On Feb 6, 2009, at 11:00 AM, TCK wrote:
How well does the read throughput from HDFS scale with the number of
data nodes ?
For example, if I had a large file (say 10GB) on a 10 data node
cluster, would the time taken to read this whole file in parallel
(ie, with multiple reader client processes requesting different
parts of the file in parallel) be halved if I had the same file on a
20 data node cluster ?
Possibly. (I don't give a firm answer because the answer depends on
the number of chunks and the number of replicas).
If there are enough replicas and enough separate reading processes
with enough network bandwidth, then yes, your read bandwidth could
double.
Is this not possible because HDFS doesn't support random seeks?
It does for reads. It does not for writes.
Trust me, our physicists have what can best be described as "the most
god-awful random read patterns you've seen in your life" and they do
fine on HDFS.
What about if the file was split up into multiple smaller files
before placing in the HDFS ?
Then things would be less efficient and you'd be less likely to scale.
Brian
Thanks for your input.
-TCK
--- On Wed, 2/4/09, Brian Bockelman <bbock...@cse.unl.edu> wrote:
From: Brian Bockelman <bbock...@cse.unl.edu>
Subject: Re: Batch processing with Hadoop -- does HDFS scale for
parallel reads?
To: core-user@hadoop.apache.org
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 <bbock...@cse.unl.edu> wrote:
From: Brian Bockelman <bbock...@cse.unl.edu>
Subject: Re: Batch processing with Hadoop -- does HDFS scale for
parallel
reads?
To: core-user@hadoop.apache.org
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
