[jira] [Created] (SPARK-9832) TPCDS Q98 Fails

["Michael Armbrust (JIRA)" <ji...@apache.org>]

Michael Armbrust created SPARK-9832:
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             Summary: TPCDS Q98 Fails
                 Key: SPARK-9832
                 URL: https://issues.apache.org/jira/browse/SPARK-9832
             Project: Spark
          Issue Type: Sub-task
          Components: SQL
            Reporter: Michael Armbrust
            Priority: Blocker


{code}
select
  i_item_desc,
  i_category,
  i_class,
  i_current_price,
  sum(ss_ext_sales_price) as itemrevenue
  -- sum(ss_ext_sales_price) * 100 / sum(sum(ss_ext_sales_price)) over (partition by i_class) as revenueratio
from
  store_sales
  join item on (store_sales.ss_item_sk = item.i_item_sk)
  join date_dim on (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
where
  i_category in('Jewelry', 'Sports', 'Books')
  -- and d_date between cast('2001-01-12' as date) and (cast('2001-01-12' as date) + 30)
  -- and d_date between '2001-01-12' and '2001-02-11'
  -- and ss_date between '2001-01-12' and '2001-02-11'
  -- and ss_sold_date_sk between 2451922 and 2451952  -- partition key filter
  and ss_sold_date_sk between 2451911 and 2451941  -- partition key filter (1 calendar month)
  and d_date between '2001-01-01' and '2001-01-31'
group by
  i_item_id,
  i_item_desc,
  i_category,
  i_class,
  i_current_price
order by
  i_category,
  i_class,
  i_item_id,
  i_item_desc
  -- revenueratio
limit 1000
{code}

{code}
Job aborted due to stage failure: Task 11 in stage 62.0 failed 4 times, most recent failure: Lost task 11.3 in stage 62.0 (TID 5289, 10.0.227.73): java.lang.IllegalArgumentException: Unscaled value too large for precision
	at org.apache.spark.sql.types.Decimal.set(Decimal.scala:76)
	at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:338)
	at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
	at org.apache.spark.sql.catalyst.expressions.UnsafeRow.getDecimal(UnsafeRow.java:386)
	at org.apache.spark.sql.catalyst.expressions.JoinedRow.getDecimal(JoinedRow.scala:97)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
	at org.apache.spark.sql.execution.joins.HashJoin$$anon$1.next(HashJoin.scala:101)
	at org.apache.spark.sql.execution.joins.HashJoin$$anon$1.next(HashJoin.scala:74)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at org.apache.spark.sql.execution.joins.HashJoin$$anon$1.fetchNext(HashJoin.scala:115)
	at org.apache.spark.sql.execution.joins.HashJoin$$anon$1.hasNext(HashJoin.scala:93)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
	at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:353)
	at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.<init>(TungstenAggregationIterator.scala:587)
	at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$1.apply(TungstenAggregate.scala:72)
	at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$1.apply(TungstenAggregate.scala:64)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
	at org.apache.spark.scheduler.Task.run(Task.scala:88)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:745)
{code}



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