Thanks Mark, I guess this may be broadened to the concept of separate compute from storage. Your point on " ... can kind of disappear after the data is first read from the storage layer." reminds of performing Logical IOs as opposed to Physical IOs. But again as you correctly pointed out on the amount of available cache and concurrency that can saturate the hits on the storage. I personally believe that Data locality helps by avoiding these remote IO calls
Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On 15 April 2018 at 21:22, Mark Hamstra <[email protected]> wrote: > This is a sort of your mileage varies type question. >> > > Yes, it really does. Not only does it depend heavily on the configuration > of your compute and storage, but it also depends a lot on any caching that > you are doing between compute and storage and on the nature of your Spark > queries/Jobs. If you are mostly doing cold full scans, then you're going to > see a big performance hit. If you are reusing a lot of prior or > intermediate results, then you are frequently not going all the way back to > a slow storage layer, but rather to a Spark CachedTable, some other cache, > or even the OS buffer cache for shuffle files -- or to local disk spillage. > All of that is typically going to be local to your compute nodes, so the > data locality issue can kind of disappear after the data is first read from > the storage layer. > > > On Sat, Apr 14, 2018 at 12:17 PM, Mich Talebzadeh < > [email protected]> wrote: > >> Hi, >> >> This is a sort of your mileage varies type question. >> >> In a classic Hadoop cluster, one has data locality when each node >> includes the Spark libraries and HDFS data. this helps certain queries like >> interactive BI. >> >> However running Spark over remote storage say Isilon scaled out NAS >> instead of LOCAL HDFS becomes problematic. The full-scan Spark needs to >> do will take much longer when it is done over the network (access the >> remote Isilon storage) instead of local I/O request to HDFS. >> >> Has anyone done some comparative studies on this? >> >> >> Thanks >> >> >> Dr Mich Talebzadeh >> >> >> >> LinkedIn * >> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >> >> >> >> http://talebzadehmich.wordpress.com >> >> >> *Disclaimer:* Use it at your own risk. Any and all responsibility for >> any loss, damage or destruction of data or any other property which may >> arise from relying on this email's technical content is explicitly >> disclaimed. The author will in no case be liable for any monetary damages >> arising from such loss, damage or destruction. >> >> >> > >
