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https://issues.apache.org/jira/browse/SPARK-15810?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15720404#comment-15720404
]
koert kuipers commented on SPARK-15810:
---------------------------------------
similar test:
{noformat}
val agg1 = new Aggregator[Int, Tuple1[Option[(Int, Int)]],
Tuple1[Option[(Int, Int)]]] {
def zero: Tuple1[Option[(Int, Int)]] = {
val x = Tuple1(None)
println(s"zero ${x}")
x
}
def reduce(b: Tuple1[Option[(Int, Int)]], a: Int): Tuple1[Option[(Int,
Int)]] = {
println(s"reduce ${b} and ${a}")
Tuple1(b._1.map(minMax => (math.min(minMax._1, a), math.max(minMax._2,
a))).orElse(Some((a, a))))
}
def merge(b1: Tuple1[Option[(Int, Int)]], b2: Tuple1[Option[(Int, Int)]]):
Tuple1[Option[(Int, Int)]] = {
println(s"merge ${b1} and ${b2}")
(b1._1, b2._1) match {
case (Some((min1, max1)), Some((min2, max2))) =>
Tuple1(Some((math.min(min1, min2), math.max(max1, max2))))
case (Some(minMax1), _) => Tuple1(Some(minMax1))
case (_, Some(minMax2)) => Tuple1(Some(minMax2))
case _ => Tuple1(None)
}
}
def finish(reduction: Tuple1[Option[(Int, Int)]]): Tuple1[Option[(Int,
Int)]] = {
println(s"finish ${reduction}")
reduction
}
def bufferEncoder: Encoder[Tuple1[Option[(Int, Int)]]] =
ExpressionEncoder[Tuple1[Option[(Int, Int)]]]()
def outputEncoder: Encoder[Tuple1[Option[(Int, Int)]]] =
ExpressionEncoder[Tuple1[Option[(Int, Int)]]]()
}
val y = Seq(1, 2, 3)
.toDS
.repartition(5)
.select(agg1.toColumn)
y.printSchema
y.show
{noformat}
gives:
{noformat}
root
|-- _1: struct (nullable = true)
| |-- _1: integer (nullable = false)
| |-- _2: integer (nullable = false)
zero (None)
zero (None)
zero (None)
zero (None)
reduce (None) and 1
reduce (Some((1,1))) and 2
zero (None)
reduce (None) and 3
zero (None)
merge (None) and (None)
merge (None) and (Some((1,2)))
merge (Some((1,2))) and (None)
merge (Some((1,2))) and (None)
merge (Some((1,2))) and (Some((3,3)))
finish (Some((1,3)))
+-----+
| _1|
+-----+
|[1,3]|
+-----+
{noformat}
> Aggregator doesn't play nice with Option
> ----------------------------------------
>
> Key: SPARK-15810
> URL: https://issues.apache.org/jira/browse/SPARK-15810
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Environment: spark 2.0.0-SNAPSHOT
> Reporter: koert kuipers
>
> {code}
> val ds1 = List(("a", 1), ("a", 2), ("a", 3)).toDS
> val ds2 = ds1.map{ case (k, v) => (k, if (v > 1) Some(v) else None) }
> val ds3 = ds2.groupByKey(_._1).agg(new Aggregator[(String, Option[Int]),
> Option[Int], Option[Int]]{
> def zero: Option[Int] = None
> def reduce(b: Option[Int], a: (String, Option[Int])): Option[Int] =
> b.map(bv => a._2.map(av => bv + av).getOrElse(bv)).orElse(a._2)
> def merge(b1: Option[Int], b2: Option[Int]): Option[Int] = b1.map(b1v =>
> b2.map(b2v => b1v + b2v).getOrElse(b1v)).orElse(b2)
> def finish(reduction: Option[Int]): Option[Int] = reduction
> def bufferEncoder: Encoder[Option[Int]] = implicitly[Encoder[Option[Int]]]
> def outputEncoder: Encoder[Option[Int]] = implicitly[Encoder[Option[Int]]]
> }.toColumn)
> ds3.printSchema
> ds3.show
> {code}
> i get as output a somewhat odd looking schema, and after that the program
> just hangs pinning one cpu at 100%. the data never shows.
> output:
> {noformat}
> root
> |-- value: string (nullable = true)
> |-- $anon$1(scala.Tuple2): struct (nullable = true)
> | |-- value: integer (nullable = true)
> {noformat}
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