-Re: Suitability of HDFS for live file store
Vinod Kumar Vavilapalli 2012-10-15, 23:25
For your original use case, HDFS indeed sounded like an overkill. But once you start thinking of thumbnail generation, PDFs etc, MapReduce obviously fits the bill.
If you wish to do stuff like streaming the stored digital films, clearly, you may want to move your serving somewhere else that works in tandem with Hadoop.
On Oct 15, 2012, at 1:59 PM, Matt Painter wrote:
> Sorry, I should have provided a bit more detail. Currently our data set comprises of 50-100Mb TIFF files. In the near future we'd like to store and process preservation-quality digitised film, which will individually exceed this size by orders of magnitude (and has currently been in the "too-hard" basket with our current infrastructure). In general, our thinking thus far has been very much based on what our current infrastructure can provide - so I'm excited to have alternatives available.
> There will also be thumbnail generation as well as generation of the screen-resolution JPEGs that I alluded to, and PDF generation. Whether the JPEG/PDF derivatives are stored in HDFS remains to be seen - these can be easily regenerated at any stage and their total size will be relatively small, so it may not be the best fit for storage of these guys.
> On 16 October 2012 09:35, Brian Bockelman <[EMAIL PROTECTED]> wrote:
> 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.
> 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: