Yes, I had similar views from the netapp paper. My usecase is io heavy and that's why ( atleast IMO), when data set grows, a shared SAN begins to make less sense as opposed to DAS for MR type of jobs.
As Lucas pointed out, sharing the same data with other apps is a great adv. w SAN. Thanks Abhishek i Sent from my iPad with iMstakes On Oct 18, 2012, at 6:59, "Michael Segel" <michael_se...@hotmail.com<mailto:michael_se...@hotmail.com>> wrote: I haven't played with a NetApp box, but the way it has been explained to me is that your SAN appears as if its direct attached storage. Its possible, based on drives and other hardware, plus it looks like they are focusing on read times only. I'd contact a NetApp rep for a better answer. Actually if you are looking at a higher density in terms of storage, going with a storage / compute cluster makes sense. On Oct 18, 2012, at 8:48 AM, Jitendra Kumar Singh <jksingh26...@gmail.com<mailto:jksingh26...@gmail.com>> wrote: Hi, In the NetApp whitepaper on SAN solution (link given by Kevin) it makes following statement. Can someone please elaborate (or give a link that explains) how 12-disk in SAN can give 2000 IOPS while if used as JBOD would give 600 IOPS? "The E2660 can deliver up to 2,000 IOPS from a 12-disk stripe (the bottleneck being the 12 disks). This headroom translates into better read times for those 64KB blocks. Twelve copies of 12 MapReduce jobs reading from 12 SATA disks can at best never exceed 12 x 50 IOPS, or 600 IOPS. The E2660 volume has five times the IOPS headroom, which translates into faster read times and high MapReduce throughput " Thanks and Regards, -- Jitendra Kumar Singh On Thu, Oct 18, 2012 at 6:02 PM, Luca Pireddu <pire...@crs4.it<mailto:pire...@crs4.it>> wrote: On 10/18/2012 02:21 AM, Pamecha, Abhishek wrote: Tom Do you mean you are using GPFS instead of HDFS? Also, if you can share, are you deploying it as DAS set up or a SAN? Thanks, Abhishek Though I don't think I'd buy a SAN for a new Hadoop cluster, we have a SAN and are using it *instead of HDFS* with a small/medium Hadoop MapReduce cluster (up to 100 nodes or so, depending on our need). We still use the local node disks for intermediate data (mapred local storage). Although this set-up does limit our possibility to scale to a large number of nodes, that's not a concern for us. On the plus, we gain the flexibility to be able to share our cluster with non-Hadoop users at our centre. -- Luca Pireddu CRS4 - Distributed Computing Group Loc. Pixina Manna Edificio 1 09010 Pula (CA), Italy Tel: +39 0709250452
