Github user gatorsmile commented on the pull request:
https://github.com/apache/spark/pull/9548#issuecomment-155835390
@cloud-fan Before discussing the solution details, let us first talk about
the design issues.
IMO, the `DataFrame` is a query language, kind of a dialect of SQL. Or,
maybe, SQL is a dialect of `DataFrame`. We need to formalize it and clearly
define the concepts of each major classes like `DataFrame` and `Column`. If
`Column` represents a concept independent of `DataFrame`, could you define what
it is? If one `Column` with the same ID can appear in different `DataFrame`,
how to enforce such a "referential integrity" between different `DataFrame`? If
two `Column` with different ID could represent the same entity, should we keep
such a relation for generating a better physical plan?
In the current implementation, each `Column` actually corresponds to an
expression in logical plans, but we are unable to apply an expression above
`Column` instances to generate a new expression. So far, `Column` is kind of a
wrapper, but it is not a subclass of `TreeNode`.
When more components are built on `DataFrame` to access and operate, we
have to carefully think about this problem. If possible, I think we need to
resolve it in the release of Spark 2.0.
Will answer your design suggestion in a separate post.
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