scala - How to catch an exception that occurred on a spark worker? - 


val htf = new hashingtf(50000) val tf = case.map(row=>     htf.transform(row) ).cache() val idf = new idf().fit(tf)  try {   idf.transform(tf).map(x=>labeledpoint(1,x)) } catch {   case ex:throwable=>     println(ex.getmessage) }

code isn't working.

hashingtf/idf belongs org.spark.mllib.feature.

i'm still getting exception says

org.apache.spark.sparkexception: failed broadcast_5_piece0 of broadcast_5

i cannot see of files in error log, how debug this?

it seems worker ran out of memory.

instant temporary fix:

run application without caching.

just remove .cache()

how debug:

probably spark ui might have complete exception details.

  • check stage details

  • check logs , thread dump in executor tab

if find multiple exceptions or errors try resolve in sequence.

most of times resolving 1st error resolve subsequent errors.


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