Glad you like it :) This sounds like a bug, and we should fix it as we merge DataFrame / Dataset for 2.0. Could you open JIRA targeted at 2.0?
On Wed, Feb 17, 2016 at 2:22 PM, Koert Kuipers <ko...@tresata.com> wrote: > first of all i wanted to say that i am very happy to see > org.apache.spark.sql.expressions.Aggregator, it is a neat api, especially > when compared to the UDAF/AggregateFunction stuff. > > its doc/comments says: A base class for user-defined aggregations, which > can be used in [[DataFrame]] and [[Dataset]] > > it works well with Dataset/GroupedDataset, but i am having no luck using > it with DataFrame/GroupedData. does anyone have an example how to use it > with a DataFrame? > > in particular i would like to use it with this method in GroupedData: > def agg(expr: Column, exprs: Column*): DataFrame > > clearly it should be possible, since GroupedDataset uses that very same > method to do the work: > private def agg(exprs: Column*): DataFrame = > groupedData.agg(withEncoder(exprs.head), exprs.tail.map(withEncoder): > _*) > > the trick seems to be the wrapping in withEncoder, which is private. i > tried to do something like it myself, spending my usual daily 30 mins > getting around private restrictions in spark on this, but i had no luck > since it uses more private stuff on TypedColumn. also this column/catalyst > stuff makes me instantly sleepy so i didn't try to hard. > > anyhow, my attempt at using it in DataFrame: > > val simpleSum = new SqlAggregator[Int, Int, Int] { > def zero: Int = 0 // The initial value. > def reduce(b: Int, a: Int) = b + a // Add an element to the running > total > def merge(b1: Int, b2: Int) = b1 + b2 // Merge intermediate values. > def finish(b: Int) = b // Return the final result. > }.toColumn > > val df = sc.makeRDD(1 to 3).map(i => (i, i)).toDF("k", "v") > df.groupBy("k").agg(simpleSum).show > > and the resulting error: > org.apache.spark.sql.AnalysisException: unresolved operator 'Aggregate > [k#104], [k#104,($anon$3(),mode=Complete,isDistinct=false) AS sum#106]; > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:38) > at > org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:46) > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:241) > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50) > at > org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:122) > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50) > at > org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:46) > at > org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34) > at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:130) > at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:49) > > best, koert > > > >