Spark：如何使用RowEncoder創建流數據集？

Question

我有一個使用Spark結構化流創建的流數據幀。 像這樣-

val dataStream =
    spark
      .readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", bootstrapServer)
      .option("subscribe", topic)
      .load()

現在，當我嘗試使用名為newKey的附加列從datastream創建數據集時，出現以下錯誤-

[error] (run-main-0) java.lang.UnsupportedOperationException: No Encoder found for org.apache.spark.sql.Row
[error] - field (class: "org.apache.spark.sql.Row", name: "_2")
[error] - root class: "scala.Tuple2"
java.lang.UnsupportedOperationException: No Encoder found for org.apache.spark.sql.Row
- field (class: "org.apache.spark.sql.Row", name: "_2")
- root class: "scala.Tuple2"
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:642)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:444)
    at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
    at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:820)
    at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:444)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1$$anonfun$8.apply(ScalaReflection.scala:636)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1$$anonfun$8.apply(ScalaReflection.scala:624)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:392)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:355)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:624)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:444)
    at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
    at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:820)
    at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:444)
    at org.apache.spark.sql.catalyst.ScalaReflection$.serializerFor(ScalaReflection.scala:433)
    at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:71)
    at org.apache.spark.sql.Encoders$.product(Encoders.scala:275)
    at org.apache.spark.sql.LowPrioritySQLImplicits$class.newProductEncoder(SQLImplicits.scala:248)
    at org.apache.spark.sql.SQLImplicits.newProductEncoder(SQLImplicits.scala:34)

我正在使用的代碼如下：

import spark.implicits._
implicit val rowEncoder: ExpressionEncoder[Row] = RowEncoder(dataStream.schema)

val dsStream =
    dataStream
      .select(lit("a").as("newKey"), col("*"))
      .as[(String, Row)]
      .writeStream
      .format("console")
      .start()

誰能幫我解決這個問題？

Answer 1

以下任何一種都可以工作：

• map ：

   import org.apache.spark.sql.{Encoder, Encoders, Row} import org.apache.spark.sql.functions._ val df = Seq((1L, "a", 4.0)).toDF("x", "y", "z") val encoder = Encoders.tuple(Encoders.STRING, RowEncoder(df.schema)) df.map(row => ("a", row))(encoder) 

• 用struct和as select ：

   df.select(lit("a"), struct(df.columns map col: _*)).as[(String, Row)](encoder)