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From "Pamecha, Abhishek" <apame...@x.com>
Subject RE: HDFS using SAN
Date Thu, 18 Oct 2012 00:26:06 GMT
In a SAN? Would it be a concern if I am relying on HDFS to do the replication and using SAN
only for dumb storage tier.  In that case, the only difference is remote vs local access.

Reliability may be, actually,  even better in a SAN coz I would assume any reasonable SAN
would provide decent fault-tolerance when its controller(s) fail.

Thanks,
Abhishek

From: Mohamed Riadh Trad [mailto:Mohamed.trad@inria.fr]
Sent: Wednesday, October 17, 2012 6:37 AM
To: user@hadoop.apache.org
Subject: Re: HDFS using SAN

Sauvegarde tes données!

Le 17 oct. 2012 à 15:25, Kevin O'dell a écrit :


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 <apamecha@x.com<mailto:apamecha@x.com>>
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:lohit.vijayarenu@gmail.com<mailto:lohit.vijayarenu@gmail.com>]
Sent: Tuesday, October 16, 2012 3:26 PM
To: user@hadoop.apache.org<mailto:user@hadoop.apache.org>
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 <jbuell@vmware.com<mailto:jbuell@vmware.com>>
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 the datanode in a VM which is stored on the SAN and using VMware HA to automatically
restart the VM on another host in case of a failure.  Hortonworks has demonstrated this use-case
but this strategy is a bit bleeding-edge.

Jeff

From: Pamecha, Abhishek [mailto:apamecha@x.com<mailto:apamecha@x.com>]
Sent: Tuesday, October 16, 2012 11:28 AM
To: user@hadoop.apache.org<mailto:user@hadoop.apache.org>
Subject: HDFS using SAN

Hi

I have read scattered documentation across the net which mostly say HDFS doesn't go well with
SAN being used to store data. While some say, it is an emerging trend. I would love to know
if there have been any tests performed which hint on what aspects does a direct storage excels/falls
behind a SAN.

We are investigating whether a direct storage option is better than a SAN storage for a modest
cluster with data in 100 TBs in steady state. The SAN of course can support order of magnitude
more of iops we care about for now, but given it is a shared infrastructure and we may expand
our data size, it may not be an advantage in the future.

Another thing I am interested in: for MR jobs, where data locality is the key driver, how
does that span out when using a SAN instead of direct storage?

And of course on the subjective topics of availability and reliability on using a SAN for
data storage in HDFS, I would love to receive your views.

Thanks,
Abhishek




--
Have a Nice Day!
Lohit



--
Kevin O'Dell
Customer Operations Engineer, Cloudera


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