CODEWITHSUNDEEP
scalaapache-spark
Sidd
Sidd

Reputation: 291

Converting String to Date in Spark Dataframe

I have a dataframe (df1) with 2 StringType fields.

Field1 (StringType) Value-X

Field2 (StringType) value-20180101

All I am trying to do is create another dataframe (df2) from df1 with 2 fields-

Field1 (StringType) Value-X

Field2 (Date Type) Value-2018-01-01

I am using the below code-

df2=df1.select(
     col("field1").alias("f1"),
     unix_timestamp(col("field2"),"yyyyMMdd").alias("f2")
)

df2.show

df2.printSchema

For this field 2, I tried multiple things - unix_timestamp , from_unixtimestamp, to_date, cast(“date”) but nothing worked

I need the following schema as output:

df2.printSchema
|-- f1: string (nullable = false)
|-- f2: date (nullable = false)

I'm using Spark 2.1

Upvotes: 1

Views: 10249

Answers (1)

Leo C
Leo C

Reputation: 22449

to_date seems to work fine for what you need:

import org.apache.spark.sql.functions._

val df1 = Seq( ("X", "20180101"), ("Y", "20180406") ).toDF("c1", "c2")

val df2 = df1.withColumn("c2", to_date($"c2", "yyyyMMdd"))

df2.show
// +---+----------+
// | c1|        c2|
// +---+----------+
// |  X|2018-01-01|
// |  Y|2018-04-06|
// +---+----------+

df2.printSchema
// root
//  |-- c1: string (nullable = true)
//  |-- c2: date (nullable = true)

[UPDATE]

For Spark 2.1 or prior, to_date doesn't take format string as a parameter, hence explicit string formatting to the standard yyyy-MM-dd format using, say, regexp_replace is needed:

val df2 = df1.withColumn(
  "c2", to_date(regexp_replace($"c2", "(\\d{4})(\\d{2})(\\d{2})", "$1-$2-$3"))
)

Upvotes: 4

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