diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
index d823275d857b..04752ec5fe7b 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
@@ -60,6 +60,7 @@
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Types;
 import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.StructType$;
 
 /**
  * Base class for custom RecordReaders for Parquet that directly materialize to `T`.
@@ -136,7 +137,9 @@ public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptCont
     ReadSupport.ReadContext readContext = readSupport.init(new InitContext(
         taskAttemptContext.getConfiguration(), toSetMultiMap(fileMetadata), fileSchema));
     this.requestedSchema = readContext.getRequestedSchema();
-    this.sparkSchema = new ParquetSchemaConverter(configuration).convert(requestedSchema);
+    String sparkRequestedSchemaString =
+        configuration.get(ParquetReadSupport$.MODULE$.SPARK_ROW_REQUESTED_SCHEMA());
+    this.sparkSchema = StructType$.MODULE$.fromString(sparkRequestedSchemaString);
     this.reader = new ParquetFileReader(configuration, file, blocks, requestedSchema.getColumns());
     for (BlockMetaData block : blocks) {
       this.totalRowCount += block.getRowCount();
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 02b94452a129..7e83bcbb6e24 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -680,6 +680,30 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
       )
     }
   }
+
+  test("SPARK-16632: read Parquet int32 as ByteType and ShortType") {
+    withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> "true") {
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
+
+        // When being written to Parquet, `TINYINT` and `SMALLINT` should be converted into
+        // `int32 (INT_8)` and `int32 (INT_16)` respectively. However, Hive doesn't add the `INT_8`
+        // and `INT_16` annotation properly (HIVE-14294). Thus, when reading files written by Hive
+        // using Spark with the vectorized Parquet reader enabled, we may hit error due to type
+        // mismatch.
+        //
+        // Here we are simulating Hive's behavior by writing a single `INT` field and then read it
+        // back as `TINYINT` and `SMALLINT` in Spark to verify this issue.
+        Seq(1).toDF("f").write.parquet(path)
+
+        val withByteField = new StructType().add("f", ByteType)
+        checkAnswer(spark.read.schema(withByteField).parquet(path), Row(1: Byte))
+
+        val withShortField = new StructType().add("f", ShortType)
+        checkAnswer(spark.read.schema(withShortField).parquet(path), Row(1: Short))
+      }
+    }
+  }
 }
 
 object TestingUDT {