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Kmer index
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Fundamental invariant
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DataStore — slot-indexed data
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Distance matrix API on DataStore types
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Full distance matrices
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Traits — obicompactvec::traits
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LayeredStore< S> — obilayeredmap
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Normalised metrics — two-pass cascade
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Parallelism model
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Point query — kmer → Option< Item>
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DataStore derivation
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Fundamental invariant
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Three-level hierarchy
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MphfLayer — autonomous mapping layer
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DataStore — slot-indexed data
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Distance matrix API on DataStore types
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Full distance matrices
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Partial distance matrices
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Progressive aggregation principle
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Traits — obicompactvec::traits
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LayeredStore< S> — obilayeredmap
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Normalised metrics — two-pass cascade
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Parallelism model
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Query model
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Point query — kmer → Option< Item>
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Aggregation — → Result
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DataStore derivation
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Relationship to current implementation
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What is implemented
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< span class = "md-ellipsis" >
What is not yet implemented
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< li class = "md-nav__item" >
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< span class = "md-ellipsis" >
Planned refactoring
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2026-05-15 21:07:23 +08:00
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< div class = "md-content" data-md-component = "content" >
< article class = "md-content__inner md-typeset" >
< h1 id = "kmer-index-architecture" > Kmer index architecture</ h1 >
< h2 id = "fundamental-invariant" > Fundamental invariant</ h2 >
< p > A given canonical kmer belongs to < strong > exactly one partition</ strong > and < strong > exactly one layer</ strong > within that partition. This is the property that makes all aggregation operations decomposable and parallelisable without coordination.</ p >
< hr />
< h2 id = "three-level-hierarchy" > Three-level hierarchy</ h2 >
< div class = "highlight" >< pre >< span ></ span >< code > PartitionedIndex
├── LayeredPartition (one per minimiser bucket)
│ ├── MphfLayer 0 kmer → slot (immutable bijection)
│ │ ├── DataStore A slot → T (e.g. counts)
│ │ └── DataStore B slot → T (e.g. presence/absence, derived)
│ ├── MphfLayer 1
│ │ └── DataStore A
│ └── ...
├── LayeredPartition
│ └── ...
</ code ></ pre ></ div >
< p >< strong > PartitionedIndex</ strong > : routes queries to partitions via canonical minimiser hash. Owns the partition count and routing scheme (fixed at creation). Dispatches aggregations across partitions in parallel.</ p >
< p >< strong > LayeredPartition</ strong > : one directory per minimiser bucket. Holds a < code > Vec< MphfLayer> </ code > . Each layer covers a disjoint kmer set — layer 0 is built from dataset A; layer 1 covers kmers in B absent from layer 0; and so on. Layers within a partition are always disjoint.</ p >
< p >< strong > MphfLayer</ strong > : the MPHF + evidence + unitig spine. Maps < code > kmer → slot</ code > for its disjoint kmer set. Immutable once built. Independent of any data attached to it.</ p >
< p >< strong > DataStore</ strong > : a slot-indexed data array (e.g. < code > PersistentCompactIntMatrix</ code > , < code > PersistentBitMatrix</ code > ). Attached to a < code > MphfLayer</ code > externally. Multiple stores of different types can coexist on the same < code > MphfLayer</ code > .</ p >
< hr />
< h2 id = "mphflayer-autonomous-mapping-layer" > MphfLayer — autonomous mapping layer</ h2 >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "n" > MphfLayer</ span >< span class = "p" > ::</ span >< span class = "n" > find</ span >< span class = "p" > (</ span >< span class = "n" > kmer</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nc" > CanonicalKmer</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nb" > Option</ span >< span class = "o" > < </ span >< span class = "kt" > usize</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // slot, or None if absent</ span >
< span class = "n" > MphfLayer</ span >< span class = "p" > ::</ span >< span class = "n" > n</ span >< span class = "p" > ()</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "kt" > usize</ span >< span class = "w" > </ span >< span class = "c1" > // number of slots</ span >
< span class = "n" > MphfLayer</ span >< span class = "p" > ::</ span >< span class = "n" > build</ span >< span class = "p" > (</ span >< span class = "n" > dir</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Path</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > OLMResult</ span >< span class = "o" > < </ span >< span class = "p" > (</ span >< span class = "bp" > Self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "kt" > usize</ span >< span class = "p" > )</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // from unitigs.bin</ span >
< span class = "n" > MphfLayer</ span >< span class = "p" > ::</ span >< span class = "n" > open</ span >< span class = "p" > (</ span >< span class = "n" > dir</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Path</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > OLMResult</ span >< span class = "o" > < </ span >< span class = "bp" > Self</ span >< span class = "o" > > </ span >
</ code ></ pre ></ div >
< p >< code > find</ code > returns < code > Some(slot)</ code > only if the kmer is actually in this layer (evidence check included). Returns < code > None</ code > for kmers present in other layers or absent from the index.</ p >
< p > The MPHF (< code > mphf.bin</ code > , < code > evidence.bin</ code > , < code > unitigs.bin</ code > ) is built once and never rebuilt. All data derivation operations (count → presence, thresholding, merging) reuse the same < code > MphfLayer</ code > .</ p >
< hr />
< h2 id = "datastore-slot-indexed-data" > DataStore — slot-indexed data</ h2 >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "k" > trait</ span >< span class = "w" > </ span >< span class = "n" > DataStore</ span >< span class = "w" > </ span >< span class = "p" > {</ span >
< span class = "w" > </ span >< span class = "k" > type</ span >< span class = "w" > </ span >< span class = "nc" > Item</ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > get</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > slot</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kt" > usize</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Self</ span >< span class = "p" > ::</ span >< span class = "n" > Item</ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > n</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "kt" > usize</ span >< span class = "p" > ;</ span >
< span class = "p" > }</ span >
</ code ></ pre ></ div >
< p > Concrete types from < code > obicompactvec</ code > :</ p >
< table >
< thead >
< tr >
< th > Type</ th >
< th >< code > Item</ code ></ th >
< th > Column stats</ th >
< th > Use</ th >
</ tr >
</ thead >
< tbody >
< tr >
< td >< code > PersistentCompactIntMatrix</ code ></ td >
< td >< code > Box< [u32]> </ code ></ td >
< td >< code > sum() -> Array1< u64> </ code ></ td >
< td > count per sample per slot</ td >
</ tr >
< tr >
< td >< code > PersistentBitMatrix</ code ></ td >
< td >< code > Box< [bool]> </ code ></ td >
< td >< code > count_ones() -> Array1< u64> </ code ></ td >
< td > presence per sample per slot</ td >
</ tr >
</ tbody >
</ table >
< p >< code > sum()</ code > and < code > count_ones()</ code > are the bridge between the per-matrix level and cross-layer aggregation: they give the total weight of each column within one (partition, layer) pair, which can be summed to get global column weights.</ p >
< p > A < code > DataStore</ code > knows nothing about kmers or MPHFs. It is indexed by < code > usize</ code > slot only.</ p >
< hr />
< h2 id = "distance-matrix-api-on-datastore-types" > Distance matrix API on DataStore types</ h2 >
< p > Both < code > PersistentCompactIntMatrix</ code > and < code > PersistentBitMatrix</ code > expose two families of distance matrix methods.</ p >
< h3 id = "full-distance-matrices" > Full distance matrices</ h3 >
< p > Compute the final < code > n_cols × </ code > distance matrix from data within a single matrix. Internally parallelised over the upper triangle via rayon.</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "c1" > // PersistentCompactIntMatrix</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > relfreq_bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > relfreq_euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > hellinger_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > threshold_jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > threshold</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kt" > u32</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "c1" > // PersistentBitMatrix</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > hamming_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >
</ code ></ pre ></ div >
< p > These are convenience methods. For a < code > LayeredDataStore</ code > or < code > PartitionedDataStore</ code > they cannot be used directly — the partial API is required.</ p >
< h3 id = "partial-distance-matrices" > Partial distance matrices</ h3 >
< p > Return additive components that can be summed element-wise across (partition, layer) pairs before computing the final distance. This is what makes cross-layer and cross-partition aggregation possible.</ p >
< p >< strong > Category 1 — self-contained partials</ strong > : additive without any external parameter.</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "c1" > // PersistentCompactIntMatrix</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >
< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "p" > (</ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "c1" > // sum_min[i,j]</ span >
< span class = "w" > </ span >< span class = "n" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "c1" > // col_sums[k]</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // sum of squared diffs</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_threshold_jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > threshold</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kt" > u32</ span >< span class = "p" > )</ span >
< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "p" > (</ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "c1" > // inter[i,j]</ span >
< span class = "w" > </ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "c1" > // union[i,j]</ span >
< span class = "c1" > // PersistentBitMatrix</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >
< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "p" > (</ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "c1" > // inter[i,j]</ span >
< span class = "w" > </ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "c1" > // union[i,j]</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_hamming_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // differing bits</ span >
</ code ></ pre ></ div >
< p >< strong > Category 2 — normalised partials</ strong > : require global column sums as input, computed beforehand across all (partition, layer) pairs.</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "c1" > // PersistentCompactIntMatrix only</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_relfreq_bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > col_sums</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >
< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // Σ_slot min(a_slot/sum_i, b_slot/sum_j)</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_relfreq_euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > col_sums</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >
< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // Σ_slot (a_slot/sum_i - b_slot/sum_j)²</ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_hellinger_euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > col_sums</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >
< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "c1" > // Σ_slot (√(a/sum_i) - √(b/sum_j))²</ span >
</ code ></ pre ></ div >
< p > The < code > col_sums</ code > parameter must reflect the GLOBAL count across all layers and all partitions — passing a per-layer sum would give a wrong result. This constraint drives the two-pass algorithm described below.</ p >
< hr />
< h2 id = "progressive-aggregation-principle" > Progressive aggregation principle</ h2 >
< p > Aggregation is < strong > hierarchical</ strong > : each level computes its contribution by aggregating from the level immediately below it. No level skips a level or collects raw data from two levels down.</ p >
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< div class = "highlight" >< pre >< span ></ span >< code > PersistentCompactIntMatrix::col_weights() — column sums for one (partition, layer) matrix
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↓ Σ across layers
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LayeredStore< PersistentCompactIntMatrix> ::col_weights() — column sums for one partition
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↓ Σ across partitions
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LayeredStore< LayeredStore< …>> ::col_weights() — global column sums
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</ code ></ pre ></ div >
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< p > The same cascade applies to every partial:</ p >
< div class = "highlight" >< pre >< span ></ span >< code > PersistentCompactIntMatrix::partial_bray() — one (partition, layer)
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↓ element-wise Σ across layers
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LayeredStore< PersistentCompactIntMatrix> ::partial_bray() — one partition
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↓ element-wise Σ across partitions
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LayeredStore< LayeredStore< …>> ::partial_bray() — global partial → final dist
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</ code ></ pre ></ div >
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< p > Each level presents a stable trait surface to the level above; no level reaches two levels down.</ p >
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< hr />
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< h2 id = "traits-obicompactvectraits" > Traits — < code > obicompactvec::traits</ code ></ h2 >
< p > Three traits unify the aggregation API across all levels of the hierarchy.</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "k" > trait</ span >< span class = "w" > </ span >< span class = "n" > ColumnWeights</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nb" > Send</ span >< span class = "w" > </ span >< span class = "o" > +</ span >< span class = "w" > </ span >< span class = "nb" > Sync</ span >< span class = "w" > </ span >< span class = "p" > {</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > col_weights</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "p" > }</ span >
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< span class = "k" > trait</ span >< span class = "w" > </ span >< span class = "n" > CountPartials</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nc" > ColumnWeights</ span >< span class = "w" > </ span >< span class = "p" > {</ span >
< span class = "w" > </ span >< span class = "c1" > // self-contained partials (additive, no parameter)</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_bray</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_euclidean</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_threshold_jaccard</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > threshold</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kt" > u32</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "p" > (</ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > );</ span >
< span class = "w" > </ span >< span class = "c1" > // normalised partials (global col_weights passed in cascade)</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_relfreq_bray</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > global</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_relfreq_euclidean</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > global</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_hellinger</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > global</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kp" > & </ span >< span class = "nc" > Array1</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "c1" > // provided finalisation methods (default implementations)</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > threshold_jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > threshold</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "kt" > u32</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > relfreq_bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > relfreq_euclidean_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > hellinger_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "p" > }</ span >
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< span class = "k" > trait</ span >< span class = "w" > </ span >< span class = "n" > BitPartials</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nc" > ColumnWeights</ span >< span class = "w" > </ span >< span class = "p" > {</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_jaccard</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "p" > (</ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ,</ span >< span class = "w" > </ span >< span class = "n" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > );</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > partial_hamming</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "p" > ;</ span >
< span class = "w" > </ span >< span class = "c1" > // provided</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > jaccard_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
< span class = "w" > </ span >< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > hamming_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > u64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >
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< span class = "p" > }</ span >
</ code ></ pre ></ div >
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< p >< strong > Leaf implementors</ strong > (in < code > obicompactvec</ code > ):</ p >
< table >
< thead >
< tr >
< th > Type</ th >
< th > Traits</ th >
</ tr >
</ thead >
< tbody >
< tr >
< td >< code > PersistentCompactIntMatrix</ code ></ td >
< td >< code > ColumnWeights</ code > (via < code > sum()</ code > ), < code > CountPartials</ code ></ td >
</ tr >
< tr >
< td >< code > PersistentBitMatrix</ code ></ td >
< td >< code > ColumnWeights</ code > (via < code > count_ones()</ code > ), < code > BitPartials</ code ></ td >
</ tr >
</ tbody >
</ table >
< p >< code > PersistentCompactIntVec</ code > and < code > PersistentBitVec</ code > do < strong > not</ strong > implement these traits — they are single-column primitives, not matrix-level aggregators.</ p >
< hr />
< h2 id = "layeredstores-obilayeredmap" >< code > LayeredStore< S> </ code > — < code > obilayeredmap</ code ></ h2 >
< p > A single generic wrapper replaces the need for named < code > LayeredDataStore</ code > and < code > PartitionedDataStore</ code > types:</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "k" > pub</ span >< span class = "w" > </ span >< span class = "k" > struct</ span >< span class = "w" > </ span >< span class = "nc" > LayeredStore</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "o" > > </ span >< span class = "p" > (</ span >< span class = "nb" > Vec</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "o" > > </ span >< span class = "p" > );</ span >
</ code ></ pre ></ div >
< p > Three blanket impls propagate the traits up the hierarchy:</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "k" > impl</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nc" > ColumnWeights</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "n" > ColumnWeights</ span >< span class = "w" > </ span >< span class = "k" > for</ span >< span class = "w" > </ span >< span class = "n" > LayeredStore</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >< span class = "w" > </ span >< span class = "c1" > // Σ across inner stores</ span >
< span class = "k" > impl</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nc" > CountPartials</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "n" > CountPartials</ span >< span class = "w" > </ span >< span class = "k" > for</ span >< span class = "w" > </ span >< span class = "n" > LayeredStore</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >< span class = "w" > </ span >< span class = "c1" > // same pattern</ span >
< span class = "k" > impl</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "p" > :</ span >< span class = "w" > </ span >< span class = "nc" > BitPartials</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "n" > BitPartials</ span >< span class = "w" > </ span >< span class = "k" > for</ span >< span class = "w" > </ span >< span class = "n" > LayeredStore</ span >< span class = "o" > < </ span >< span class = "n" > S</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >< span class = "w" > </ span >< span class = "err" > …</ span >< span class = "w" > </ span >< span class = "p" > }</ span >< span class = "w" > </ span >< span class = "c1" > // same pattern</ span >
</ code ></ pre ></ div >
< p > Because the blanket impl is recursive, < strong >< code > LayeredStore< LayeredStore< S>> </ code ></ strong > automatically inherits all three traits when < code > S</ code > does — no separate < code > PartitionedStore</ code > type is needed:</ p >
< div class = "highlight" >< pre >< span ></ span >< code > PersistentCompactIntMatrix implements CountPartials
LayeredStore< PersistentCompactIntMatrix> via blanket impl (= one partition)
LayeredStore< LayeredStore< …>> via blanket impl (= partitioned index)
</ code ></ pre ></ div >
< h3 id = "normalised-metrics-two-pass-cascade" > Normalised metrics — two-pass cascade</ h3 >
< p > The normalised finalisation methods call < code > col_weights()</ code > first (pass 1), then the normalised partial (pass 2). Both calls go through the same blanket impl, so the cascade is automatic:</ p >
< div class = "highlight" >< pre >< span ></ span >< code >< span class = "c1" > // called on LayeredStore< LayeredStore< PersistentCompactIntMatrix>> </ span >
< span class = "k" > fn</ span >< span class = "w" > </ span >< span class = "nf" > relfreq_bray_dist_matrix</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "bp" > self</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "p" > -> </ span >< span class = "w" > </ span >< span class = "nc" > Array2</ span >< span class = "o" > < </ span >< span class = "kt" > f64</ span >< span class = "o" > > </ span >< span class = "w" > </ span >< span class = "p" > {</ span >
< span class = "w" > </ span >< span class = "kd" > let</ span >< span class = "w" > </ span >< span class = "n" > global</ span >< span class = "w" > </ span >< span class = "o" > =</ span >< span class = "w" > </ span >< span class = "bp" > self</ span >< span class = "p" > .</ span >< span class = "n" > col_weights</ span >< span class = "p" > ();</ span >< span class = "w" > </ span >< span class = "c1" > // pass 1 — progressive sum at every level</ span >
< span class = "w" > </ span >< span class = "kd" > let</ span >< span class = "w" > </ span >< span class = "n" > p</ span >< span class = "w" > </ span >< span class = "o" > =</ span >< span class = "w" > </ span >< span class = "bp" > self</ span >< span class = "p" > .</ span >< span class = "n" > partial_relfreq_bray</ span >< span class = "p" > (</ span >< span class = "o" > & </ span >< span class = "n" > global</ span >< span class = "p" > );</ span >< span class = "w" > </ span >< span class = "c1" > // pass 2 — global passed in cascade</ span >
< span class = "w" > </ span >< span class = "n" > p</ span >< span class = "p" > .</ span >< span class = "n" > mapv</ span >< span class = "p" > (</ span >< span class = "o" > |</ span >< span class = "n" > v</ span >< span class = "o" > |</ span >< span class = "w" > </ span >< span class = "mf" > 1.0</ span >< span class = "w" > </ span >< span class = "o" > -</ span >< span class = "w" > </ span >< span class = "n" > v</ span >< span class = "p" > )</ span >< span class = "w" > </ span >< span class = "c1" > // finalise (diagonal zeroed separately)</ span >
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< span class = "p" > }</ span >
</ code ></ pre ></ div >
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< p >< code > global</ code > is exact: each kmer belongs to exactly one < code > (partition, layer)</ code > pair, so there is no double-counting across the hierarchy.</ p >
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< hr />
< h2 id = "parallelism-model" > Parallelism model</ h2 >
< table >
< thead >
< tr >
< th > Level</ th >
< th > Unit</ th >
< th > Coordination</ th >
</ tr >
</ thead >
< tbody >
< tr >
< td > Across partitions</ td >
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< td >< code > LayeredStore< LayeredStore< S>> </ code > inner stores</ td >
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< td > none — fully independent</ td >
</ tr >
< tr >
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< td > Across layers within a partition</ td >
< td >< code > LayeredStore< S> </ code > inner stores</ td >
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< td > none — disjoint kmer sets</ td >
</ tr >
< tr >
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< td > Normalised pass 1 (< code > col_weights</ code > )</ td >
< td > per inner store</ td >
< td > none — additive</ td >
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</ tr >
< tr >
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< td > Normalised pass 2 (partial)</ td >
< td > per inner store</ td >
< td >< code > global</ code > broadcast read-only</ td >
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</ tr >
< tr >
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< td > Within a matrix (distance)</ td >
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< td > upper-triangle pair < code > (i,j)</ code ></ td >
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< td > none — rayon < code > par_iter</ code ></ td >
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</ tr >
</ tbody >
</ table >
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< p > All levels use rayon < code > par_iter</ code > internally; < code > reduce_with</ code > performs a parallel tree reduction.</ p >
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< hr />
< h2 id = "query-model" > Query model</ h2 >
< h3 id = "point-query-kmer-optionitem" > Point query — < code > kmer → Option< Item> </ code ></ h3 >
< div class = "highlight" >< pre >< span ></ span >< code > minimiser(kmer) → partition p
for each layer l in p:
slot = MphfLayer_l.find(kmer)
if slot is Some:
return DataStore_l.get(slot)
return None
</ code ></ pre ></ div >
< p > O(n_layers) MPHF probes worst case; O(1) expected. No cross-layer fusion — the result comes from exactly one (partition, layer).</ p >
< h3 id = "aggregation-result" > Aggregation — < code > → Result</ code ></ h3 >
< div class = "highlight" >< pre >< span ></ span >< code > result = reduce(
for p in partitions: // parallel
for l in layers(p): // parallel
partial(DataStore_p_l)
)
</ code ></ pre ></ div >
< p > For normalised metrics replace with the two-pass scheme above.</ p >
< hr />
< h2 id = "datastore-derivation" > DataStore derivation</ h2 >
< p > Because the < code > MphfLayer</ code > is independent of its data stores, new stores can be derived from existing ones without rebuilding the MPHF:</ p >
< div class = "highlight" >< pre >< span ></ span >< code > // count → presence/absence, parallel across (partition, layer)
for (p, l) in all_partition_layer_pairs().par_iter():
count_store = open PersistentCompactIntMatrix at (p, l)
presence_store = PersistentBitMatrix::from_count_matrix(count_store, threshold, dir)
</ code ></ pre ></ div >
< p > Other derivations: threshold a count matrix → binary presence matrix; union two presence matrices; merge two count matrices (saturating add, column-wise). All are local to one < code > (partition, layer)</ code > pair.</ p >
< hr />
< h2 id = "relationship-to-current-implementation" > Relationship to current implementation</ h2 >
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< h3 id = "what-is-implemented" > What is implemented</ h3 >
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< ul >
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< li >< strong >< code > obicompactvec::traits</ code ></ strong > : < code > ColumnWeights</ code > , < code > CountPartials</ code > , < code > BitPartials</ code > are defined and implemented on < code > PersistentCompactIntMatrix</ code > and < code > PersistentBitMatrix</ code > .</ li >
< li >< strong >< code > obilayeredmap::LayeredStore< S> </ code ></ strong > : generic wrapper with blanket impls for all three traits. < code > LayeredStore< LayeredStore< S>> </ code > is the partitioned level — no separate type needed. Tests confirm that splitting data across layers and across partitions gives the same distance matrices as computing on flat combined data.</ li >
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</ ul >
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< h3 id = "what-is-not-yet-implemented" > What is not yet implemented</ h3 >
< ul >
< li >< code > Layer< D: LayerData> </ code > still fuses < code > MphfLayer</ code > and one < code > DataStore</ code > . Multiple data stores on the same MPHF are not supported.</ li >
< li >< code > LayeredMap</ code > is a single-partition structure without distance matrix API; it does not yet use < code > LayeredStore</ code > .</ li >
< li > No < code > PartitionedIndex</ code > type for point queries with parallel partition dispatch.</ li >
</ ul >
< h3 id = "planned-refactoring" > Planned refactoring</ h3 >
< ol >
< li > Extract < code > MphfLayer</ code > from < code > Layer< D> </ code > as an autonomous type.</ li >
< li > Replace < code > LayerData</ code > trait with the < code > DataStore</ code > / < code > ColumnWeights</ code > / < code > CountPartials</ code > / < code > BitPartials</ code > system.</ li >
< li > Rewire < code > LayeredMap</ code > to hold < code > LayeredStore< PersistentCompactIntMatrix> </ code > (or bit variant) alongside the MPHF layers.</ li >
< li > Implement < code > PartitionedIndex</ code > using < code > LayeredStore< LayeredStore< S>> </ code > for data and parallel dispatch for queries.</ li >
</ ol >
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