Hi,
I have the following schema:
-root
|-timestamp
|-date
|-year
|-month
|-day
|-some_column
|-some_other_column
I'd like to achieve either of these:
1) Use the timestamp field to partition by year, month and day.
This looks weird though, as Spark wouldn't magically know how to load the data
back since the year, month and day columns don't exist in the schema.
2) If 1) is not possible, partition data by date.year, date.month and date.day.
`df.write.partitionBy('date.year')` does not work, since the `date.year` column
does not exist in the schema.
If 2) isn't possible either, I'll just move year, month and day to the root of
the schema, which I don't like as it bloats it.
Do you know if any of these is possible?
Thank you,
Samy
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