Github user deanchen commented on the issue:
https://github.com/apache/spark/pull/13912
@srowen @rxin Would love to see this get merged as this has been a pain
point for us. Not a fan of timezoneless dates as an engineer but the need to
passthrough or write timezoneless dates to csv's has been a necessary task due
to a variety of reasons in the past.
We use Spark to parse large amounts of daily financial asset data with
pre-agreed upon date conventions. Most of the dates we deal doesn't belong to a
timezone and are treated more like indices with the maximum granularity of a
day. The CSV reports we produce for our customers do not ever contain timezone
as a result and almost all dates as passthrough from the original timezoneless
dates.
This is not just a common pattern with financial data, but also common
practice in large companies I've worked at in the past. A large tech company
I've worked at in the past used the convention of MST date and datetime for all
internal systems(database included).
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