[jira] [Created] (FLINK-7371) user defined aggregator assumes nr of arguments smaller or equal than number of row fields

["Stefano Bortoli (JIRA)" <ji...@apache.org>]

Stefano Bortoli created FLINK-7371:
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             Summary: user defined aggregator assumes nr of arguments smaller or equal than number of row fields
                 Key: FLINK-7371
                 URL: https://issues.apache.org/jira/browse/FLINK-7371
             Project: Flink
          Issue Type: Bug
          Components: Table API & SQL
    Affects Versions: 1.3.1
            Reporter: Stefano Bortoli


The definition of user define aggregations with a number of parameters larger than the row fields causes ArrayIndexOutOfBoundsException because the indexing is based on a linear iteration over row fields. This does not consider cases where fields can be used more than once and constant values are passed to the aggregation function.

for example:
{code}
window(partition {} order by [2] rows between $5 PRECEDING and CURRENT ROW aggs [myAgg($0, $1, $3, $0, $4)])
{code}

where $3 and $4 are reference to constants, and $0 and $1 are fields causes:

{code}
java.lang.ArrayIndexOutOfBoundsException: 4
	at org.apache.flink.table.plan.schema.RowSchema.mapIndex(RowSchema.scala:134)
	at org.apache.flink.table.plan.schema.RowSchema$$anonfun$mapAggregateCall$1.apply(RowSchema.scala:147)
	at org.apache.flink.table.plan.schema.RowSchema$$anonfun$mapAggregateCall$1.apply(RowSchema.scala:147)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
	at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
	at scala.collection.AbstractTraversable.map(Traversable.scala:105)
	at org.apache.flink.table.plan.schema.RowSchema.mapAggregateCall(RowSchema.scala:147)
	at org.apache.flink.table.plan.nodes.datastream.DataStreamOverAggregate$$anonfun$9.apply(DataStreamOverAggregate.scala:362)
{code}



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