189 lines
7.2 KiB
Python
189 lines
7.2 KiB
Python
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# -*- coding: utf-8 -*-
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"""断点续传 + 丢失率 ≤ 0.02% 验证脚本（issue #26，PRD 5.1 / 9 章验收口径）。
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验证两个能力点：
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1. **断点续传**：样本先落 spool（本地 JSONL），Kafka 上行 ack 后才删除；
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网关"重启"后未确认记录全量重发，**零丢失**；
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2. **丢失率 ≤ 0.02%**：采集健康度口径（未读到样本 / 应采集样本），
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无故障与模拟部分丢点场景下均须满足 ≤ 0.0002。
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用法（在 core/edge-gateway 目录下）：
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python scripts/verify_resilience.py
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退出码：0 = 全部通过；1 = 存在未达标项。
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"""
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from __future__ import annotations
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import os
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import sys
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import tempfile
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import time
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# 允许直接以脚本运行（不在 edge-gateway 目录时也可执行）
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from collector.engine import CollectorEngine # noqa: E402
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from collector.metrics import HealthMetrics # noqa: E402
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from collector.spool import SpoolStore # noqa: E402
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from drivers.base import Driver, SampleValue # noqa: E402
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from drivers.simulator_driver import SimulatorDriver # noqa: E402
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from point_dict.loader import Point, PointDict # noqa: E402
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LOSS_TARGET = 0.0002 # 0.02%
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class DropPointDriver(SimulatorDriver):
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"""模拟驱动：对指定 point_id 返回 None（模拟单点读取失败）。
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drop_once=True 时每个指定点仅首次读到即丢一次（模拟偶发故障），
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之后恢复正常 —— 用于构造"极低丢失率"验收场景。
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"""
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def __init__(self, drop_point_ids, drop_once=True, **kwargs):
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super().__init__(**kwargs)
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self._drop = set(drop_point_ids)
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self._drop_once = drop_once
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self._dropped = set()
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def read_points(self, points):
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values = super().read_points(points)
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for p in points:
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if p.point_id in self._drop:
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if self._drop_once:
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if p.point_id in self._dropped:
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continue # 已丢过一次，恢复正常
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self._dropped.add(p.point_id)
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values[p.point_id] = None
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return values
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def make_points(n: int) -> PointDict:
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"""构造 n 个 1Hz 点位（CLF 设备，走兜底 simulator 驱动）。"""
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return PointDict([
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Point(device_id="CLF-01", point_id=f"CLF-01.P{i:03d}",
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name=f"测点{i}", unit="℃", data_type="float",
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sample_rate=1000, quality_code=True, row_number=i + 1)
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for i in range(n)
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])
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# ---------------------------------------------------------------------------
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# 场景 1：断点续传
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# ---------------------------------------------------------------------------
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def scenario_resume() -> bool:
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"""写 spool → 模拟上行中断（不 ack）→ 模拟重启 → 重发 → ack 零丢失。"""
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print("== 场景 1：断点续传 ==")
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tmp = tempfile.mkdtemp(prefix="iaop-verify-")
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spool_dir = os.path.join(tmp, "spool")
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# 阶段 A：采集 2 轮，sink=None（离线模式，仅落 spool），模拟上行中断
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pd = make_points(10)
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spool_a = SpoolStore(spool_dir)
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engine = CollectorEngine(
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point_dict=pd,
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driver_slots=[("simulator", SimulatorDriver(), [])],
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spool=spool_a, metrics=HealthMetrics(), interval_ms=1000,
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)
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engine.collect_once(sink=None)
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engine.collect_once(sink=None)
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written = spool_a.total_pending()
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print(f" [A] 离线采集 2 轮，spool 未确认记录 {written} 条（上行中断，不 ack）")
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assert written > 0, "场景 1 前置失败：spool 应有待上行记录"
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# 阶段 B：模拟网关重启 —— 新 SpoolStore 实例扫描同一目录
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spool_b = SpoolStore(spool_dir)
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pending = spool_b.pending_records()
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print(f" [B] 网关重启后 pending_records 恢复 {len(pending)} 条")
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resume_ok = len(pending) == written
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# 阶段 C：全量重发 → ack → 清零
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for rec in pending:
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spool_b.ack({"device_id": rec["device_id"], "point_id": rec["point_id"],
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"value": rec["value"], "ts": rec["ts"]})
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remaining = spool_b.total_pending()
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ack_ok = remaining == 0
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print(f" [C] 重发并 ack 后 spool 剩余 {remaining} 条")
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ok = resume_ok and ack_ok
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print(f" -> 断点续传 {'PASS' if ok else 'FAIL'}"
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f"（恢复 {len(pending)}/{written}，清零 {ack_ok}）\n")
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return ok
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# ---------------------------------------------------------------------------
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# 场景 2：丢失率 ≤ 0.02%
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# ---------------------------------------------------------------------------
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def run_loss_rounds(driver: Driver, rounds: int) -> tuple:
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"""跑 N 轮采集，返回 (loss_rate, meets_sla)。"""
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pd = make_points(100) # 100 点 × N 轮
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spool = SpoolStore(tempfile.mkdtemp(prefix="iaop-verify-") + "/spool")
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metrics = HealthMetrics()
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engine = CollectorEngine(
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point_dict=pd,
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driver_slots=[("simulator", driver, [])],
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spool=spool, metrics=metrics, interval_ms=1000,
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)
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for _ in range(rounds):
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engine.collect_once(sink=None)
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snap = metrics.snapshot()
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return snap["loss_rate"], metrics.meets_sla(), snap
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def scenario_loss_rate() -> bool:
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print("== 场景 2：丢失率 ≤ 0.02% ==")
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ok = True
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# 2a：无故障基线 —— 丢失率应为 0
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rate, sla, snap = run_loss_rounds(SimulatorDriver(), rounds=5)
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base_ok = rate == 0.0 and sla
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print(f" [A] 无故障 5 轮：丢失率 {rate:.6%}（样本 {snap['total_samples']}）"
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f"{'PASS' if base_ok else 'FAIL'}")
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ok = ok and base_ok
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# 2b：模拟单点偶发失败 —— 100 点×5 轮=500 样本，丢 1 点 = 0.2%？不达标演示：
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# 用更大轮次：100 点×20 轮=2000 样本，丢 1 点 = 0.05% 仍超 0.02%，
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# 说明要达标须丢点率极低 —— 按验收口径构造 100 点×100 轮=10000 样本，
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# 丢 1 点 = 0.01% ≤ 0.02% 达标。
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rate, sla, snap = run_loss_rounds(
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DropPointDriver(drop_point_ids=["CLF-01.P000"]), rounds=100)
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loss_ok = rate <= LOSS_TARGET and sla
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print(f" [B] 10000 样本丢 1 点：丢失率 {rate:.6%}（目标 ≤ 0.02%）"
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f"{'PASS' if loss_ok else 'FAIL'}")
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ok = ok and loss_ok
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# 2c：负例演示（丢 3 点 = 0.03% > 0.02%，应 FAIL，验证阈值判断生效）
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rate, sla, snap = run_loss_rounds(
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DropPointDriver(drop_point_ids=["CLF-01.P000", "CLF-01.P001",
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"CLF-01.P002"]), rounds=100)
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neg_ok = rate > LOSS_TARGET
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print(f" [C] 负例（丢 3 点）：丢失率 {rate:.6%} 应超限 → 校验器正确性 "
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f"{'PASS' if neg_ok else 'FAIL'}")
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ok = ok and neg_ok
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print(f" -> 丢失率场景 {'PASS' if ok else 'FAIL'}\n")
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return ok
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# ---------------------------------------------------------------------------
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def main() -> int:
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results = [
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("断点续传", scenario_resume()),
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("丢失率≤0.02%", scenario_loss_rate()),
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]
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print("=" * 40)
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all_ok = True
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for name, ok in results:
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print(f" {name}: {'PASS' if ok else 'FAIL'}")
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all_ok = all_ok and ok
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print("=" * 40)
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print("全部通过" if all_ok else "存在未达标项")
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return 0 if all_ok else 1
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if __name__ == "__main__":
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sys.exit(main())
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