What about if the file was split up into multiple smaller files before placing
in the HDFS ?
Thanks for your input.
-TCK
--- On Wed, 2/4/09, Brian Bockelman <[email protected]> wrote:
From: Brian Bockelman <[email protected]>
Subject: Re: Batch processing with Hadoop -- does HDFS scale for parallel reads?
To: [email protected]
Date: Wednesday, February 4, 2009, 1:50 PM
Sounds overly complicated. Complicated usually leads to mistakes :)
What about just having a single cluster and only running the tasktrackers on
the fast CPUs? No messy cross-cluster transferring.
Brian
On Feb 4, 2009, at 12:46 PM, TCK wrote:
Thanks, Brian. This sounds encouraging for us.
What are the advantages/disadvantages of keeping a persistent storage
(HD/K)FS cluster separate from a processing Hadoop+(HD/K)FS cluster ?
The advantage I can think of is that a permanent storage cluster has
different requirements from a map-reduce processing cluster -- the permanent
storage cluster would need faster, bigger hard disks, and would need to grow as
the total volume of all collected logs grows, whereas the processing cluster
would need fast CPUs and would only need to grow with the rate of incoming data.
So it seems to make sense to me to copy a piece of data from the permanent
storage cluster to the processing cluster only when it needs to be processed. Is
my line of thinking reasonable? How would this compare to running the map-reduce
processing on same cluster as the data is stored in? Which approach is used by
most people?
Best Regards,
TCK
--- On Wed, 2/4/09, Brian Bockelman <[email protected]> wrote:
From: Brian Bockelman <[email protected]>
Subject: Re: Batch processing with Hadoop -- does HDFS scale for parallel
reads?
To: [email protected]
Date: Wednesday, February 4, 2009, 1:06 PM
Hey TCK,
We use HDFS+FUSE solely as a storage solution for a application which
doesn't understand MapReduce. We've scaled this solution to
around
80Gbps. For 300 processes reading from the same file, we get about
20Gbps.
Do consider your data retention policies -- I would say that Hadoop as a
storage system is thus far about 99% reliable for storage and is not a
backup
solution. If you're scared of getting more than 1% of your logs lost,
have
a good backup solution. I would also add that when you are learning your
operational staff's abilities, expect even more data loss. As you
gain
experience, data loss goes down.
I don't believe we've lost a single block in the last month, but
it
took us 2-3 months of 1%-level losses to get here.
Brian
On Feb 4, 2009, at 11:51 AM, TCK wrote:
Hey guys,
We have been using Hadoop to do batch processing of logs. The logs get
written and stored on a NAS. Our Hadoop cluster periodically copies a
batch of
new logs from the NAS, via NFS into Hadoop's HDFS, processes them, and
copies the output back to the NAS. The HDFS is cleaned up at the end of
each
batch (ie, everything in it is deleted).
The problem is that reads off the NAS via NFS don't scale even if
we
try to scale the copying process by adding more threads to read in
parallel.
If we instead stored the log files on an HDFS cluster (instead of
NAS), it
seems like the reads would scale since the data can be read from multiple
data
nodes at the same time without any contention (except network IO, which
shouldn't be a problem).
I would appreciate if anyone could share any similar experience they
have
had with doing parallel reads from a storage HDFS.
Also is it a good idea to have a separate HDFS for storage vs for
doing
the batch processing ?
Best Regards,
TCK
