diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
index ae65f29c32264..80a7d4efe4e52 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -1787,6 +1787,14 @@ object SQLConf {
     .booleanConf
     .createWithDefault(false)
 
+  val LEGACY_BUCKETED_TABLE_SCAN_OUTPUT_ORDERING =
+    buildConf("spark.sql.legacy.bucketedTableScan.outputOrdering")
+      .internal()
+      .doc("When true, the bucketed table scan will list files during planning to figure out the " +
+        "output ordering, which is expensive and may make the planning quite slow.")
+    .booleanConf
+    .createWithDefault(false)
+
   val ARITHMETIC_OPERATIONS_FAIL_ON_OVERFLOW =
     buildConf("spark.sql.arithmeticOperations.failOnOverFlow")
       .doc("If it is set to true, all arithmetic operations on non-decimal fields throw an " +
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
index 984f4d3474b03..b76cd9fc07b47 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
@@ -35,6 +35,7 @@ import org.apache.spark.sql.catalyst.util.truncatedString
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat => ParquetSource}
 import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.sources.{BaseRelation, Filter}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.vectorized.ColumnarBatch
@@ -187,6 +188,9 @@ case class FileSourceScanExec(
     val optimizerMetadataTimeNs = relation.location.metadataOpsTimeNs.getOrElse(0L)
     val startTime = System.nanoTime()
     val ret = relation.location.listFiles(partitionFilters, dataFilters)
+    if (relation.partitionSchemaOption.isDefined) {
+      driverMetrics("numPartitions") = ret.length
+    }
     driverMetrics("numFiles") = ret.map(_.files.size.toLong).sum
     val timeTakenMs = NANOSECONDS.toMillis(
       (System.nanoTime() - startTime) + optimizerMetadataTimeNs)
@@ -237,8 +241,12 @@ case class FileSourceScanExec(
           val partitioning = HashPartitioning(bucketColumns, spec.numBuckets)
           val sortColumns =
             spec.sortColumnNames.map(x => toAttribute(x)).takeWhile(x => x.isDefined).map(_.get)
+          val shouldCalculateSortOrder =
+            conf.getConf(SQLConf.LEGACY_BUCKETED_TABLE_SCAN_OUTPUT_ORDERING) &&
+              sortColumns.nonEmpty &&
+              !hasPartitionsAvailableAtRunTime
 
-          val sortOrder = if (sortColumns.nonEmpty && !hasPartitionsAvailableAtRunTime) {
+          val sortOrder = if (shouldCalculateSortOrder) {
             // In case of bucketing, its possible to have multiple files belonging to the
             // same bucket in a given relation. Each of these files are locally sorted
             // but those files combined together are not globally sorted. Given that,
@@ -287,12 +295,6 @@ case class FileSourceScanExec(
         "PushedFilters" -> seqToString(pushedDownFilters),
         "DataFilters" -> seqToString(dataFilters),
         "Location" -> locationDesc)
-    val withOptPartitionCount = if (relation.partitionSchemaOption.isDefined &&
-      !hasPartitionsAvailableAtRunTime) {
-      metadata + ("PartitionCount" -> selectedPartitions.size.toString)
-    } else {
-      metadata
-    }
 
     val withSelectedBucketsCount = relation.bucketSpec.map { spec =>
       val numSelectedBuckets = optionalBucketSet.map { b =>
@@ -300,10 +302,10 @@ case class FileSourceScanExec(
       } getOrElse {
         spec.numBuckets
       }
-      withOptPartitionCount + ("SelectedBucketsCount" ->
+      metadata + ("SelectedBucketsCount" ->
         s"$numSelectedBuckets out of ${spec.numBuckets}")
     } getOrElse {
-      withOptPartitionCount
+      metadata
     }
 
     withSelectedBucketsCount
@@ -346,6 +348,12 @@ case class FileSourceScanExec(
     } else {
       None
     }
+  } ++ {
+    if (relation.partitionSchemaOption.isDefined) {
+      Some("numPartitions" -> SQLMetrics.createMetric(sparkContext, "number of partitions read"))
+    } else {
+      None
+    }
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala
index d52a78033e6cd..c3edec39979ae 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala
@@ -49,6 +49,16 @@ class BucketedReadWithoutHiveSupportSuite extends BucketedReadSuite with SharedS
 abstract class BucketedReadSuite extends QueryTest with SQLTestUtils {
   import testImplicits._
 
+  protected override def beforeAll(): Unit = {
+    super.beforeAll()
+    spark.sessionState.conf.setConf(SQLConf.LEGACY_BUCKETED_TABLE_SCAN_OUTPUT_ORDERING, true)
+  }
+
+  protected override def afterAll(): Unit = {
+    spark.sessionState.conf.unsetConf(SQLConf.LEGACY_BUCKETED_TABLE_SCAN_OUTPUT_ORDERING)
+    super.afterAll()
+  }
+
   private val maxI = 5
   private val maxJ = 13
   private lazy val df = (0 until 50).map(i => (i % maxI, i % maxJ, i.toString)).toDF("i", "j", "k")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index d413dfb2b2dc5..68ccee5e6623a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -17,12 +17,12 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.apache.spark.metrics.source.HiveCatalogMetrics
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.catalyst.TableIdentifier
 import org.apache.spark.sql.catalyst.parser.ParseException
 import org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand
 import org.apache.spark.sql.hive.HiveUtils
-import org.apache.spark.sql.hive.execution._
 import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.test.SQLTestUtils
@@ -207,4 +207,29 @@ class HiveExplainSuite extends QueryTest with SQLTestUtils with TestHiveSingleto
       }
     }
   }
+
+  test("SPARK-28595: explain should not trigger partition listing") {
+    Seq(true, false).foreach { legacyBucketedScan =>
+      withSQLConf(
+        SQLConf.LEGACY_BUCKETED_TABLE_SCAN_OUTPUT_ORDERING.key -> legacyBucketedScan.toString) {
+        HiveCatalogMetrics.reset()
+        withTable("t") {
+          sql(
+            """
+              |CREATE TABLE t USING json
+              |PARTITIONED BY (j)
+              |CLUSTERED BY (i) SORTED BY (i) INTO 4 BUCKETS
+              |AS SELECT 1 i, 2 j
+            """.stripMargin)
+          assert(HiveCatalogMetrics.METRIC_PARTITIONS_FETCHED.getCount == 0)
+          spark.table("t").sort($"i").explain()
+          if (legacyBucketedScan) {
+            assert(HiveCatalogMetrics.METRIC_PARTITIONS_FETCHED.getCount > 0)
+          } else {
+            assert(HiveCatalogMetrics.METRIC_PARTITIONS_FETCHED.getCount == 0)
+          }
+        }
+      }
+    }
+  }
 }