Writes are optimised by queueing up multiple writes when possible.
The queue is flushed after the Annex monad action finishes. That makes it
happen on program termination, and also whenever a nested Annex monad action
finishes.
Reads are optimised by checking once (per AnnexState) if the database
exists. If the database doesn't exist yet, all reads return mempty.
Reads also cause queued writes to be flushed, so reads will always be
consistent with writes (as long as they're made inside the same Annex monad).
A future optimisation path would be to determine when that's not necessary,
which is probably most of the time, and avoid flushing unncessarily.
Design notes for this commit:
- separate reads from writes
- reuse a handle which is left open until program
exit or until the MVar goes out of scope (and autoclosed then)
- writes are queued
- queue is flushed periodically
- immediate queue flush before any read
- auto-flush queue when database handle is garbage collected
- flush queue on exit from Annex monad
(Note that this may happen repeatedly for a single database connection;
or a connection may be reused for multiple Annex monad actions,
possibly even concurrent ones.)
- if database does not exist (or is empty) the handle
is not opened by reads; reads instead return empty results
- writes open the handle if it was not open previously
Fsck can use the queue for efficiency since it is write-heavy, and only
reads a value before writing it. But, the queue is not suited to the Keys
database.
The problem is that shutdown is not always called, particularly in the test
suite. So, a database connection would be opened, possibly some changes
queued, and then not shut down.
One way this can happen is when using Annex.eval or Annex.run with a new
state. A better fix might be to make both of them call Keys.shutdown
(and be sure to do it even if the annex action threw an error).
Complication: Sometimes they're run reusing an existing state, so shutting
down a database connection could cause problems for other users of that
same state. I think this would need a MVar holding the database handle,
so it could be emptied once shut down, and another user of the database
connection could then start up a new one if it got shut down. But, what if
2 threads were concurrently using the same database handle and one shut it
down while the other was writing to it? Urgh.
Might have to go that route eventually to get the database access to run
fast enough. For now, a quick fix to get the test suite happier, at the
expense of speed.
If a DbHandle is in use by another thread, it could be queueing changes
while shutdown is running. So, wait for the worker to finish before
flushing the queue, so that any last-minute writes are included. Before
this fix, they would be silently dropped.
Of course, if the other thread continues to try to use a DbHandle once it's
closed, it will block forever as the worker is no longer reading from the
jobs MVar. So, that would crash with
"thread blocked indefinitely in an MVar operation".
The Keys database can hold multiple inode caches for a given key. One for
the annex object, and one for each pointer file, which may not be hard
linked to it.
Inode caches for a key are recorded when its content is added to the annex,
but only if it has known pointer files. This is to avoid the overhead of
maintaining the database when not needed.
When the smudge filter outputs a file's content, the inode cache is not
updated, because git's smudge interface doesn't let us write the file. So,
dropping will fall back to doing an expensive verification then. Ideally,
git's interface would be improved, and then the inode cache could be
updated then too.
Renamed the db to keys, since it is various info about a Keys.
Dropping a key will update its pointer files, as long as their content can
be verified to be unmodified. This falls back to checksum verification, but
I want it to use an InodeCache of the key, for speed. But, I have not made
anything populate that cache yet.
The one exception is in Utility.Daemon. As long as a process only
daemonizes once, which seems reasonable, and as long as it avoids calling
checkDaemon once it's already running as a daemon, the fcntl locking
gotchas won't be a problem there.
Annex.LockFile has it's own separate lock pool layer, which has been
renamed to LockCache. This is a persistent cache of locks that persist
until closed.
This is not quite done; lockContent stil needs to be converted.
Also, moved the database to a subdir, as there are multiple files.
This seems to work well with concurrent fscks, although they still do
redundant work due to the commit granularity. Occasionally two writes will
conflict, and one is then deferred and happens later.
Except, with 3 concurrent fscks, I got failures:
git-annex: user error (SQLite3 returned ErrorBusy while attempting to perform prepare "SELECT \"fscked\".\"key\"\nFROM \"fscked\"\nWHERE \"fscked\".\"key\" = ?\n": database is locked)
Argh!!!
Still not robust enough. I have 3 fscks running concurrently, and am
seeing:
("commit deferred",user error (SQLite3 returned ErrorBusy while attempting
to perform step.))
and
git-annex: user error (SQLite3 returned ErrorBusy while attempting to perform prepare "SELECT \"fscked\".\"key\"\nFROM \"fscked\"\nWHERE \"fscked\".\"key\" = ?\n": database is locked)
Sqlite doesn't support multiple concurrent writers
at all. One of them will fail to write. It's not even possible to have two
processes building up separate transactions at the same time. Before using
sqlite, incremental fsck could work perfectly well with multiple fsck
processes running concurrently. I'd like to keep that working.
My partial solution, so far, is to make git-annex buffer writes, and every
so often send them all to sqlite at once, in a transaction. So most of the
time, nothing is writing to the database. (And if it gets unlucky and
a write fails due to a collision with another writer, it can just wait and
retry the write later.) This lets multiple processes write to the database
successfully.
But, for the purposes of concurrent, incremental fsck, it's not ideal.
Each process doesn't immediately learn of files that another process has
checked. So they'll tend to do redundant work.
Only way I can see to improve this is to use some other mechanism for
short-term IPC between the fsck processes. Not yet done.
----
Also, make addDb check if an item is in the database already, and not try
to re-add it. That fixes an intermittent crash with
"SQLite3 returned ErrorConstraint while attempting to perform step."
I am not 100% sure why; it only started happening when I moved write
buffering into the queue. It seemed to generally happen on the same file
each time, so could just be due to multiple files having the same key.
However, I doubt my sound repo has many duplicate keys, and I suspect
something else is going on.
----
Updated benchmark, with the 1000 item queue: 6m33.808s
Turns out sqlite does not like having its database deleted out from
underneath it. It might suffice to empty the table, but I would rather
start each fsck over with a new database, so I added a lock file, and
running incremental fscks use a shared lock.
This leaves one concurrency bug left; running two concurrent fsck --more
will lead to: "SQLite3 returned ErrorBusy while attempting to perform step."
and one or both will fail. This is a concurrent writers problem.
Database.Handle can now be given a CommitPolicy, making it easy to specify
transaction granularity.
Benchmarking the old git-annex incremental fsck that flips sticky bits
to the new that uses sqlite, running in a repo with 37000 annexed files,
both from cold cache:
old: 6m6.906s
new: 6m26.913s
This commit was sponsored by TasLUG.
Did not keep backwards compat for sticky bit records. An incremental fsck
that is already in progress will start over on upgrade to this version.
This is not yet ready for merging. The autobuilders need to have sqlite
installed.
Also, interrupting a fsck --incremental does not commit the database.
So, resuming with fsck --more restarts from beginning.
Memory: Constant during a fsck of tens of thousands of files.
(But, it does seem to buffer whole transation in memory, so
may really scale with number of files.)
CPU: ?
