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From Giuseppe Sarno <GiuseppeSa...@fico.com>
Subject Scaling spark jobs returning large amount of data
Date Thu, 04 Jun 2015 14:30:09 GMT
Hello,
I am relatively new to spark and I am currently trying to understand how to scale large numbers
of jobs with spark.
I understand that spark architecture is split in "Driver", "Master" and "Workers". Master
has a standby node in case of failure and workers can scale out.
All the examples I have seen show Spark been able to distribute the load to the workers and
returning small amount of data to the Driver. In my case I would like to explore the scenario
where I need to generate a large report on data stored on Cassandra and understand how Spark
architecture will handle this case when multiple report jobs will be running in parallel.
According to this  presentation https://trongkhoanguyenblog.wordpress.com/2015/01/07/understand-the-spark-deployment-modes/
responses from workers go through the Master and finally to the Driver. Does this mean that
the Driver and/ or Master is a single point for all the responses coming back from workers
?
Is it possible to start multiple concurrent Drivers ?

Regards,
Giuseppe.


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