scala - Add new column in DataFrame base on existing column

Question

I have a csv file with datetime column: "2011-05-02T04:52:09+00:00".

I am using scala, the file is loaded into spark DataFrame and I can use jodas time to parse the date:

val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
val df = new SQLContext(sc).load("com.databricks.spark.csv", Map("path" -> "data.csv", "header" -> "true")) 
val d = org.joda.time.format.DateTimeFormat.forPattern("yyyy-mm-dd'T'kk:mm:ssZ")

I would like to create new columns base on datetime field for timeserie analysis.

In DataFrame, how do I create a column base on value of another column?

I notice DataFrame has following function: df.withColumn("dt",column), is there a way to create a column base on value of existing column?

Thanks

Answer 1

import org.apache.spark.sql.types.DateType
import org.apache.spark.sql.functions._
import org.joda.time.DateTime
import org.joda.time.format.DateTimeFormat

val d = DateTimeFormat.forPattern("yyyy-mm-dd'T'kk:mm:ssZ")
val dtFunc: (String => Date) = (arg1: String) => DateTime.parse(arg1, d).toDate
val x = df.withColumn("dt", callUDF(dtFunc, DateType, col("dt_string")))

The callUDF, col are included in functions as the import show

The dt_string inside col("dt_string") is the origin column name of your df, which you want to transform from.

Alternatively, you could replace the last statement with:

val dtFunc2 = udf(dtFunc)
val x = df.withColumn("dt", dtFunc2(col("dt_string")))