具有复杂输入参数的Spark SQL UDF

Question

我正在尝试将UDF与输入类型Array of struct一起使用.我有以下数据结构,这只是更大结构的相关部分

|--investments: array (nullable = true)
    |    |-- element: struct (containsNull = true)
    |    |    |-- funding_round: struct (nullable = true)
    |    |    |    |-- company: struct (nullable = true)
    |    |    |    |    |-- name: string (nullable = true)
    |    |    |    |    |-- permalink: string (nullable = true)
    |    |    |    |-- funded_day: long (nullable = true)
    |    |    |    |-- funded_month: long (nullable = true)
    |    |    |    |-- funded_year: long (nullable = true)
    |    |    |    |-- raised_amount: long (nullable = true)
    |    |    |    |-- raised_currency_code: string (nullable = true)
    |    |    |    |-- round_code: string (nullable = true)
    |    |    |    |-- source_description: string (nullable = true)
    |    |    |    |-- source_url: string (nullable = true)

我宣布了案例类:

case class Company(name: String, permalink: String)
case class FundingRound(company: Company, funded_day: Long, funded_month: Long, funded_year: Long, raised_amount: Long, raised_currency_code: String, round_code: String, source_description: String, source_url: String)
case class Investments(funding_round: FundingRound)

UDF声明:

sqlContext.udf.register("total_funding", (investments:Seq[Investments])  => {
     val totals = investments.map(r => r.funding_round.raised_amount)
     totals.sum
})

当我执行以下转换时,结果如预期

scala> sqlContext.sql("""select total_funding(investments) from companies""")
res11: org.apache.spark.sql.DataFrame = [_c0: bigint]

但是当像collect一样执行的操作时我有一个错误:

Executor: Exception in task 0.0 in stage 4.0 (TID 10)
java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to $line33.$read$$iwC$$iwC$Investments

感谢您的任何帮助.

Answer 1

你看到的错误应该是不言自明的.Catalyst/SQL类型和Scala类型之间存在严格的映射,可以在Spark SQL,DataFrames和Datasets Guide 的相关部分中找到.

特定struct类型转换为o.a.s.sql.Row(在您的特定情况下,数据将被公开为Seq[Row]).

有不同的方法可用于将数据公开为特定类型:

• 定义已在2.0.0中删除但现在无法替换的UDT(用户定义类型).
• 转换DataFrame到Dataset[T]其中T是所需的局部类型.

只有前一种方法才适用于这种特殊情况.

如果你想investments.funding_round.raised_amount使用UDF 访问,你需要这样的东西:

val getRaisedAmount = udf((investments: Seq[Row]) => scala.util.Try(
  investments.map(_.getAs[Row]("funding_round").getAs[Long]("raised_amount"))
).toOption)

但简单select应该更安全,更清洁:

df.select($"investments.funding_round.raised_amount")