Hello All,
Our team is having a lot of issues with the Spark API particularly with large
schema tables. We currently have a program written in Scala that utilizes the
Apache spark API to create two tables from raw files. We have one particularly
very large raw data file that contains around ~4700 columns and ~200,000 rows.
Every week we get a new file that shows the updates, inserts and deletes that
happened in the last week. Our program will create two files – a master file
and a history file. The master file will be the most up to date version of this
table while the history table shows all changes inserts and updates that
happened to this table and showing what changed. For example, if we have the
following schema where A and B are unique:
Week 1
Week 2
A B C
A B C
1 2 3
1 2 4
Then the master table will now be
A B C
1 2 4
and History table will be
A B change_column change_type old_value
new_value
1 2 C Update
3 4
This process is working flawlessly for shorter schema tables. We have a table
that has 300 columns but over 100,000,000 rows and this code still runs. The
process above for the larger schema table runs for around 15 hours, and then
crashes with the following error:
Exception in thread "main" java.lang.StackOverflowError
at scala.collection.generic.Growable$class.loop$1(Growable.scala:52)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:57)
at
scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:183)
at
scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:45)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:381)
at
scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.immutable.List.flatMap(List.scala:344)
…
Some other notes… This is running on a very large MAPR cluster. We have tried
running the job with upwards of ½ a TB of RAM and this still happens. All of
our other smaller schema tables run except for this one.
Here is a code example that takes around 4 hours to run for this larger table,
but runs in 20 seconds for other tables:
var dataframe_result = dataframe1.join(broadcast(dataframe2),
Seq(listOfUniqueIds:_*)).repartition(100).cache()
We have tried all of the following with no success:
* Using hash broad-cast joins (dataframe2 is smaller, dataframe1 is huge)
* Repartioining on different numbers, as well as not repartitioning at all
* Caching the result of the dataframe (we originally did not do this).
What is causing this error and how do we go about fixing it? This code just
takes in 1 parameter (the table to run) so it’s the exact same code for every
table. It runs flawlessly for every other table except for this one. The only
thing different between this table and all the other ones is the number of
columns. This has the most columns at 4700 where the second most is 800.
If anyone has any ideas on how to fix this it would be greatly appreciated.
Thank you in advance for the help!!
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