apache-spark - Spark 中 bigint 的兼容数据类型是什么？我们如何将 bigint 转换为 spark 兼容的数据类型？

Question

我正在尝试使用 Spark 将数据从 greenplum 移动到 HDFS。我可以从源表中成功读取数据，数据框(greenplum 表)的 spark 推断模式是:

数据框架构:

 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表对应的schema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 表模式，我使用逻辑创建了以下 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)

当我尝试在数据框架构上应用我的新架构时，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 但它失败了，但是谁能告诉我如何将 bigint 转换为 spark 兼容的数据类型？如果不是，我如何修改我的逻辑以在 case 语句中为这个 bigint/bigdecimal 问题提供正确的数据类型？

Answer 1

在这里看到你的问题，似乎你正在尝试将 bigint 值转换为 big decimal，这是不正确的。 Bigdecimal 是一个必须具有固定精度(最大位数)和小数位数(点右侧的位数)的小数。而你的似乎是长期值(value)。

这里不使用BigDecimal 数据类型，而是尝试使用LongType 来正确转换bigint 值。看看这是否解决了您的目的。