diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 0e2ae19ca39a..2de2c2fd1a60 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -2029,12 +2029,12 @@ def coalesce(self, numPartitions, shuffle=False):
         [[1, 2, 3, 4, 5]]
         """
         if shuffle:
-            # In Scala's repartition code, we will distribute elements evenly across output
-            # partitions. However, the RDD from Python is serialized as a single binary data,
-            # so the distribution fails and produces highly skewed partitions. We need to
-            # convert it to a RDD of java object before repartitioning.
-            data_java_rdd = self._to_java_object_rdd().coalesce(numPartitions, shuffle)
-            jrdd = self.ctx._jvm.SerDeUtil.javaToPython(data_java_rdd)
+            # Decrease the batch size in order to distribute evenly the elements across output
+            # partitions. Otherwise, repartition will possibly produce highly skewed partitions.
+            batchSize = min(10, self.ctx._batchSize or 1024)
+            ser = BatchedSerializer(PickleSerializer(), batchSize)
+            selfCopy = self._reserialize(ser)
+            jrdd = selfCopy._jrdd.coalesce(numPartitions, shuffle)
         else:
             jrdd = self._jrdd.coalesce(numPartitions, shuffle)
         return RDD(jrdd, self.ctx, self._jrdd_deserializer)