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[SPARK-11889] [SQL] Fix type inference for GroupedDataset.agg in REPL #9870

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[SPARK-11889] [SQL] Fix type inference for GroupedDataset.agg in REPL #9870

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24 changes: 24 additions & 0 deletions repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -315,6 +315,30 @@ class ReplSuite extends SparkFunSuite {
}
}

test("Datasets agg type-inference") {
val output = runInterpreter("local",
"""
|import org.apache.spark.sql.functions._
|import org.apache.spark.sql.Encoder
|import org.apache.spark.sql.expressions.Aggregator
|import org.apache.spark.sql.TypedColumn
|/** An `Aggregator` that adds up any numeric type returned by the given function. */
|class SumOf[I, N : Numeric](f: I => N) extends Aggregator[I, N, N] with Serializable {
| val numeric = implicitly[Numeric[N]]
| override def zero: N = numeric.zero
| override def reduce(b: N, a: I): N = numeric.plus(b, f(a))
| override def merge(b1: N,b2: N): N = numeric.plus(b1, b2)
| override def finish(reduction: N): N = reduction
|}
|
|def sum[I, N : Numeric : Encoder](f: I => N): TypedColumn[I, N] = new SumOf(f).toColumn
|val ds = Seq((1, 1, 2L), (1, 2, 3L), (1, 3, 4L), (2, 1, 5L)).toDS()
|ds.groupBy(_._1).agg(sum(_._2), sum(_._3)).collect()
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@marmbrus marmbrus Nov 20, 2015

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@dragos any idea why it fails to infer the type only in the REPL?

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@dragos dragos Nov 21, 2015

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I can't reproduce the difference. It won't infer it in a standalone program either.

As I mentioned in our conversation, it's a chicken and egg problem: type inference is guided by the expected type, but if the method is overloaded, the expected type is not known. And the argument type is what guides overload resolution. It works in simple cases, when the overloads have different aritites, but with varargs that's no longer the case.

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@marmbrus marmbrus Nov 22, 2015

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It always works fine here though. Why is this different?

""".stripMargin)
assertDoesNotContain("error:", output)
assertDoesNotContain("Exception", output)
}

test("collecting objects of class defined in repl") {
val output = runInterpreter("local[2]",
"""
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27 changes: 3 additions & 24 deletions sql/core/src/main/scala/org/apache/spark/sql/GroupedDataset.scala
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Expand Up @@ -146,31 +146,10 @@ class GroupedDataset[K, T] private[sql](
reduce(f.call _)
}

/**
* Compute aggregates by specifying a series of aggregate columns, and return a [[DataFrame]].
* We can call `as[T : Encoder]` to turn the returned [[DataFrame]] to [[Dataset]] again.
*
* The available aggregate methods are defined in [[org.apache.spark.sql.functions]].
*
* {{{
* // Selects the age of the oldest employee and the aggregate expense for each department
*
* // Scala:
* import org.apache.spark.sql.functions._
* df.groupBy("department").agg(max("age"), sum("expense"))
*
* // Java:
* import static org.apache.spark.sql.functions.*;
* df.groupBy("department").agg(max("age"), sum("expense"));
* }}}
*
* We can also use `Aggregator.toColumn` to pass in typed aggregate functions.
*
* @since 1.6.0
*/
// This is here to prevent us from adding overloads that would be ambiguous.
@scala.annotation.varargs
def agg(expr: Column, exprs: Column*): DataFrame =
groupedData.agg(withEncoder(expr), exprs.map(withEncoder): _*)
private def agg(exprs: Column*): DataFrame =
groupedData.agg(withEncoder(exprs.head), exprs.tail.map(withEncoder): _*)

private def withEncoder(c: Column): Column = c match {
case tc: TypedColumn[_, _] =>
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8 changes: 3 additions & 5 deletions sql/core/src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java
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Expand Up @@ -404,11 +404,9 @@ public String call(Tuple2<String, Integer> value) throws Exception {
grouped.agg(new IntSumOf().toColumn(Encoders.INT(), Encoders.INT()));
Assert.assertEquals(Arrays.asList(tuple2("a", 3), tuple2("b", 3)), agged.collectAsList());

Dataset<Tuple4<String, Integer, Long, Long>> agged2 = grouped.agg(
new IntSumOf().toColumn(Encoders.INT(), Encoders.INT()),
expr("sum(_2)"),
count("*"))
.as(Encoders.tuple(Encoders.STRING(), Encoders.INT(), Encoders.LONG(), Encoders.LONG()));
Dataset<Tuple2<String, Integer>> agged2 = grouped.agg(
new IntSumOf().toColumn(Encoders.INT(), Encoders.INT()))
.as(Encoders.tuple(Encoders.STRING(), Encoders.INT()));
Assert.assertEquals(
Arrays.asList(
new Tuple4<>("a", 3, 3L, 2L),
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