在下面的代碼中，出現 java.lang.String 之類的錯誤不是雙模式的有效外部類型

Question

我的代碼如下所示：

object DataTypeValidation extends Logging {

  def main(args: Array[String]) {

    val spark = SparkSession.builder()
      .appName("SparkProjectforDataTypeValidation")
      .master("local")
      .getOrCreate();
    spark.sparkContext.setLogLevel("ERROR")
    try {

      breakable {
        val format = new SimpleDateFormat("d-M-y hh:mm:ss.SSSSS")
        println("*********Data Type Validation Started*************** " + format.format(Calendar.getInstance().getTime()))

        val data = Seq(Row(873131558, "ABC22"), Row(29000000, 99.00), Row(27000000, 2.34))
        val schema = StructType(Array(
            StructField("oldcl", IntegerType, nullable = true),
            StructField("newcl", DoubleType, nullable = true))
            )
        
        val ONE = 1
        var erroredRecordRow = new scala.collection.mutable.ListBuffer[Row]()
        val newSchema = schema.fields.map({
          case StructField(name, _: IntegerType, nullorNotnull, _) => StructField(name, StringType, nullorNotnull)
          case StructField(name, _: DoubleType, nullorNotnull, _) => StructField(name, StringType, nullorNotnull)
          case fields => fields
        }).dropRight(ONE)
        val newStructType = StructType { newSchema }
        val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
        df.show()
        print(df.schema)
 }

    } catch {
      case exception: Exception =>
        println("exception caught in Data Type Mismatch In Schema Validation: " + exception.toString())
        exception.printStackTrace();
    }
    spark.stop()
  }
}

exception caught in Data Type Mismatch In Schema Validation: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.lang.RuntimeException: Error while encoding: java.lang.RuntimeException: java.lang.String is not a valid external type for schema of double
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, oldcl), IntegerType) AS oldcl#0
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, newcl), DoubleType) AS newcl#1
    at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:292)

Answer 1

@AnkitTomar，此錯誤是由於字符串值ABC22映射到DoubleType 。

請更新以下行

val data = Seq(Row(873131558, "ABC22"), Row(29000000, 99.00), Row(27000000, 2.34))
        val schema = StructType(Array(
            StructField("oldcl", IntegerType, nullable = true),
            StructField("newcl", DoubleType, nullable = true))
            )

和

val data = Seq(Row(873131558, "ABC22"), Row(29000000, "99.00"), Row(27000000, "2.34"))

 val schema = StructType(Array(
            StructField("oldcl", IntegerType, nullable = true),
            StructField("newcl", StringType, nullable = true))
            )

這樣您就可以檢索預期的結果，

val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)

df.show()

/*
+---------+-----+
|    oldcl|newcl|
+---------+-----+
|873131558|ABC22|
| 29000000|99.00|
| 27000000| 2.34|
+---------+-----+
*/

注意：我在您的代碼中找不到 newSchema 的用法，如果您遵循任何其他方法，請發表評論

        val ONE = 1
        var erroredRecordRow = new scala.collection.mutable.ListBuffer[Row]()
        val newSchema = schema.fields.map({
          case StructField(name, _: IntegerType, nullorNotnull, _) => StructField(name, StringType, nullorNotnull)
          case StructField(name, _: DoubleType, nullorNotnull, _) => StructField(name, StringType, nullorNotnull)
          case fields => fields
        }).dropRight(ONE)
        val newStructType = StructType { newSchema }