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From "jeevan.ks" <jeevansriva...@gmail.com>
Subject Parallelism: behavioural difference in version 1.2 and 2.1!?
Date Wed, 29 Aug 2018 13:55:43 GMT
Hi,

I've two systems. One is built on Spark 1.2 and the other on 2.1. I am
benchmarking both with the same benchmarks (wordcount, grep, sort, etc.)
with the same data set from S3 bucket (size ranges from 50MB to 10 GB). The
Spark cluster I made use of is r3.xlarge, 8 instances, 4 cores each, and
28GB RAM. I observed a strange behaviour while running the benchmarks and is
as follows:

- When I ran Spark 1.2 version with default partition number
(sc.defaultParallelism), the jobs would take forever to complete. So I
changed it to the number of cores, i.e., 32 times 3 = 96. This did a magic
and the jobs completed quickly.

- However, when I tried the above magic number on the version 2.1, the jobs
are taking forever. Deafult parallelism works better, but not that
efficient.

I'm having problem to rationalise this and compare both the systems. My
question is: what changes were made from 1.2 to 2.1 with respect to default
parallelism for this behaviour to occur? How can I have both versions behave
similary on the same software/hardware configuration so that I can compare?

I'd really appreciate your help on this!

Cheers,
Jeevan 



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