<p dir="ltr">As I thought about that I decided it's worth expanding on stereotypical MPI user vs. stereotypical Spark user. In general if I ask each about the I/O pattern of their codes, I'll get:</p>
<p dir="ltr">MPI user: "We open N files for read, M files for write, typically Y% of this is random r/w with some streaming at stage ....."</p>
<p dir="ltr">Spark user: "What does 'I/O mean? I didn't see that in the 'Spark for Budding Data Scientists' tutorial I just finished earlier today..."</p>
<p dir="ltr">The "data science" area has some maturing to do which should be exciting and fun for all of us :)</p>
<p dir="ltr">jbh</p>
<br><div class="gmail_quote"><div dir="ltr">On Fri, Dec 30, 2016, 10:47 AM John Hanks <<a href="mailto:griznog@gmail.com">griznog@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><p dir="ltr" class="gmail_msg">This often gets presented as an either/or proposition and it's really not. We happily use SLURM to schedule the setup, run and teardown of spark clusters. At the end of the day it's all software, even the kernel and OS. The big secret of HPC is that in a job scheduler we have an amazingly powerful tool to manage resources. Once you are scheduling spark clusters, hadoop clusters, VMs as jobs, containers, long running web services, ...., you begin to feel sorry for those poor "cloud" people trapped in buzzword land.</p>
<p dir="ltr" class="gmail_msg">But, directly to your question what we are learning as we dive deeper into spark (interest in hadoop here seems to be minimal and fading) is that it is just as hard or maybe harder to tune for than MPI and the people who want to use it tend to have a far looser grasp of how to tune it than those using MPI. In the short term I think it is beneficial as a sysadmin to spend some time learning the inner squishy bits to compensate for that. A simple wordcount example or search can show that wc and grep can often outperform spark and it takes some experience to understand when a particular approach is the better one for a given problem. (Where better is measured by efficiency, not by the number of cool new technical toys were employed :)</p>
<p dir="ltr" class="gmail_msg">jbh</p>
<br class="gmail_msg"><div class="gmail_quote gmail_msg"><div dir="ltr" class="gmail_msg">On Fri, Dec 30, 2016, 9:32 AM Jonathan Aquilina <<a href="mailto:jaquilina@eagleeyet.net" class="gmail_msg" target="_blank">jaquilina@eagleeyet.net</a>> wrote:<br class="gmail_msg"></div><blockquote class="gmail_quote gmail_msg" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="font-size:10pt;font-family:Verdana,Geneva,sans-serif" class="gmail_msg">
<p class="gmail_msg">Hi All,</p>
<p class="gmail_msg">Seeing the new activity about new clusters for 2017, this sparked a thought in my mind here. Beowulf Cluster vs hadoop/spark</p>
<p class="gmail_msg">In this day and age given that there is the technology with hadoop and spark to crunch large data sets, why build a cluster of pc's instead of use something like hadoop/spark?</p>
<p class="gmail_msg"><br class="gmail_msg"></p>
<p class="gmail_msg">Happy New Year</p>
<p class="gmail_msg">Jonathan Aquilina</p>
<p class="gmail_msg">Owner EagleEyeT</p>
</div>
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</blockquote></div><div dir="ltr" class="gmail_msg">-- <br class="gmail_msg"></div><div data-smartmail="gmail_signature" class="gmail_msg"><div dir="ltr" class="gmail_msg"><div class="gmail_msg">‘[A] talent for following the ways of yesterday, is not sufficient to improve the world of today.’</div><div class="gmail_msg"> - King Wu-Ling, ruler of the Zhao state in northern China, 307 BC</div></div></div></blockquote></div><div dir="ltr">-- <br></div><div data-smartmail="gmail_signature"><div dir="ltr"><div>‘[A] talent for following the ways of yesterday, is not sufficient to improve the world of today.’</div><div> - King Wu-Ling, ruler of the Zhao state in northern China, 307 BC</div></div></div>