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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
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)
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