koert kuipers created SPARK-13363:
-------------------------------------
Summary: Aggregator not working with DataFrame
Key: SPARK-13363
URL: https://issues.apache.org/jira/browse/SPARK-13363
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.0.0
Reporter: koert kuipers
Priority: Minor
org.apache.spark.sql.expressions.Aggregator 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:
{noformat}
def agg(expr: Column, exprs: Column*): DataFrame
{noformat}
clearly it should be possible, since GroupedDataset uses that very same method
to do the work:
{noformat}
private def agg(exprs: Column*): DataFrame =
groupedData.agg(withEncoder(exprs.head), exprs.tail.map(withEncoder): _*)
{noformat}
the trick seems to be the wrapping in withEncoder, which is private. i tried to
do something like it myself, but i had no luck since it uses more private stuff
in TypedColumn.
anyhow, my attempt at using it in DataFrame:
{noformat}
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
{noformat}
and the resulting error:
{noformat}
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)
{noformat}
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