MapReduce counters do not count duplications. In MapReduce, if a task needs to be re-run, the value of the counter from the second task overwrites the value from the first task.
-Sandy On Mon, Sep 22, 2014 at 4:55 AM, Nan Zhu <zhunanmcg...@gmail.com> wrote: > If you think it as necessary to fix, I would like to resubmit that PR > (seems to have some conflicts with the current DAGScheduler) > > My suggestion is to make it as an option in accumulator, e.g. some > algorithms utilizing accumulator for result calculation, it needs a > deterministic accumulator, while others implementing something like Hadoop > counters may need the current implementation (count everything happened, > including the duplications) > > Your thoughts? > > -- > Nan Zhu > > On Sunday, September 21, 2014 at 6:35 PM, Matei Zaharia wrote: > > Hmm, good point, this seems to have been broken by refactorings of the > scheduler, but it worked in the past. Basically the solution is simple -- > in a result stage, we should not apply the update for each task ID more > than once -- the same way we don't call job.listener.taskSucceeded more > than once. Your PR also tried to avoid this for resubmitted shuffle stages, > but I don't think we need to do that necessarily (though we could). > > Matei > > On September 21, 2014 at 1:11:13 PM, Nan Zhu (zhunanmcg...@gmail.com) > wrote: > > Hi, Matei, > > Can you give some hint on how the current implementation guarantee the > accumulator is only applied for once? > > There is a pending PR trying to achieving this ( > https://github.com/apache/spark/pull/228/files), but from the current > implementation, I didn’t see this has been done? (maybe I missed something) > > Best, > > -- > Nan Zhu > > On Sunday, September 21, 2014 at 1:10 AM, Matei Zaharia wrote: > > Hey Sandy, > > On September 20, 2014 at 8:50:54 AM, Sandy Ryza (sandy.r...@cloudera.com) > wrote: > > Hey All, > > A couple questions came up about shared variables recently, and I wanted > to > confirm my understanding and update the doc to be a little more clear. > > *Broadcast variables* > Now that tasks data is automatically broadcast, the only occasions where > it > makes sense to explicitly broadcast are: > * You want to use a variable from tasks in multiple stages. > * You want to have the variable stored on the executors in deserialized > form. > * You want tasks to be able to modify the variable and have those > modifications take effect for other tasks running on the same executor > (usually a very bad idea). > > Is that right? > Yeah, pretty much. Reason 1 above is probably the biggest, but 2 also > matters. (We might later factor tasks in a different way to avoid 2, but > it's hard due to things like Hadoop JobConf objects in the tasks). > > > *Accumulators* > Values are only counted for successful tasks. Is that right? KMeans seems > to use it in this way. What happens if a node goes away and successful > tasks need to be resubmitted? Or the stage runs again because a different > job needed it. > Accumulators are guaranteed to give a deterministic result if you only > increment them in actions. For each result stage, the accumulator's update > from each task is only applied once, even if that task runs multiple times. > If you use accumulators in transformations (i.e. in a stage that may be > part of multiple jobs), then you may see multiple updates, from each run. > This is kind of confusing but it was useful for people who wanted to use > these for debugging. > > Matei > > > > > > thanks, > Sandy > > > >
