I was tried using reduceByKey, without success. 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
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! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/flatMap-output-on-disk-flatMap-memory-overhead-tp23098p23108.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org