feat(convert): integrate process metrics into JSON and SQLite converters

- add process_metrics function to record peak RSS and CPU time
- update JSON converter to include metrics in completion marker
- modify SQLite converter to utilize new metrics for performance tracking
- enhance storage size updates with locking mechanism for concurrent access

common/queries/csv_queries.py

@@ -0,0 +1,139 @@
+"""Q1-Q7 over the raw CSV tree: pure python + csv module, streaming.
+
+Q1 is a direct path read (the honest CSV strength, spec 08); Q2-Q7 are
+deterministic directory walks with programmatic joins. No manifest
+validation here - queries measure retrieval, not integrity checking.
+"""
+
+from __future__ import annotations
+
+import csv
+from pathlib import Path
+
+from common.queries import Q1_TRACK_CODE
+from common.track_summary import summarize_track
+
+
+def _rows(path: Path) -> tuple[list[str], list[list[str]]]:
+	with open(path, newline="", encoding="ascii") as fh:
+		reader = csv.reader(fh)
+		header = [h.lower() for h in next(reader)]
+		return header, list(reader)
+
+
+def _dict_row(path: Path) -> dict[str, str]:
+	header, rows = _rows(path)
+	return dict(zip(header, rows[0]))
+
+
+def _track_path(csv_root: Path, track_code: str) -> Path:
+	batch, wafer, coupon, track = track_code.split("-")
+	return csv_root / f"batch_{batch}" / f"wafer_{wafer}" / f"coupon_{coupon}" / f"track_{track}"
+
+
+def _coupon_dirs(csv_root: Path):
+	for batch_dir in sorted(csv_root.glob("batch_*")):
+		batch_code = batch_dir.name[6:]
+		for wafer_dir in sorted(batch_dir.glob("wafer_*")):
+			for coupon_dir in sorted(wafer_dir.glob("coupon_*")):
+				yield batch_code, coupon_dir
+
+
+def _track_dirs(coupon_dir: Path) -> list[Path]:
+	return sorted(coupon_dir.glob("track_T*"))
+
+
+def _cofs(track_dir: Path) -> list[float]:
+	_, rows = _rows(track_dir / "cof_vs_cycle.csv")
+	return [float(r[1]) for r in rows]
+
+
+def _mean(values: list[float]) -> float:
+	total = 0.0
+	for v in values:
+		total += v
+	return total / len(values)
+
+
+def q1(csv_root: Path) -> list[tuple]:
+	_, rows = _rows(_track_path(csv_root, Q1_TRACK_CODE) / "cof_vs_cycle.csv")
+	return [(int(r[0]), float(r[1])) for r in rows]
+
+
+def q2(csv_root: Path) -> list[tuple]:
+	out = []
+	for _, coupon_dir in _coupon_dirs(csv_root):
+		info = _dict_row(coupon_dir / "coupon_info.csv")
+		if info["run_code"] == "RESERVE" or not 9.5 <= float(info["au_wtpct_mean"]) <= 10.5:
+			continue
+		tracks = _track_dirs(coupon_dir)
+		env = _dict_row(tracks[0] / "track_info.csv")["environment"]
+		cof_ss = _mean([summarize_track(_cofs(t))[0] for t in tracks])
+		out.append((info["coupon_code"], env, cof_ss))
+	return out
+
+
+def q3(csv_root: Path) -> list[tuple]:
+	out = []
+	for batch_code, coupon_dir in _coupon_dirs(csv_root):
+		info = _dict_row(coupon_dir / "coupon_info.csv")
+		if info["run_code"] == "RESERVE":
+			continue
+		header, rows = _rows(coupon_dir / "nanoindentation.csv")
+		h_col = header.index("hardness_gpa")
+		hardness = _mean([float(r[h_col]) for r in rows])
+		cof_ss = _mean([summarize_track(_cofs(t))[0] for t in _track_dirs(coupon_dir)])
+		out.append((info["coupon_code"], batch_code, hardness, cof_ss))
+	return out
+
+
+def q4(csv_root: Path) -> list[tuple]:
+	out = []
+	for _, coupon_dir in _coupon_dirs(csv_root):
+		for track_dir in _track_dirs(coupon_dir):
+			info = _dict_row(track_dir / "track_info.csv")
+			if info["environment"] != "dry_n2" or float(info["load_mn"]) != 100.0:
+				continue
+			cof_ss = summarize_track(_cofs(track_dir))[0]
+			if cof_ss > 0.20:
+				out.append((info["track_code"], cof_ss))
+	return out
+
+
+def q5(csv_root: Path) -> list[tuple]:
+	sums: dict[str, list[float]] = {}
+	for batch_code, coupon_dir in _coupon_dirs(csv_root):
+		for track_dir in _track_dirs(coupon_dir):
+			run_in = summarize_track(_cofs(track_dir))[2]
+			acc = sums.setdefault(batch_code, [0.0, 0.0])
+			acc[0] += run_in
+			acc[1] += 1
+	return [(batch, acc[0] / acc[1]) for batch, acc in sorted(sums.items())]
+
+
+def q6(csv_root: Path) -> list[tuple]:
+	out = []
+	for _, coupon_dir in _coupon_dirs(csv_root):
+		info = _dict_row(coupon_dir / "coupon_info.csv")
+		if info["run_code"] == "RESERVE":
+			continue
+		tracks = _track_dirs(coupon_dir)
+		load = float(_dict_row(tracks[0] / "track_info.csv")["load_mn"])
+		wear = _mean([float(_dict_row(t / "wear.csv")["wear_volume_um3"]) for t in tracks])
+		out.append((info["coupon_code"], load, wear))
+	return out
+
+
+def q7(csv_root: Path) -> list[tuple]:
+	groups: dict[tuple[str, int], list[float]] = {}
+	for _, coupon_dir in _coupon_dirs(csv_root):
+		for track_dir in _track_dirs(coupon_dir):
+			info = _dict_row(track_dir / "track_info.csv")
+			bucket = int(round(float(info["speed_mm_s"]) * float(info["load_mn"])))
+			cofs = _cofs(track_dir)
+			run_in = summarize_track(cofs)[2]
+			acc = groups.setdefault((info["environment"], bucket), [0.0, 0.0])
+			for cof in cofs[run_in:]:  # cycles strictly past run-in
+				acc[0] += cof
+				acc[1] += 1
+	return [(env, bucket, acc[0] / acc[1]) for (env, bucket), acc in sorted(groups.items())]