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From "Kumar, Saurabh 5. (Nokia - IN/Bangalore)" <>
Subject RE: Query related to spark cluster
Date Mon, 30 May 2016 07:16:43 GMT
Hi All,

@Deepak: Thanks for your suggestion, we are using Mesos to handle spark cluster.

@Jorn : the reason we chose postgresXL was of its geo-spational support as we store location

We were seeing how to quickly put things better and what is the right approach

Our original thinking was to use different cluster for different needs.

Eg.  Instead of 1 cluster we were thinking having 3 cluster

1) Spark cluster -- including HDFS we need HDFS because we have to read data from an SFTP
location and we thought best is if we write it first to HDFS

2) Distributed R cluster since R does not scale and we have a need for scaling and no time
to move to SparkR we thought we try distributed R.

3) PostgresXL cluster -- This is the DB cluster so the Spark cluster would write to PostgresXl
cluster and R will read/write to postgresXL cluster

In current setup we have included all component into same cluster. Can you please help me
out to choose best approach which will not compromise scalability and failover mechanism?


From: Deepak Sharma []
Sent: Monday, May 30, 2016 12:17 PM
To: Jörn Franke <>
Cc: Kumar, Saurabh 5. (Nokia - IN/Bangalore) <>;;
Sawhney, Prerna (Nokia - IN/Bangalore) <>
Subject: Re: Query related to spark cluster

Hi Saurabh
You can have hadoop cluster running YARN as scheduler.
Configure spark to run with the same YARN setup.
Then you need R only on 1 node , and connect to the cluster using the SparkR.


On Mon, May 30, 2016 at 12:12 PM, Jörn Franke <<>>

Well if you require R then you need to install it (including all additional packages) on each
node. I am not sure why you store the data in Postgres . Storing it in Parquet and Orc is
sufficient in HDFS (sorted on relevant columns) and you use the SparkR libraries to access

On 30 May 2016, at 08:38, Kumar, Saurabh 5. (Nokia - IN/Bangalore) <<>>
Hi Team,

I am using Apache spark to build scalable Analytic engine. My setup is as follows.

Flow of processing is as follows:

Raw Files > Store to HDFS > Process by Spark and Store to Postgre_XL Database > R
process data fom Postgre-XL to process in distributed mode.

I have 6 nodes cluster setup for ETL operations which have

1.      Spark slaves installed on all 6 of them.
2.      HDFS data nodes on each of 6 nodes with replication factor 2.
3.      PosGRE –XL 9.5 Database coordinator on each of 6 nodes.
4.      R software is installed on all nodes and Uses process Data from Postgre-XL in distributed

Can you please guide me about pros and cons of this setup.
Installing all component on every machines is recommended or there is any drawback?
R software should run on spark cluster ?

Thanks & Regards
Saurabh Kumar
R&D Engineer, T&I TED Technology Explorat&Disruption
Nokia Networks
L5, Manyata Embassy Business Park, Nagavara, Bangalore, India 560045
Mobile: +91-8861012418<tel:%2B91-8861012418>

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