158 lines
5.5 KiB
Python
158 lines
5.5 KiB
Python
# -*- coding: utf-8 -*-
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"""
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并排对比两个(或多个)机器的 数据.jsonl，定位谁慢、慢在哪个阶段。
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用法:
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python temp/compare_jsonl.py 2号机器数据.jsonl 3号机器数据.jsonl
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python temp/compare_jsonl.py a.jsonl b.jsonl c.jsonl ...
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"""
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import json
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import sys
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from datetime import datetime
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def parse_dt(s):
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try:
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return datetime.fromisoformat(s)
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except (ValueError, TypeError):
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return None
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def analyze(path):
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"""读一个 jsonl，返回汇总指标。"""
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s = {
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"n": 0, "ok": 0, "err": 0, "no_sub": 0,
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"dur": 0.0, "elapsed": 0.0,
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"stage": {"split_frame": 0.0, "merge": 0.0, "getsrt": 0.0},
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"ocr": 0, "n_ocr": 0,
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"starts": [], "ends": [],
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}
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try:
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f = open(path, encoding="utf-8")
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except FileNotFoundError:
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return None
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with f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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try:
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r = json.loads(line)
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except json.JSONDecodeError:
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continue
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s["n"] += 1
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st = r.get("status")
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if st == "success":
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s["ok"] += 1
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elif st == "no_subtitle":
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s["no_sub"] += 1
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else:
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s["err"] += 1
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try:
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s["dur"] += float(r.get("duration") or 0)
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except (TypeError, ValueError):
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pass
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el = r.get("elapsed_seconds")
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if el:
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try:
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s["elapsed"] += float(el)
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except (TypeError, ValueError):
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pass
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sd = r.get("stage_durations") or {}
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for k in s["stage"]:
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try:
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s["stage"][k] += float(sd.get(k) or 0)
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except (TypeError, ValueError):
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pass
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ic = r.get("image_counts") or {}
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toc = ic.get("total_ocr_count")
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if toc:
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try:
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s["ocr"] += int(toc)
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s["n_ocr"] += 1
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except (TypeError, ValueError):
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pass
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ds = parse_dt(r.get("process_start"))
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de = parse_dt(r.get("process_end"))
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if ds:
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s["starts"].append(ds)
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if de:
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s["ends"].append(de)
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return s
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def derive(s):
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"""从汇总算出对比指标。"""
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d = {}
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n = max(1, s["n"])
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d["记录数"] = s["n"]
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d["成功率%"] = round(s["ok"] / n * 100, 1)
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d["视频总时长h"] = round(s["dur"] / 3600, 1)
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# 墙钟跨度 + 实时倍率
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span = 0.0
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if s["starts"] and s["ends"]:
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span = (max(s["ends"]) - min(s["starts"])).total_seconds()
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d["墙钟跨度h"] = round(span / 3600, 2)
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d["实时倍率x"] = round(s["dur"] / span, 2) if span > 0 else 0
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d["视频/天"] = round(s["n"] / span * 86400) if span > 0 else 0
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d["有效并发"] = round(s["elapsed"] / span, 1) if span > 0 else 0
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# 平均每视频各阶段秒数
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d["抽帧s/视频"] = round(s["stage"]["split_frame"] / n, 1)
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d["拼接s/视频"] = round(s["stage"]["merge"] / n, 1)
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d["getsrt s/视频"] = round(s["stage"]["getsrt"] / n, 1)
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total_stage = sum(s["stage"].values()) or 1
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d["抽帧占比%"] = round(s["stage"]["split_frame"] / total_stage * 100)
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d["拼接占比%"] = round(s["stage"]["merge"] / total_stage * 100)
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d["getsrt占比%"] = round(s["stage"]["getsrt"] / total_stage * 100)
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d["平均OCR次/视频"] = round(s["ocr"] / s["n_ocr"]) if s["n_ocr"] else 0
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d["平均耗时s/视频"] = round(s["elapsed"] / n, 1)
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return d
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def main():
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paths = sys.argv[1:]
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if len(paths) < 2:
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print("用法: python temp/compare_jsonl.py a.jsonl b.jsonl [c.jsonl ...]")
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return
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cols, names = [], []
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for p in paths:
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s = analyze(p)
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if s is None:
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print(f"找不到文件: {p}")
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return
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cols.append(derive(s))
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names.append(p.replace("数据.jsonl", "").replace(".jsonl", "") or p)
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keys = list(cols[0].keys())
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w = max(14, max(len(n) for n in names) + 2)
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print("=" * (18 + w * len(cols)))
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print(f"{'指标':<16}" + "".join(f"{n:>{w}}" for n in names))
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print("-" * (18 + w * len(cols)))
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for k in keys:
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print(f"{k:<16}" + "".join(f"{str(c[k]):>{w}}" for c in cols))
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print("=" * (18 + w * len(cols)))
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# 自动点评(两台时)
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if len(cols) == 2:
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a, b = cols
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slow_i = 0 if a["实时倍率x"] < b["实时倍率x"] else 1
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fast_i = 1 - slow_i
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print(f"\n【分析】{names[slow_i]} 更慢(实时倍率 "
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f"{cols[slow_i]['实时倍率x']}x vs {cols[fast_i]['实时倍率x']}x)")
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# 找慢在哪个阶段
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for stage, key in [("抽帧", "抽帧s/视频"), ("拼接", "拼接s/视频"), ("getsrt(OCR)", "getsrt s/视频")]:
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sv, fv = cols[slow_i][key], cols[fast_i][key]
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if fv and sv > fv * 1.3:
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print(f" ▸ {stage} 慢: {sv}s vs {fv}s (慢 {sv/fv:.1f}x)")
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if cols[slow_i]["有效并发"] < cols[fast_i]["有效并发"] * 0.8:
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print(f" ▸ 并发低: {cols[slow_i]['有效并发']} vs {cols[fast_i]['有效并发']}"
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f"(CPU/内存/OCR 排队?)")
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if cols[slow_i]["平均OCR次/视频"] > cols[fast_i]["平均OCR次/视频"] * 1.3:
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print(f" ▸ OCR 次数多: {cols[slow_i]['平均OCR次/视频']} vs "
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f"{cols[fast_i]['平均OCR次/视频']}(视频更长/composite更多)")
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if __name__ == "__main__":
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main()
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