GitBackend will use it to configure gix::Repository. I think UserSettings
is generally useful to pass store-specific parameters, so I've updated all
factory functions.
Since the concurrent diff algorithm is significantly slower when using
the Git backend, I think we'll have to use switch between the two
algorithms depending on backend. Even if the concurrent version always
performed as well as the sequential version, exactly how concurrent it
should be probably still depends on the backend. This commit therefore
adds a function to the `Backend` trait, so each backend can say how
much concurrency they deal well with. I then use that number for
choosing between the sequential and concurrent versions in
`MergedTree::diff_stream()`, and also to decide the number of
concurrent reads to do in the concurrent version.
This avoids https://github.com/rust-lang/futures-rs/issues/2090. I
don't think we need to worry about reading legacy conflicts
asynchronously - async is really only useful for Google's backend
right now, and we don't use the legacy format at Google. In
particular, I don't want `MergedTree::value()` to have to be async.
`ReadonlyRepo::init()` takes callbacks for initializing each kind of
backend. We called these things like `op_store_initializer`. I found
that confusing because it is not a `OpStoreFactory` (which is for
loading an existing backend). This patch tries to clarify that by
renaming the arguments and adding types for each kind of callback
function.
The commit backend at Google is cloud-based (and so are the other
backends); it reads and writes commits from/to a server, which stores
them in a database. That makes latency much higher than for disk-based
backends. To reduce the latency, we have a local daemon process that
caches and prefetches objects. There are still many cases where
latency is high, such as when diffing two uncached commits. We can
improve that by changing some of our (jj's) algorithms to read many
objects concurrently from the backend. In the case of tree-diffing, we
can fetch one level (depth) of the tree at a time. There are several
ways of doing that:
* Make the backend methods `async`
* Use many threads for reading from the backend
* Add backend methods for batch reading
I don't think we typically need CPU parallelism, so it's wasteful to
have hundreds of threads running in order to fetch hundreds of objects
in parallel (especially when using a synchronous backend like the Git
backend). Batching would work well for the tree-diffing case, but it's
not as composable as `async`. For example, if we wanted to fetch some
commits at the same time as we were doing a diff, it's hard to see how
to do that with batching. Using async seems like our best bet.
I didn't make the backend interface's write functions async because
writes are already async with the daemon we have at Google. That
daemon will hash the object and immediately return, and then send the
object to the server in the background. I think any cloud-based
solution will need a similar daemon process. However, we may need to
reconsider this if/when jj gets used on a server with a custom backend
that writes directly to a database (i.e. no async daemon in between).
I've tried to measure the performance impact. That's the largest
difference I've been able to measure was on `jj diff
--ignore-working-copy -s --from v5.0 --to v6.0` in the Linux repo,
which increases from 749 ms to 773 ms (3.3%). In most cases I've
tested, there's no measurable difference. I've tried diffing from the
root commit, as well as `jj --ignore-working-copy log --no-graph -r
'::v3.0 & author(torvalds)' -T 'commit_id ++ "\n"'` (to test a
commit-heavy load).
