Cast column containing multiple string date formats to DateTime in Spark

Question

I have a date column in my Spark DataDrame that contains multiple string formats. I would like to cast these to DateTime.

The two formats in my column are:

mm/dd/yyyy; and
yyyy-mm-dd

My solution so far is to use a UDF to change the first date format to match the second as follows:

import re

def parseDate(dateString):
    if re.match('d{1,2}/d{1,2}/d{4}', dateString) is not None:
        return datetime.strptime(dateString, '%M/%d/%Y').strftime('%Y-%M-%d')
    else:
        return dateString

# Create Spark UDF based on above function
dateUdf = udf(parseDate)

df = (df.select(to_date(dateUdf(raw_transactions_df['trans_dt']))))

This works, but is not all that fault-tolerant. I am specifically concerned about:

Date formats I am yet to encounter.
Distinguishing between mm/dd/yyyy and dd/mm/yyyy (the regex I’m using clearly doesn’t do this at the moment).

Is there a better way to do this?

Asked By: W05aDePQw6h8e7

||

Source

Answer 1

Personally I would recommend using SQL functions directly without expensive and inefficient reformatting:

from pyspark.sql.functions import coalesce, to_date

def to_date_(col, formats=("MM/dd/yyyy", "yyyy-MM-dd")):
    # Spark 2.2 or later syntax, for < 2.2 use unix_timestamp and cast
    return coalesce(*[to_date(col, f) for f in formats])

This will choose the first format, which can successfully parse input string.

Usage:

df = spark.createDataFrame([(1, "01/22/2010"), (2, "2018-12-01")], ("id", "dt"))
df.withColumn("pdt", to_date_("dt")).show()

+---+----------+----------+
| id|        dt|       pdt|
+---+----------+----------+
|  1|01/22/2010|2010-01-22|
|  2|2018-12-01|2018-12-01|
+---+----------+----------+

It will be faster than udf, and adding new formats is just a matter of adjusting formats parameter.

However it won’t help you with format ambiguities. In general case it might not be possible to do it without manual intervention and cross referencing with external data.

The same thing can be of course done in Scala:

import org.apache.spark.sql.Column
import org.apache.spark.sql.functions.{coalesce, to_date}

def to_date_(col: Column, 
             formats: Seq[String] = Seq("MM/dd/yyyy", "yyyy-MM-dd")) = {
  coalesce(formats.map(f => to_date(col, f)): _*)
}

Answered By: zero323

Answer 2

You can do this in 100% sql with something like this:

create database delete_me;
use delete_me;
create table test (enc_date string);

insert into test values ('10/28/2019');
insert into test values ('2020-03-31 00:00:00.000');
insert into test values ('2019-10-18');
insert into test values ('gobledie-gook');
insert into test values ('');
insert into test values (null);
insert into test values ('NULL');

-- you might need the following line depending on your version of spark
-- set spark.sql.legacy.timeParserPolicy = LEGACY;
select enc_date, coalesce(to_date(enc_date, "yyyy-MM-dd"), to_date(enc_date, "MM/dd/yyyy")) as date from test;


enc_date                    date
--------                    ----
2020-03-31 00:00:00.000     2020-03-31
2019-10-18                  2019-10-18
null                        null
10/28/2019                  2019-10-28
gobledie-gook               null
NULL                        null
                            null

Answered By: John