onesvm-browser-server/bench/searxng-global/summarize.py
chii 983259836d chore: init workspace with onesvm-dev-md + casa-commander
docs: 联网搜索服务架构方案全套(plan-final/design-arch/选型决策/整合导览/MCP文档/部署预设/联调手册)
bench: 5 方案 + 代理 + 站点矩阵本机实测工程(无密钥)
部署目标:primary mgr1 先行测试(待批准后执行)
2026-09-01 15:19:52 +08:00

283 lines
8.7 KiB
Python

#!/usr/bin/env python3
"""Build searxng-global/results.json from raw artifacts. No secrets."""
import json, math, os, statistics
from pathlib import Path
ROOT = Path(__file__).resolve().parent
RAW = ROOT / "raw"
def percentile(xs, p):
if not xs:
return None
s = sorted(xs)
if len(s) == 1:
return round(s[0], 4)
k = (len(s) - 1) * p / 100.0
f = math.floor(k)
c = math.ceil(k)
if f == c:
return round(s[int(k)], 4)
return round(s[f] + (s[c] - s[f]) * (k - f), 4)
def load_jsonl(p):
rows = []
if not p.exists():
return rows
for line in p.read_text(encoding="utf-8").splitlines():
line = line.strip()
if not line:
continue
rows.append(json.loads(line))
return rows
def tsv_mibs(p, col="mem_mib"):
vals = []
if not p.exists():
return vals
lines = p.read_text(encoding="utf-8").splitlines()
if len(lines) < 2:
return vals
hdr = lines[0].split("\t")
try:
i = hdr.index(col)
except ValueError:
i = 4 if col == "mem_mib" else 5
for line in lines[1:]:
parts = line.split("\t")
if len(parts) <= i:
continue
try:
v = float(parts[i])
except ValueError:
continue
if v > 0:
vals.append(v)
return vals
def median(xs):
if not xs:
return None
return round(statistics.median(xs), 3)
def pmax(xs):
return round(max(xs), 3) if xs else None
def mem_score(idle, delta, burst):
def band(v, cuts):
if v is None:
return None
for score, lim in cuts:
if v <= lim:
return score
return 0
idle_s = band(idle, [(100, 60), (80, 100), (60, 200), (40, 400)])
delta_s = band(delta, [(100, 30), (80, 80), (60, 200), (40, 350)])
burst_s = band(burst, [(100, 200), (80, 400), (60, 700), (40, 1000)])
parts = [x for x in (idle_s, delta_s, burst_s) if x is not None]
if not parts:
return None
return round(sum(parts) / 3, 1)
def quality_search(t3_rows):
if not t3_rows:
return 0
oks = [r for r in t3_rows if r.get("assert_ok")]
if any(r.get("assert_ok") for r in t3_rows):
# 100 if >=5 and >=2 engines
best = max(t3_rows, key=lambda r: (r.get("assert_ok"), r.get("n_results", 0), len(r.get("engines") or [])))
if best.get("assert_ok"):
return 100
if best.get("n_results", 0) >= 5 and len(best.get("engines") or []) == 1:
return 70
if best.get("n_results", 0) >= 1:
return 40
# maybe some results even if assert failed
if any(r.get("n_results", 0) >= 5 for r in t3_rows):
if any(len(r.get("engines") or []) >= 2 for r in t3_rows):
return 100
return 70
if any(r.get("n_results", 0) >= 1 for r in t3_rows):
return 40
return 0
def stab_score(rate, oom):
if oom:
return 0
if rate is None:
return None
if rate >= 0.99:
return 100
if rate >= 0.95:
return 80
if rate >= 0.85:
return 60
if rate >= 0.70:
return 40
return 0
def lat_score_t3(p95):
if p95 is None:
return None
if p95 <= 2:
return 100
if p95 <= 5:
return 70
if p95 <= 12:
return 40
return 0
def lat_score_t5(t5_p95, t3_p95):
if t5_p95 is None:
return None
if t3_p95 and t3_p95 > 0:
ratio = t5_p95 / t3_p95
if ratio <= 3:
return 100
if ratio <= 5:
return 70
if ratio <= 8:
return 40
return 0
if t5_p95 <= 12:
return 40
return 0
def main():
t3 = load_jsonl(RAW / "t3.jsonl")
t3e = load_jsonl(RAW / "t3-engines.jsonl")
t5 = load_jsonl(RAW / "t5.jsonl")
idle = tsv_mibs(RAW / "idle.tsv")
sess = tsv_mibs(RAW / "session.tsv")
burst = tsv_mibs(RAW / "t5-stats.tsv")
meta = {}
mp = RAW / "image-meta.json"
if mp.exists():
meta = json.loads(mp.read_text(encoding="utf-8"))
idle_p50 = median(idle)
sess_peak = pmax(sess)
burst_peak = pmax(burst)
delta = None
if idle_p50 is not None and sess_peak is not None:
delta = round(max(0.0, sess_peak - idle_p50), 3)
t3_ok = [r for r in t3 if r.get("assert_ok")]
t3_times = [r["t_total"] for r in t3 if r.get("t_total") is not None]
t5_ok = [r for r in t5 if r.get("assert_ok")]
t5_times = [r["t_total"] for r in t5 if r.get("t_total") is not None]
t5_n = len(t5)
t5_rate = (len(t5_ok) / t5_n) if t5_n else None
engine_matrix = {}
for r in t3e:
name = r.get("engines_param") or "unknown"
engine_matrix.setdefault(name, {"n": 0, "http_200": 0, "n_results": [], "unresponsive": [], "assert_like": 0})
e = engine_matrix[name]
e["n"] += 1
if r.get("http_code") == 200:
e["http_200"] += 1
e["n_results"].append(r.get("n_results") or 0)
e["unresponsive"].extend(r.get("unresponsive_engines") or [])
requested = (r.get("engines_param") or "").lower()
hit = requested in [str(x).lower() for x in (r.get("engines") or [])]
e["engine_contributed_n"] = e.get("engine_contributed_n", 0) + (1 if hit else 0)
if hit and r.get("http_code") == 200:
e["assert_like"] += 1
for k, v in engine_matrix.items():
v["success_rate"] = round(v["assert_like"] / v["n"], 3) if v["n"] else 0
v["results_p50"] = percentile(v["n_results"], 50)
oom = bool(meta.get("oom"))
q = quality_search(t3)
t3_p95 = percentile(t3_times, 95)
t5_p95 = percentile(t5_times, 95)
t3_p50 = percentile(t3_times, 50)
t5_p50 = percentile(t5_times, 50)
# combined latency: average of T3 and T5 scores if both
l3 = lat_score_t3(t3_p95)
l5 = lat_score_t5(t5_p95, t3_p95)
lat_parts = [x for x in (l3, l5) if x is not None]
lat = round(sum(lat_parts) / len(lat_parts), 1) if lat_parts else None
mem = mem_score(idle_p50, delta, burst_peak)
stab = stab_score(t5_rate if t5_rate is not None else (len(t3_ok) / len(t3) if t3 else None), oom)
ops = 80
total = None
if None not in (mem, q, stab, lat, ops):
total = round(mem * 0.30 + q * 0.25 + stab * 0.20 + lat * 0.15 + ops * 0.10, 1)
out = {
"scheme": "searxng-global",
"group": "foreign_proxy",
"image": {
"ref": meta.get("ref") or "docker.io/searxng/searxng:2026.8.29-d226b78bc",
"digest": meta.get("digest"),
"id": meta.get("id"),
"arch": meta.get("arch"),
"pull": meta.get("pull"),
},
"probed_at": meta.get("probed_at"),
"commands": meta.get("commands") or [],
"proxy": meta.get("proxy") or {},
"rss_source": "docker_stats+cgroup",
"idle_mb_p50": idle_p50,
"session_delta_mb": delta,
"burst_peak_mb": burst_peak,
"session_peak_mb": sess_peak,
"templates": {
"T3": {
"n": len(t3),
"success": len(t3_ok),
"http_codes": [r.get("http_code") for r in t3],
"p50_s": t3_p50,
"p95_s": t3_p95,
"assert_ok": bool(t3_ok),
"engines_seen": sorted({e for r in t3 for e in (r.get("engines") or [])}),
"unresponsive_union": sorted({e for r in t3 for e in (r.get("unresponsive_engines") or [])}),
"reddit_ref_n": sum(1 for r in t3 if r.get("reddit_ref")),
"note": "; ".join(sorted({r.get("note") for r in t3 if r.get("note")})),
},
"T3_engine_matrix": engine_matrix,
},
"t5": {
"n": t5_n,
"parallelism": 60,
"queue_impl": "none",
"success": len(t5_ok),
"success_rate": t5_rate,
"p50_s": t5_p50,
"p95_s": t5_p95,
"oom": oom,
"http_429": sum(1 for r in t5 if r.get("http_code") == 429),
"http_503": sum(1 for r in t5 if r.get("http_code") == 503),
"first_ttfb_s": min((r.get("ttfb") or 999) for r in t5) if t5 else None,
"last_t_total_s": max((r.get("t_total") or 0) for r in t5) if t5 else None,
"wall_s": meta.get("t5_wall_s"),
},
"t4_waf": [],
"scorecard": {
"mem": mem,
"quality": q,
"stability": stab,
"latency": lat,
"ops": ops,
"total": total,
},
"notes": meta.get("notes") or [],
}
(ROOT / "results.json").write_text(json.dumps(out, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
print(json.dumps({"wrote": str(ROOT / "results.json"), "scorecard": out["scorecard"]}, ensure_ascii=False))
if __name__ == "__main__":
main()