spark sql 数据类型转换_如何在Spark SQL的DataFrame中更改列类型？
我想基础上的解决方案withColumn，withColumnRenamed并cast提出msemelman，马丁Senne等是简单和清晰。
我认为您的方法还可以，请记住，Spark DataFrame是行的(不可变的)RDD，因此我们从来没有真正替换过列，只是DataFrame每次使用新的架构创建新列。
假设您有一个具有以下架构的原始df：
scala> df.printSchema
root
|-- Year: string (nullable = true)
|-- Month: string (nullable = true)
|-- DayofMonth: string (nullable = true)
|-- DayOfWeek: string (nullable = true)
|-- DepDelay: string (nullable = true)
|-- Distance: string (nullable = true)
|-- CRSDepTime: string (nullable = true)
在一列或几列上定义了一些UDF：
import org.apache.spark.sql.functions._
val toInt = udf[Int, String]( _.toInt)
val toDouble = udf[Double, String]( _.toDouble)
val toHour = udf((t: String) => "%04d".format(t.toInt).take(2).toInt )
val days_since_nearest_holidays = udf(
(year:String, month:String, dayOfMonth:String) => year.toInt + 27 + month.toInt-12
)
更改列类型甚至从另一个构建新的DataFrame都可以这样写：
val featureDf = df
.withColumn("departureDelay", toDouble(df("DepDelay")))
.withColumn("departureHour", toHour(df("CRSDepTime")))
.withColumn("dayOfWeek", toInt(df("DayOfWeek")))
.withColumn("dayOfMonth", toInt(df("DayofMonth")))
.withColumn("month", toInt(df("Month")))
.withColumn("distance", toDouble(df("Distance")))
.withColumn("nearestHoliday", days_since_nearest_holidays(
df("Year"), df("Month"), df("DayofMonth"))
)
.select("departureDelay", "departureHour", "dayOfWeek", "dayOfMonth",
"month", "distance", "nearestHoliday")
产生：
scala> df.printSchema
root
|-- departureDelay: double (nullable = true)
|-- departureHour: integer (nullable = true)
|-- dayOfWeek: integer (nullable = true)
|-- dayOfMonth: integer (nullable = true)
|-- month: integer (nullable = true)
|-- distance: double (nullable = true)
|-- nearestHoliday: integer (nullable = true)
这非常接近您自己的解决方案。简而言之，将类型更改和其他转换作为单独的udf vals使代码更易于阅读和重用。