Hello,
the following statement :
df.write.mode("overwrite").saveAsTable("dev_mgd.ema_elklogs_pp");causes:
2019-07-17 15:08:57 WARN Utils:66 - Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.debug.maxToStringFields' in SparkEnv.conf.
Exception in thread "dispatcher-event-loop-68" java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118)
at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
at org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
at org.apache.spark.scheduler.TaskSetManager$$anonfun$resourceOffer$1.apply(TaskSetManager.scala:486)
at org.apache.spark.scheduler.TaskSetManager$$anonfun$resourceOffer$1.apply(TaskSetManager.scala:467)
at scala.Option.map(Option.scala:146)
at org.apache.spark.scheduler.TaskSetManager.resourceOffer(TaskSetManager.scala:467)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$org$apache$spark$scheduler$TaskSchedulerImpl$$resourceOfferSingleTaskSet$1.apply$mcVI$sp(TaskSchedulerImpl.scala:315)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)
at org.apache.spark.scheduler.TaskSchedulerImpl.org$apache$spark$scheduler$TaskSchedulerImpl$$resourceOfferSingleTaskSet(TaskSchedulerImpl.scala:310)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$4$$anonfun$apply$11.apply(TaskSchedulerImpl.scala:412)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$4$$anonfun$apply$11.apply(TaskSchedulerImpl.scala:409)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$4.apply(TaskSchedulerImpl.scala:409)
at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$4.apply(TaskSchedulerImpl.scala:396)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:396)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.org$apache$spark$scheduler$cluster$CoarseGrainedSchedulerBackend$DriverEndpoint$$makeOffers(CoarseGrainedSchedulerBackend.scala:248)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anonfun$receive$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:136)
at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:117)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:205)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:101)tried to increase memory for spark driver and executors but no luck.
Any suggestion will be highly appreciated :-)
Regards
