In current aptly, each repository and snapshot has its own reflist in
the database. This brings a few problems with it:
- Given a sufficiently large repositories and snapshots, these lists can
get enormous, reaching >1MB. This is a problem for LevelDB's overall
performance, as it tends to prefer values around the confiruged block
size (defaults to just 4KiB).
- When you take these large repositories and snapshot them, you have a
full, new copy of the reflist, even if only a few packages changed.
This means that having a lot of snapshots with a few changes causes
the database to basically be full of largely duplicate reflists.
- All the duplication also means that many of the same refs are being
loaded repeatedly, which can cause some slowdown but, more notably,
eats up huge amounts of memory.
- Adding on more and more new repositories and snapshots will cause the
time and memory spent on things like cleanup and publishing to grow
roughly linearly.
At the core, there are two problems here:
- Reflists get very big because there are just a lot of packages.
- Different reflists can tend to duplicate much of the same contents.
*Split reflists* aim at solving this by separating reflists into 64
*buckets*. Package refs are sorted into individual buckets according to
the following system:
- Take the first 3 letters of the package name, after dropping a `lib`
prefix. (Using only the first 3 letters will cause packages with
similar prefixes to end up in the same bucket, under the assumption
that packages with similar names tend to be updated together.)
- Take the 64-bit xxhash of these letters. (xxhash was chosen because it
relatively good distribution across the individual bits, which is
important for the next step.)
- Use the first 6 bits of the hash (range [0:63]) as an index into the
buckets.
Once refs are placed in buckets, a sha256 digest of all the refs in the
bucket is taken. These buckets are then stored in the database, split
into roughly block-sized segments, and all the repositories and
snapshots simply store an array of bucket digests.
This approach means that *repositories and snapshots can share their
reflist buckets*. If a snapshot is taken of a repository, it will have
the same contents, so its split reflist will point to the same buckets
as the base repository, and only one copy of each bucket is stored in
the database. When some packages in the repository change, only the
buckets containing those packages will be modified; all the other
buckets will remain unchanged, and thus their contents will still be
shared. Later on, when these reflists are loaded, each bucket is only
loaded once, short-cutting loaded many megabytes of data. In effect,
split reflists are essentially copy-on-write, with only the changed
buckets stored individually.
Changing the disk format means that a migration needs to take place, so
that task is moved into the database cleanup step, which will migrate
reflists over to split reflists, as well as delete any unused reflist
buckets.
All the reflist tests are also changed to additionally test out split
reflists; although the internal logic is all shared (since buckets are,
themselves, just normal reflists), some special additions are needed to
have native versions of the various reflist helper methods.
In our tests, we've observed the following improvements:
- Memory usage during publish and database cleanup, with
`GOMEMLIMIT=2GiB`, goes down from ~3.2GiB (larger than the memory
limit!) to ~0.7GiB, a decrease of ~4.5x.
- Database size decreases from 1.3GB to 367MB.
*In my local tests*, publish times had also decreased down to mere
seconds but the same effect wasn't observed on the server, with the
times staying around the same. My suspicions are that this is due to I/O
performance: my local system is an M1 MBP, which almost certainly has
much faster disk speeds than our DigitalOcean block volumes. Split
reflists include a side effect of requiring more random accesses from
reading all the buckets by their keys, so if your random I/O
performance is slower, it might cancel out the benefits. That being
said, even in that case, the memory usage and database size advantages
still persist.
Signed-off-by: Ryan Gonzalez <ryan.gonzalez@collabora.com>
In some local tests w/ a slowed down filesystem, this massively cut down
on the time to clean up a repository by ~3x, bringing a total 'publish
update' time from ~16s to ~13s.
Signed-off-by: Ryan Gonzalez <ryan.gonzalez@collabora.com>
