ParquetTableScan always prepends the values of partition columns in output rows irrespective of the order of the partition columns in the original SELECT query

This happens when ParquetTableScan is being used by turning on spark.sql.hive.convertMetastoreParquet

For example:

spark-sql> set spark.sql.hive.convertMetastoreParquet=true;

spark-sql> create table table1(a int , b int) partitioned by (p1 string, p2 int) ROW FORMAT SERDE 'parquet.hive.serde.ParquetHiveSerDe' STORED AS INPUTFORMAT 'parquet.hive.DeprecatedParquetInputFormat' OUTPUTFORMAT 'parquet.hive.DeprecatedParquetOutputFormat';

spark-sql> insert into table table1 partition(p1='January',p2=1) select key, 10 from src;

spark-sql> select a, b, p1, p2 from table1 limit 10;

January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10
January 1 484 10

The correct output should be

484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1
484 10 January 1

This also leads to schema mismatch if the query is run using HiveContext and the result is a SchemaRDD.
For example :

scala> import org.apache.spark.sql.hive._
scala> val hc = new HiveContext(sc)
scala> hc.setConf("spark.sql.hive.convertMetastoreParquet", "true")
scala> val res = hc.sql("select a, b, p1, p2 from table1 limit 10")
scala> res.collect
res2: Array[org.apache.spark.sql.Row] = Array([January,1,238,10], [January,1,86,10], [January,1,311,10], [January,1,27,10], [January,1,165,10], [January,1,409,10], [January,1,255,10], [January,1,278,10], [January,1,98,10], [January,1,484,10])

scala> res.schema
res5: org.apache.spark.sql.StructType = StructType(ArrayBuffer(StructField(a,IntegerType,true), StructField(b,IntegerType,true), StructField(p1,StringType,true), StructField(p2,IntegerType,true)))