Question

我正在尝试使用Spark将数据从greenplum移到HDFS。我可以从源表成功读取数据，并且数据框（greenplum表）的火花推断模式为：

DataFrame架构：

 je_header_id: long (nullable = true)
 je_line_num: long (nullable = true)
 last_updated_by: decimal(15,0) (nullable = true)
 last_updated_by_name: string (nullable = true)
 ledger_id: long (nullable = true)
 code_combination_id: long (nullable = true)
 balancing_segment: string (nullable = true)
 cost_center_segment: string (nullable = true)
 period_name: string (nullable = true)
 effective_date: timestamp (nullable = true)
 status: string (nullable = true)
 creation_date: timestamp (nullable = true)
 created_by: decimal(15,0) (nullable = true)
 entered_dr: decimal(38,20) (nullable = true)
 entered_cr: decimal(38,20) (nullable = true)
 entered_amount: decimal(38,20) (nullable = true)
 accounted_dr: decimal(38,20) (nullable = true)
 accounted_cr: decimal(38,20) (nullable = true)
 accounted_amount: decimal(38,20) (nullable = true)
 xx_last_update_log_id: integer (nullable = true)
 source_system_name: string (nullable = true)
 period_year: decimal(15,0) (nullable = true)
 period_num: decimal(15,0) (nullable = true)

Hive表的对应架构为：

je_header_id:bigint|je_line_num:bigint|last_updated_by:bigint|last_updated_by_name:string|ledger_id:bigint|code_combination_id:bigint|balancing_segment:string|cost_center_segment:string|period_name:string|effective_date:timestamp|status:string|creation_date:timestamp|created_by:bigint|entered_dr:double|entered_cr:double|entered_amount:double|accounted_dr:double|accounted_cr:double|accounted_amount:double|xx_last_update_log_id:int|source_system_name:string|period_year:bigint|period_num:bigint

使用上面提到的Hive表架构，我使用以下逻辑创建了以下StructType：

def convertDatatype(datatype: String): DataType = {
  val convert = datatype match {
    case "string"     => StringType
    case "bigint"     => LongType
    case "int"        => IntegerType
    case "double"     => DoubleType
    case "date"       => TimestampType
    case "boolean"    => BooleanType
    case "timestamp"  => TimestampType
  }
  convert
}

准备好的架构：

 je_header_id: long (nullable = true)
 je_line_num: long (nullable = true)
 last_updated_by: long (nullable = true)
 last_updated_by_name: string (nullable = true)
 ledger_id: long (nullable = true)
 code_combination_id: long (nullable = true)
 balancing_segment: string (nullable = true)
 cost_center_segment: string (nullable = true)
 period_name: string (nullable = true)
 effective_date: timestamp (nullable = true)
 status: string (nullable = true)
 creation_date: timestamp (nullable = true)
 created_by: long (nullable = true)
 entered_dr: double (nullable = true)
 entered_cr: double (nullable = true)
 entered_amount: double (nullable = true)
 accounted_dr: double (nullable = true)
 accounted_cr: double (nullable = true)
 accounted_amount: double (nullable = true)
 xx_last_update_log_id: integer (nullable = true)
 source_system_name: string (nullable = true)
 period_year: long (nullable = true)
 period_num: long (nullable = true)

当我尝试将newSchema应用于数据框Schema时，出现异常：

java.lang.RuntimeException: java.math.BigDecimal is not a valid external type for schema of bigint

我知道它正在尝试将BigDecimal转换为Bigint，但失败了，但是有人可以告诉我如何将bigint转换为spark兼容的数据类型吗？如果没有，我该如何修改我的逻辑以在case语句中为此bigint / bigdecimal问题提供适当的数据类型？

Answer 1

在这里看到您的问题，似乎您正在尝试将bigint值转换为大十进制，这是不对的。 Bigdecimal是一个十进制数，必须具有固定的精度（最大位数）和小数位数（点右侧的位数）。而且您的价值似乎很长。

在这里，而不是使用BigDecimal数据类型，请尝试使用LongType正确转换bigint值。看看这是否解决了您的目的。

Spark中bigint的兼容数据类型是什么？如何将bigint转换为Spark兼容数据类型？

1 个答案: