你可以使用join:

val usersToKeep = sc.parallelize(
  listOfUsersToKeep.map(Tuple1(_))).toDF("userID_")

val finalDataFrame = usersToKeep
  .join(initialDataFrame, $"userID" === $"userID_")
  .drop("userID_")

或广播变量和 UDF:

or a broadcast variable and an UDF:

import org.apache.spark.sql.functions.udf

val usersToKeepBD = sc.broadcast(listOfUsersToKeep.toSet)
val checkUser = udf((id: Long) => usersToKeepBD.value.contains(id))
val finalDataFrame = initialDataFrame.where(checkUser($"userID"))

也应该可以广播数据帧:

It should be also possible to broadcast a DataFrame:

import org.apache.spark.sql.functions.broadcast

initialDataFrame.join(broadcast(usersToKeep), $"userID" === $"userID_")