There are several confusing things going on here. I think this is part of the explanation, not 100% sure:
'bigint' is the Spark SQL type of an 8-byte long. 'long' is the type of a JVM primitive. Both are the same, conceptually, but represented differently internally as they are logically somewhat different ideas. The first thing I'm not sure about is why the toString of Dataset[Long] reports a 'bigint' and printSchema() reports 'long'. That might be a (cosmetic) bug. Second, in Scala 2.12, its SAM support causes calls to reduce() and other methods, using an Object type, to be ambiguous, because Spark has long since had Java-friendly overloads that support a SAM interface for Java callers. Those weren't removed to avoid breakage, at the cost of having to explicitly tell it what overload you want. (They are equivalent) This is triggered because range() returns java.lang.Longs, not long primitives (i.e. scala.Long). I assume that is to make it versatile enough to use in Java too, and because it's hard to write an overload (would have to rename it) But that means you trigger the SAM overload issue. Anything you do that makes this a Dataset[scala.Long] resolves it, as it is no longer ambiguous (Java-friendly Object-friendly overload does not apply). For example: spark.range(10).map(_.toLong).reduce(_+_) If you collect(), you still have an Array[java.lang.Long]. But Scala implicits and conversions make .reduce(_+_) work fine on that; there is no "Java-friendly" overload in the way. Normally all of this just works and you can ignore these differences. This is a good example of a corner case in which it's inconvenient, because of the old Java-friendly overloads. This is by design though. On Sat, Jun 27, 2020 at 8:29 AM Anwar AliKhan <anwaralikhan...@gmail.com> wrote: > > As you know I have been puzzling over this issue : > How come spark.range(100).reduce(_+_) > worked in earlier spark version but not with the most recent versions. > > well, > > When you first create a dataset, by default the column "id" datatype is > [BigInt], > It is a bit like a coin Long on one side and bigint on the other side. > > scala> val myrange = spark.range(1,100) > myrange: org.apache.spark.sql.Dataset[Long] = [id: bigint] > > The Spark framework error message after parsing the reduce(_+_) method > confirms this > and moreover stresses its constraints of expecting data type long as > parameter argument(s). > > scala> myrange.reduce(_+_) > <console>:26: error: overloaded method value reduce with alternatives: > (func: > org.apache.spark.api.java.function.ReduceFunction[java.lang.Long])java.lang.Long > <and> > (func: (java.lang.Long, java.lang.Long) => java.lang.Long)java.lang.Long > cannot be applied to ((java.lang.Long, java.lang.Long) => scala.Long) > myrange.reduce(_+_) > ^ > > But if you ask the printSchema method it disagrees with both of the above and > says the column "id" data is Long. > scala> range100.printSchema() > root > |-- id: long (nullable = false) > > If I ask the collect() method, the collect() method agrees with > printSchema() that the datatype of column "id" is Long and not BigInt. > > scala> range100.collect() > res10: Array[Long] = Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, > 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, > 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, > 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, > 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, > 91, 92, 93, 94, 95, 96, 97, 98, 99) > > To settle the dispute between the methods and get the collect() to "show me > the money" I called the collect() to pass its return type to reduce(_+_). > > "Here is the money" > scala> range100.collect().reduce(_+_) > res11: Long = 4950 > > The collect() and printSchema methods could be implying there is no > difference between a Long or a BingInt. > > Questions : These return type differentials, are they by design or an > oversight bug ? > Questions : Why the change from earlier version to later version ? > Question : Will you be updating the reduce(_+_) method ? > > When it comes to creating a dataset using toDs there is no dispute, > all the methods agree that it is neither a BigInt or a Long but an int even > integer. > > scala> val dataset = Seq(1, 2, 3).toDS() > dataset: org.apache.spark.sql.Dataset[Int] = [value: int] > > scala> dataset.collect() > res29: Array[Int] = Array(1, 2, 3) > > scala> dataset.printSchema() > root > |-- value: integer (nullable = false) > > scala> dataset.show() > +-----+ > |value| > +-----+ > | 1| > | 2| > | 3| > +-----+ > > scala> dataset.reduce(_+_) > res7: Int = 6 > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
