Monotonic timers are paused during standby. Thus these timers won't fire
after waking up. Fall back to periodic polling to detect too large clock
jumps. See https://github.com/golang/go/issues/35012 for a discussion of
go timers during standby.
While searching for lock file from concurrently running restic
instances, restic ignored unreadable lock files. These can either be
in fact invalid or just be temporarily unreadable. As it is not really
possible to differentiate between both cases, just err on the side of
caution and consider the repository as already locked.
The code retries searching for other locks up to three times to smooth
out temporarily unreadable lock files.
Restic continued e.g. a backup task even when it failed to renew the
lock or failed to do so in time. For example if a backup client enters
standby during the backup this can allow other operations like `prune`
to run in the meantime (after calling `unlock`). After leaving standby
the backup client will continue its backup and upload indexes which
refer pack files that were removed in the meantime.
This commit introduces a goroutine explicitly monitoring for locks that
are not refreshed in time. To simplify the implementation there's now a
separate goroutine to refresh the lock and monitor for timeouts for each
lock. The monitoring goroutine would now cause the backup to fail as the
client has lost it's lock in the meantime.
The lock refresh goroutines are bound to the context used to lock the
repository initially. The context returned by `lockRepo` is also
cancelled when any of the goroutines exits. This ensures that the
context is cancelled whenever for any reason the lock is no longer
refreshed.
Previously the global context was either accessed via gopts.ctx,
stored in a local variable and then used within that function or
sometimes both. This makes it very hard to follow which ctx or a wrapped
version of it reaches which method.
Thus just drop the context from the globalOptions struct and pass it
explicitly to every command line handler method.
Some backends generate additional files for each existing file, e.g.
1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef
1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef.sha256
For some commands this leads to an "multiple IDs with prefix" error when
trying to reference a snapshot.
Failing to process data requested from the cache usually indicates a
problem with the returned data. Assume that the cache entry is somehow
damaged and retry downloading it once.
The cross compilation tasks are currently the slowest part of the CI
runs. Splitting it into three parts should reduce its time to roughly
that of the windows CI run.
Sparse files contain large regions containing only zero bytes. Checking
that a blob only contains zeros is possible with over 100GB/s for modern
x86 CPUs. Calculating sha256 hashes is only possible with 500MB/s (or
2GB/s using hardware acceleration). Thus we can speed up the hash
calculation for all zero blobs (which always have length
chunker.MinSize) by checking for zero bytes and then using the
precomputed hash.
The all zeros check is only performed for blobs with the minimal chunk
size, and thus should add no overhead most of the time. For chunks which
are not all zero but have the minimal chunks size, the overhead will be
below 2% based on the above performance numbers.
This allows reading sparse sections of files as fast as the kernel can
return data to us. On my system using BTRFS this resulted in about
4GB/s.
The restorer can issue multiple calls to WriteAt in parallel. This can
result in unexpected orderings of the Truncate and WriteAt calls and
sometimes too short restored files.
