12.Basic API Concepts之Scala API Extensions_the argument types of an anonymous function must b-优快云博客

flink 1.8

Scala API Extensions

为了保证Scala和Java APIs在语法间的一致性，在批处理和流处理的标准APIs 中省略了Scala才具有的高级表达能力的特性。

如果您想更好的体验Scala的用法，您可以选择选择通过隐式转换增强Scala API的扩展。

要使用所有可用的扩展，您只需为DataSet API添加一个简单的import

import org.apache.flink.api.scala.extensions._

或DataStream API

import org.apache.flink.streaming.api.scala.extensions._

或者，您可以导入单独的扩展名a-la-carte来只使用您喜欢的扩展名。

Accept partial functions

通常，DataSet和DataStream APIs 都不接受匿名模式匹配函数来解构元组tuples、case类或集合collections，如下所示:

val data: DataSet[(Int, String, Double)] = // [...]
data.map {
  case (id, name, temperature) => // [...]
  // The previous line causes the following compilation error:
  // "The argument types of an anonymous function must be fully known. (SLS 8.5)"
}

这个扩展在DataSet和DataStream Scala API中引入了新的方法，这些方法在扩展的API中具有一对一的对应关系。这些委派方法确实支持匿名模式匹配函数。

DataSet API

Method	Original	Example
mapWith	map (DataSet)	data.mapWith { case (_, value) => value.toString }
mapPartitionWith	mapPartition (DataSet)	data.mapPartitionWith { case head #:: _ => head }
flatMapWith	flatMap (DataSet)	data.flatMapWith { case (_, name, visitTimes) => visitTimes.map(name -> _) }
filterWith	filter (DataSet)	data.filterWith { case Train(_, isOnTime) => isOnTime }
reduceWith	reduce (DataSet, GroupedDataSet)	data.reduceWith { case ((_, amount1), (_, amount2)) => amount1 + amount2 }
reduceGroupWith	reduceGroup (GroupedDataSet)	data.reduceGroupWith { case id #:: value #:: _ => id -> value }
groupingBy	groupBy (DataSet)	data.groupingBy { case (id, _, _) => id }
sortGroupWith	sortGroup (GroupedDataSet)	grouped.sortGroupWith(Order.ASCENDING) { case House(_, value) => value }
combineGroupWith	combineGroup (GroupedDataSet)	grouped.combineGroupWith { case header #:: amounts => amounts.sum }
projecting	apply (JoinDataSet, CrossDataSet)	data1.join(data2). whereClause(case (pk, _) => pk). isEqualTo(case (_, fk) => fk). projecting { case ((pk, tx), (products, fk)) => tx -> products } data1.cross(data2).projecting { case ((a, _), (_, b) => a -> b }
projecting	apply (CoGroupDataSet)	data1.coGroup(data2). whereClause(case (pk, _) => pk). isEqualTo(case (_, fk) => fk). projecting { case (head1 #:: _, head2 #:: _) => head1 -> head2 } }

DataStream API

Method	Original	Example
mapWith	map (DataStream)	data.mapWith { case (_, value) => value.toString }
flatMapWith	flatMap (DataStream)	data.flatMapWith { case (_, name, visits) => visits.map(name -> _) }
filterWith	filter (DataStream)	data.filterWith { case Train(_, isOnTime) => isOnTime }
keyingBy	keyBy (DataStream)	data.keyingBy { case (id, _, _) => id }
mapWith	map (ConnectedDataStream)	data.mapWith( map1 = case (_, value) => value.toString, map2 = case (_, _, value, _) => value + 1 )
flatMapWith	flatMap (ConnectedDataStream)	data.flatMapWith( flatMap1 = case (_, json) => parse(json), flatMap2 = case (_, _, json, _) => parse(json) )
keyingBy	keyBy (ConnectedDataStream)	data.keyingBy( key1 = case (_, timestamp) => timestamp, key2 = case (id, _, _) => id )
reduceWith	reduce (KeyedStream, WindowedStream)	data.reduceWith { case ((_, sum1), (_, sum2) => sum1 + sum2 }
foldWith	fold (KeyedStream, WindowedStream)	data.foldWith(User(bought = 0)) { case (User(b), (_, items)) => User(b + items.size) }
applyWith	apply (WindowedStream)	data.applyWith(0)( foldFunction = case (sum, amount) => sum + amount windowFunction = case (k, w, sum) => // [...] )
projecting	apply (JoinedStream)	data1.join(data2). whereClause(case (pk, _) => pk). isEqualTo(case (_, fk) => fk). projecting { case ((pk, tx), (products, fk)) => tx -> products }

有关每个方法的语义的更多信息，请参阅 DataSet和DataStream API文档。要完全使用此扩展，可以添加以下import:

import org.apache.flink.api.scala.extensions.acceptPartialFunctions

对与流处理的数据集DataSet，需要引入一下包：

import org.apache.flink.streaming.api.scala.extensions.acceptPartialFunctions

下面的代码片段展示了如何结合使用这些扩展方法(对应DataSet API)的最小示例:

object Main {
  import org.apache.flink.api.scala.extensions._
  case class Point(x: Double, y: Double)
  def main(args: Array[String]): Unit = {
    val env = ExecutionEnvironment.getExecutionEnvironment
    val ds = env.fromElements(Point(1, 2), Point(3, 4), Point(5, 6))
    ds.filterWith {
      case Point(x, _) => x > 1
    }.reduceWith {
      case (Point(x1, y1), (Point(x2, y2))) => Point(x1 + y1, x2 + y2)
    }.mapWith {
      case Point(x, y) => (x, y)
    }.flatMapWith {
      case (x, y) => Seq("x" -> x, "y" -> y)
    }.groupingBy {
      case (id, value) => id
    }
  }
}

https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/scala_api_extensions.html

https://flink.sojb.cn/dev/scala_api_extensions.html