This removes the switch for using a Badger database, because it has bugs
that it seems there is no interest in fixing, and no actual bug tracker
to track them in.
It retains the actual implementation for the sole purpose of being able
to do the conversion back to LevelDB if anyone is actually running with
USE_BADGER. At some point in a couple of versions we can remove the
implementation as well.
Since iterators must be released before committing or discarding a
transaction we have the pattern of both deferring a release plus doing
it manually. But we can't release twice because we track this with a
WaitGroup that will panic when there are more Done()s than Add()s. This
just adds a boolean to let an iterator keep track.
If the GC finds a key k that it wants to keep, it records that in a
Bloom filter. If a key k' can be removed but its hash collides with k,
it will be kept. Since the old Bloom filter code was completely
deterministic, the next run would encounter the same collision, assuming
k must still be kept.
A randomized hash function that uses all the SHA-256 bits solves this
problem: the second run has a non-zero probability of removing k', as
long as the Bloom filter is not completely full.
This makes Go 1.15 test/vet happy, avoiding "conversion from untyped int
to string yields a string of one rune" warning where we do
string(KeyTypeWhatever) in namespaced.go.
It also clarifies and enforces the currently allowed range of these
numbers so I think it's fine.
This matches the convention of the stdlib and avoids ambiguity: when
customErr{} and &customErr{} both implement error, client code needs to
check for both.
Memory use should remain the same, since storing a non-pointer type in
an interface value still copies the value to the heap.
Group the global list of files by version, instead of having one flat list for all devices. This removes lots of duplicate protocol.Vectors.
Co-authored-by: Jakob Borg <jakob@kastelo.net>
This reduces the size of our write batches before we flush them. This
has two effects: reducing the amount of data lost if we crash when
updating the database, and reducing the amount of memory used when we do
large updates without checkpoint (e.g., deleting a folder).
I ran our SyncManyFiles benchmark as it is the one doing most
transactions, however there was no relevant change in any metric (it's
limited by our fsync I expect). This is good as any visible change would
just be a decrease in performance.
I don't have a benchmark on deleting a large folder, taking that part on
trust for now...
Simon FreiandGitHub
Jakob BorgandGitHub
Audrius ButkeviciusandGitHub
greatroarandGitHub
André ColombandGitHub