Hi, We use HDFS to process data for the LHC - somewhat similar case here. Our files are a bit larger, our total local data size if ~1PB logical, and we "bring our own" batch system, so no Map-Reduce. We perform many random reads, so we are quite sensitive to underlying latency.
I don't see any obvious mismatches between your requirements and HDFS capabilities that you can eliminate it as a candidate without an evaluation. Do note that HDFS does not provide complete POSIX semantics - but you don't appear to need them? IMHO, if you are looking for the following requirements: 1) Proven petascale data store (never want to be on the bleeding edge of your filesystem's scaling!). 2) Has self-healing semantics (can recover from the loss of RAIDs or entire storage targets). 3) Open source (but do consider commercial companies - your time is worth something!). You end up at looking at a very small number of candidates. Others filesystems that should be on your list: 1) Gluster. A quite viable alternate. Like HDFS, you can buy commercial support. I personally don't know enough to provide a pros/cons list, but we keep it on our radar. 2) Ceph. Not as proven IMHO. I don't know of multiple petascale deploys. Requires a quite recent kernel. Quite good on-paper design. 3) Lustre. I think you'd be disappointed with the self-healing. A very "traditional" HPC/clustered filesystem design. For us, HDFS wins. I think it has the possibility of being a winner in your case too. Brian On Oct 15, 2012, at 3:21 PM, Jay Vyas <[email protected]> wrote: > Seems like a heavyweight solution unless you are actually processing the > images? > > Wow, no mapreduce, no streaming writes, and relatively small files. Im > surprised that you are considering hadoop at all ? > > Im surprised there isnt a simpler solution that uses redundancy without all > the > daemons and name nodes and task trackers and stuff. > > Might make it kind of awkward as a normal file system. > > On Mon, Oct 15, 2012 at 4:08 PM, Harsh J <[email protected]> wrote: > Hey Matt, > > What do you mean by 'real-time' though? While HDFS has pretty good > contiguous data read speeds (and you get N x replicas to read from), > if you're looking to "cache" frequently accessed files into memory > then HDFS does not natively have support for that. Otherwise, I agree > with Brock, seems like you could make it work with HDFS (sans > MapReduce - no need to run it if you don't need it). > > The presence of NameNode audit logging will help your file access > analysis requirement. > > On Tue, Oct 16, 2012 at 1:17 AM, Matt Painter <[email protected]> wrote: > > Hi, > > > > I am a new Hadoop user, and would really appreciate your opinions on whether > > Hadoop is the right tool for what I'm thinking of using it for. > > > > I am investigating options for scaling an archive of around 100Tb of image > > data. These images are typically TIFF files of around 50-100Mb each and need > > to be made available online in realtime. Access to the files will be > > sporadic and occasional, but writing the files will be a daily activity. > > Speed of write is not particularly important. > > > > Our previous solution was a monolithic, expensive - and very full - SAN so I > > am excited by Hadoop's distributed, extensible, redundant architecture. > > > > My concern is that a lot of the discussion on and use cases for Hadoop is > > regarding data processing with MapReduce and - from what I understand - > > using HDFS for the purpose of input for MapReduce jobs. My other concern is > > vague indication that it's not a 'real-time' system. We may be using > > MapReduce in small components of the application, but it will most likely be > > in file access analysis rather than any processing on the files themselves. > > > > In other words, what I really want is a distributed, resilient, scalable > > filesystem. > > > > Is Hadoop suitable if we just use this facility, or would I be misusing it > > and inviting grief? > > > > M > > > > -- > Harsh J > > > > -- > Jay Vyas > MMSB/UCHC
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