Major Description
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)))
