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@viirya Thanks for the reply and additional info. My (mis)understanding was
that when I access the individual struct fields that compose a UDT (say, in an
Expression), that I was actually accessing
Github user viirya commented on the issue:
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@metasim Thanks for comment. This patch takes the approach to use Scala
data types on UDT and let SparkSQL's encoder to convert user data to internal
format.
I'm not sure which part the
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Does this PR remove the ability to read directly from Tungsten memory, in
cases where one wants to avoid the serialization/deserialization costs? IOW, in
an `Expression` over a UDT, can one still
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Thanks @lossyrob for sharing those examples here. As a followup, I want to
point out that my experience extending `UserDefinedType[_]`, as an external
user, has been very positive, and feels like a
Github user lossyrob commented on the issue:
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Using geospatial data types with Spark SQL require UDTs, and solutions are
hacking around the fact that they are private to the package. e.g.:
-
Github user MLnick commented on the issue:
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The JIRA has 37 watchers (ranked 17 across all Spark JIRAs) and 22 votes
(ranked 8 overall across all JIRAs). I'd have thought that's some indication of
user interest.
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Github user rxin commented on the issue:
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I don't know how important it is. It seems like it's primarily used by
MLlib and very few other things ...
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Is there no SQL committer support for this? Seems like a critical feature
for Spark users with no response from any SQL folks.
Making UDT public in some way is pretty important no?
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also cc @cloud-fan @hvanhovell
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Github user viirya commented on the issue:
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ping @rxin @marmbrus Can you take a brief look and let me know what you
think about this change to UDT? Thank you.
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cc @marmbrus @rxin @mengxr User defined types (UDT) is a critical feature
to developers. As we have marked current UDT API private for a while,
developers can't use UDT and that is a problem for
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