diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapper.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapper.scala
index 9a0bdc6bcfd1..90e3bdcd082c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapper.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapper.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, Murmur3HashFunctio
 import org.apache.spark.sql.catalyst.plans.physical.KeyGroupedPartitioning
 import org.apache.spark.sql.connector.read.{HasPartitionKey, InputPartition}
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.util.NonFateSharingCache
 
 /**
  * Wraps the [[InternalRow]] with the corresponding [[DataType]] to make it comparable with
@@ -34,9 +35,10 @@ import org.apache.spark.sql.types.{DataType, StructField, StructType}
  * @param dataTypes the data types for the row
  */
 class InternalRowComparableWrapper(val row: InternalRow, val dataTypes: Seq[DataType]) {
+  import InternalRowComparableWrapper._
 
-  private val structType = StructType(dataTypes.map(t => StructField("f", t)))
-  private val ordering = RowOrdering.createNaturalAscendingOrdering(dataTypes)
+  private val structType = structTypeCache.get(dataTypes)
+  private val ordering = orderingCache.get(dataTypes)
 
   override def hashCode(): Int = Murmur3HashFunction.hash(row, structType, 42L).toInt
 
@@ -53,6 +55,21 @@ class InternalRowComparableWrapper(val row: InternalRow, val dataTypes: Seq[Data
 }
 
 object InternalRowComparableWrapper {
+  private final val MAX_CACHE_ENTRIES = 1024
+
+  private val orderingCache = {
+    val loadFunc = (dataTypes: Seq[DataType]) => {
+      RowOrdering.createNaturalAscendingOrdering(dataTypes)
+    }
+    NonFateSharingCache(loadFunc, MAX_CACHE_ENTRIES)
+  }
+
+  private val structTypeCache = {
+    val loadFunc = (dataTypes: Seq[DataType]) => {
+      StructType(dataTypes.map(t => StructField("f", t)))
+    }
+    NonFateSharingCache(loadFunc, MAX_CACHE_ENTRIES)
+  }
 
   def apply(
       partition: InputPartition with HasPartitionKey,
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapperBenchmark.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapperBenchmark.scala
new file mode 100644
index 000000000000..cc28e8552516
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/InternalRowComparableWrapperBenchmark.scala
@@ -0,0 +1,74 @@
+/*
+ * 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.catalyst.util
+
+import org.apache.spark.benchmark.{Benchmark, BenchmarkBase}
+import org.apache.spark.sql.catalyst.expressions.Literal
+import org.apache.spark.sql.catalyst.plans.physical.KeyGroupedPartitioning
+import org.apache.spark.sql.connector.catalog.PartitionInternalRow
+import org.apache.spark.sql.types.IntegerType
+
+/**
+ * Benchmark for [[InternalRowComparableWrapper]].
+ * To run this benchmark:
+ * {{{
+ *   1. without sbt:
+ *      bin/spark-submit --class <this class> --jars <spark core test jar> <spark catalyst test jar>
+ *   2. build/sbt "catalyst/Test/runMain <this class>"
+ *   3. generate result:
+ *      SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "catalyst/Test/runMain <this class>"
+ *      Results will be written to "benchmarks/InternalRowComparableWrapperBenchmark-results.txt".
+ * }}}
+ */
+object InternalRowComparableWrapperBenchmark extends BenchmarkBase {
+
+  private def constructAndRunBenchmark(): Unit = {
+    val partitionNum = 200_000
+    val bucketNum = 4096
+    val day = 20240401
+    val partitions = (0 until partitionNum).map { i =>
+      val bucketId = i % bucketNum
+      PartitionInternalRow.apply(Array(day, bucketId));
+    }
+    val benchmark = new Benchmark("internal row comparable wrapper", partitionNum, output = output)
+
+    benchmark.addCase("toSet") { _ =>
+      val distinct = partitions
+        .map(new InternalRowComparableWrapper(_, Seq(IntegerType, IntegerType)))
+        .toSet
+      assert(distinct.size == bucketNum)
+    }
+
+    benchmark.addCase("mergePartitions") { _ =>
+      // just to mock the data types
+      val expressions = (Seq(Literal(day, IntegerType), Literal(0, IntegerType)))
+
+      val leftPartitioning = KeyGroupedPartitioning(expressions, bucketNum, partitions)
+      val rightPartitioning = KeyGroupedPartitioning(expressions, bucketNum, partitions)
+      val merged = InternalRowComparableWrapper.mergePartitions(
+        leftPartitioning, rightPartitioning, expressions)
+      assert(merged.size == bucketNum)
+    }
+
+    benchmark.run()
+  }
+
+  override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
+    constructAndRunBenchmark()
+  }
+}