PySpark dataframe convert unusual string format to Timestamp - apache-spark

Question

I am using PySpark through Spark 1.5.0.
I have an unusual String format in rows of a column for datetime values. It looks like this:
Row[(datetime='2016_08_21 11_31_08')]
Is there a way to convert this unorthodox yyyy_mm_dd hh_mm_dd format into a Timestamp?
Something that can eventually come along the lines of
df = df.withColumn("date_time",df.datetime.astype('Timestamp'))
I had thought that Spark SQL functions like regexp_replace could work, but of course I need to replace
_ with - in the date half
and _ with : in the time part.
I was thinking I could split the column in 2 using substring and count backward from the end of time. Then do the 'regexp_replace' separately, then concatenate. But this seems to many operations? Is there an easier way?

Answer 1

Spark >= 2.2
from pyspark.sql.functions import to_timestamp
(sc
.parallelize([Row(dt='2016_08_21 11_31_08')])
.toDF()
.withColumn("parsed", to_timestamp("dt", "yyyy_MM_dd HH_mm_ss"))
.show(1, False))
## +-------------------+-------------------+
## |dt |parsed |
## +-------------------+-------------------+
## |2016_08_21 11_31_08|2016-08-21 11:31:08|
## +-------------------+-------------------+
Spark < 2.2
It is nothing that unix_timestamp cannot handle:
from pyspark.sql import Row
from pyspark.sql.functions import unix_timestamp
(sc
.parallelize([Row(dt='2016_08_21 11_31_08')])
.toDF()
.withColumn("parsed", unix_timestamp("dt", "yyyy_MM_dd HH_mm_ss")
# For Spark <= 1.5
# See issues.apache.org/jira/browse/SPARK-11724
.cast("double")
.cast("timestamp"))
.show(1, False))
## +-------------------+---------------------+
## |dt |parsed |
## +-------------------+---------------------+
## |2016_08_21 11_31_08|2016-08-21 11:31:08.0|
## +-------------------+---------------------+
In both cases the format string should be compatible with Java SimpleDateFormat.

Answer 2

zero323's answer answers the question, but I wanted to add that if your datetime string has a standard format, you should be able to cast it directly into timestamp type:
df.withColumn('datetime', col('datetime_str').cast('timestamp'))
It has the advantage of handling milliseconds, while unix_timestamp only has only second-precision (to_timestamp works with milliseconds too but requires Spark >= 2.2 as zero323 stated). I tested it on Spark 2.3.0, using the following format: '2016-07-13 14:33:53.979' (with milliseconds, but it also works without them).

Answer 3

I add some more code lines from Florent F's answer for better understanding and running the snippet in local machine:
import os, pdb, sys
import pyspark
from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql.types import StructType, ArrayType
from pyspark.sql.types import StringType
from pyspark.sql.functions import col
sc = pyspark.SparkContext('local[*]')
spark = SparkSession.builder.getOrCreate()
# preparing some example data - df1 with String type and df2 with Timestamp type
df1 = sc.parallelize([{"key":"a", "date":"2016-02-01"},
{"key":"b", "date":"2016-02-02"}]).toDF()
df1.show()
df2 = df1.withColumn('datetime', col('date').cast("timestamp"))
df2.show()

Answer 4

Just want to add more resources and example into this discussion.
https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
For example, if your ts string is "22 Dec 2022 19:06:36 EST", then the format is "dd MMM yyyy HH:mm:ss zzz"