Major Description
I am running a SparkSQL GROUP BY query on a largish Parquet table (a few hundred million rows), weighing it at about 50GB. My cluster has 1.7 TB of RAM, so it should have more than plenty resources to cope with this query.
WARN TaskSetManager: Lost task 279.0 in stage 22.0 (TID 1229, ds-model-w-21.c.eastern-gravity-771.internal): java.lang.OutOfMemoryError: GC overhead limit exceeded
at scala.collection.SeqLike$class.distinct(SeqLike.scala:493)
at scala.collection.AbstractSeq.distinct(Seq.scala:40)
at org.apache.spark.sql.catalyst.expressions.Coalesce.resolved$lzycompute(nullFunctions.scala:33)
at org.apache.spark.sql.catalyst.expressions.Coalesce.resolved(nullFunctions.scala:33)
at org.apache.spark.sql.catalyst.expressions.Coalesce.dataType(nullFunctions.scala:37)
at org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:100)
at org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:101)
at org.apache.spark.sql.catalyst.expressions.Coalesce.eval(nullFunctions.scala:50)
at org.apache.spark.sql.catalyst.expressions.MutableLiteral.update(literals.scala:81)
at org.apache.spark.sql.catalyst.expressions.SumFunction.update(aggregates.scala:571)
at org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:167)
at org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:151)
at org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:615)
at org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:615)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:264)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:231)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:264)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:231)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:183)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)