I have a Spark Dataframe in that consists of a series of dates:
from pyspark.sql import SQLContext
from pyspark.sql import Row
from pyspark.sql.types import
As of Spark 1.5 you can use unix_timestamp:
from pyspark.sql import functions as F
timeFmt = "yyyy-MM-dd'T'HH:mm:ss.SSS"
timeDiff = (F.unix_timestamp('EndDateTime', format=timeFmt)
- F.unix_timestamp('StartDateTime', format=timeFmt))
df = df.withColumn("Duration", timeDiff)
Note the Java style time format.
>>> df.show()
+---+--------------------+--------------------+--------+
| ID| EndDateTime| StartDateTime|Duration|
+---+--------------------+--------------------+--------+
|X01|2014-02-13T12:36:...|2014-02-13T12:31:...| 258|
|X02|2014-02-13T12:35:...|2014-02-13T12:32:...| 204|
|X03|2014-02-13T12:36:...|2014-02-13T12:32:...| 228|
|XO4|2014-02-13T12:37:...|2014-02-13T12:32:...| 269|
|XO5|2014-02-13T12:36:...|2014-02-13T12:33:...| 202|
+---+--------------------+--------------------+--------+