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From ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>
Subject Re: Join highly skewed datasets
Date Sun, 28 Jun 2015 22:45:48 GMT
Regarding # of executors.

I get 342 executors in parallel each time and i set executor-cores to 1.
Hence i need to set 342 * 2 * x (x = 1,2,3, ..) as number of partitions
while running blockJoin. Is this correct.

And is my assumptions on replication levels correct.

Did you get a chance to look at my processing.



On Sun, Jun 28, 2015 at 3:31 PM, Koert Kuipers <koert@tresata.com> wrote:

> regarding your calculation of executors... RAM in executor is not really
> comparable to size on disk.
>
> if you read from from file and write to file you do not have to set
> storage level.
>
> in the join or blockJoin specify number of partitions  as a multiple (say
> 2 times) of number of cores available to you across all executors (so not
> just number of executors, on yarn you can have many cores per executor).
>
> On Sun, Jun 28, 2015 at 6:04 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepujain@gmail.com>
> wrote:
>
>> Could you please suggest and help me understand further.
>>
>> This is the actual sizes
>>
>> -sh-4.1$ hadoop fs -count dw_lstg_item
>>            1          764      2041084436189
>> /sys/edw/dw_lstg_item/snapshot/2015/06/25/00
>> *This is not skewed there is exactly one etntry for each item but its 2TB*
>> So should its replication be set to 1 ?
>>
>> The below two datasets (RDD) are unioned and their total size is 150G.
>> These can be skewed and
>> hence we use block join with Scoobi + MR.
>> *So should its replication be set to 3 ?*
>> -sh-4.1$ hadoop fs -count
>> /apps/hdmi-prod/b_um/epdatasets/exptsession/2015/06/20
>>            1          101        73796345977
>> /apps/hdmi-prod/b_um/epdatasets/exptsession/2015/06/20
>> -sh-4.1$ hadoop fs -count
>> /apps/hdmi-prod/b_um/epdatasets/exptsession/2015/06/21
>>            1          101        85559964549
>> /apps/hdmi-prod/b_um/epdatasets/exptsession/2015/06/21
>>
>> Also can you suggest the number of executors to be used in this case ,
>> each executor is started with max 14G of memory?
>>
>> Is it equal to 2TB + 150G (Total data) = 20150 GB/14GB = 1500 executors
>> ? Is this calculation correct ?
>>
>> And also please suggest on the
>> "(should be memory-and-disk or disk-only), number of partitions (should
>> be large, multiple of num executors),"
>>
>>
>> https://spark.apache.org/docs/latest/programming-guide.html#which-storage-level-to-choose
>>
>> When do i choose this setting ?  (Attached is my code for reference)
>>
>>
>>
>> On Sun, Jun 28, 2015 at 2:57 PM, Koert Kuipers <koert@tresata.com> wrote:
>>
>>> a blockJoin spreads out one side while replicating the other. i would
>>> suggest replicating the smaller side. so if lstgItem is smaller try 3,1
>>> or else 1,3. this should spread the "fat" keys out over multiple (3)
>>> executors...
>>>
>>>
>>> On Sun, Jun 28, 2015 at 5:35 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepujain@gmail.com>
>>> wrote:
>>>
>>>> I am able to use blockjoin API and it does not throw compilation error
>>>>
>>>> val viEventsWithListings: RDD[(Long, (DetailInputRecord, VISummary,
>>>> Long))] = lstgItem.blockJoin(viEvents,1,1).map {
>>>>
>>>> }
>>>>
>>>> Here viEvents is highly skewed and both are on HDFS.
>>>>
>>>> What should be the optimal values of replication, i gave 1,1
>>>>
>>>>
>>>>
>>>> On Sun, Jun 28, 2015 at 1:47 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepujain@gmail.com>
>>>> wrote:
>>>>
>>>>> I incremented the version of spark from 1.4.0 to 1.4.0.1 and ran
>>>>>
>>>>>  ./make-distribution.sh  --tgz -Phadoop-2.4 -Pyarn  -Phive
>>>>> -Phive-thriftserver
>>>>>
>>>>> Build was successful but the script faild. Is there a way to pass the
>>>>> incremented version ?
>>>>>
>>>>>
>>>>> [INFO] BUILD SUCCESS
>>>>>
>>>>> [INFO]
>>>>> ------------------------------------------------------------------------
>>>>>
>>>>> [INFO] Total time: 09:56 min
>>>>>
>>>>> [INFO] Finished at: 2015-06-28T13:45:29-07:00
>>>>>
>>>>> [INFO] Final Memory: 84M/902M
>>>>>
>>>>> [INFO]
>>>>> ------------------------------------------------------------------------
>>>>>
>>>>> + rm -rf /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist
>>>>>
>>>>> + mkdir -p /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/lib
>>>>>
>>>>> + echo 'Spark 1.4.0.1 built for Hadoop 2.4.0'
>>>>>
>>>>> + echo 'Build flags: -Phadoop-2.4' -Pyarn -Phive -Phive-thriftserver
>>>>>
>>>>> + cp
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/assembly/target/scala-2.10/spark-assembly-1.4.0.1-hadoop2.4.0.jar
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/lib/
>>>>>
>>>>> + cp
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/examples/target/scala-2.10/spark-examples-1.4.0.1-hadoop2.4.0.jar
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/lib/
>>>>>
>>>>> + cp
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/network/yarn/target/scala-2.10/spark-1.4.0.1-yarn-shuffle.jar
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/lib/
>>>>>
>>>>> + mkdir -p
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/examples/src/main
>>>>>
>>>>> + cp -r
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/examples/src/main
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/examples/src/
>>>>>
>>>>> + '[' 1 == 1 ']'
>>>>>
>>>>> + cp
>>>>> '/Users/dvasthimal/ebay/projects/ep/spark-1.4.0/lib_managed/jars/datanucleus*.jar'
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/dist/lib/
>>>>>
>>>>> cp:
>>>>> /Users/dvasthimal/ebay/projects/ep/spark-1.4.0/lib_managed/jars/datanucleus*.jar:
>>>>> No such file or directory
>>>>>
>>>>> LM-SJL-00877532:spark-1.4.0 dvasthimal$ ./make-distribution.sh  --tgz
>>>>> -Phadoop-2.4 -Pyarn  -Phive -Phive-thriftserver
>>>>>
>>>>>
>>>>>
>>>>> On Sun, Jun 28, 2015 at 1:41 PM, Koert Kuipers <koert@tresata.com>
>>>>> wrote:
>>>>>
>>>>>> you need 1) to publish to inhouse maven, so your application can
>>>>>> depend on your version, and 2) use the spark distribution you compiled
to
>>>>>> launch your job (assuming you run with yarn so you can launch multiple
>>>>>> versions of spark on same cluster)
>>>>>>
>>>>>> On Sun, Jun 28, 2015 at 4:33 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepujain@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> How can i import this pre-built spark into my application via
maven
>>>>>>> as i want to use the block join API.
>>>>>>>
>>>>>>> On Sun, Jun 28, 2015 at 1:31 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepujain@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> I ran this w/o maven options
>>>>>>>>
>>>>>>>> ./make-distribution.sh  --tgz -Phadoop-2.4 -Pyarn  -Phive
>>>>>>>> -Phive-thriftserver
>>>>>>>>
>>>>>>>> I got this spark-1.4.0-bin-2.4.0.tgz in the same working
directory.
>>>>>>>>
>>>>>>>> I hope this is built with 2.4.x hadoop as i did specify -P
>>>>>>>>
>>>>>>>> On Sun, Jun 28, 2015 at 1:10 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
<deepujain@gmail.com
>>>>>>>> > wrote:
>>>>>>>>
>>>>>>>>>  ./make-distribution.sh  --tgz --*mvn* "-Phadoop-2.4
-Pyarn
>>>>>>>>> -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -DskipTests
clean package"
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> or
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>  ./make-distribution.sh  --tgz --*mvn* -Phadoop-2.4 -Pyarn
>>>>>>>>> -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -DskipTests
clean package"
>>>>>>>>> ​Both fail with
>>>>>>>>>
>>>>>>>>> + echo -e 'Specify the Maven command with the --mvn flag'
>>>>>>>>>
>>>>>>>>> Specify the Maven command with the --mvn flag
>>>>>>>>>
>>>>>>>>> + exit -1
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Deepak
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Deepak
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Deepak
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Deepak
>>>>
>>>>
>>>
>>
>>
>> --
>> Deepak
>>
>>
>


-- 
Deepak

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