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[英]How can I optimize spark function to round a double value to 2 decimals?
[英]How can I optimize the spark function to replace nulls with zeroes?
下面是我的Spark函數,該函數處理DataFrame列中的空值,而不管其數據類型如何。
def nullsToZero(df:DataFrame,nullsToZeroColsList:Array[String]): DataFrame ={
var y:DataFrame = df
for(colDF <- y.columns){
if(nullsToZeroColsList.contains(colDF)){
y = y.withColumn(colDF,expr("case when "+colDF+" IS NULL THEN 0 ELSE "+colDF+" end"))
}
}
return y
}
import spark.implicits._
val personDF = Seq(
("miguel", Some(12),100,110,120), (null, Some(22),200,210,220), ("blu", None,300,310,320)
).toDF("name", "age","number1","number2","number3")
println("Print Schema")
personDF.printSchema()
println("Show Original DF")
personDF.show(false)
val myColsList:Array[String] = Array("name","age","age")
println("NULLS TO ZERO")
println("Show NullsToZeroDF")
val fixedDF = nullsToZero(personDF,myColsList)
在上面的代碼中,我有一個Integer類型和String類型的數據類型,它們都由我的函數處理。 但是我懷疑下面的代碼,在我的函數中可能會影響性能,但不確定。
y = y.withColumn(colDF,expr("case when "+colDF+" IS NULL THEN 0 ELSE "+colDF+" end"))
有沒有更優化的方式可以編寫此函數,執行.withColumn()並一次又一次地分配DF的意義何在? 先感謝您。
我建議為na.fill(valueMap)
組裝valueMap
以根據數據類型用特定值填充null
列,如下所示:
import org.apache.spark.sql.functions._
import spark.implicits._
val df = Seq(
(Some(1), Some("a"), Some("x"), None),
(None, Some("b"), Some("y"), Some(20.0)),
(Some(3), None, Some("z"), Some(30.0))
).toDF("c1", "c2", "c3", "c4")
val nullColList = List("c1", "c2", "c4")
val valueMap = df.dtypes.filter(x => nullColList.contains(x._1)).
collect{ case (c, t) => t match {
case "StringType" => (c, "n/a")
case "IntegerType" => (c, 0)
case "DoubleType" => (c, Double.MinValue)
} }.toMap
// valueMap: scala.collection.immutable.Map[String,Any] =
// Map(c1 -> 0, c2 -> n/a, c4 -> -1.7976931348623157E308)
df.na.fill(valueMap).show
// +---+---+---+--------------------+
// | c1| c2| c3| c4|
// +---+---+---+--------------------+
// | 1| a| x|-1.79769313486231...|
// | 0| b| y| 20.0|
// | 3|n/a| z| 30.0|
// +---+---+---+--------------------+
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