After updating the worktree for an add/drop, update git's index, so git
status will not show the files as modified.
What actually happens is that the index update removes the inode
information from the index. The next git status (or similar) run
then has to do some work. It runs the clean filter.
So, this depends on the clean filter being reasonably fast and on git
not leaking memory when running it. Both problems were fixed in
a96972015d, but only for git 2.5. Anyone
using an older git will see very expensive git status after an add/drop.
This uses the same git update-index queue as other parts of git-annex, so
the actual index update is fairly efficient. Of course, updating the index
does still have some overhead. The annex.queuesize config will control how
often the index gets updated when working on a lot of files.
This is an imperfect workaround... Added several todos about new
problems this workaround causes. Still, this seems a lot better than the
old behavior.
This commit was supported by the NSF-funded DataLad project.
On second thought, git passes filepaths, which may not be valid utf8, so
can't use Text here.
String will be a little bit slower, but not enough to worry about.
Git uses pkt-line in the pack and http protocols, and for the long-running
filter processes protocol as well.
This should be a quite efficient parser and builder since it uses
attoparsec and bytestring-builder.
This adds a dependency on attoparsec, but it's a free dependency because
eg aeson depends on attoparsec and git-annex depends on aeson.
This commit was supported by the NSF-funded DataLad project.
v6 add: Take advantage of improved SIGPIPE handler in git 2.5 to speed up
the clean filter by not reading the file content from the pipe. This also
avoids git buffering the whole file content in memory.
When built with an older git, still consumes stdin. If built with a newer
git and used with an older one, it breaks, but that's acceptable --
checking the git version every time would make repeated smudge runs slow.
This commit was supported by the NSF-funded DataLad project.
