023b5c44e9c,DISK]
>>
>> DatanodeInfoWithStorage[10.11.0.226:50010,DS-b3a00781-c6bd-4
>> 98c-a487-5ce6aaa66f48,DISK],
>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-fa5aa339-e266-4
>> e20-a360-e7cdad5dacc3,DISK],
>> DatanodeInfoWithStorage[10.11.0.225:50010,DS-9d597d3f-cd4f-4
>> c8f-8a13-7be37ce769c9,DISK]]
>>
>> and in the spark UI i see the Locality Level is  RACK_LOCAL. for that task
>>
>> if it is RACK_LOCAL then it should run either in node 10.11.0.226 or
>> 10.11.0.228, because these 2 nodes has all the four blocks needed for
>> computation
>> but the executor is running in 10.11.0.225
>>
>> my theory is not applying anywhere.
>>
>> please help me in understanding how spark/yarn calculates number of
>> executors/tasks.
>>
>> Thanks,
>> -Yeshwanth
>>
>>
>

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