Rahul Aggarwal created SPARK-5049:
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Summary: ParquetTableScan always prepends the values of partition
columns in output rows irrespective of the order of the partition columns in
the original SELECT query
Key: SPARK-5049
URL: https://issues.apache.org/jira/browse/SPARK-5049
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.2.0, 1.1.0
Reporter: Rahul Aggarwal
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)))
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