I am assuming that the storage of vertices (NoSQL or any other format) need not be updated after every iteration.
Based on the above assumption, I have the following suggestions: - Instead of running a separate job, we inject a partitioning superstep before the first superstep of the job. (This has a dependency on the Superstep API) - The partitions instead of being written to HDFS, which is creating a copy of input files in HDFS Cluster (too costly I believe), should be written to local files and read from. - For graph jobs, we can configure this partitioning superstep class specific to graph partitioning class that partitions and loads vertices. This sure has some dependencies. But would be a graceful solution and can tackle every problem. This is what I want to achieve in the end. Please proceed if you have any intermediate ways to reach here faster. Regards, Suraj On Mon, May 6, 2013 at 3:14 AM, Edward J. Yoon <[email protected]>wrote: > P.S., BSPJob (with table input) also the same. It's not only for GraphJob. > > On Mon, May 6, 2013 at 4:09 PM, Edward J. Yoon <[email protected]> > wrote: > > All, > > > > I've also roughly described details about design of Graph APIs[1]. To > > reduce our misunderstandings (please read first Partitioning and > > GraphModuleInternals documents), > > > > * In NoSQLs case, there's obviously no need to Hash-partitioning or > > rewrite partition files on HDFS. So, in these input cases, I think > > vertex structure should be parsed at GraphJobRunner.loadVertices() > > method. > > > > At here, we faced two options: 1) The current implementation of > > 'PartitioningRunner' writes converted vertices on sequence format > > partition files. And GraphJobRunner reads only Vertex Writable > > objects. If input is table, we maybe have to skip the Partitioning job > > and have to parse vertex structure at loadVertices() method after > > checking some conditions. 2) PartitioningRunner just writes raw > > records to proper partition files after checking its partition ID. And > > GraphJobRunner.loadVertices() always parses and loads vertices. > > > > I was mean that I prefer the latter and there's no need to write > > VertexWritable files. It's not related whether graph will support only > > Seq format or not. Hope my explanation is enough! > > > > 1. http://wiki.apache.org/hama/GraphModuleInternals > > > > On Mon, May 6, 2013 at 10:00 AM, Edward J. Yoon <[email protected]> > wrote: > >> I've described my big picture here: > http://wiki.apache.org/hama/Partitioning > >> > >> Please review and feedback whether this is acceptable. > >> > >> > >> On Mon, May 6, 2013 at 8:18 AM, Edward <[email protected]> wrote: > >>> p.s., i think theres mis understand. it doesn't mean that graph will > support only sequence file format. Main is whether converting at > patitioning stage or loadVertices stage. > >>> > >>> Sent from my iPhone > >>> > >>> On May 6, 2013, at 8:09 AM, Suraj Menon <[email protected]> wrote: > >>> > >>>> Sure, Please go ahead. > >>>> > >>>> > >>>> On Sun, May 5, 2013 at 6:52 PM, Edward J. Yoon <[email protected] > >wrote: > >>>> > >>>>>>> Please let me know before this is changed, I would like to work on > a > >>>>>>> separate branch. > >>>>> > >>>>> I personally, we have to focus on high priority tasks. and more > >>>>> feedbacks and contributions from users. So, if changes made, I'll > >>>>> release periodically. If you want to work on another place, please > do. > >>>>> I don't want to wait your patches. > >>>>> > >>>>> > >>>>> On Mon, May 6, 2013 at 7:49 AM, Edward J. Yoon < > [email protected]> > >>>>> wrote: > >>>>>> For preparing integration with NoSQLs, of course, maybe condition > >>>>>> check (whether converted or not) can be used without removing record > >>>>>> converter. > >>>>>> > >>>>>> We need to discuss everything. > >>>>>> > >>>>>> On Mon, May 6, 2013 at 7:11 AM, Suraj Menon <[email protected] > > > >>>>> wrote: > >>>>>>> I am still -1 if this means our graph module can work only on > sequential > >>>>>>> file format. > >>>>>>> Please note that you can set record converter to null and make > changes > >>>>> to > >>>>>>> loadVertices for what you desire here. > >>>>>>> > >>>>>>> If we came to this design, because TextInputFormat is inefficient, > would > >>>>>>> this work for Avro or Thrift input format? > >>>>>>> Please let me know before this is changed, I would like to work on > a > >>>>>>> separate branch. > >>>>>>> You may proceed as you wish. > >>>>>>> > >>>>>>> Regards, > >>>>>>> Suraj > >>>>>>> > >>>>>>> > >>>>>>> On Sun, May 5, 2013 at 4:09 PM, Edward J. Yoon < > [email protected] > >>>>>> wrote: > >>>>>>> > >>>>>>>> I think 'record converter' should be removed. It's not good idea. > >>>>>>>> Moreover, it's unnecessarily complex. To keep vertex input > reader, we > >>>>>>>> can move related classes into common module. > >>>>>>>> > >>>>>>>> Let's go with my original plan. > >>>>>>>> > >>>>>>>> On Sat, May 4, 2013 at 9:32 AM, Edward J. Yoon < > [email protected]> > >>>>>>>> wrote: > >>>>>>>>> Hi all, > >>>>>>>>> > >>>>>>>>> I'm reading our old discussions about record converter, superstep > >>>>>>>>> injection, and common module: > >>>>>>>>> > >>>>>>>>> - http://markmail.org/message/ol32pp2ixfazcxfc > >>>>>>>>> - http://markmail.org/message/xwtmfdrag34g5xc4 > >>>>>>>>> > >>>>>>>>> To clarify goals and objectives: > >>>>>>>>> > >>>>>>>>> 1. A parallel input partition is necessary for obtaining > scalability > >>>>>>>>> and elasticity of a Bulk Synchronous Parallel processing (It's > not a > >>>>>>>>> memory issue, or Disk/Spilling Queue, or HAMA-644. Please don't > >>>>>>>>> shake). > >>>>>>>>> 2. Input partitioning should be handled at BSP framework level, > and > >>>>> it > >>>>>>>>> is for every Hama jobs, not only for Graph jobs. > >>>>>>>>> 3. Unnecessary I/O Overhead need to be avoided, and NoSQLs input > also > >>>>>>>>> should be considered. > >>>>>>>>> > >>>>>>>>> The current problem is that every input of graph jobs should be > >>>>>>>>> rewritten on HDFS. If you have a good idea, Please let me know. > >>>>>>>>> > >>>>>>>>> -- > >>>>>>>>> Best Regards, Edward J. Yoon > >>>>>>>>> @eddieyoon > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> -- > >>>>>>>> Best Regards, Edward J. Yoon > >>>>>>>> @eddieyoon > >>>>>> > >>>>>> > >>>>>> > >>>>>> -- > >>>>>> Best Regards, Edward J. Yoon > >>>>>> @eddieyoon > >>>>> > >>>>> > >>>>> > >>>>> -- > >>>>> Best Regards, Edward J. Yoon > >>>>> @eddieyoon > >>>>> > >> > >> > >> > >> -- > >> Best Regards, Edward J. Yoon > >> @eddieyoon > > > > > > > > -- > > Best Regards, Edward J. Yoon > > @eddieyoon > > > > -- > Best Regards, Edward J. Yoon > @eddieyoon >
