Scala API扩展

为了在Scala和Java API之间保持相当大的一致性,在批处理和流式传输的标准API中省略了一些允许Scala高级表达性的函数。

如果您想享受完整的Scala体验,可以选择选择关联通过隐式转换增强Scala API的扩展。

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

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

或DataStream API

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

或者,您可以导入单个扩展名a-là-carte以仅使用您喜欢的扩展名

接受部分函数

通常,DataSet和DataStream API都不接受匿名模式匹配函数来解构元组,案例类或集合,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)"
}

此扩展在DataSet和DataStream Scala API中引入了新方法,这些方法在扩展API中具有一对一的对应关系。这些委托方法确实支持匿名模式匹配函数。

DataSet API

方法 原生 DEMO
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 原生 DEMO
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(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
  }

有关每种方法的语义的更多信息,请参阅 DataSetDataStream API文档。

要仅使用此扩展程序,您可以添加以下内容import

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

对于DataSet扩展和

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

以下代码段显示了如何一起使用这些扩展方法的最小示例(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
    }
  }
}

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