【发布时间】：2020-01-07 20:58:05
【问题描述】：
我正在尝试重构一些代码并将一般逻辑放入一个特征中。我基本上想处理数据集，按一些键对它们进行分组并聚合：
import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.sql.{ Dataset, Encoder, Encoders, TypedColumn }
case class SomeKey(a: String, b: Boolean)
case class InputRow(
SomeKey,
v: Double
)
trait MyTrait {
def processInputs: Dataset[InputRow]
def groupAndAggregate(
logs: Dataset[InputRow]
): Dataset[(SomeKey, Long)] = {
import logs.sparkSession.implicits._
logs
.groupByKey(i => i.key)
.agg(someAggFunc)
}
//Whatever agg function: here, it counts the number of v that are >= 0.5
def someAggFunc: TypedColumn[InputRow, Long] =
new Aggregator[
/*input type*/ InputRow,
/* "buffer" type */ Long,
/* output type */ Long
] with Serializable {
def zero = 0L
def reduce(b: Long, a: InputRow) = {
if (a.v >= 0.5)
b + 1
else
b
}
def merge(b1: Long, b2: Long) =
b1 + b2
// map buffer to output type
def finish(b: Long) = b
def bufferEncoder: Encoder[Long] = Encoders.scalaLong
def outputEncoder: Encoder[Long] = Encoders.scalaLong
}.toColumn
}
一切正常：我可以实例化一个继承自 MyTrait 的类并覆盖我处理输入的方式：
import spark.implicits._
case class MyTraitTest(testDf: DataFrame) extends MyTrait {
override def processInputs: Dataset[InputRow] = {
val ds = testDf
.select(
$"a",
$"b",
$"v",
)
.rdd
.map(
r =>
InputRow(
SomeKey(r.getAs[String]("a"), r.getAs[Boolean]("b")),
r.getAs[Double]("v")
)
)
.toDS
ds
}
val df: DataFrame = Seq(
("1", false, 0.40),
("1", false, 0.54),
("0", true, 0.85),
("1", true, 0.39)
).toDF("a", "b", "v")
val myTraitTest = MyTraitTest(df)
val ds: Dataset[InputRow] = myTraitTest.processInputs
val res = myTraitTest.groupAndAggregate(ds)
res.show(false)
+----------+----------------------------------+
|key |InputRow |
+----------+----------------------------------+
|[1, false]|1 |
|[0, true] |1 |
|[1, true] |0 |
+----------+----------------------------------+
现在的问题：我希望 SomeKey 从更通用的特征 Key 派生，因为键不会总是只有两个字段，字段不会有相同的类型等。它总是一些简单的元组基本的原始类型。
所以我尝试执行以下操作：
trait Key extends Product
case class SomeKey(a: String, b: Boolean) extends Key
case class SomeOtherKey(x: Int, y: Boolean, z: String) extends Key
case class InputRow[T <: Key](
key: T,
v: Double
)
trait MyTrait[T <: Key] {
def processInputs: Dataset[InputRow[T]]
def groupAndAggregate(
logs: Dataset[InputRow[T]]
): Dataset[(T, Long)] = {
import logs.sparkSession.implicits._
logs
.groupByKey(i => i.key)
.agg(someAggFunc)
}
def someAggFunc: TypedColumn[InputRow[T], Long] = {...}
我现在这样做：
case class MyTraitTest(testDf: DataFrame) extends MyTrait[SomeKey] {
override def processInputs: Dataset[InputRow[SomeKey]] = {
...
}
等等
但现在我得到了错误：Unable to find encoder for type T. An implicit Encoder[T] is needed to store T instances in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for serializing other types will be added in future releases.
.groupByKey(i => i.key)
我真的不知道如何解决这个问题，我尝试了很多东西都没有成功。抱歉，这个描述太长了，但希望你有所有的元素来帮助我理解......谢谢！
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标签： scala apache-spark serialization apache-spark-dataset