chore: 移除误提交的并发验证脚本 verify_breakpoint_resume.py（避免与并行开发冲突）
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# -*- coding: utf-8 -*-
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"""断点续传与丢失率 ≤0.02% 验证脚本 —— issue #26 / PRD 5.1·9 章验收口径。
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验证内容（对齐父 Issue #3「① 边缘采集网关 模板化封装」验收基线）：
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1. **断点续传**：Kafka 故障（上行降级 spool-only）期间样本全部落盘本地 spool；
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模拟网关重启后 pending 记录全量重发、逐条 ack，零丢失、内容完全一致；
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2. **ack 删除**：上行确认（ack）后 spool 记录精确删除，不重复、不残留；
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3. **端到端丢失率 ≤ 0.02%**：600 点位 1Hz（对齐 PRD 5.1 压测口径）+ 故障窗口，
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HealthMetrics 丢失率 ≤ 0.02%、P99 ≤ 1.8s、可用性 ≥ 99.8%（meets_sla），
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故障结束后 spool 最终排空（全部上行确认）。
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用法：
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python verify_breakpoint_resume.py [--points 600] [--rounds 60] \
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[--outage-rounds 10] [--seed 2026]
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退出码：0 = 全部通过（PASS）；1 = 任一检查失败（FAIL）。
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"""
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from __future__ import annotations
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import argparse
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import os
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import random
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import sys
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import tempfile
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import time
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from typing import Dict, List, Optional
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# 允许从任意 cwd 以脚本方式运行（python verify_breakpoint_resume.py）
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from collector import CollectorEngine, HealthMetrics, SpoolStore # noqa: E402
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from drivers import SimulatorDriver # noqa: E402
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from drivers.base import Driver, SampleValue # noqa: E402
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from point_dict.loader import Point, PointDict # noqa: E402
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LOSS_RATE_SLA = 0.0002 # 丢失率 ≤ 0.02%（PRD 5.1）
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P99_SLA = 1.8 # 采集 P99 ≤ 1.8s
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AVAILABILITY_SLA = 0.998 # 可用性 ≥ 99.8%
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# ----------------------------------------------------------------------
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# 辅助：点位字典 / 抖动驱动 / 模拟 Kafka 上行（含故障窗口）
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# ----------------------------------------------------------------------
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def make_point_dict(n_points: int, seed: int = 2026) -> PointDict:
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"""生成 n_points 个测点（设备前缀 CLF-01..CLF-05，对齐压测口径）。"""
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points: List[Point] = []
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n_devices = 5
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for i in range(n_points):
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dev = f"CLF-{i % n_devices + 1:02d}"
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points.append(
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Point(
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device_id=dev,
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point_id=f"{dev}.P{i:04d}",
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name=f"测点{i + 1}",
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unit="℃",
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data_type="float",
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sample_rate=1000,
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quality_code=True,
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row_number=i + 2,
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)
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)
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return PointDict(points)
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class FlakySimulatorDriver(Driver):
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"""模拟驱动包装：以 drop_probability 随机丢点，验证丢失率统计口径。
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丢点比例默认 0.01%（=0.0001），低于 0.02% 验收基线，
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用于证明“采集侧偶发未读”被正确计入丢失率且仍满足 SLA。
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"""
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protocol = "simulator-flaky"
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def __init__(self, drop_probability: float = 0.0001, seed: int = 2026):
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super().__init__(None)
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self._inner = SimulatorDriver({"seed": seed})
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self.drop_probability = drop_probability
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self._rng = random.Random(seed + 1)
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def connect(self) -> None:
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self._inner.connect()
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def read_points(self, points: List[Point]) -> Dict[str, SampleValue]:
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values = self._inner.read_points(points)
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for p in points:
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if self._rng.random() < self.drop_probability:
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values.pop(p.point_id, None) # 未读到 → 引擎计入丢失
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return values
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def close(self) -> None:
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self._inner.close()
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class FakeKafkaSink:
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"""模拟 Kafka 上行通道：up 时确认删除 spool，down 时保留（断点续传场景）。
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行为对齐 upstream/kafka_sink.py：发送成功即按样本 ack 删除 spool 记录；
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down 期间样本留在 spool，等待恢复后重发。
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"""
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def __init__(self, spool: SpoolStore):
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self.spool = spool
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self.up = True
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self.published = 0
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def publish(self, samples: List[dict]) -> int:
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if not self.up:
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return 0 # 上行故障：样本保留在 spool
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ok = 0
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for s in samples:
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self.spool.ack(
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{"device_id": s["device_id"], "point_id": s["point_id"],
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"value": s["value"], "ts": s["ts"]}
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)
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ok += 1
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self.published += ok
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return ok
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# ----------------------------------------------------------------------
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# 检查 1：断点续传 —— 重启重发零丢失、内容一致
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# ----------------------------------------------------------------------
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def check_resume_replay(workdir: str, n_points: int, n_rounds: int, seed: int) -> dict:
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"""Kafka 故障期间样本全部落盘；模拟重启后全量重发、逐条 ack。"""
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spool_dir = os.path.join(workdir, "spool-resume")
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spool = SpoolStore(spool_dir)
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metrics = HealthMetrics()
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engine = CollectorEngine(
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point_dict=make_point_dict(n_points, seed),
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driver_slots=[("simulator-flaky", FlakySimulatorDriver(seed=seed), [])],
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spool=spool,
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metrics=metrics,
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interval_ms=1000,
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max_pending=10 ** 9,
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)
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# 故障窗口：sink=None（spool-only 降级），样本只落盘不上行
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for _ in range(n_rounds):
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engine.collect_once(sink=None)
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before = spool.pending_records()
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assert len(before) > 0, "故障窗口内应产生待上行样本"
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# 模拟网关重启：新 SpoolStore（同一目录）+ 重发 pending
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spool2 = SpoolStore(spool_dir)
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replay = spool2.pending_records()
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assert len(replay) == len(before), "重启后重发条数应与故障期间采集数一致"
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for rec, orig in zip(replay, before):
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assert rec["device_id"] == orig["device_id"]
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assert rec["point_id"] == orig["point_id"]
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assert rec["value"] == orig["value"]
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assert abs(rec["ts"] - orig["ts"]) < 1e-6
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# 重发成功 → 逐条 ack 删除
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for rec in replay:
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spool2.ack(rec)
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assert spool2.total_pending() == 0, "重发并 ack 后 spool 应排空"
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return {"collected": len(before), "replayed": len(replay), "remaining": 0}
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# ----------------------------------------------------------------------
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# 检查 2：ack 删除 —— 上行确认后精确删除、不残留
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# ----------------------------------------------------------------------
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def check_ack_delete(workdir: str, n_points: int, seed: int) -> dict:
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spool = SpoolStore(os.path.join(workdir, "spool-ack"))
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engine = CollectorEngine(
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point_dict=make_point_dict(n_points, seed),
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driver_slots=[("simulator", SimulatorDriver({"seed": seed}), [])],
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spool=spool,
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metrics=HealthMetrics(),
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interval_ms=1000,
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max_pending=10 ** 9,
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)
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engine.collect_once(sink=None)
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n = spool.total_pending()
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assert n == n_points, f"单轮应写入 {n_points} 条，实际 {n}"
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# 模拟 Kafka 投递确认：按 point_id ack（与 kafka_sink._on_delivery 相同口径）
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for rec in spool.pending_records():
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spool.ack({"device_id": None, "point_id": rec["point_id"],
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"value": None, "ts": None})
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assert spool.total_pending() == 0, "全部确认后 spool 应清零"
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# 再采一轮：确认新样本正常追加、无残留干扰
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engine.collect_once(sink=None)
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assert spool.total_pending() == n_points, "ack 后新样本应精确追加"
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return {"acked": n, "remaining": 0}
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# ----------------------------------------------------------------------
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# 检查 3：端到端丢失率 ≤ 0.02%（600 点位 1Hz + 故障窗口 + 恢复重发）
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# ----------------------------------------------------------------------
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def check_end_to_end_loss_rate(
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workdir: str, n_points: int, n_rounds: int, outage_rounds: int, seed: int
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) -> dict:
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spool_dir = os.path.join(workdir, "spool-e2e")
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spool = SpoolStore(spool_dir)
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metrics = HealthMetrics()
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engine = CollectorEngine(
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point_dict=make_point_dict(n_points, seed),
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driver_slots=[("simulator-flaky", FlakySimulatorDriver(seed=seed), [])],
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spool=spool,
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metrics=metrics,
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interval_ms=1000,
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max_pending=10 ** 9,
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)
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sink = FakeKafkaSink(spool)
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outage_start = max(1, n_rounds - outage_rounds - 1)
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peak_pending = 0
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for r in range(n_rounds):
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sink.up = r >= outage_start # 故障窗口内 Kafka 不可用
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engine.collect_once(sink=sink)
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peak_pending = max(peak_pending, spool.total_pending())
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# 恢复 + 模拟重启重发：pending 全量上行确认
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sink.up = True
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for rec in spool.pending_records():
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sink.spool.ack(rec)
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snap = metrics.snapshot()
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ok_loss = metrics.loss_rate <= LOSS_RATE_SLA
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ok_p99 = metrics.p99_latency() <= P99_SLA
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ok_avail = metrics.availability >= AVAILABILITY_SLA
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ok_drain = spool.total_pending() == 0
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assert ok_loss, f"丢失率 {metrics.loss_rate:.6f} > {LOSS_RATE_SLA}（0.02%）"
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assert ok_p99, f"P99 {metrics.p99_latency():.3f}s > {P99_SLA}s"
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assert ok_avail, f"可用性 {metrics.availability:.6f} < {AVAILABILITY_SLA}"
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assert ok_drain, "故障恢复后 spool 应排空（全部上行确认）"
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assert metrics.meets_sla(), "HealthMetrics.meets_sla() 应为 True"
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snap["peak_pending"] = peak_pending
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snap["outage_rounds"] = outage_rounds
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return snap
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# ----------------------------------------------------------------------
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# 主流程
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# ----------------------------------------------------------------------
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def parse_args(argv: List[str]) -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="iAOP 边缘采集网关：断点续传与丢失率≤0.02% 验证脚本（issue #26）"
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)
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parser.add_argument("--points", type=int, default=600,
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help="模拟点位数量（默认 600，对齐 PRD 5.1 压测口径）")
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parser.add_argument("--rounds", type=int, default=60,
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help="端到端采集轮数（默认 60）")
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parser.add_argument("--outage-rounds", type=int, default=10,
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help="Kafka 故障窗口轮数（默认 10，验证断点续传）")
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parser.add_argument("--seed", type=int, default=2026,
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help="随机种子（默认 2026，保证可复现）")
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return parser.parse_args(argv)
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def main(argv: Optional[List[str]] = None) -> int:
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args = parse_args(argv if argv is not None else sys.argv[1:])
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print("=" * 68)
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print("iAOP 边缘采集网关 · 断点续传与丢失率≤0.02% 验证脚本")
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print(f"点位={args.points} 轮数={args.rounds} 故障窗口={args.outage_rounds} "
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f"种子={args.seed}")
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print("=" * 68)
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results: List[tuple] = []
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with tempfile.TemporaryDirectory(prefix="verify-bpr-") as workdir:
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# 检查 1：断点续传
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t0 = time.monotonic()
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r1 = check_resume_replay(workdir, args.points, args.outage_rounds, args.seed)
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r1["elapsed"] = time.monotonic() - t0
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results.append(("断点续传（故障期全落盘 → 重启全量重发 → ack 排空）",
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f"采集 {r1['collected']} 条 / 重发 {r1['replayed']} 条 / 残留 {r1['remaining']} 条",
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r1["collected"] == r1["replayed"] and r1["remaining"] == 0))
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# 检查 2：ack 删除
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t0 = time.monotonic()
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r2 = check_ack_delete(workdir, min(args.points, 200), args.seed)
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r2["elapsed"] = time.monotonic() - t0
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results.append(("上行确认删除（ack 后精确删除、无残留）",
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f"确认 {r2['acked']} 条 / 残留 {r2['remaining']} 条",
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r2["remaining"] == 0))
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# 检查 3：端到端丢失率
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t0 = time.monotonic()
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r3 = check_end_to_end_loss_rate(
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workdir, args.points, args.rounds, args.outage_rounds, args.seed
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)
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r3["elapsed"] = time.monotonic() - t0
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results.append(
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("端到端丢失率 ≤ 0.02%",
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f"样本 {r3['total_samples']} / 丢失 {r3['lost_samples']} / "
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f"丢失率 {r3['loss_rate']:.6f} / P99 {r3['p99_latency_sec']}s / "
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f"可用性 {r3['availability']:.6f} / spool 峰值 {r3['peak_pending']}",
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r3["loss_rate"] <= LOSS_RATE_SLA
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and r3["p99_latency_sec"] <= P99_SLA
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and r3["availability"] >= AVAILABILITY_SLA
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and r3["peak_pending"] > 0 # 故障窗口确实产生了 spool 堆积
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and r3["total_samples"] - r3["lost_samples"] >= 0))
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print("-" * 68)
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all_pass = True
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for name, detail, ok in results:
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all_pass = all_pass and ok
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print(f"[{'PASS' if ok else 'FAIL'}] {name}")
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print(f" {detail}")
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print("-" * 68)
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if all_pass:
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print("结论：全部通过 —— 断点续传零丢失，丢失率/P99/可用性满足 PRD 5.1 验收基线。")
|
|
||||||
return 0
|
|
||||||
print("结论：存在失败项 —— 请检查网关实现后重跑。")
|
|
||||||
return 1
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
sys.exit(main())
|
|
||||||
Reference in New Issue
Block a user