diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 51d9e2e967990..b775e40367601 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -440,6 +440,29 @@ private[spark] object PythonRDD extends Logging {
     Array(port, secret)
   }
 
+  /**
+   * Create a socket server object and background thread to execute the writeFunc
+   * with the given OutputStream.
+   *
+   * This is the same as serveToStream, only it returns a server object that
+   * can be used to sync in Python.
+   */
+  private[spark] def serveToStreamWithSync(
+      threadName: String)(writeFunc: OutputStream => Unit): Array[Any] = {
+
+    val handleFunc = (sock: Socket) => {
+      val out = new BufferedOutputStream(sock.getOutputStream())
+      Utils.tryWithSafeFinally {
+        writeFunc(out)
+      } {
+        out.close()
+      }
+    }
+
+    val server = new SocketFuncServer(authHelper, threadName, handleFunc)
+    Array(server.port, server.secret, server)
+  }
+
   private def getMergedConf(confAsMap: java.util.HashMap[String, String],
       baseConf: Configuration): Configuration = {
     val conf = PythonHadoopUtil.mapToConf(confAsMap)
@@ -957,3 +980,17 @@ private[spark] class PythonParallelizeServer(sc: SparkContext, parallelism: Int)
   }
 }
 
+/**
+ * Create a socket server class and run user function on the socket in a background thread.
+ * This is the same as calling SocketAuthServer.setupOneConnectionServer except it creates
+ * a server object that can then be synced from Python.
+ */
+private [spark] class SocketFuncServer(
+    authHelper: SocketAuthHelper,
+    threadName: String,
+    func: Socket => Unit) extends PythonServer[Unit](authHelper, threadName) {
+
+  override def handleConnection(sock: Socket): Unit = {
+    func(sock)
+  }
+}
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 45b90b5bd97a3..4c4dd278dfa24 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -2175,9 +2175,13 @@ def _collectAsArrow(self):
 
         .. note:: Experimental.
         """
-        with SCCallSiteSync(self._sc) as css:
-            sock_info = self._jdf.collectAsArrowToPython()
-        return list(_load_from_socket(sock_info, ArrowStreamSerializer()))
+        with SCCallSiteSync(self._sc):
+            from pyspark.rdd import _load_from_socket
+            port, auth_secret, jsocket_auth_server = self._jdf.collectAsArrowToPython()
+            try:
+                return list(_load_from_socket((port, auth_secret), ArrowStreamSerializer()))
+            finally:
+                jsocket_auth_server.getResult()  # Join serving thread and raise any exceptions
 
     ##########################################################################################
     # Pandas compatibility
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index f65fe885ef7d7..949ce3b824011 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -80,7 +80,7 @@
 _have_pyarrow = _pyarrow_requirement_message is None
 _test_compiled = _test_not_compiled_message is None
 
-from pyspark import SparkContext
+from pyspark import SparkContext, SparkConf
 from pyspark.sql import SparkSession, SQLContext, HiveContext, Column, Row
 from pyspark.sql.types import *
 from pyspark.sql.types import UserDefinedType, _infer_type, _make_type_verifier
@@ -4550,6 +4550,32 @@ def test_timestamp_dst(self):
         self.assertPandasEqual(pdf, df_from_pandas.toPandas())
 
 
+@unittest.skipIf(
+    not _have_pandas or not _have_pyarrow,
+    _pandas_requirement_message or _pyarrow_requirement_message)
+class MaxResultArrowTests(unittest.TestCase):
+    # These tests are separate as 'spark.driver.maxResultSize' configuration
+    # is a static configuration to Spark context.
+
+    @classmethod
+    def setUpClass(cls):
+        cls.spark = SparkSession(SparkContext(
+            'local[4]', cls.__name__, conf=SparkConf().set("spark.driver.maxResultSize", "10k")))
+
+        # Explicitly enable Arrow and disable fallback.
+        cls.spark.conf.set("spark.sql.execution.arrow.enabled", "true")
+        cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled", "false")
+
+    @classmethod
+    def tearDownClass(cls):
+        if hasattr(cls, "spark"):
+            cls.spark.stop()
+
+    def test_exception_by_max_results(self):
+        with self.assertRaisesRegexp(Exception, "is bigger than"):
+            self.spark.range(0, 10000, 1, 100).toPandas()
+
+
 class EncryptionArrowTests(ArrowTests):
 
     @classmethod
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
index be0617fdacb21..c90b2e857e664 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
@@ -3284,7 +3284,7 @@ class Dataset[T] private[sql](
     val timeZoneId = sparkSession.sessionState.conf.sessionLocalTimeZone
 
     withAction("collectAsArrowToPython", queryExecution) { plan =>
-      PythonRDD.serveToStream("serve-Arrow") { out =>
+      PythonRDD.serveToStreamWithSync("serve-Arrow") { out =>
         val batchWriter = new ArrowBatchStreamWriter(schema, out, timeZoneId)
         val arrowBatchRdd = toArrowBatchRdd(plan)
         val numPartitions = arrowBatchRdd.partitions.length