Kmer indexing per partition proceeds in two phases. The separation is necessary because the exact number of surviving unique kmers is not known until after counting and filtering low-abundance kmers.
1.**External sort**: read the dereplicated superkmer file; extract the raw `u64` canonical kmer value for every kmer of every superkmer. Sort in RAM-bounded chunks (adaptive budget: 40% of available RAM ÷ n_threads, minimum 1 M kmers per chunk), then k-way merge with inline dedup. Result: `sorted_unique.bin` — a flat array of f0 distinct sorted `u64` values. Exact kmer count f0 is known at this point.
2.**Build provisional MPHF** (ptr_hash, same configuration as phase 2) over `sorted_unique.bin` using `new_from_par_iter`. Delete `sorted_unique.bin` immediately after. Persist to `mphf1.bin`.
3.**Create `counts1.bin`**: `PersistentCompactIntVec` with f0 slots, zero-initialised.
4.**Accumulation pass**: re-read the dereplicated superkmer file; for each kmer in each superkmer, compute `slot = mphf.index(kmer.raw())` and increment `counts1[slot]` by the superkmer's COUNT.
5.**Build kmer frequency spectrum** from `counts1`: histogram `{count → n_kmers}`, totals f0 (distinct kmers) and f1 (total abundance). Written to `kmer_spectrum_raw.json` per partition, then merged globally.
After filtering (applying a min-count threshold derived from the spectrum) and building the local De Bruijn graph + unitigs (see [Construction pipeline](pipeline.md)), the exact filtered kmer set is available via `unitigs.bin`.
1.**Pass 1** (parallel): a `CanonicalKmerIter` — clonable via `Arc<Mmap>`, no file reopening — is passed directly to `new_from_par_iter` via `par_bridge()`. No `.idx` is read or created at this stage; parallelism is at partition/layer level, not within a single MPHF. Produces `mphf.bin`.
2.**Pass 2** (sequential): iterate with `iter_indexed_canonical_kmers`; fill evidence files; call `fill_slot(slot, kmer)` callback per kmer. For Exact/Hybrid, `.idx` is written at the end of this pass — never earlier.
-`GOFunction` (group-oriented variant) was the original phase-1 choice; eliminated when the external sort made the exact count available at phase 1 as well
**Both phases**: **ptr_hash**, same type alias and construction parameters. The external sort (phase 1) and the unitig index (phase 2) both provide the exact key count before MPHF construction, so ptr_hash's requirement is satisfied in both cases. Using a single MPHF implementation removes the `ph` crate dependency.
boomphf: eliminated — largest space overhead, streaming advantage no longer needed. FMPH/GOFunction: eliminated — exact count available, ptr_hash is faster at equivalent compactness.
For a human genome at 30× coverage with 1 024 partitions, realistic partition sizes are 3–30 M unique kmers → 1–8 MB per phase-2 MPHF, well within RAM.
**Bucket function — `CubicEps`**: λ=3.5, α=0.99. Balanced tradeoff: 2× slower construction than `Linear/λ=3.0`, 20% less space. `default_compact` (λ=4.0) saves a further 12.5% at 2× more construction time — not chosen.
**Remap — `CachelineEfVec`**: Elias-Fano variant packing 44 sorted 40-bit values per 64-byte cacheline (11.6 bits/value vs 32 for `Vec<u32>`). One cacheline per query; space win dominates at billion-scale key counts.
**Exact** (`IndexMode::Exact`): `evidence.bin` stores one `(chunk_id, rank)` pair per MPHF slot. Verification reconstructs the kmer and compares to the query. Zero false positives. `.idx` required at query time.
**Approx** (`IndexMode::Approx { b, z }`): `fingerprint.bin` stores a b-bit hash per slot. False-positive rate 1/2^b per query; Findere z-parameter reduces window FP to ≈ 1/2^(b·z). No `.idx` written or needed.
**Hybrid** (`IndexMode::Hybrid { b, z }`): both `fingerprint.bin` and `evidence.bin` + `.idx`. `find()` uses the fingerprint (O(1)); `find_strict()` uses exact evidence (O(1)).
In `obikpartitionner`, `build_index_layer` receives `block_bits: u8` from `IndexConfig::block_bits` and forwards it directly to `Layer::build` and `Layer::build_approx_evidence`.
`MphfLayer::find` dispatches on `LayerEvidence` at O(1) — no panicking `find_exact`/`find_approx` methods. `find_strict` always performs an exact check: O(1) for Exact/Hybrid, O(n) sequential scan for Approx. Expected probe depth: 1 for kmers in layer 0. Each probe is a ptr_hash lookup (~10 ns) plus one evidence check.
Two layer chains can be merged by re-indexing their union through the full pipeline. This is expensive (full rebuild) but produces an optimal single-layer index. Merge is a maintenance operation, not a query-path requirement.