Re: Hadoop with Netapp

[Steve Loughran <st...@apache.org>]

On 25/08/11 08:20, Sagar Shukla wrote:
> Hi Hakan,
>
>          Please find my comments inline in blue :
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>
>
> -----Original Message-----
> From: Hakan (c)lter [mailto:hakanilter@gmail.com]
> Sent: Thursday, August 25, 2011 12:28 PM
> To: common-user@hadoop.apache.org
> Subject: Hadoop with Netapp
>
>
>
> Hi everyone,
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>
>
> We are going to create a new Hadoop cluster in our company, i have to get some advises from you:
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> 1. Does anyone have stored whole Hadoop data not on local disks but on Netapp or other storage system? Do we have to store datas on local disks, if so is it because of performace issues?
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> <sagar>: Yes, we were using SAN LUNs for storing Hadoop data. SAN works faster than NAS in terms of performance while writing the data to the storage. Also SAN LUNs can be auto-mounted while booting up the system.

Silly question: why? SANs are SPOFs (Gray & van Ingen, MS, 2005; SAN 
responsible for 11% of terraserver downtime).

Was it because you had the rack and wanted to run Hadoop, or did you 
want a more agile cluster? Because it's going to increase your cost of 
storage dramatically, which means you pay more per TB, or end up with 
less TB of storage. I wouldn't go this way for a dedicated Hadoop 
cluster. For a multi-use cluster, it's a different story


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> 2. What do you think about running Hadoop nodes in virtual (VMware) servers?
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> <sagar>: If high speed computing is not a requirement for you then Hadoop nodes in VM environment could be a good option, but one other slight drawback is when the VM crashes recovery of the in-memory data would be gone. Hadoop takes care of some amount of failover, but there is some amount of risk involved and requires good HA building capabilities.


I do it for dev and test work, and for isolated clusters in a shared 
environment.

-for CPU bound stuff, it actually works quite well, as there's no 
significant overhead

-for HDD access, reading from the FS, writing to the FS and to store 
transient spill data you take a tangible performance hit. That's OK if 
you can afford to wait or rent a few extra CPUs -and your block size is 
such that those extra servers can help out -which may be in the map 
phase more than the reduce phase


Some Hadoop-ish projects -Stratosphere from TuB in particular- are 
designed for VM infrastructure so come up with execution plans to use 
VMs efficiently.

-steve