Johnu, Keep in mind HDFS was more less designed and thus optimized for MR jobs versus general filesystem use. It was also optimized for a case of hardware in the past, eg. slower networks then today (1gigE or less). Theres's lots of little hacks in hadoop to optimize for that, for example local mmaped reads in hdfs client). It will tough to beat MR on HDFS in that scenario and hadoop. If hadoop is a smaller piece in a large data-pipeline (that includes non-hadoop, regular fs work) then it makes more sense. Now if you're talking about the hardware and network of tomorrow (10gigE or 40gigE) then locality of placement starts to matter less. For example the Mellanox people claim that they are able to get 20% more performance out of Ceph in the 40gigE scenario. And if we're designing for the network for future then there's a lot we can clean from the Quantcast hadoop filesystem (http://quantcast.github.io/qfs/). Take a look at their recent publication: http://db.disi.unitn.eu/pages/VLDBProgram/pdf/industry/p808-ovsiannikov.pdf. They essentially forked KFS, added erasure coding support created a hadoop filesystem driver for it. They were able to get much better write performance by reducing write amplifications (1.5x copies versus 3 copies) thus reducing network traffic and possibly freeing up that previous bandwidth for read traffic. They claim to have improved read performance compared to HDFS a tad. QFS unlike Ceph places the erasure coding logic inside of the client so it's not a apples-to-apples comparison. but I think you get my point, and it would be possible to implement a rich Ceph (filesystem/hadoop) client like this as well. In summary, if Hadoop on Ceph is a major priority I think it would be best to "borrow" the good ideas for QFS and implement them in Hadoop Ceph filesystem and Ceph it self (letting a smart client get chunks directly, write chunks directly). I don't doubt that it's a lot of work but the results might be worth it in in terms of performance you get for the cost. Some food for though. I don't have a horse in this particular game but I am interested in DFSs and VLDBs so I'm constantly reading into research / what folks are building. Cheers, - Milosz P.S: Forgot to Reply-to-all, haven't had my coffee yet. On Thu, Sep 4, 2014 at 3:16 AM, Johnu George (johnugeo) <johnugeo@xxxxxxxxx> wrote: > Hi All, > I was reading more on Hadoop over ceph. I heard from Noah that > tuning of Hadoop on Ceph is going on. I am just curious to know if there > is any reason to keep default object size as 64MB. Is it because of the > fact that it becomes difficult to encode > getBlockLocations if blocks are divided into objects and to choose the > best location for tasks if no nodes in the system has a complete block.? > > I am wondering if someone any benchmark results for various object sizes. > If you have them, it will be helpful if you share them. > > I see that Ceph doesn¹t place objects considering the client location or > distance between client and the osds where data is stored.(data-locality) > While, data locality is the key idea for HDFS block placement and > retrieval for maximum throughput. So, how does ceph plan to perform better > than HDFS as ceph relies on random placement > using hashing unlike HDFS block placement? Can someone also point out > some performance results comparing ceph random placements vs hdfs locality > aware placement? > > Also, Sage wrote about a way to specify a node to be primary for hadoop > like environments. > (http://comments.gmane.org/gmane.comp.file-systems.ceph.devel/1548 ) Is > this through primary affinity configuration? > > Thanks, > Johnu > -- Milosz Tanski CTO 16 East 34th Street, 15th floor New York, NY 10016 p: 646-253-9055 e: milosz@xxxxxxxxx -- To unsubscribe from this list: send the line "unsubscribe ceph-devel" in the body of a message to majordomo@xxxxxxxxxxxxxxx More majordomo info at http://vger.kernel.org/majordomo-info.html
