Yes its the same data.
1) The number of partitions are the same (8, which is an argument to the
HashPartitioner). In the first case, these partitions are spread across
both the worker nodes. In the second case, all the partitions are on the
same node.
2) What resources would be of interest here? Scala shell takes the default
parameters since we use "bin/spark-shell --master <master-URL>" to run the
scala-shell. For the scala program, we do set some configuration options
such as driver memory (12GB), parallelism is set to 8 and we use Kryo
serializer.
We are running this on Azure D3-v2 machines which have 4 cores and 14GB
RAM.1 executor runs on each worker node. Following configuration options
are set for the scala program -- perhaps we should move it to the spark
config file.
Driver memory and executor memory are set to 12GB
parallelism is set to 8
Kryo serializer is used
Number of retainedJobs and retainedStages has been increased to check them
in the UI.
What information regarding Spark Context would be of interest here?
Regards,
Raghava.
On Sun, Apr 17, 2016 at 10:54 PM, Anuj Kumar <anujs...@gmail.com> wrote:
> If the data file is same then it should have similar distribution of keys.
> Few queries-
>
> 1. Did you compare the number of partitions in both the cases?
> 2. Did you compare the resource allocation for Spark Shell vs Scala
> Program being submitted?
>
> Also, can you please share the details of Spark Context, Environment and
> Executors when you run via Scala program?
>
> On Mon, Apr 18, 2016 at 4:41 AM, Raghava Mutharaju <
> m.vijayaragh...@gmail.com> wrote:
>
>> Hello All,
>>
>> We are using HashPartitioner in the following way on a 3 node cluster (1
>> master and 2 worker nodes).
>>
>> val u = sc.textFile("hdfs://x.x.x.x:8020/user/azureuser/s.txt").map[(Int,
>> Int)](line => { line.split("\\|") match { case Array(x, y) => (y.toInt,
>> x.toInt) } }).partitionBy(new HashPartitioner(8)).setName("u").persist()
>>
>> u.count()
>>
>> If we run this from the spark shell, the data (52 MB) is split across the
>> two worker nodes. But if we put this in a scala program and run it, then
>> all the data goes to only one node. We have run it multiple times, but this
>> behavior does not change. This seems strange.
>>
>> Is there some problem with the way we use HashPartitioner?
>>
>> Thanks in advance.
>>
>> Regards,
>> Raghava.
>>
>
>
--
Regards,
Raghava
http://raghavam.github.io
