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hadoop datanode kernel build and HDFS multiplier factor
Hi hadoopers,

I just got my hands on ten servers (hp 2950 iii) that were upgraded by
another set of servers, and these are the production grid servers.

This is a grid to compute exographic metrics from webserver accesslogs like
geolocation, ISP, and all kind of metrics related to our portal's audience,
to support our operations and content delivery teams with complimentary
metrics than Google Analytics and Omniture already provides, and the daily
log rotation should be around 400GB uncompressed Apache's CustomLog. We
won't hold raw data in HDFS as it would increase hardware requirements to a
level we're not yet able to compromise. We're going to Map Reduce these raw
logs to meningful metrics.

They all have 6 slots for SAS 15K HDD, and I already asked hardware guys to
install CentOS distribution on RAID1 using 2 disks of 73GB. The remaining 4
slots will be filled with 300GB 15K SAS HDDs and I want them to be handled
by hadoop, ending up with 8 x 1.2TB total DataNode storage. 2 servers to
NN, SNN and JobTracker, and 8 DN/TT servers.

Now comes the questions:

#1: I'm following the list and there are some questions regarding building
the kernel for this hardware using different I/O scheduler approaches. I
have yet customize one kernel to upgrade our default CentOS6 stock kernel
with new I/O schedulers if it seems to enhance performance, maximizing
throughput. Should I do it?

#2: With 400GB of raw input data, and 9.6TB total HDFS storage, with a
daily or maybe hourly batch jobs, what should be the optimal multiplier to
HDFS redundat copies of HDFS blocks? Would the answer to #1 impacts what
value I'd configure to be the multiplier on #2 to have optimal HDFS usage
and to meet the processing time requirements for our batch jobs?

Thank you for your attention and time!

Best regards,
Marcel