diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveJoinOrientation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveJoinOrientation.scala
new file mode 100644
index 0000000000000..8131f6d58f504
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveJoinOrientation.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.adaptive
+
+import org.apache.spark.sql.catalyst.plans.InnerLike
+import org.apache.spark.sql.catalyst.plans.logical.{Join, LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.internal.SQLConf
+
+/**
+ * This optimization rule detects if the probe side of the SortMerge join is smaller
+ * than build side. While joining, probe side is streamed and build side is buffered,
+ * so having a larger build side can cause memory issues.
+ */
+case class AdaptiveJoinOrientation(conf: SQLConf) extends Rule[LogicalPlan] {
+
+  private def isMaterializedShuffleStage(plan: LogicalPlan): Boolean = plan match {
+    case LogicalQueryStage(_, shuffleExec: ShuffleQueryStageExec)
+      if shuffleExec.resultOption.get.isDefined => true
+    case _ =>
+      false
+  }
+
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case j @ Join(left, right, _: InnerLike, _, _)
+      if Seq(left, right).forall(plan => isMaterializedShuffleStage(plan) &&
+        plan.stats.sizeInBytes > conf.autoBroadcastJoinThreshold) &&
+          left.stats.sizeInBytes < right.stats.sizeInBytes =>
+      Project(j.output, j.copy(left = right, right = left))
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanExec.scala
index bc924e6978ddc..1f7a22705b972 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanExec.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanExec.scala
@@ -79,7 +79,8 @@ case class AdaptiveSparkPlanExec(
   @transient private val optimizer = new RuleExecutor[LogicalPlan] {
     // TODO add more optimization rules
     override protected def batches: Seq[Batch] = Seq(
-      Batch("Demote BroadcastHashJoin", Once, DemoteBroadcastHashJoin(conf))
+      Batch("Demote BroadcastHashJoin", Once, DemoteBroadcastHashJoin(conf)),
+      Batch("Orient SortMergeJoin", Once, AdaptiveJoinOrientation(conf))
     )
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
index c696d3f648ed1..a616882f2b51d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
@@ -25,6 +25,7 @@ import org.apache.log4j.Level
 import org.apache.spark.scheduler.{SparkListener, SparkListenerEvent, SparkListenerJobStart}
 import org.apache.spark.sql.{Dataset, QueryTest, Row, SparkSession, Strategy}
 import org.apache.spark.sql.catalyst.optimizer.{BuildLeft, BuildRight}
+import org.apache.spark.sql.catalyst.plans.InnerLike
 import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan}
 import org.apache.spark.sql.execution.{PartialReducerPartitionSpec, ReusedSubqueryExec, ShuffledRowRDD, SparkPlan}
 import org.apache.spark.sql.execution.command.DataWritingCommandExec
@@ -1147,4 +1148,62 @@ class AdaptiveQueryExecSuite
       }
     }
   }
+
+  test("SPARK-SMJ_ORIENTATION: reorient SMJ using adaptive stats") {
+    withTable("testTbl1", "testTbl2") {
+      withSQLConf(
+        SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "true",
+        SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") {
+
+        def getProbeBuildSides(plan: SparkPlan): Option[(SparkPlan, SparkPlan)] = {
+          plan collectFirst {
+            case SortMergeJoinExec(_, _, _: InnerLike, _, left, right, _) =>
+              (left, right)
+          }
+        }
+
+        def validateOrientation(initialPlan: SparkPlan, updatedPlan: SparkPlan,
+                                orderShouldBeChanged: Boolean): Unit = {
+          val (initialProbePlan, initialBuildPlan) = getProbeBuildSides(initialPlan).get
+          val (updatedProbePlan, updatedBuildPlan) = getProbeBuildSides(updatedPlan).get
+          if (orderShouldBeChanged) {
+            assert(updatedBuildPlan.output == initialProbePlan.output)
+            assert(updatedProbePlan.output == initialBuildPlan.output)
+          } else {
+            assert(updatedBuildPlan.output == initialBuildPlan.output)
+            assert(updatedProbePlan.output == initialProbePlan.output)
+          }
+        }
+
+        val df1 = (0 until 10).toDF("col1").as("df1")
+        df1.write.format("parquet").saveAsTable("testTbl1")
+
+        val df2 = (0 until 100).toDF("col1").as("df2")
+        df2.write.format("parquet").saveAsTable("testTbl2")
+
+        val dfWrongOrder =
+          spark.sql("SELECT * from testTbl1 JOIN testTbl2 ON testTbl1.col1 = testTbl2.col1")
+        val dfCorrectOrder =
+          spark.sql("SELECT * from testTbl2 JOIN testTbl1 ON testTbl1.col1 = testTbl2.col1")
+
+        val initialAdaptivePlan1 = dfWrongOrder.queryExecution.executedPlan
+        val initialPlan1 = initialAdaptivePlan1.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
+        dfWrongOrder.collect
+        val adaptivePlan1 = dfWrongOrder.queryExecution.executedPlan
+        val updatedPlan1 = adaptivePlan1.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
+        // Join orientation should have changed
+        validateOrientation(initialPlan1, updatedPlan1, true)
+        // Project should correct the result order
+        assert(initialPlan1.output == adaptivePlan1.output)
+
+        val initialAdaptivePlan2 = dfCorrectOrder.queryExecution.executedPlan
+        val initialPlan2 = initialAdaptivePlan2.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
+        dfCorrectOrder.collect
+        val adaptivePlan2 = dfCorrectOrder.queryExecution.executedPlan
+        val updatedPlan2 = adaptivePlan2.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
+        // Join orientation should not have changed
+        validateOrientation(initialPlan2, updatedPlan2, false)
+      }
+    }
+  }
 }