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https://issues.apache.org/jira/browse/SPARK-33632?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17243858#comment-17243858
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Frank Oosterhuis commented on SPARK-33632:
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[~qwe1398775315] is right, and the spec is actually somewhat clear about this.
"Year: The count of letters determines the minimum field width below which
padding is used. If the count of letters is two, then a reduced two digit form
is used. For printing, this outputs the rightmost two digits. *For parsing,
this will parse using the base value of 2000, resulting in a year within the
range 2000 to 2099 inclusive.* If the count of letters is less than four (but
not two), then the sign is only output for negative years. Otherwise, the sign
is output if the pad width is exceeded when ‘G’ is not present. 7 or more
letters will fail."
The table could be a bit clearer.
!screenshot-1.png!
> to_date doesn't behave as documented
> ------------------------------------
>
> Key: SPARK-33632
> URL: https://issues.apache.org/jira/browse/SPARK-33632
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.0.1
> Reporter: Frank Oosterhuis
> Priority: Major
> Attachments: image-2020-12-04-11-45-10-379.png, screenshot-1.png
>
>
> I'm trying to use to_date on a string formatted as "10/31/20".
> Expected output is "2020-10-31".
> Actual output is "0020-01-31".
> The
> [documentation|https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html]
> suggests 2020 or 20 as input for "y".
> Example below. Expected behaviour is included in the udf.
> {code:scala}
> import java.sql.Date
> import org.apache.spark.sql.SparkSession
> import org.apache.spark.sql.functions.{to_date, udf}
> object ToDate {
> val toDate = udf((date: String) => {
> val split = date.split("/")
> val month = "%02d".format(split(0).toInt)
> val day = "%02d".format(split(1).toInt)
> val year = split(2).toInt + 2000
> Date.valueOf(s"${year}-${month}-${day}")
> })
> def main(args: Array[String]): Unit = {
> val spark = SparkSession.builder().master("local[2]").getOrCreate()
> spark.sparkContext.setLogLevel("ERROR")
> import spark.implicits._
> Seq("1/1/20", "10/31/20")
> .toDF("raw")
> .withColumn("to_date", to_date($"raw", "m/d/y"))
> .withColumn("udf", toDate($"raw"))
> .show
> }
> }
> {code}
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