diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala
index 9251d3301114..5b08f51850b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala
@@ -36,6 +36,7 @@ import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
+import org.apache.spark.sql.execution.WholeStageCodegenExec
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
@@ -132,7 +133,7 @@ class OrcFileFormat
   override def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean = {
     val conf = sparkSession.sessionState.conf
     conf.orcVectorizedReaderEnabled && conf.wholeStageEnabled &&
-      schema.length <= conf.wholeStageMaxNumFields &&
+      !WholeStageCodegenExec.isTooManyFields(conf, schema) &&
       schema.forall(s => supportDataType(s.dataType) &&
         !s.dataType.isInstanceOf[UserDefinedType[_]]) &&
       supportBatchForNestedColumn(sparkSession, schema)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala
index 9acf59cbd94e..348ef6f9eca6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala
@@ -659,4 +659,30 @@ class OrcSourceSuite extends OrcSuite with SharedSparkSession {
       checkAnswer(spark.sql("SELECT _col0, _col2.c1 FROM t1"), Seq(Row(1, "a")))
     }
   }
+
+  test("SPARK-36594: ORC vectorized reader should properly check maximal number of fields") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = spark.range(10).map { x =>
+        val stringColumn = s"$x" * 10
+        val structColumn = (x, s"$x" * 100)
+        val arrayColumn = (0 until 5).map(i => (x + i, s"$x" * 5))
+        val mapColumn = Map(s"$x" -> (x * 0.1, (x, s"$x" * 100)))
+        (x, stringColumn, structColumn, arrayColumn, mapColumn)
+      }.toDF("int_col", "string_col", "struct_col", "array_col", "map_col")
+      df.write.format("orc").save(path)
+
+      Seq(("5", false), ("10", true)).foreach {
+        case (maxNumFields, vectorizedEnabled) =>
+          withSQLConf(SQLConf.ORC_VECTORIZED_READER_NESTED_COLUMN_ENABLED.key -> "true",
+            SQLConf.WHOLESTAGE_MAX_NUM_FIELDS.key -> maxNumFields) {
+            val scanPlan = spark.read.orc(path).queryExecution.executedPlan
+            assert(scanPlan.find {
+              case scan: FileSourceScanExec => scan.supportsColumnar
+              case _ => false
+            }.isDefined == vectorizedEnabled)
+          }
+      }
+    }
+  }
 }