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
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173
common/queries/rdf_queries.py
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173
common/queries/rdf_queries.py
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"""Q1-Q7 over the oxigraph triplestore: SPARQL retrieval (spec 08).
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The store holds raw data only (no precomputed summaries), so queries that
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need steady-state COF or run-in retrieve the raw cycles via SPARQL and
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derive the values with the shared algorithm (common/track_summary.py) -
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identical semantics to every other format. The SPARQL text of each query
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is exported to out/bench/queries/rdf/q<N>.sparql by make_queries.py.
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"""
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from __future__ import annotations
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from pathlib import Path
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from pyoxigraph import Store
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from common.queries import Q1_TRACK_CODE
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from common.track_summary import summarize_track
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PREFIX = "PREFIX tribo: <https://sandia.gov/ontology/tribology#>\n"
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TRACK_IRI = f"<https://sandia.gov/ontology/tribology/id/track/{Q1_TRACK_CODE}>"
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SPARQL = {
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"q1": PREFIX + f"""SELECT ?cycle ?cof WHERE {{
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{TRACK_IRI} tribo:hasCycle ?c .
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?c tribo:cycle ?cycle ; tribo:cof ?cof .
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}}""",
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"q2": PREFIX + """SELECT ?ccode ?env ?track ?cycle ?cof WHERE {
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?coupon tribo:au_wtpct_mean ?au ; tribo:coupon_code ?ccode .
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FILTER(?au >= 9.5 && ?au <= 10.5)
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?track tribo:partOf ?coupon ; tribo:environment ?env ; tribo:hasCycle ?c .
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?c tribo:cycle ?cycle ; tribo:cof ?cof .
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}""",
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"q3_hardness": PREFIX + """SELECT ?ccode ?bcode (AVG(?h) AS ?hardness) WHERE {
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?coupon tribo:coupon_code ?ccode ; tribo:cutFrom ?wafer ; tribo:nanoindentation ?m .
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?wafer tribo:partOf ?batch .
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?batch tribo:batch_code ?bcode .
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?m tribo:hasIndent ?i .
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?i tribo:hardness_gpa ?h .
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} GROUP BY ?ccode ?bcode""",
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"q3_cycles": PREFIX + """SELECT ?ccode ?track ?cycle ?cof WHERE {
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?track a tribo:FrictionTest ; tribo:partOf ?coupon ; tribo:hasCycle ?c .
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?coupon tribo:coupon_code ?ccode .
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?c tribo:cycle ?cycle ; tribo:cof ?cof .
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}""",
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"q4": PREFIX + """SELECT ?tcode ?track ?cycle ?cof WHERE {
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?track tribo:environment "dry_n2" ; tribo:load_mn ?load ; tribo:track_code ?tcode ; tribo:hasCycle ?c .
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FILTER(?load = 100)
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?c tribo:cycle ?cycle ; tribo:cof ?cof .
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}""",
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"q5": PREFIX + """SELECT ?bcode ?track ?cycle ?cof WHERE {
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?track a tribo:FrictionTest ; tribo:partOf ?coupon ; tribo:hasCycle ?c .
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?coupon tribo:cutFrom ?wafer .
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?wafer tribo:partOf ?batch .
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?batch tribo:batch_code ?bcode .
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?c tribo:cycle ?cycle ; tribo:cof ?cof .
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}""",
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"q6": PREFIX + """SELECT ?ccode ?load (AVG(?wv) AS ?wear) WHERE {
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?track tribo:partOf ?coupon ; tribo:load_mn ?load ; tribo:wear ?w .
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?w tribo:wear_volume_um3 ?wv .
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?coupon tribo:coupon_code ?ccode .
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} GROUP BY ?ccode ?load""",
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"q7": PREFIX + """SELECT ?env ?speed ?load ?track ?cycle ?cof WHERE {
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?track tribo:environment ?env ; tribo:speed_mm_s ?speed ; tribo:load_mn ?load ; tribo:hasCycle ?c .
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?c tribo:cycle ?cycle ; tribo:cof ?cof .
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}""",
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}
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def _store(store_path: Path) -> Store:
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try:
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return Store.read_only(str(store_path))
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except AttributeError:
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return Store(str(store_path))
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def _track_cofs(solutions, track_var: str, keep: tuple[str, ...]) -> dict[str, dict]:
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"""Group cycle solutions by track: ordered cof list + kept literals."""
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tracks: dict[str, dict] = {}
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for sol in solutions:
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key = str(sol[track_var])
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entry = tracks.get(key)
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if entry is None:
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entry = {"pairs": []}
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for name in keep:
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entry[name] = sol[name].value
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tracks[key] = entry
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entry["pairs"].append((int(sol["cycle"].value), float(sol["cof"].value)))
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for entry in tracks.values():
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entry["pairs"].sort()
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entry["cofs"] = [cof for _, cof in entry["pairs"]]
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return tracks
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def _mean(values: list[float]) -> float:
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total = 0.0
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for v in values:
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total += v
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return total / len(values)
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def q1(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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return [(int(s["cycle"].value), float(s["cof"].value)) for s in store.query(SPARQL["q1"])]
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def q2(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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tracks = _track_cofs(store.query(SPARQL["q2"]), "track", ("ccode", "env"))
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coupons: dict[tuple[str, str], list[float]] = {}
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for entry in sorted(tracks.values(), key=lambda e: e["ccode"]):
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coupons.setdefault((entry["ccode"], entry["env"]), []).append(summarize_track(entry["cofs"])[0])
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return [(code, env, _mean(values)) for (code, env), values in sorted(coupons.items())]
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def q3(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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hardness = {
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s["ccode"].value: (s["bcode"].value, float(s["hardness"].value))
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for s in store.query(SPARQL["q3_hardness"])
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}
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tracks = _track_cofs(store.query(SPARQL["q3_cycles"]), "track", ("ccode",))
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coupons: dict[str, list[float]] = {}
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for entry in sorted(tracks.values(), key=lambda e: e["ccode"]):
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coupons.setdefault(entry["ccode"], []).append(summarize_track(entry["cofs"])[0])
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return [
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(code, hardness[code][0], hardness[code][1], _mean(values))
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for code, values in sorted(coupons.items())
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]
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def q4(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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tracks = _track_cofs(store.query(SPARQL["q4"]), "track", ("tcode",))
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out = []
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for entry in sorted(tracks.values(), key=lambda e: e["tcode"]):
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cof_ss = summarize_track(entry["cofs"])[0]
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if cof_ss > 0.20:
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out.append((entry["tcode"], cof_ss))
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return out
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def q5(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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tracks = _track_cofs(store.query(SPARQL["q5"]), "track", ("bcode",))
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sums: dict[str, list[float]] = {}
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for entry in sorted(tracks.values(), key=lambda e: e["bcode"]):
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acc = sums.setdefault(entry["bcode"], [0.0, 0.0])
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acc[0] += summarize_track(entry["cofs"])[2]
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acc[1] += 1
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return [(batch, acc[0] / acc[1]) for batch, acc in sorted(sums.items())]
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def q6(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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return [
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(s["ccode"].value, float(s["load"].value), float(s["wear"].value))
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for s in store.query(SPARQL["q6"])
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]
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def q7(store_path: Path) -> list[tuple]:
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store = _store(store_path)
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tracks = _track_cofs(store.query(SPARQL["q7"]), "track", ("env", "speed", "load"))
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groups: dict[tuple[str, int], list[float]] = {}
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for key in sorted(tracks):
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entry = tracks[key]
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bucket = int(round(float(entry["speed"]) * float(entry["load"])))
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run_in = summarize_track(entry["cofs"])[2]
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acc = groups.setdefault((entry["env"], bucket), [0.0, 0.0])
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for cof in entry["cofs"][run_in:]:
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acc[0] += cof
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acc[1] += 1
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return [(env, bucket, acc[0] / acc[1]) for (env, bucket), acc in sorted(groups.items())]
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