The new diff algorithm produces pretty bad diffs in some cases, such
as cc4b1e9230 in this repo (the parent of this commit). I think the
problem there is that many words are repeated over and over. Diffing
first at the line level and then refining the diff of the changed
ranges at the word level gives much better results. That's what this
patch does. After this patch, `jj diff -r cc4b1e923091` looks pretty
similar to the diff in GitHub's UI.
I hope to get around to doing the same for the merge code soon.
Impact on benchmarks:
Before:
test bench_diff_10k_lines_reversed ... bench: 42,647,532 ns/iter (+/- 765,347)
test bench_diff_10k_modified_lines ... bench: 21,407,980 ns/iter (+/- 126,366)
test bench_diff_10k_unchanged_lines ... bench: 4,235 ns/iter (+/- 16)
test bench_diff_1k_lines_reversed ... bench: 1,190,483 ns/iter (+/- 7,192)
test bench_diff_1k_modified_lines ... bench: 1,919,766 ns/iter (+/- 9,665)
test bench_diff_1k_unchanged_lines ... bench: 231 ns/iter (+/- 1)
test bench_diff_git_git_read_tree_c ... bench: 174,702 ns/iter (+/- 1,199)
After:
test bench_diff_10k_lines_reversed ... bench: 38,289,509 ns/iter (+/- 129,004)
test bench_diff_10k_modified_lines ... bench: 33,140,659 ns/iter (+/- 3,989,339)
test bench_diff_10k_unchanged_lines ... bench: 3,099 ns/iter (+/- 14)
test bench_diff_1k_lines_reversed ... bench: 973,551 ns/iter (+/- 94,895)
test bench_diff_1k_modified_lines ... bench: 3,033,818 ns/iter (+/- 29,513)
test bench_diff_1k_unchanged_lines ... bench: 230 ns/iter (+/- 1)
test bench_diff_git_git_read_tree_c ... bench: 79,100 ns/iter (+/- 963)
So most of them get slower, as expected. The last one, taken from a
real diff in the git.git repo, get faster, however (which is also what
I would have expected).
The current diff algorithm does a full LCS on the words of the texts,
which is really slow. Diffing the working copy when e.g.
`src/commands.py` has changes far apart takes seconds. This patch adds
an implementation inspired by JGit's Histogram diff. I say "inspired"
because I just didn't quite understand it :P In particular, I didn't
understand what it does when it finds non-unique elements. I decided
to line up the leading common elements on both sides of the merge. I
don't know if that usually gives good enough results in practice.
I'm sure this can still be optimized a lot, but this seems good enough
as a start. There is also many things to improve about the quality of
the diffs.
