原文链接:https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/scala_api_extensions.html
为了保持Scala API和Java API之间相当的一致性，Scala 的一些高阶表达式被从标准的API中剔除，包括批处理的和流处理的。
如果你想享受完整的Scala体验，你可以选择通过隐式转换来添加扩展提升Scala的API。
为了使用所有的扩展，你仅需要导入如下的包，在DataSet API中:

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

或者在DataStream API中:

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

或者你可以根据你自己的需要导入单独的扩展。
接受部分函数(Accept partial functions)
通常，DataSet API和DataStream API都不支持通过匿名模式匹配来构建tuple、case class和集合，如下:

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)"
}

这个扩展为DataSet API和DataStream API引入了新的方法，这些方法与扩展API有着一一对应的关系，这些委派方法支持匿名模式匹配功能:
DataSet API
方法 原形 案例

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
方法 原形 案例

mapWith map (DataStream)    data.mapWith {
  case (_, value) => value.toString
}
mapPartitionWith    mapPartition (DataStream)   data.mapPartitionWith {
  case head #:: _ => head
}
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 (KeyedDataStream, WindowedDataStream)    data.reduceWith {
  case ((_, sum1), (_, sum2) => sum1 + sum2
}
foldWith    fold (KeyedDataStream, WindowedDataStream)  data.foldWith(User(bought = 0)) {
  case (User(b), (_, items)) => User(b + items.size)
}
applyWith   apply (WindowedDataStream)  data.applyWith(0)(
  foldFunction = case (sum, amount) => sum + amount
  windowFunction = case (k, w, sum) => // [...]
)
projecting  apply (JoinedDataStream)    data1.join(data2).
  whereClause(case (pk, _) => pk).
  isEqualTo(case (_, fk) => fk).
  projecting {
    case ((pk, tx), (products, fk)) => tx -> products
  }

想了解更多关于每个方法的语义信息，请参考DataSet和DataStream API文档。
为了使用这些扩展方法，你需要引入如下类对于DataSet 来说:

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

对于DataStream来说:

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

下面的小例子中展示了如何组合使用这些扩展的方法(使用DataSet API):

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
    }
  }
}