spark - java heap space issue - ExecutorLostFailure - container exited with status 143 - scala

Question

I am reading the string which is of length more than 100k bytes and splitting the columns based on width. I have close to 16K columns which I split from above string based on width.
but while writing into parquet i am using below code
rdd1=spark.sparkContext.textfile("file1")
{ var now=0
{ val collector= new array[String] (ColLenghth.length)
val recordlength=line.length
for (k<- 0 to colLength.length -1)
{ collector(k) = line.substring(now,now+colLength(k))
now =now+colLength(k)
}
collector.toSeq}
StringArray=rdd1.map(SubstrSting(_,ColLengthSeq))
#here ColLengthSeq is read from another schema file which is column lengths
StringArray.toDF("StringCol").select(0 until ColCount).map(j=>$"StringCol"(j) as column_seq(j):_*).write.mode("overwrite").parquet("c"\home\")
here ColCount = 16000 and column_seq is seq(string) with 16K column names.
I am running this on Yarn with 16GB executor memory and 20 executors.
File size is 4GB.
I am getting the error as
Lost task 113.0 in stage 0.0 (TID 461, gsta32512.foo.com): ExecutorLostFailure (executor 28 exited caused by one of the running tasks) Reason:
Container marked as failed:
container_e05_1472185459203_255575_01_000183 on host: gsta32512.foo.com. Exit status: 143. Diagnostics:
Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Killed by external signal
when i checked the status on UI its showing
#java.lang.outofmemoryerror java heap space
#java.lang.outofmemoryerror gc overhead limit exceeded
Please guide on performance tuning of above mentioned code and spark submit parameter optimization