我会用scala Try 键入以处理读取avro文件目录时出现故障的可能性。通过“try”，我们可以在代码中明确失败的可能性，并以功能性的方式进行处理：

object Main extends App {

  import scala.util.{Success, Try}
  import org.apache.spark.{SparkConf, SparkContext}
  import com.databricks.spark.avro._

  val sc = new SparkContext(new SparkConf().setMaster("local[*]").setAppName("example"))
  val sqlContext = new org.apache.spark.sql.SQLContext(sc)

  //the first path exists, the second one doesn't
  val paths = List("/data/1", "/data/2")

  //Wrap the attempt to read the paths in a Try, then use collect to filter
  //and map with a single partial function.
  val avros =
    paths
      .map(p => Try(sqlContext.read.avro(p)))
      .collect{
        case Success(df) => df
      }
  //Do whatever you want with your list of dataframes
  avros.foreach{ df =>
    println(df.collect())
  }
  sc.stop()
}