In 2013, I wrote "Cryptohash benchmarks 90 to 101% faster than external
hashers". Re-benchmarking today, I found cryptonite's sha256 consistently
outperformed coreutils by 10% for large files. Tested 10 mb, 100 mb, 1 gb
files with both sha256 and sha512. And for smaller files, the external
process startup time swamps the hash time.
Perhaps cryptonite has improved. Or it could just do better on my
current CPU Intel(R) Pentium(R) CPU 4410Y @ 1.50GHz). Anyway, even if cryptonite
is slower in some situations, seems likely it would only be marginally slower;
it's got the same class of highly optimised C code under the hood as coreutils.
The main difference between the two sha256 implementations seems to be
how much of the inner loop they unroll..
This commit was sponsored by Henrik Riomar on Patreon.
Probably not noticed until now because the queue is large enough that two
threads each filling theirs at the same time and flushing is unlikely to
happen.
Also made explicit that each worker thread gets its own queue.
I think that was the case before, but if something was put in the queue
before worker threads were forked off, they could have each inherited the
same queue.
Could have gone with a single shared queue, but per-worker queues is more
efficient, because a worker can add lots of stuff to its own queue without
any locking.
This commit was sponsored by Ole-Morten Duesund on Patreon.
Avoids annex.largefiles inconsitency and also avoids a lot of
unneccessary calls to the clean filter when a large repo's clone
is being initialized.
This commit was supported by the NSF-funded DataLad project.
v6: When annex.largefiles is not configured for a file, running git add or
git commit, or otherwise using git to stage a file will add it to the annex
if the file was in the annex before, and to git otherwise. This is to avoid
accidental conversion.
Note that git-annex add's behavior has not changed, for reasons explained
in the added comment.
Performance: No added overhead when annex.largefiles is configured.
When not configured, there is an added call to catObjectMetaData,
which involves a round trip through git cat-file --batch.
However, the earlier catKeyFile primes the cache for it.
This commit was supported by the NSF-funded DataLad project.
Last of the known v6 races.
This also makes git add of a pointer file populate it when its content
is present in the annex. Which makes sense to do, I think.
This commit was supported by the NSF-funded DataLad project.
Update pointer file next time reconcileStaged is run to recover from the
race.
Note that restagePointerFile causes git to run the clean filter,
and that will run reconcileStaged. So, normally by the time the git
annex get/drop command finishes, the race has already been dealt with.
It may be that, in some case, that won't happen and the race will be
dealt with at a later point. git-annex could run reconcileStaged at
shutdown if that becomes a problem.
This does not handle the situation where the git mv is committed before
git-annex gets a chance to run again. git commit does run the clean
filter, and that happens to re-inject the content if it was supposed to
be dropped but is still populated. But, the case where the file was
supposed to be gotten but is not populated is not handled yet.
This commit was supported by the NSF-funded DataLad project.
If a pointer file is being populated and something modifies it at the
same time, there was a race there the modified file's InodeCache
could get added into the keys database.
Note that replaceFile normally renames the temp file into place, so the
inode cache caculated for the temp file will still be good. If it has to
fall back to a copy, the worktree file won't be put in the inode cache.
This has the same result as if the worktree file gets touched, and will
be handled the same way. Eg, when dropping, isUnmodified will do an
expensive comparison and notice that the worktree file does have the
same content, and so drop it.
This commit was supported by the NSF-funded DataLad project.
It can be set to an empty string to use the system locales too. Of course
whether that will work depends on the amount of divergence.
This commit was supported by the NSF-funded DataLad project.
