hdaly0 opened a new pull request, #42541:
URL: https://github.com/apache/spark/pull/42541

   ### What changes were proposed in this pull request?
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   This PR proposes to change the way that python `datetime.timedelta` objects 
are converted to `pyspark.sql.types.DayTimeIntervalType` objects. Specifically, 
it modifies the logic inside `toInternal` which returns the timedelta as a 
python integer (would be int64 in other languages) storing the timedelta as 
microseconds. The current logic inadvertently adds an extra second when doing 
the conversion for certain python timedelta objects, thereby returning an 
incorrect value.
   
   An illustrative example is as follows:
   
   ```
   from datetime import timedelta
   from pyspark.sql.types import DayTimeIntervalType, StructField, StructType
   
   spark = ...spark session setup here...
   
   td = timedelta(days=4498031, seconds=16054, microseconds=999981)
   df = spark.createDataFrame([(td,)], 
StructType([StructField(name="timedelta_col", dataType=DayTimeIntervalType(), 
nullable=False)]))
   df.show(truncate=False)
   
   > +------------------------------------------------+
   > |timedelta_col                                   | 
   > +------------------------------------------------+ 
   > |INTERVAL '4498031 04:27:35.999981' DAY TO SECOND| 
   > +------------------------------------------------+
   
   print(str(td))
   
   >  '4498031 days, 4:27:34.999981' 
   ```
   
   In the above example, look at the seconds. The original python timedelta 
object has 34 seconds, the pyspark DayTimeIntervalType column has 35 seconds.
   
   
   ### Why are the changes needed?
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     1. If you propose a new API, clarify the use case for a new API.
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   To fix a bug. It is a bug because the wrong value is returned after 
conversion. Adding the above timedelta entry to existing unit tests causes the 
test to fail.
   
   ### Does this PR introduce _any_ user-facing change?
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   Note that it means *any* user-facing change including all aspects such as 
the documentation fix.
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   Yes. Users should now see the correct timedelta values in pyspark dataframes 
for similar such edge cases.
   
   
   ### How was this patch tested?
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   Illustrative edge case examples were added to the unit test 
(`python/pyspark/sql/tests/test_types.py` the `test_daytime_interval_type` 
test), verified that the existing code failed the test, new code was added, and 
verified that the unit test now passes.
   


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