Scala API 扩展

All Flink Scala APIs are deprecated and will be removed in a future Flink version. You can still build your application in Scala, but you should move to the Java version of either the DataStream and/or Table API.

See FLIP-265 Deprecate and remove Scala API support

Scala API 扩展 #

为了在 Scala 和 Java API 之间保持大致相同的使用体验，在批处理和流处理的标准 API 中省略了一些允许 Scala 高级表达的特性。

如果你想拥有完整的 Scala 体验，可以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，你只需为 DataStream API 添加一个简单的引入

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

或者，您可以引入单个扩展 a-là-carte 来使用您喜欢的扩展。

Accept partial functions #

通常，DataStream API 不接受匿名模式匹配函数来解构元组、case 类或集合，如下所示：

val data: DataStream[(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 DataStream Scala API 中引入了新的方法，这些方法在扩展 API 中具有一对一的对应关系。这些委托方法支持匿名模式匹配函数。

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
}
projecting apply (JoinedStream) 
data1.join(data2).
  whereClause(case (pk, _) => pk).
  isEqualTo(case (_, fk) => fk).
  projecting {
    case ((pk, tx), (products, fk)) => tx -> products
  }

有关每个方法语义的更多信息, 请参考 DataStream API 文档。

要单独使用此扩展，你可以添加以下引入：

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

用于 DataSet 扩展

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

下面的代码片段展示了如何一起使用这些扩展方法 (以及 DataSet API) 的最小示例:

object Main {
  import org.apache.flink.streaming.api.scala.extensions._

  case class Point(x: Double, y: Double)

  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.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)
    }.keyingBy {
      case (id, value) => id
    }
  }
}

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