A dataset represents a table with data, populated in certain way, with
certain characteristics of the schema and data.
Before this change, datasets were implicitly defined, with population
hard-coded inside the populate() function.
This change gathers logic related to datasets into classes, in order to:
- make it easier to define new datasets.
- be able to measure performance of dataset population in a
standardized way.
- being able to express constraints on datasets imposed by different
test cases. Test cases are matched with possible datasets based
on the abstract interface they accept (e.g. clustered_ds,
multipartition_ds), and which must be implemented by a compatible
dataset. To facilitate this matching, test function is now wrapped
into a dataset_acceptor object, with an automatically-generated can_run()
virtual method, deduced by make_test_fn().
- be able to select tests to run based on the dataset name.
Only tests which are compatible with that dataset will be run.
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