Convert date from String to Date format in Dataframes
Since your main aim was to convert the type of a column in a DataFrame from String to Timestamp, I think this approach would be better.
import org.apache.spark.sql.functions.{to_date, to_timestamp}
val modifiedDF = DF.withColumn("Date", to_date($"Date", "MM/dd/yyyy"))
You could also use to_timestamp
(I think this is available from Spark 2.x) if you require fine grained timestamp.
you can also do this query...!
sqlContext.sql("""
select from_unixtime(unix_timestamp('08/26/2016', 'MM/dd/yyyy'), 'yyyy:MM:dd') as new_format
""").show()
I solved the same problem without the temp table/view and with dataframe functions.
Of course I found that only one format works with this solution and that's yyyy-MM-DD
.
For example:
val df = sc.parallelize(Seq("2016-08-26")).toDF("Id")
val df2 = df.withColumn("Timestamp", (col("Id").cast("timestamp")))
val df3 = df2.withColumn("Date", (col("Id").cast("date")))
df3.printSchema
root
|-- Id: string (nullable = true)
|-- Timestamp: timestamp (nullable = true)
|-- Date: date (nullable = true)
df3.show
+----------+--------------------+----------+
| Id| Timestamp| Date|
+----------+--------------------+----------+
|2016-08-26|2016-08-26 00:00:...|2016-08-26|
+----------+--------------------+----------+
The timestamp of course has 00:00:00.0
as a time value.
Use to_date
with Java SimpleDateFormat
.
TO_DATE(CAST(UNIX_TIMESTAMP(date, 'MM/dd/yyyy') AS TIMESTAMP))
Example:
spark.sql("""
SELECT TO_DATE(CAST(UNIX_TIMESTAMP('08/26/2016', 'MM/dd/yyyy') AS TIMESTAMP)) AS newdate"""
).show()
+----------+
| dt|
+----------+
|2016-08-26|
+----------+