Spark 2.1 Hive Partition Adding Issue ORC Format

[#1]

I am using pyspark 2.1 to create partitions dynamically from table A to table B. Below are the DDL's

<code>create table A (
objid bigint,
occur_date timestamp)
STORED AS ORC;

create table B (
objid bigint,
occur_date timestamp)
PARTITIONED BY (
occur_date_pt date)
STORED AS ORC;

I am then using a pyspark code where I am trying to determine the partitions that need to be merged, below is the portion of code where I am actually doing that

<code>for row in  incremental_df.select(partitioned_column).distinct().collect():
    path            = '/apps/hive/warehouse/B/' + partitioned_column + '=' + format(row[0])
    old_df          = merge_df.where(col(partitioned_column).isin(format(row[0])))
    new_df          = incremental_df.where(col(partitioned_column).isin(format(row[0])))
    output_df       = old_df.subtract(new_df)
    output_df       = output_df.unionAll(new_df)
    output_df.write.option("compression","none").mode("overwrite").format("orc").save(path)
    refresh_metadata_sql = 'MSCK REPAIR TABLE ' + table_name
    sqlContext.sql(refresh_metadata_sql)

On Execution of the code I am able to see the partitions in HDFS

Found 3 items drwx------ - 307010265 hdfs 0 2017-06-01 10:31 /apps/hive/warehouse/B/occur_date_pt=2017-06-01 drwx------ - 307010265 hdfs 0 2017-06-01 10:31 /apps/hive/warehouse/B/occur_date_pt=2017-06-02 drwx------ - 307010265 hdfs 0 2017-06-01 10:31 /apps/hive/warehouse/B/occur_date_pt=2017-06-03

But when I am trying to access the table inside Spark I am getting array out of bound error

<code>>> merge_df = sqlContext.sql('select * from B')
DataFrame[]
>>> merge_df.show()
17/06/01 10:33:13 ERROR Executor: Exception in task 0.0 in stage 200.0 (TID 4827)
java.lang.IndexOutOfBoundsException: toIndex = 3
        at java.util.ArrayList.subListRangeCheck(ArrayList.java:1004)
        at java.util.ArrayList.subList(ArrayList.java:996)
        at org.apache.hadoop.hive.ql.io.orc.RecordReaderFactory.getSchemaOnRead(RecordReaderFactory.java:161)
        at org.apache.hadoop.hive.ql.io.orc.RecordReaderFactory.createTreeReader(RecordReaderFactory.java:66)
        at org.apache.hadoop.hive.ql.io.orc.RecordReaderImpl.<init>(RecordReaderImpl.java:202)
        at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.rowsOptions(ReaderImpl.java:539)
        at org.apache.hadoop.hive.ql.io.orc.OrcRawRecordMerger$ReaderPair.<init>(OrcRawRecordMerger.java:183)
        at org.apache.hadoop.hive.ql.io.orc.OrcRawRecordMerger$OriginalReaderPair.<init>(OrcRawRecordMerger.java:226)
        at org.apache.hadoop.hive.ql.io.orc.OrcRawRecordMerger.<init>(OrcRawRecordMerger.java:437)
        at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getReader(OrcInputFormat.java:1215)
        at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getRecordReader(OrcInputFormat.java:1113)
        at org.apache.spark.rdd.HadoopRDD$anon$1.liftedTree1$1(HadoopRDD.scala:252)
        at org.apache.spark.rdd.HadoopRDD$anon$1.<init>(HadoopRDD.scala:251)
        at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:211)
        at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:102)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)

[#2 · Accepted solution]

I resolved it by removing the column on which the table was partitioned from the dataframe