[SPARK-16632][SQL] Use Spark requested schema to guide vectorized Parquet reader initialization

## What changes were proposed in this pull request?

In `SpecificParquetRecordReaderBase`, which is used by the vectorized Parquet reader, we convert the Parquet requested schema into a Spark schema to guide column reader initialization. However, the Parquet requested schema is tailored from the schema of the physical file being scanned, and may have inaccurate type information due to bugs of other systems (e.g. HIVE-14294).

On the other hand, we already set the real Spark requested schema into Hadoop configuration in [`ParquetFileFormat`][1]. This PR simply reads out this schema to replace the converted one.

## How was this patch tested?

New test case added in `ParquetQuerySuite`.

[1]: https://github.com/apache/spark/blob/v2.0.0-rc5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L292-L294

Author: Cheng Lian <lian@databricks.com>

Closes apache#14278 from liancheng/spark-16632-simpler-fix.

Author: liancheng

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();

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 {