Most of our tests use overly simplistic schemas (`simple_schema`) or
very specialized ones that focus on exercising a specific area of the
tested code. This is fine in most places as not all code is schema
dependent, however practice has showed that there can be nasty bugs
hiding in dark corners that only appear with a schema that has a
specific combination of types.
This series introduces `tests::random_schema` a utility class for
generating random schemas and random data for them. An important goal is
to make using random schemas in tests as simple and convenient as
possible, therefore fostering the appearance of tests using random
schemas.
Random schema was developed to help testing code I'm currently working
on, which segregates data by time-windows. As I wasn't confident in my
ability to think of every possible combination of types that can break
my code I came up with random-schema to help me finding these corner
cases. So far I consider it a success, it already found bugs in my code
that I'm not sure I would have found if I had relied on specific
schemas. It also found bugs in unrelated areas of the code which proves
my point in the first paragraph.
* https://github.com/denesb/scylla.git random_schema/v5:
tests/data_model: approximate to the modeled data structures
data_value: add ascii constructor
tests/random-utils.hh: add stepped_int_distribution
tests/random-utils.hh: get_int() add overloads that accept external
rand engine
tests/random-utils.hh: add get_real()
tests: introduce random_schema
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