[
https://issues.apache.org/jira/browse/FLINK-8828?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jelmer Kuperus updated FLINK-8828:
----------------------------------
Description:
A collect function is a method that takes a Partial Function as its parameter
and applies it to all the elements in the collection to create a new collection
which satisfies the Partial Function.
It can be found on all [core scala collection
classes|http://www.scala-lang.org/api/2.9.2/scala/collection/TraversableLike.html]
as well as on spark's [rdd
interface|https://spark.apache.org/docs/2.2.0/api/scala/index.html#org.apache.spark.rdd.RDD]
To understand its utility imagine the following scenario :
Given a DataStream that produces events of type _Purchase_ and _View_
Transform this stream into a stream of purchase amounts over 1000 euros.
Currently an implementation might look like
{noformat}
val x = dataStream
.filter(_.isInstanceOf[Purchase])
.map(_.asInstanceOf[Purchase])
.filter(_.amount > 1000)
.map(_.amount){noformat}
Or alternatively you could do this
{noformat}
dataStream.flatMap(_ match {
case p: Purchase if p.amount > 1000 => Some(p.amount)
case _ => None
}){noformat}
But with collect implemented it could look like
{noformat}
dataStream.collect {
case p: Purchase if p.amount > 1000 => p.amount
}{noformat}
Which is a lot nicer to both read and write
was:
A collect function is a method that takes a Partial Function as its parameter
and applies it to all the elements in the collection to create a new collection
which satisfies the Partial Function.
It can be found on all [core scala collection
classes|http://www.scala-lang.org/api/2.9.2/scala/collection/TraversableLike.html]
as well as on spark's [rdd
interface|https://spark.apache.org/docs/2.2.0/api/scala/index.html#org.apache.spark.rdd.RDD]
To understand its utility imagine the following scenario :
You have a DataStream that produces events of type _Purchase_ and _View_
You would like to transform this stream into a stream of purchase amounts over
1000 euros.
Currently an implementation might look like
{noformat}
val x = dataStream
.filter(_.isInstanceOf[Purchase])
.map(_.asInstanceOf[Purchase])
.filter(_.amount > 1000)
.map(_.amount){noformat}
Or alternatively you could do this
{noformat}
dataStream.flatMap(_ match {
case p: Purchase if p.amount > 1000 => Some(p.amount)
case _ => None
}){noformat}
But with collect implemented it could look like
{noformat}
dataStream.collect {
case p: Purchase if p.amount > 1000 => p.amount
}{noformat}
Which is a lot nicer to both read and write
> Add collect method to DataStream / DataSet scala api
> ----------------------------------------------------
>
> Key: FLINK-8828
> URL: https://issues.apache.org/jira/browse/FLINK-8828
> Project: Flink
> Issue Type: Improvement
> Components: Core, DataSet API, DataStream API, Scala API
> Affects Versions: 1.4.0
> Reporter: Jelmer Kuperus
> Priority: Major
>
> A collect function is a method that takes a Partial Function as its parameter
> and applies it to all the elements in the collection to create a new
> collection which satisfies the Partial Function.
> It can be found on all [core scala collection
> classes|http://www.scala-lang.org/api/2.9.2/scala/collection/TraversableLike.html]
> as well as on spark's [rdd
> interface|https://spark.apache.org/docs/2.2.0/api/scala/index.html#org.apache.spark.rdd.RDD]
> To understand its utility imagine the following scenario :
> Given a DataStream that produces events of type _Purchase_ and _View_
> Transform this stream into a stream of purchase amounts over 1000 euros.
> Currently an implementation might look like
> {noformat}
> val x = dataStream
> .filter(_.isInstanceOf[Purchase])
> .map(_.asInstanceOf[Purchase])
> .filter(_.amount > 1000)
> .map(_.amount){noformat}
> Or alternatively you could do this
> {noformat}
> dataStream.flatMap(_ match {
> case p: Purchase if p.amount > 1000 => Some(p.amount)
> case _ => None
> }){noformat}
> But with collect implemented it could look like
> {noformat}
> dataStream.collect {
> case p: Purchase if p.amount > 1000 => p.amount
> }{noformat}
>
> Which is a lot nicer to both read and write
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