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https://issues.apache.org/jira/browse/SPARK-15598?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15305651#comment-15305651
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koert kuipers commented on SPARK-15598:
---------------------------------------
how will change this impact usage like this:
{noformat}
val customSummer = new Aggregator[Data, Int, Int] {
def zero: Int = 0
def reduce(b: Int, a: Data): Int = b + a.i
def merge(b1: Int, b2: Int): Int = b1 + b2
def finish(r: Int): Int = r
}.toColumn()
val ds: Dataset[Data] = ...
val aggregated = ds.select(customSummer)
{noformat}
what if df is empty? currently it will return zero
> Change Aggregator.zero to Aggregator.init
> -----------------------------------------
>
> Key: SPARK-15598
> URL: https://issues.apache.org/jira/browse/SPARK-15598
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Reynold Xin
>
> org.apache.spark.sql.expressions.Aggregator currently requires defining the
> zero value for an aggregator. This is actually a limitation making it
> difficult to implement APIs such as reduce. In reduce (or reduceByKey), a
> single associative and commutative reduce function is specified by the user,
> and there is no definition of zero value.
> A small tweak to the API is to change zero to init, taking an input, similar
> to the following:
> {code}
> abstract class Aggregator[-IN, BUF, OUT] extends Serializable {
> def init(a: IN): BUF
> def reduce(b: BUF, a: IN): BUF
> def merge(b1: BUF, b2: BUF): BUF
> def finish(reduction: BUF): OUT
> }
> {code}
> Then reduce can be implemented using:
> {code}
> f: (T, T) => T
> new Aggregator[T, T, T] {
> override def init(a: T): T = identify
> override def reduce(b: T, a: T): T = f(b, a)
> override def merge(b1: T, b2: T): T = f(b1, b2)
> override def finish(reduction: T): T = identify
> }
> {code}
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