You shouldn't have to persist the RDD at all, just call flatMap and reduce on it directly. If you try to persist it, that will try to load the original dat into memory, but here you are only scanning through it once.
Matei > On Jun 2, 2015, at 2:09 AM, Octavian Ganea <octavian.ga...@inf.ethz.ch> wrote: > > Thanks, > > I was actually using reduceByKey, not groupByKey. > > I also tried this: rdd.persist(MEMORY_AND_DISK).flatMap(...).reduceByKey . > However, I got the same error as before, namely the error described here: > http://apache-spark-user-list.1001560.n3.nabble.com/flatMap-output-on-disk-flatMap-memory-overhead-td23098.html > > <http://apache-spark-user-list.1001560.n3.nabble.com/flatMap-output-on-disk-flatMap-memory-overhead-td23098.html> > > My task is to count the frequencies of pairs of words that occur in a set of > documents at least 5 times. I know that this final output is sparse and > should comfortably fit in memory. However, the intermediate pairs that are > spilled by flatMap might need to be stored on the disk, but I don't > understand why the persist option does not work and my job fails. > > My code: > > rdd.persist(StorageLevel.MEMORY_AND_DISK) > .flatMap(x => outputPairsOfWords(x)) // outputs pairs of type > ((word1,word2) , 1) > .reduceByKey((a,b) => (a + b).toShort) > .filter({case((x,y),count) => count >= 5}) > > > My cluster has 8 nodes, each with 129 GB of RAM and 16 cores per node. One > node I keep for the master, 7 nodes for the workers. > > my conf: > > conf.set("spark.cores.max", "128") > conf.set("spark.akka.frameSize", "1024") > conf.set("spark.executor.memory", "115g") > conf.set("spark.shuffle.file.buffer.kb", "1000") > > my spark-env.sh: > ulimit -n 200000 > SPARK_JAVA_OPTS="-Xss1g -Xmx129g -d64 -XX:-UseGCOverheadLimit > -XX:-UseCompressedOops" > SPARK_DRIVER_MEMORY=129G > > spark version: 1.1.1 > > Thank you a lot for your help! > > > 2015-06-02 4:40 GMT+02:00 Matei Zaharia <matei.zaha...@gmail.com > <mailto:matei.zaha...@gmail.com>>: > As long as you don't use cache(), these operations will go from disk to disk, > and will only use a fixed amount of memory to build some intermediate > results. However, note that because you're using groupByKey, that needs the > values for each key to all fit in memory at once. In this case, if you're > going to reduce right after, you should use reduceByKey, which will be more > efficient. > > Matei > > > On Jun 1, 2015, at 2:21 PM, octavian.ganea <octavian.ga...@inf.ethz.ch > > <mailto:octavian.ga...@inf.ethz.ch>> wrote: > > > > Dear all, > > > > Does anyone know how can I force Spark to use only the disk when doing a > > simple flatMap(..).groupByKey.reduce(_ + _) ? Thank you! > > > > > > > > -- > > View this message in context: > > http://apache-spark-user-list.1001560.n3.nabble.com/map-reduce-only-with-disk-tp23102.html > > > > <http://apache-spark-user-list.1001560.n3.nabble.com/map-reduce-only-with-disk-tp23102.html> > > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > > <mailto:user-unsubscr...@spark.apache.org> > > For additional commands, e-mail: user-h...@spark.apache.org > > <mailto:user-h...@spark.apache.org> > > > > > > > -- > Octavian Ganea > > Research assistant at ETH Zurich > octavian.ga...@inf.ethz.ch <mailto:octavian.ga...@inf.ethz.ch> > http://da.inf.ethz.ch/people/OctavianGanea/ > <http://da.inf.ethz.ch/people/OctavianGanea/>
