Scala API 扩展

为了在Scala和JavaAPI之间保持一定的一致性，在批处理和流的标准API中忽略了一些在Scala中允许高表达性的特性。

如果要 享受完整的Scala体验，您可以选择选择-intoExtensions（通过隐式转换来增强ScalaAPI）。

要使用所有可用的扩展，您可以为数据集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Extensions a-là-carte 仅使用您更喜欢的那些。

接受部分函数

通常，DataSet和DataS treamAPI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DataS treamScalaAPI中都引入了新的方法，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

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

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
  }

|

有关每个方法的语义的详细信息，请参阅数据集和数据流API文档。

要专门使用此扩展名，您可以添加以下import：

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

对于数据集扩展,

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

下面的片段展示了如何(与DataSet API)一起使用这些扩展方法的最小示例：

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
    }
  }
}