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Data Engineering
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Help with Identifying and Parsing Varying Date Formats in Spark DataFrame

New Contributor


Hello Spark Community,

I'm encountering an issue with parsing dates in a Spark DataFrame due to inconsistent date formats across my datasets. I need to identify and parse dates correctly, irrespective of their format. Below is a brief outline of my problem and the code that I'm currently using:

Issue: I have multiple CSV files that contain date fields with different formats, such as "dd/MM/yyyy", "yyyy-MM-dd", and sometimes with timestamps. When I attempt to convert string columns to date columns in Spark, the conversion results in null values if the format doesn't match perfectly.

Objective: My goal is to have a robust solution that can automatically detect the date format and parse it correctly into a date or timestamp type in Spark, avoiding null outcomes.

Current Approach: Here is the piece of code that processes the date columns:

# Simplified code snippet for clarity
from pyspark.sql.functions import to_date, col

date_formats = ["dd/MM/yyyy", "yyyy-MM-dd", "MM/dd/yyyy"]
for date_format in date_formats:
df = df.withColumn('Date', to_date(col('Date'), date_format))


This code works under the assumption that one of the specified formats is correct, but it fails when none of the formats match, resulting in null values.

Question: Can someone suggest a method or best practice in Spark to handle multiple date formats, where the format is not known in advance and can vary from one record to another?


Any help or pointers towards the right approach would be greatly appreciated!

Thank you in advance!


Esteemed Contributor III

How about not specifying the format?  This will already match common formats.
When you still have nulls, you can use your list with known exotic formats.
Another solution is working with regular expressions.  looking for 2 digit numbers not larger than 12 probably means it is the month. the other 2 digit number is day etc.
This is where regex shines.