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From Koert Kuipers <ko...@tresata.com>
Subject Re: frustration with field names in Dataset
Date Thu, 02 Feb 2017 15:41:58 GMT
another example is if i have a Dataset[(K, V)] and i want to repartition it
by the key K. repartition requires a Column which means i am suddenly back
to worrying about duplicate field names. i would like to be able to say:

dataset.repartition(dataset(0))

On Thu, Feb 2, 2017 at 10:39 AM, Koert Kuipers <koert@tresata.com> wrote:

> since a dataset is a typed object you ideally don't have to think about
> field names.
>
> however there are operations on Dataset that require you to provide a
> Column, like for example joinWith (and joinWith returns a strongly typed
> Dataset, not DataFrame). once you have to provide a Column you are back to
> thinking in field names, and worrying about duplicate field names, which is
> something that can easily happen in a Dataset without you realizing it.
>
> so under the hood Dataset has unique identifiers for every column, as in
> dataset.queryExecution.logical.output, but these are expressions
> (attributes) that i cannot turn back into columns since the constructors
> for this are private in spark.
>
> so.... how about having Dataset.apply(i: Int): Column to allow me to pick
> columns by position without having to worry about (duplicate) field names?
> then i could do something like:
>
> dataset.joinWith(otherDataset, dataset(0) === otherDataset(0), joinType)
>

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