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This commit introduces sparse index support for KDI files to enable fast random access during k-mer matching. It adds a new .kdx index file format and updates the KDI reader and writer to handle index creation and seeking. The changes include: - New KdxIndex struct and related functions for loading, searching, and writing .kdx files - Modified KdiReader to support seeking with the new index - Updated KdiWriter to create .kdx index files during writing - Enhanced KmerSetGroup.Contains to use the new index for faster lookups - Added a new 'match' command to annotate sequences with k-mer match positions The index is created automatically during KDI file creation and allows for O(log N / stride) binary search followed by at most stride linear scan steps, significantly improving performance for large datasets.
191 lines
4.5 KiB
Go
191 lines
4.5 KiB
Go
package obikmer
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import (
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"sort"
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"sync"
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"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiseq"
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)
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// QueryEntry represents a canonical k-mer to look up, together with
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// metadata to trace the result back to the originating sequence and position.
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type QueryEntry struct {
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Kmer uint64 // canonical k-mer value
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SeqIdx int // index within the batch
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Pos int // 0-based position in the sequence
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}
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// MatchResult maps sequence index → sorted slice of matched positions.
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type MatchResult map[int][]int
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// seqMatchResult collects matched positions for a single sequence.
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type seqMatchResult struct {
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mu sync.Mutex
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positions []int
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}
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// PrepareQueries extracts all canonical k-mers from a batch of sequences
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// and groups them by partition using super-kmer minimizers.
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//
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// Returns queries[partition] where each slice is sorted by Kmer value.
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func (ksg *KmerSetGroup) PrepareQueries(sequences []*obiseq.BioSequence) [][]QueryEntry {
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P := ksg.partitions
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k := ksg.k
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m := ksg.m
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// Pre-allocate partition buckets
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buckets := make([][]QueryEntry, P)
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for i := range buckets {
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buckets[i] = make([]QueryEntry, 0, 64)
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}
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for seqIdx, seq := range sequences {
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bseq := seq.Sequence()
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if len(bseq) < k {
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continue
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}
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// Iterate super-kmers to get minimizer → partition mapping
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for sk := range IterSuperKmers(bseq, k, m) {
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partition := int(sk.Minimizer % uint64(P))
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// Iterate canonical k-mers within this super-kmer
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skSeq := sk.Sequence
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if len(skSeq) < k {
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continue
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}
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localPos := 0
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for kmer := range IterCanonicalKmers(skSeq, k) {
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buckets[partition] = append(buckets[partition], QueryEntry{
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Kmer: kmer,
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SeqIdx: seqIdx,
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Pos: sk.Start + localPos,
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})
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localPos++
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}
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}
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}
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// Sort each bucket by k-mer value for merge-scan
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for p := range buckets {
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sort.Slice(buckets[p], func(i, j int) bool {
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return buckets[p][i].Kmer < buckets[p][j].Kmer
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})
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}
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return buckets
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}
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// MatchBatch looks up pre-sorted queries against one set of the index.
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// Partitions are processed in parallel. For each partition, a merge-scan
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// compares the sorted queries against the sorted KDI stream.
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//
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// Returns a MatchResult mapping sequence index to sorted matched positions.
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func (ksg *KmerSetGroup) MatchBatch(setIndex int, queries [][]QueryEntry) MatchResult {
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P := ksg.partitions
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// Per-sequence result collectors
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var resultMu sync.Mutex
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resultMap := make(map[int]*seqMatchResult)
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getResult := func(seqIdx int) *seqMatchResult {
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resultMu.Lock()
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sr, ok := resultMap[seqIdx]
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if !ok {
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sr = &seqMatchResult{}
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resultMap[seqIdx] = sr
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}
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resultMu.Unlock()
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return sr
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}
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var wg sync.WaitGroup
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for p := 0; p < P; p++ {
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if len(queries[p]) == 0 {
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continue
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}
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wg.Add(1)
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go func(part int) {
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defer wg.Done()
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ksg.matchPartition(setIndex, part, queries[part], getResult)
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}(p)
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}
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wg.Wait()
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// Build final result with sorted positions
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result := make(MatchResult, len(resultMap))
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for seqIdx, sr := range resultMap {
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if len(sr.positions) > 0 {
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sort.Ints(sr.positions)
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result[seqIdx] = sr.positions
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}
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}
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return result
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}
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// matchPartition processes one partition: opens the KDI reader (with index),
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// seeks to the first query, then merge-scans queries against the KDI stream.
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func (ksg *KmerSetGroup) matchPartition(
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setIndex int,
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partIndex int,
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queries []QueryEntry, // sorted by Kmer
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getResult func(int) *seqMatchResult,
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) {
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r, err := NewKdiIndexedReader(ksg.partitionPath(setIndex, partIndex))
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if err != nil {
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return
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}
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defer r.Close()
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if r.Count() == 0 || len(queries) == 0 {
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return
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}
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// Seek to the first query's neighborhood
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if err := r.SeekTo(queries[0].Kmer); err != nil {
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return
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}
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// Read first kmer from the stream after seek
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currentKmer, ok := r.Next()
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if !ok {
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return
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}
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qi := 0 // query index
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for qi < len(queries) {
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q := queries[qi]
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// Advance KDI stream until >= query kmer
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for currentKmer < q.Kmer {
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currentKmer, ok = r.Next()
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if !ok {
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return // KDI exhausted
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}
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}
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if currentKmer == q.Kmer {
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// Match! Record all queries with this same k-mer value
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matchedKmer := q.Kmer
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for qi < len(queries) && queries[qi].Kmer == matchedKmer {
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sr := getResult(queries[qi].SeqIdx)
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sr.mu.Lock()
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sr.positions = append(sr.positions, queries[qi].Pos)
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sr.mu.Unlock()
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qi++
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}
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} else {
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// currentKmer > q.Kmer: skip all queries with this kmer value
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skippedKmer := q.Kmer
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for qi < len(queries) && queries[qi].Kmer == skippedKmer {
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qi++
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}
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}
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}
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}
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