I think it's saying a string isn't being sent properly from the JVM side. Does it work for you if you change the dapply UDF to something simpler?
Do you have any log from YARN? _____________________________ From: Shane Lee <shane_y_...@yahoo.com.invalid<mailto:shane_y_...@yahoo.com.invalid>> Sent: Tuesday, August 9, 2016 12:19 AM Subject: Re: SparkR error when repartition is called To: Sun Rui <sunrise_...@163.com<mailto:sunrise_...@163.com>> Cc: User <user@spark.apache.org<mailto:user@spark.apache.org>> Sun, I am using spark in yarn client mode in a 2-node cluster with hadoop-2.7.2. My R version is 3.3.1. I have the following in my spark-defaults.conf: spark.executor.extraJavaOptions =-XX:+PrintGCDetails -XX:+HeapDumpOnOutOfMemoryError spark.r.command=c:/R/R-3.3.1/bin/x64/Rscript spark.ui.killEnabled=true spark.executor.instances = 3 spark.serializer = org.apache.spark.serializer.KryoSerializer spark.shuffle.file.buffer = 1m spark.driver.maxResultSize=0 spark.executor.memory=8g spark.executor.cores = 6 I also ran into some other R errors that I was able to bypass by modifying the worker.R file (attached). In a nutshell I was getting the "argument is length of zero" error sporadically so I put in extra checks for it. Thanks, Shane On Monday, August 8, 2016 11:53 PM, Sun Rui <sunrise_...@163.com<mailto:sunrise_...@163.com>> wrote: I can't reproduce your issue with len=10000 in local mode. Could you give more environment information? On Aug 9, 2016, at 11:35, Shane Lee <shane_y_...@yahoo.com.INVALID<mailto:shane_y_...@yahoo.com.invalid>> wrote: Hi All, I am trying out SparkR 2.0 and have run into an issue with repartition. Here is the R code (essentially a port of the pi-calculating scala example in the spark package) that can reproduce the behavior: schema <- structType(structField("input", "integer"), structField("output", "integer")) library(magrittr) len = 3000 data.frame(n = 1:len) %>% as.DataFrame %>% SparkR:::repartition(10L) %>% dapply(., function (df) { library(plyr) ddply(df, .(n), function (y) { data.frame(z = { x1 = runif(1) * 2 - 1 y1 = runif(1) * 2 - 1 z = x1 * x1 + y1 * y1 if (z < 1) { 1L } else { 0L } }) }) } , schema ) %>% SparkR:::summarize(total = sum(.$output)) %>% collect * 4 / len For me it runs fine as long as len is less than 5000, otherwise it errors out with the following message: Error in invokeJava(isStatic = TRUE, className, methodName, ...) : org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage 56.0 failed 4 times, most recent failure: Lost task 6.3 in stage 56.0 (TID 899, LARBGDV-VM02): org.apache.spark.SparkException: R computation failed with Error in readBin(con, raw(), stringLen, endian = "big") : invalid 'n' argument Calls: <Anonymous> -> readBin Execution halted at org.apache.spark.api.r.RRunner.compute(RRunner.scala:108) at org.apache.spark.sql.execution.r.MapPartitionsRWrapper.apply(MapPartitionsRWrapper.scala:59) at org.apache.spark.sql.execution.r.MapPartitionsRWrapper.apply(MapPartitionsRWrapper.scala:29) at org.apache.spark.sql.execution.MapPartitionsExec$$anonfun$6.apply(objects.scala:178) at org.apache.spark.sql.execution.MapPartitionsExec$$anonfun$6.apply(objects.scala:175) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$ If the repartition call is removed, it runs fine again, even with very large len. After looking through the documentations and searching the web, I can't seem to find any clues how to fix this. Anybody has seen similary problem? Thanks in advance for your help. Shane
