Azure-Databricks autoloader Binaryfile option with foreach() gives java.lang.OutOfMemoryError: Java heap space

I am trying to do copying file from one location to another location using BinaryFile option and foreach(copy) in autoloader. It runs well with smaller files(upto 150 MB) but fails with bigger files throws below exception :

22/09/07 10:25:51 INFO FileScanRDD: Reading File path: dbfs:/mnt/somefile.csv, range: 0-1652464461, partition values: [empty row], modificationTime: 1662542176000. 22/09/07 10:25:52 ERROR Utils: Uncaught exception in thread stdout writer for /databricks/python/bin/python java.lang.OutOfMemoryError: Java heap space at org.apache.spark.sql.catalyst.expressions.UnsafeRow.getBinary(UnsafeRow.java:416) at org.apache.spark.sql.catalyst.expressions.SpecializedGettersReader.read(SpecializedGettersReader.java:75) at org.apache.spark.sql.catalyst.expressions.UnsafeRow.get(UnsafeRow.java:333) at org.apache.spark.sql.execution.python.EvaluatePython$.toJava(EvaluatePython.scala:58) at org.apache.spark.sql.execution.python.PythonForeachWriter.$anonfun$inputByteIterator$1(PythonForeachWriter.scala:43) at org.apache.spark.sql.execution.python.PythonForeachWriter$$Lambda$1830/1643360976.apply(Unknown Source) at scala.collection.Iterator$$anon$10.next(Iterator.scala:461) at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:92) at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:82) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:82) at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:442) at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:871) at org.apache.spark.api.python.BasePythonRunner$WriterThread.$anonfun$run$1(PythonRunner.scala:573) at org.apache.spark.api.python.BasePythonRunner$WriterThread$$Lambda$2008/2134044540.apply(Unknown Source) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2275) at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:365) 22/09/07 10:25:52 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[stdout writer for /databricks/python/bin/python,5,main] java.lang.OutOfMemoryError: Java heap space at org.apache.spark.sql.catalyst.expressions.UnsafeRow.getBinary(UnsafeRow.java:416) at org.apache.spark.sql.catalyst.expressions.SpecializedGettersReader.read(SpecializedGettersReader.java:75) at org.apache.spark.sql.catalyst.expressions.UnsafeRow.get(UnsafeRow.java:333) at org.apache.spark.sql.execution.python.EvaluatePython$.toJava(EvaluatePython.scala:58) at org.apache.spark.sql.execution.python.PythonForeachWriter.$anonfun$inputByteIterator$1(PythonForeachWriter.scala:43) at org.apache.spark.sql.execution.python.PythonForeachWriter$$Lambda$1830/1643360976.apply(Unknown Source) at scala.collection.Iterator$$anon$10.next(Iterator.scala:461) at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:92) at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:82) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:82) at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:442) at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:871) at org.apache.spark.api.python.BasePythonRunner$WriterThread.$anonfun$run$1(PythonRunner.scala:573) at org.apache.spark.api.python.BasePythonRunner$WriterThread$$Lambda$2008/2134044540.apply(Unknown Source) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2275) at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:365)

Below is the high-level code snippet for reference:

Cluster size is 2 workers and 1 driver with 14 Gb ram and 4 cores each

cloudfile_options = {
    "cloudFiles.subscriptionId":subscription_ID,
    "cloudFiles.connectionString": queue_SAS_connection_string,
    "cloudFiles.format": "BinaryFile", 
    "cloudFiles.tenantId":tenant_ID,
    "cloudFiles.clientId":client_ID,
    "cloudFiles.clientSecret":client_secret,
    "cloudFiles.useNotifications" :"true"
}

def copy(row):
    source = row['path']
    destination = "somewhere"
    shutil.copy(source,destination)

spark.readStream.format("cloudFiles")
                        .options(**cloudfile_options)
                        .load(storage_input_path)              
                        .writeStream
                        .foreach(copy)
                        .option("checkpointLocation", checkpoint_location)
                        .trigger(once=True)
                        .start()

I also tested shutil.copy with huge file sizes (20GB) outside foreach() and it works seemlessly.

Any leads on this would be much appreciated