The previous system of relying on test name prefixes for ordering was
simple, but organizing tests by dependencies and topologically sorting
during compilation is 1. more flexible and 2. simplifies test names,
which get typed a lot.
Note these are not "hard" dependencies, each test suite should work fine
in isolation. These "after" dependencies just hint an ordering when all
tests are ran.
As such, it's worth noting the tests should NOT error of a dependency is
missing. This unfortunately makes it a bit hard to catch typos, but
allows faster compilation of a subset of tests.
---
To make this work the way tests are linked has changed from using custom
linker section (fun linker magic!) to a weakly linked array appended to
every source file (also fun linker magic!).
At least with this method test.py has strict control over the test
ordering, and doesn't depend on 1. the order in which the linker merges
sections, and 2. the order tests are passed to test.py. I didn't realize
the previous system was so fragile.
It doesn't make sense to test more complex logic, such as t2_btree.toml,
when the logic it is built on, t1_rbyd.toml, does not past testing. The
test runner already guarantees a consistent lexicographic order, so all
we need to do is renamed these from test_* -> tn_*.
Note, if we every have more than 10 tests, we will need to bump up the
number of digits for all tests, so t1_rbyd.toml -> t01_rbyd.toml. This
is the main downside of lexicographic ordering. But we'll cross that
bridge when we get to it.
Note that because we amortize the traversal cost over the number of
entries, mtree traversal may have some strange looking results when
compared to mtree lookup.
Though it's interesting to note this is a valid result. In mtree lookups
we need to fetch the mdir for each entry, which is expensive. However
mtree traversal can strictly avoid fetching each mdir more than once.
This does make mdir traversal faster when iterating over all mdirs in
order.
This can be represented in big O notation if we treat the number of
entries (n) and block size (b) as variables:
- mtree traversal via lookup = O(nb+nlog(b)logb(n))
- mtree traversal via traversal = O(nlog(b)logb(n))
It's interesting to note the different performance characteristics of
purely CoW btrees vs our mutable mtree.
The main downside of our mtree is the need to fetch leaf mdirs. This
fetch is expensive, and can be avoided in CoW btrees by storing the
trunk in each branch's parent.
On the other hand, btrees need to propagate all changes upwards to the
root.
An interesting takeaway is that a sort of mdir-trunk cache may be a very
interesting optimization for relatively little RAM cost. This may be
something to explore in the future.
Christopher Haster