Hi Burak, After I added .repartition(sc.defaultParallelism), I can see from the log the partition number is set to 32. But in the Spark UI, it seems all the data are loaded onto one executor. Previously they were loaded onto 4 executors.
Any idea? Thanks, David On Fri, Mar 27, 2015 at 11:01 AM Xi Shen <davidshe...@gmail.com> wrote: > How do I get the number of cores that I specified at the command line? I > want to use "spark.default.parallelism". I have 4 executors, each has 8 > cores. According to > https://spark.apache.org/docs/1.2.0/configuration.html#execution-behavior, > the "spark.default.parallelism" value will be 4 * 8 = 32...I think it is > too large, or inappropriate. Please give some suggestion. > > I have already used cache, and count to pre-cache. > > I can try with smaller k for testing, but eventually I will have to use k > = 5000 or even large. Because I estimate our data set would have that much > of clusters. > > > Thanks, > David > > > On Fri, Mar 27, 2015 at 10:40 AM Burak Yavuz <brk...@gmail.com> wrote: > >> Hi David, >> The number of centroids (k=5000) seems too large and is probably the >> cause of the code taking too long. >> >> Can you please try the following: >> 1) Repartition data to the number of available cores with >> .repartition(numCores) >> 2) cache data >> 3) call .count() on data right before k-means >> 4) try k=500 (even less if possible) >> >> Thanks, >> Burak >> >> On Mar 26, 2015 4:15 PM, "Xi Shen" <davidshe...@gmail.com> wrote: >> > >> > The code is very simple. >> > >> > val data = sc.textFile("very/large/text/file") map { l => >> > // turn each line into dense vector >> > Vectors.dense(...) >> > } >> > >> > // the resulting data set is about 40k vectors >> > >> > KMeans.train(data, k=5000, maxIterations=500) >> > >> > I just kill my application. In the log I found this: >> > >> > 15/03/26 11:42:43 INFO storage.BlockManagerMaster: Updated info of >> block broadcast_26_piece0 >> > 15/03/26 23:02:57 WARN server.TransportChannelHandler: Exception in >> connection from workernode0.xshe3539-hadoop-sydney.q10.internal.cloudapp. >> net/100.72.84.107:56277 >> > java.io.IOException: An existing connection was forcibly closed by the >> remote host >> > >> > Notice the time gap. I think it means the work node did not generate >> any log at all for about 12hrs...does it mean they are not working at all? >> > >> > But when testing with very small data set, my application works and >> output expected data. >> > >> > >> > Thanks, >> > David >> > >> > >> > On Fri, Mar 27, 2015 at 10:04 AM Burak Yavuz <brk...@gmail.com> wrote: >> >> >> >> Can you share the code snippet of how you call k-means? Do you cache >> the data before k-means? Did you repartition the data? >> >> >> >> On Mar 26, 2015 4:02 PM, "Xi Shen" <davidshe...@gmail.com> wrote: >> >>> >> >>> OH, the job I talked about has ran more than 11 hrs without a >> result...it doesn't make sense. >> >>> >> >>> >> >>> On Fri, Mar 27, 2015 at 9:48 AM Xi Shen <davidshe...@gmail.com> >> wrote: >> >>>> >> >>>> Hi Burak, >> >>>> >> >>>> My iterations is set to 500. But I think it should also stop of the >> centroid coverages, right? >> >>>> >> >>>> My spark is 1.2.0, working in windows 64 bit. My data set is about >> 40k vectors, each vector has about 300 features, all normalised. All work >> node have sufficient memory and disk space. >> >>>> >> >>>> Thanks, >> >>>> David >> >>>> >> >>>> >> >>>> On Fri, 27 Mar 2015 02:48 Burak Yavuz <brk...@gmail.com> wrote: >> >>>>> >> >>>>> Hi David, >> >>>>> >> >>>>> When the number of runs are large and the data is not properly >> partitioned, it seems that K-Means is hanging according to my experience. >> Especially setting the number of runs to something high drastically >> increases the work in executors. If that's not the case, can you give more >> info on what Spark version you are using, your setup, and your dataset? >> >>>>> >> >>>>> Thanks, >> >>>>> Burak >> >>>>> >> >>>>> On Mar 26, 2015 5:10 AM, "Xi Shen" <davidshe...@gmail.com> wrote: >> >>>>>> >> >>>>>> Hi, >> >>>>>> >> >>>>>> When I run k-means cluster with Spark, I got this in the last two >> lines in the log: >> >>>>>> >> >>>>>> 15/03/26 11:42:42 INFO spark.ContextCleaner: Cleaned broadcast 26 >> >>>>>> 15/03/26 11:42:42 INFO spark.ContextCleaner: Cleaned shuffle 5 >> >>>>>> >> >>>>>> >> >>>>>> >> >>>>>> Then it hangs for a long time. There's no active job. The driver >> machine is idle. I cannot access the work node, I am not sure if they are >> busy. >> >>>>>> >> >>>>>> I understand k-means may take a long time to finish. But why no >> active job? no log? >> >>>>>> >> >>>>>> >> >>>>>> Thanks, >> >>>>>> David >> >>>>>> >> >
