PySpark 1.5 How to Truncate Timestamp to Nearest Minute from seconds

Question

I am using PySpark. I have a column (\'dt\') in a dataframe (\'canon_evt\') that this a timestamp. I am trying to remove seconds from a DateTime value. It is originally read in

Answer 1

Converting to Unix timestamps and basic arithmetics should to the trick:

from pyspark.sql import Row
from pyspark.sql.functions import col, unix_timestamp, round

df = sc.parallelize([
    Row(dt='1970-01-01 00:00:00'),
    Row(dt='2015-09-16 05:39:46'),
    Row(dt='2015-09-16 05:40:46'),
    Row(dt='2016-03-05 02:00:10'),
]).toDF()


## unix_timestamp converts string to Unix timestamp (bigint / long)
## in seconds. Divide by 60, round, multiply by 60 and cast
## should work just fine.
## 
dt_truncated = ((round(unix_timestamp(col("dt")) / 60) * 60)
    .cast("timestamp"))

df.withColumn("dt_truncated", dt_truncated).show(10, False)
## +-------------------+---------------------+
## |dt                 |dt_truncated         |
## +-------------------+---------------------+
## |1970-01-01 00:00:00|1970-01-01 00:00:00.0|
## |2015-09-16 05:39:46|2015-09-16 05:40:00.0|
## |2015-09-16 05:40:46|2015-09-16 05:41:00.0|
## |2016-03-05 02:00:10|2016-03-05 02:00:00.0|
## +-------------------+---------------------+