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From Felix Cheung <felixcheun...@hotmail.com>
Subject Re: SparkR error when repartition is called
Date Tue, 09 Aug 2016 09:15:26 GMT
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_lee@yahoo.com.invalid<mailto:shane_y_lee@yahoo.com.invalid>>
Sent: Tuesday, August 9, 2016 12:19 AM
Subject: Re: SparkR error when repartition is called
To: Sun Rui <sunrise_win@163.com<mailto:sunrise_win@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_win@163.com<mailto:sunrise_win@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_lee@yahoo.com.INVALID<mailto:shane_y_lee@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







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