I am currently using Spark 1.1.0 that has been compiled against Hadoop 2.3. Our cluster is CDH5.1.2 which is runs Hive 0.12. I have two external Hive tables that point to Parquet (compressed with Snappy), which were converted over from Avro if that matters.
I am trying to perform a join with these two Hive tables, but am encountering an exception. In a nutshell, I launch a spark shell, create my HiveContext (pointing to the correct metastore on our cluster), and then proceed to do the following: scala> val hc = new HiveContext(sc) scala> val txn = hc.sql(“select * from pqt_rdt_snappy where transdate >= 1325376000000 and translate <= 1340063999999”) scala> val segcust = hc.sql(“select * from pqt_segcust_snappy where coll_def_id=‘abcd’”) scala> txn.registerAsTable(“segTxns”) scala> segcust.registerAsTable(“segCusts”) scala> val joined = hc.sql(“select t.transid, c.customer_id from segTxns t join segCusts c on t.customerid=c.customer_id”) Straight forward enough, but I get the following exception: 14/10/13 14:37:12 ERROR Executor: Exception in task 1.0 in stage 18.0 (TID 51) java.lang.IndexOutOfBoundsException: Index: 21, Size: 21 at java.util.ArrayList.rangeCheck(ArrayList.java:635) at java.util.ArrayList.get(ArrayList.java:411) at org.apache.hadoop.hive.ql.io.parquet.read.DataWritableReadSupport.init(DataWritableReadSupport.java:94) at org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.getSplit(ParquetRecordReaderWrapper.java:206) at org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.<init>(ParquetRecordReaderWrapper.java:81) at org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.<init>(ParquetRecordReaderWrapper.java:67) at org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat.getRecordReader(MapredParquetInputFormat.java:51) at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:197) at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:188) at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:97) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62) at org.apache.spark.scheduler.Task.run(Task.scala:54) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) The number of columns in my table, pqt_segcust_snappy, has 21 columns and two partitions defined. Does this error look familiar to anyone? Could my usage of SparkSQL with Hive be incorrect or is support with Hive/Parquet/partitioning still buggy at this point in Spark 1.1.0? Thanks, -Terry