Add benchmark pipeline for dense and sparse query testing

Introduces a complete query benchmark track to evaluate performance and verify consistency between dense and sparse index formats. Adds scripts to simulate fixed-size paired-end reads, pack a sparse presence index, execute queries in both modes, and capture wall time and RSS metrics. Includes a verification step that compares outputs by read ID to ensure content identity across parallel processing. Updates build configuration, documentation, and ignore patterns to support the new pipeline for two microbial specimens.

benchmark/verify_query.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+"""Compare dense vs sparse obikmer query output for one specimen.
+
+Both files are `obikmer query --count-missing` output: OBITools4-style
+FASTA, one record per read — `>id {"kmer_count":N,"kmer_missing":M,
+"kmer_strict_matches":{"label":count,...}}`. Packing format (dense vs
+sparse presence matrix) must not change query results — only I/O access
+pattern differs. Matched by read id rather than by stream position: the
+query pipeline processes input in chunks across worker threads and does
+not guarantee output order matches input order.
+
+Output to stdout: one CSV row
+  species, strain, n_reads, n_common, missing_in_dense, missing_in_sparse, mismatched, mismatch_pct
+"""
+import argparse
+import gzip
+import json
+import sys
+
+
+def load(path):
+    records = {}
+    opener = gzip.open if path.endswith('.gz') else open
+    with opener(path, 'rt') as fh:
+        for line in fh:
+            if not line.startswith('>'):
+                continue
+            header = line[1:].rstrip('\n')
+            read_id, _, json_part = header.partition(' ')
+            records[read_id] = json.loads(json_part) if json_part else {}
+    return records
+
+
+def annotations_equal(a, b):
+    return (
+        a.get('kmer_count') == b.get('kmer_count')
+        and a.get('kmer_missing') == b.get('kmer_missing')
+        and a.get('kmer_strict_matches', {}) == b.get('kmer_strict_matches', {})
+    )
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--species', required=True)
+    ap.add_argument('--strain', required=True)
+    ap.add_argument('dense_fasta')
+    ap.add_argument('sparse_fasta')
+    args = ap.parse_args()
+
+    dense = load(args.dense_fasta)
+    sparse = load(args.sparse_fasta)
+
+    dense_ids, sparse_ids = set(dense), set(sparse)
+    common = dense_ids & sparse_ids
+    missing_in_dense = len(sparse_ids - dense_ids)
+    missing_in_sparse = len(dense_ids - sparse_ids)
+
+    mismatched = sum(1 for rid in common if not annotations_equal(dense[rid], sparse[rid]))
+
+    n_reads = len(dense_ids | sparse_ids)
+    mismatch_pct = 100.0 * (mismatched + missing_in_dense + missing_in_sparse) / n_reads if n_reads else 0.0
+
+    print(f'{args.species},{args.strain},{n_reads},{len(common)},'
+          f'{missing_in_dense},{missing_in_sparse},{mismatched},{mismatch_pct:.6f}')
+
+
+if __name__ == '__main__':
+    main()