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MapReduce >> mail # user >> HDFS using SAN


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Re: HDFS using SAN
And of source IBM has supported our GPFS and SONAS customers for a couple of years already.

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Sent from my Blackberry so please excuse typing and spelling errors.
----- Original Message -----
From: "Kevin O'dell" [[EMAIL PROTECTED]]
Sent: 10/17/2012 09:25 AM AST
To: [EMAIL PROTECTED]
Subject: Re: HDFS using SAN

You may want to take a look at the Netapp White Paper on this.  They have a
SAN solution as their Hadoop offering.

http://www.netapp.com/templates/mediaView?m=tr-3969.pdf&cc=us&wid=130618138&mid=56872393

On Tue, Oct 16, 2012 at 7:28 PM, Pamecha, Abhishek <[EMAIL PROTECTED]> wrote:

>  Yes, for MR, my impression is typically the n/w utilization is next to
> none during map and reduce tasks but jumps during shuffle.  With a SAN, I
> would assume there is no such separation. There will be network activity
> all over the job’s time window with shuffle probably doing more than what
> it should. ****
>
> ** **
>
> Moreover, I hear typically SANs by default, would split data in different
> physical disks [even w/o RAID], so contiguity is lost. But I have no idea
> on if that is a good thing or bad. Looks bad on the surface, but probably
> depends on how much parallelized data fetches from multiple physical disks
> can be done by a SAN efficiently. Any comments on this aspect?****
>
> ** **
>
> And yes, when the dataset volume increases and one needs to basically do
> full table scan equivalents, I am assuming the n/w needs to support that
> entire data move from SAN to the data node all in parallel to different
> mappers.****
>
> ** **
>
> So what I am gathering is  although storing data over SAN is possible for
> a Hadoop installation, Map-shuffle-reduce may not be the best way to
> process data in that env. Is this conclusion correct? ****
>
> ** **
>
> <3 way Replication and RAID suggestions are great. ****
>
> ** **
>
> Thanks,****
>
> Abhishek****
>
> ** **
>
> *From:* lohit [mailto:[EMAIL PROTECTED]]
> *Sent:* Tuesday, October 16, 2012 3:26 PM
> *To:* [EMAIL PROTECTED]
> *Subject:* Re: HDFS using SAN****
>
> ** **
>
> Adding to this. Locality is very important for MapReduce applications. One
> might not see much of a difference for small MapReduce jobs running on
> direct attached storage vs SAN, but when you cluster grows or you find jobs
> which are heavy on IO, you would see quite a bit of difference. One thing
> which is obviously is also cost difference. Argument for that has been that
> SAN storage is much more reliable so you do not need default of 3 way
> replication factor you would do on direct attached storage. ****
>
> ** **
>
> 2012/10/16 Jeffrey Buell <[EMAIL PROTECTED]>****
>
> It will be difficult to make a SAN work well for Hadoop, but not
> impossible.  I have done direct comparisons (but not published them yet).
> Direct local storage is likely to have much more capacity and more total
> bandwidth.  But you can do pretty well with a SAN if you stuff it with the
> highest-capacity disks and provide an independent 8 gb (FC) or 10 GbE
> connection for every host.  Watch out for overall SAN bandwidth limits
> (which may well be much less than the sum of the capacity of the wires
> connected to it).  There will definitely be a hard limit to how many hosts
> you connect to a single SAN.  Scaling to larger clusters will require
> multiple SANs.****
>
>  ****
>
> Locality is an issue.  Even though each host has a direct physical access
> to all the data, a “remote” access in HDFS will still have to go over the
> network to the host that owns the data.  “Local” access is fine with the
> constraints above.****
>
>  ****
>
> RAID is not good for Hadoop performance for both local and SAN storage, so
> you’ll want to configure one LUN for each physical disk in the SAN.  If you
> do have mirroring or RAID on the SAN, you may be tempted to use that to
> replace Hadoop replication.  But while the data is protected, access to the
> data is lost if the datanode goes down.  You can get around that by running

Kevin O'Dell
Customer Operations Engineer, Cloudera
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB