Alternatives for .skf format

[johnlees]

The current .skf format has the following issues:

• It can become very large, especially for diverse datasets. As it is all deserialised (see #22 for attempts at altering this) this means any operation, including looking at metadata is potentially slow.
• The entire object needs to fit in main memory.

This would be a large and breaking change. I think if we do this, it would make sense to have a second file format (.ska/.sklarge/.h5?) which can be selected by the user for these cases, and continue to allow the previous .skf format to work as the default.

File format alternatives

For the file format, HDF5 comes to mind (which uses btrees too), but the rust bindings require the library to be installed, which is the main disadvantage (but there is good support for building it). Range queries/slices of datasets can be returned, and it's easy to add attributes to the same file. So it definitely fits the bill here.

An alternative would be Apache Parquet which has a native rust implementation, and snap compression. This would be suitable for the kmers and variants array, but it would make more sense to store the other fields (version, k size etc) with serde as before. To keep these together as a single file, could we just use tar? Starting to feel too complex imo.

Streaming from disk

For the second issue, blocks of btrees by range, and careful merging, could allow streaming of the relevant parts during build & align phases. For example see https://github.com/wspeirs/btree

Both file formats above would work here. Arrow can read and write blocks of rows. HDF5 can take a slice/range query.

Other notes

I feel like serde should be capable of at least some of this, see e.g. https://serde.rs/stream-array.html and https://serde.rs/ignored-any.html. But intial attempts with the current format weren't working well and I'm not sure why, so if it needs to be changed anyway introducing a format more designed for streaming operations might be sensible.

[johnlees]

See also https://docs.incorta.com/5.2/tools-apache-parquet-merge-tool

[johnlees]

Talking to Tommi, he suggests that better compression and working on the compressed structure may be preferable. Some ideas:

• The split k-mers are difficult to compress. A tree structure would be one option, possibly having reinserted the middle base. As the number of samples grows larger it is the variant array not the split k-mer that take up more space (e.g. in the 28 Lm example, these are 31M or 18M gzipped).
• Most of the space is taken up by the variants, and these are highly redundant, especially the constant sites (in the Lm example ~300Mb, but ~5Mb gzipped). Going back to the idea of bitvectors for A, C, G, T for these, and not explicitly storing constant sites would likely help a lot. Tommi suggests: http://bitmagic.io/ which supports some useful operations on the compressed data.

[johnlees]

Tommi suggests:

• Static bitvector of length 4 to encode each base in variant matrix, whole matrix is then a Vec of these (with correct slicing to get the index)
• Separate constant sites
• wrt bitmagic, works for I/O and streaming in this format. Advanced operations not yet available, but this aren't necessarily needed.

[johnlees]

See branch const-refactor for a prototype But for this to work properly it needs to either be bit packed and not many vectors, or a sparse array