Hi Jian, Are you essentially proposing "work stealing"? That is to say, if a map task is taking longer than others, another map task will be able to grab some of its work from it after the job is already under way?
-Todd On Tue, Feb 9, 2010 at 1:23 AM, jian yi <[email protected]> wrote: > Just like cutting a cake, we will cut it again if the first is not > balanced. > If we can control the size of every task, system will become more simple > and > optimizing is possible. > > 2010/2/9 jian yi <[email protected]> > > > Hi Alan, > > > > In my opinion, MBR solves the skew problem with minimum cost. It is > differ > > with combiner. My English is poor, but I do my best to express myself > > clearly. > > > > In MBR, we can set the size of a task, both map task and reduce task. For > > example, 120~150MB input for a task, the motive is that we can control a > > task's run-time to keep the size of every task is almost equal, which is > the > > precondition that a task can be regarded as a timeslice switched. > > > > By hashing output of map to more splits, we can regroup smaller splits to > a > > new split which size is specified and hash bigger spits to more small > > splits, until the size of all splits is within the specified range. > > > > Balance interface is like map and reduce interface. Balance interface > shoud > > be implemented when a single key has too many values, because the key > will > > be hashed to more than one splits. In the case, we can't get the final > > results in a MBR session, it is necessary to start a next MBR session. > For a > > same key, we can hash it with key+value, the action will be telled to > > balance interface. > > > > Regards > > Jian Yi > > > > 2010/2/9 Alan Gates <[email protected]> > > > > Jian, > >> > >> Sorry if any of my questions or comments would have been answered by the > >> diagrams, but apache lists don't allow attachments, so I can't see your > >> diagrams. > >> > >> If I understand correctly, your suggestion for balancing is to apply > >> reduce on subsets of the hashed data, and then run reduce again on this > >> reduced data set. Is that correct? If so, how does this differ from > the > >> combiner? Second, some aggregation operations truly aren't algebraic > (that > >> is, they cannot be distributed across multiple iterations of reduce). > An > >> example of this is session analysis, where the algorithm truly needs to > see > >> all operations together to analyze the user session. How do you propose > to > >> handle that case? > >> > >> Alan. > >> > >> > >> On Feb 7, 2010, at 11:25 PM, jian yi wrote: > >> > >> Two targets: > >>> 1. Solving the skew problem > >>> 2. Regarding a task as a timeslice to improve on scheduler, switching a > >>> job to another job by timeslice. > >>> > >>> In MR (Map-Reduce) model, reducings are not balanced, because the scale > >>> of partitiones are unbalanced. How to balance? We can control the size > of > >>> partition, rehash the bigger parition and combine to the specified > size. If > >>> a key has many values, it's necessary to execute mapreduce twice.The > >>> following is the model digram: > >>> > >>> Scheduler can regard a task as a timeslice similarly OS scheduler. > >>> If a split is bigger than a specified size, it will be splitted again. > If > >>> a split is smaller than a specified size, it will be combined with > others, > >>> we can name the combining procedure regroup. The combining is logic, > it's > >>> not necessay to combine these smaller splits to a disk file, which will > not > >>> affect the performance.The target is that every task spent same time > >>> running. > >>> > >>> > >> > > >
