renjianbo
f2e65a8fbb
- Add company module (3-tier org, CEO planning, parallel departments) - Add company orchestrator, knowledge extractor, presets, scheduler - Add company API endpoints, models, and frontend views - Add 今天吃啥 PWA app (69 dishes, real images, offline support) - Add team_projects output directory structure - Add unified manage.ps1 for service lifecycle - Add Windows startup guide v1.0 - Add TTS troubleshooting doc - Update frontend (AgentChat UX overhaul, new views) - Update backend (voice engine fix, multi-tenant, RBAC) - Remove deprecated startup scripts and old docs Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
538 lines
21 KiB
Python
538 lines
21 KiB
Python
# ============================================================================
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# 家庭阅读激励工具 - 性能测试脚本 (Phase 6)
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# ============================================================================
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"""
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性能测试：测量关键操作的响应时间和吞吐量。
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测试指标:
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1. 大规模数据构造 — 1000本书 + 3000张照片
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2. 多孩子切换响应时间 — ≤300ms
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3. 打卡操作响应时间 — ≤200ms
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4. 积分查询响应时间 — ≤100ms
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5. 照片列表查询 — 首屏 ≤1.5s
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6. 读写混合场景 — 模拟家庭日常使用
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运行方式:
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python 14-test_performance.py
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输出:
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- 控制台打印测试结果
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- 生成 15-test_report.md 报告
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"""
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import os
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import sys
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import time
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import json
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import math
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import tempfile
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import shutil
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import statistics
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from datetime import date, timedelta
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from typing import List, Dict, Any, Tuple
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# --- 路径设置 ---
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TEST_DIR = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, TEST_DIR)
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# --- 使用临时数据库 ---
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PERF_DB_DIR = tempfile.mkdtemp(prefix="reading_perf_")
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os.environ["READING_INCENTIVE_DB_DIR"] = PERF_DB_DIR
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os.environ["READING_INCENTIVE_DB_NAME"] = "test_perf.db"
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import importlib
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import db_config
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importlib.reload(db_config)
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from db_config import get_connection, transaction, DB_PATH
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from dal import (
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ChildDAO, BookDAO, ReadingRecordDAO, PointDAO,
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RewardDAO, RedemptionDAO,
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)
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from models import Child
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from points_config import PointsConfig
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from points_engine import PointsEngine
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from redemption_engine import RedemptionEngine
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from child_context import ChildContext
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# ============================================================================
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# 性能测试基类
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# ============================================================================
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class PerfBenchmark:
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"""性能基准测试 — 测量关键操作耗时。"""
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def __init__(self):
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self.results: Dict[str, Dict] = {}
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self._init_schema()
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self.child_dao = ChildDAO()
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self.book_dao = BookDAO()
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self.reading_dao = ReadingRecordDAO()
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self.point_dao = PointDAO()
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self.reward_dao = RewardDAO()
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self.redemption_dao = RedemptionDAO()
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self.engine = PointsEngine
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self.redemption = RedemptionEngine
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self.context = ChildContext()
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self.config = PointsConfig.load()
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def _init_schema(self):
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"""初始化数据库 Schema。"""
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schema_path = os.path.join(TEST_DIR, "01-schema.sql")
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conn = db_config.get_connection()
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with open(schema_path, "r", encoding="utf-8") as f:
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conn.executescript(f.read())
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conn.commit()
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conn.close()
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PointsConfig.load() # 初始化配置表
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# ---- 测量工具 ----
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def _measure(self, name: str, fn, iterations: int = 1,
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warmup: int = 1, **context) -> Dict:
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"""测量函数平均耗时。"""
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# Warmup
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for _ in range(warmup):
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fn()
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# 测量
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times = []
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results = []
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for _ in range(iterations):
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start = time.perf_counter()
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r = fn()
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elapsed = time.perf_counter() - start
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times.append(elapsed)
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results.append(r)
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avg = statistics.mean(times) * 1000 # ms
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med = statistics.median(times) * 1000
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_min = min(times) * 1000
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_max = max(times) * 1000
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p95 = sorted(times)[int(len(times) * 0.95)] * 1000 if len(times) >= 20 else _max
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record = {
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"avg_ms": round(avg, 2),
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"median_ms": round(med, 2),
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"min_ms": round(_min, 2),
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"max_ms": round(_max, 2),
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"p95_ms": round(p95, 2),
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"iterations": iterations,
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**context,
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}
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self.results[name] = record
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return record
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# ============================================================================
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# 性能测试执行
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# ============================================================================
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class PerformanceTestSuite:
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"""执行所有性能测试并生成报告。"""
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def __init__(self):
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self.bm = PerfBenchmark()
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self.targets = {
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"打卡操作": {"target_ms": 200},
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"多孩子切换": {"target_ms": 300},
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"积分查询": {"target_ms": 100},
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"照片列表首屏": {"target_ms": 1500},
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"积分流水查询": {"target_ms": 100},
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"统计数据查询": {"target_ms": 200},
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}
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def run_all(self):
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"""执行全部性能测试。"""
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print("\n" + "=" * 70)
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print(" 家庭阅读激励工具 - 性能测试套件")
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print("=" * 70)
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print(f" 测试数据库: {DB_PATH}")
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print(f" 开始时间: {time.strftime('%Y-%m-%d %H:%M:%S')}")
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print("=" * 70)
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# 1. 大规模数据构造
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print("\n[1/7] 大规模数据构造...")
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self._construct_large_dataset()
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# 2. 打卡操作响应时间
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print("\n[2/7] 打卡操作响应时间测量...")
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self._benchmark_checkin()
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# 3. 多孩子切换响应时间
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print("\n[3/7] 多孩子切换响应时间测量...")
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self._benchmark_switch()
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# 4. 积分查询响应时间
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print("\n[4/7] 积分查询响应时间测量...")
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self._benchmark_points_query()
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# 5. 积分流水查询
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print("\n[5/7] 积分流水查询响应时间测量...")
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self._benchmark_points_history()
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# 6. 统计查询
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print("\n[6/7] 统计查询响应时间测量...")
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self._benchmark_stats()
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# 7. 读写混合场景
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print("\n[7/7] 读写混合场景测量...")
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self._benchmark_mixed_workload()
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# 输出结果
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self._print_summary()
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self._generate_report()
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def _construct_large_dataset(self):
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"""构造 1000 本书 + 3000 张测试数据。"""
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start = time.perf_counter()
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child = self.bm.child_dao.create("大数据测试孩子", age=10)
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# 1000 本书
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book_batch_size = 100
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total_books = 0
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for batch_start in range(0, 1000, book_batch_size):
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conn = get_connection()
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conn.execute("BEGIN TRANSACTION")
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try:
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for i in range(batch_start, min(batch_start + book_batch_size, 1000)):
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conn.execute(
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"INSERT INTO books (title, author, page_count) VALUES (?, ?, ?)",
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(f"测试书籍_{i:04d}", f"作者_{i % 100}", 50 + (i % 150)),
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)
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conn.commit()
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total_books += min(book_batch_size, 1000 - batch_start)
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except Exception:
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conn.rollback()
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finally:
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conn.close()
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# 每本书3张照片（用 photos 表记录）
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photo_count = 0
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for book_id in range(1, min(1001, total_books + 1)):
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conn = get_connection()
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try:
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for p in range(3):
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conn.execute(
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"""INSERT INTO photos
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(child_id, reading_record_id, file_path, thumbnail_path,
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original_name, file_size, width, height, file_hash)
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VALUES (?, NULL, ?, ?, ?, ?, ?, ?, ?)""",
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(child.id,
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f"/photos/perf/{book_id}_{p}.jpg",
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f"/photos/perf/thumb_{book_id}_{p}.jpg",
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f"photo_{book_id}_{p}.jpg",
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102400 + p * 1024,
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1024, 768,
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f"hash_{book_id}_{p}" + "0" * 48),
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)
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photo_count += 1
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conn.commit()
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except Exception:
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conn.rollback()
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finally:
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conn.close()
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elapsed = time.perf_counter() - start
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print(f" ✓ 构造完成: {total_books} 本书 + {photo_count} 张照片 "
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f"({elapsed:.1f}s)")
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self.bm.results["大规模数据构造"] = {
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"books": total_books,
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"photos": photo_count,
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"elapsed_s": round(elapsed, 1),
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}
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def _benchmark_checkin(self):
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"""测量打卡操作响应时间。"""
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def single_checkin():
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book_id = self._get_random_book()
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record = self.bm.reading_dao.create(
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child_id=1, book_id=book_id,
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read_date=date.today().isoformat(),
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duration_minutes=30, pages_read=15,
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award_points=False,
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)
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return record
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result = self.bm._measure(
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"打卡操作", single_checkin,
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iterations=30, warmup=3,
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)
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print(f" ✓ 平均: {result['avg_ms']}ms | "
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f"P95: {result['p95_ms']}ms | "
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f"目标: ≤{self.targets['打卡操作']['target_ms']}ms "
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f"{'✅ PASS' if result['avg_ms'] <= self.targets['打卡操作']['target_ms'] else '❌ FAIL'}")
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def _benchmark_switch(self):
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"""测量多孩子切换响应时间。"""
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# 创建20个孩子模拟真实场景
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for i in range(19):
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self.bm.child_dao.create(f"切换测试孩子_{i}", age=5 + (i % 10))
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def switch_to_next():
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children = self.bm.child_dao.list_all()
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if children:
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child = children[len(children) % len(children)]
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ctx = ChildContext()
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ctx.switch_to(child.id)
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ctx.get_stats()
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return True
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result = self.bm._measure(
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"多孩子切换操作", switch_to_next,
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iterations=30, warmup=3,
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)
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print(f" ✓ 平均: {result['avg_ms']}ms | "
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f"P95: {result['p95_ms']}ms | "
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f"目标: ≤{self.targets['多孩子切换']['target_ms']}ms "
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f"{'✅ PASS' if result['avg_ms'] <= self.targets['多孩子切换']['target_ms'] else '❌ FAIL'}")
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def _benchmark_points_query(self):
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"""测量积分查询响应时间。"""
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# 创建大量积分流水
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child_id = 1
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for i in range(100):
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self.bm.engine.add_bonus_points(child_id, 5)
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def query_balance():
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return self.bm.engine.get_balance(child_id)
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result = self.bm._measure(
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"积分查询", query_balance,
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iterations=50, warmup=5,
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)
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print(f" ✓ 平均: {result['avg_ms']}ms | "
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f"P95: {result['p95_ms']}ms | "
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f"目标: ≤{self.targets['积分查询']['target_ms']}ms "
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f"{'✅ PASS' if result['avg_ms'] <= self.targets['积分查询']['target_ms'] else '❌ FAIL'}")
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def _benchmark_points_history(self):
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"""测量积分流水查询响应时间。"""
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child_id = 1
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def query_history():
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return self.bm.point_dao.list_by_child(child_id, limit=50)
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result = self.bm._measure(
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"积分流水查询", query_history,
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iterations=30, warmup=3,
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)
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print(f" ✓ 平均: {result['avg_ms']}ms | "
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f"P95: {result['p95_ms']}ms | "
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f"目标: ≤{self.targets['积分流水查询']['target_ms']}ms "
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f"{'✅ PASS' if result['avg_ms'] <= self.targets['积分流水查询']['target_ms'] else '❌ FAIL'}")
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def _benchmark_stats(self):
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"""测量统计查询响应时间。"""
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child_id = 1
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def query_stats():
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return ChildContext.get_child_stats(child_id)
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result = self.bm._measure(
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"统计数据查询", query_stats,
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iterations=30, warmup=3,
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)
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print(f" ✓ 平均: {result['avg_ms']}ms | "
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f"P95: {result['p95_ms']}ms | "
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f"目标: ≤200ms "
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f"{'✅ PASS' if result['avg_ms'] <= 200 else '❌ FAIL'}")
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def _benchmark_mixed_workload(self):
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"""模拟家庭日常使用混合场景。"""
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child_id = 1
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def mixed_workload():
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# 1. 打卡
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book_id = (int(time.time()) % 1000) + 1
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record = self.bm.reading_dao.create(
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child_id=child_id, book_id=book_id,
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read_date=date.today().isoformat(),
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duration_minutes=25, pages_read=12,
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award_points=False,
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)
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# 2. 积分计算
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self.bm.engine.add_reading_points(child_id, record.id, self.bm.config)
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# 3. 查询余额
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balance = self.bm.engine.get_balance(child_id)
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# 4. 查询积分流水
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history = self.bm.point_dao.list_by_child(child_id, limit=10)
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# 5. 查统计
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stats = ChildContext.get_child_stats(child_id)
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return balance
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result = self.bm._measure(
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"读写混合场景", mixed_workload,
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iterations=20, warmup=2,
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)
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print(f" ✓ 平均: {result['avg_ms']}ms | "
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f"P95: {result['p95_ms']}ms")
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def _get_random_book(self):
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"""获取随机书籍 ID。"""
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import random
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return random.randint(1, 1000)
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def _print_summary(self):
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"""控制台输出汇总。"""
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print("\n" + "=" * 70)
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print(" 性能测试结果汇总")
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print("=" * 70)
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print(f"{'测试项':<20} {'平均值(ms)':<14} {'P95(ms)':<12} {'目标(ms)':<12} {'状态'}")
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print("-" * 70)
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all_pass = True
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for name, data in self.bm.results.items():
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if "avg_ms" not in data:
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continue
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target = self.targets.get(name, {}).get("target_ms", None)
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if target is not None:
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passed = data["avg_ms"] <= target
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if not passed:
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all_pass = False
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status = "✅ PASS" if passed else "❌ FAIL"
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else:
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status = "📊 INFO"
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print(f"{name:<20} {data['avg_ms']:<14} {data['p95_ms']:<12} "
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f"{str(target or '-'):<12} {status}")
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print("-" * 70)
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print(f"\n整体结果: {'✅ 全部通过' if all_pass else '❌ 存在未达标项'}")
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print(f"测试数据库大小: {self._get_db_size()}")
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def _get_db_size(self) -> str:
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"""获取数据库文件大小。"""
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if os.path.exists(DB_PATH):
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size = os.path.getsize(DB_PATH)
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if size < 1024:
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return f"{size} B"
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elif size < 1024 * 1024:
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return f"{size / 1024:.1f} KB"
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else:
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return f"{size / (1024*1024):.1f} MB"
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return "未知"
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def _generate_report(self):
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"""生成 Markdown 测试报告。"""
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lines = []
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lines.append("# 家庭阅读激励工具 — 性能测试报告\n")
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lines.append(f"> 生成时间: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
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lines.append(f"> 测试环境: Windows 10 / Python 3.12.8 / SQLite (WAL模式)\n")
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lines.append(f"> 数据库: {DB_PATH} ({self._get_db_size()})\n")
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# 测试环境
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lines.append("---\n")
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lines.append("## 1. 测试环境说明\n")
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lines.append("| 项目 | 说明 |")
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lines.append("|------|------|")
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lines.append("| 操作系统 | Windows 10 |")
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lines.append("| Python版本 | 3.12.8 |")
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lines.append("| 数据库 | SQLite 3 (WAL模式, mmap=256MB, cache=-8000) |")
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lines.append("| 测试数据 | 1000本书 + 3000张照片记录 + 20个孩子 |")
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lines.append("| 测量方式 | time.perf_counter(), 多次迭代取均值和中位数 |")
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lines.append("| 预热次数 | 2-5次 (因测试项而异) |")
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lines.append("")
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# 测试数据构造
|
||
lines.append("## 2. 测试数据构造方法\n")
|
||
lines.append("```")
|
||
lines.append("1000 本书 → 批量 INSERT (每批100条事务)")
|
||
lines.append("3000 张照片 → 每本书3张照片，写入 photos 表")
|
||
lines.append("20 个孩子 → 模拟多孩子家庭")
|
||
lines.append("100+ 积分流水 → 每个孩子积分变动记录")
|
||
lines.append("```\n")
|
||
|
||
if "大规模数据构造" in self.bm.results:
|
||
d = self.bm.results["大规模数据构造"]
|
||
lines.append(f"- 构造 {d['books']} 本书 & {d['photos']} 张照片耗时: {d['elapsed_s']}s\n")
|
||
|
||
# 指标测量结果
|
||
lines.append("## 3. 性能指标测量结果\n")
|
||
lines.append("| 测试项 | 平均值(ms) | 中位数(ms) | 最小值(ms) | 最大值(ms) | P95(ms) | 迭代次数 | 目标(ms) | 状态 |")
|
||
lines.append("|--------|-----------|-----------|-----------|-----------|---------|---------|---------|------|")
|
||
|
||
perf_names = ["打卡操作", "多孩子切换操作", "积分查询", "积分流水查询", "统计数据查询"]
|
||
all_pass = True
|
||
for name in perf_names:
|
||
if name in self.bm.results:
|
||
d = self.bm.results[name]
|
||
target = self.targets.get(name.replace("操作", "").replace("查询", ""), {}).get("target_ms", None)
|
||
if target is None:
|
||
target = self.targets.get(name, {}).get("target_ms", None)
|
||
if target is None:
|
||
# fallback mapping
|
||
t_map = {
|
||
"打卡操作": 200, "多孩子切换操作": 300,
|
||
"积分查询": 100, "积分流水查询": 100,
|
||
"统计数据查询": 200, "读写混合场景": 500,
|
||
}
|
||
target = t_map.get(name, "-")
|
||
passed = d["avg_ms"] <= target if isinstance(target, (int, float)) else True
|
||
if isinstance(target, (int, float)) and not passed:
|
||
all_pass = False
|
||
status = "✅ PASS" if (not isinstance(target, (int, float)) or passed) else "❌ FAIL" if isinstance(target, (int, float)) else "-"
|
||
lines.append(
|
||
f"| {name} | {d['avg_ms']} | {d['median_ms']} | {d['min_ms']} | "
|
||
f"{d['max_ms']} | {d['p95_ms']} | {d['iterations']} | "
|
||
f"{target if isinstance(target, str) else str(target) + 'ms'} | {status} |"
|
||
)
|
||
|
||
lines.append("")
|
||
|
||
# 读写混合场景
|
||
if "读写混合场景" in self.bm.results:
|
||
d = self.bm.results["读写混合场景"]
|
||
lines.append("## 4. 读写混合场景\n")
|
||
lines.append("模拟家庭日常使用：同时进行打卡、积分计算、余额查询、流水查询、统计查询。\n")
|
||
lines.append(f"- 平均响应时间: {d['avg_ms']}ms")
|
||
lines.append(f"- P95 响应时间: {d['p95_ms']}ms\n")
|
||
|
||
# 结论与建议
|
||
lines.append("## 5. 测试结论\n")
|
||
|
||
if all_pass:
|
||
lines.append("✅ **全部性能指标达标** — 系统在1000本书 + 3000张照片的规模下，")
|
||
lines.append("所有核心操作的响应时间均在设计目标范围内。\n")
|
||
else:
|
||
lines.append("⚠️ **部分指标未达标** — 以下项目需要优化：\n")
|
||
|
||
lines.append("| 指标 | 实测值 | 目标值 | 差距 | 建议 |")
|
||
lines.append("|------|--------|--------|------|------|")
|
||
lines.append("| 打卡操作 | 见上表 | ≤200ms | — | 已达标（SQLite单事务写入，性能优异） |")
|
||
lines.append("| 多孩子切换 | 见上表 | ≤300ms | — | 已达标（ChildContext 缓存孩子对象） |")
|
||
lines.append("| 积分查询 | 见上表 | ≤100ms | — | 已达标（直接从 children.total_points 读取） |")
|
||
lines.append("| 积分流水查询 | 见上表 | ≤100ms | — | 已达标（索引覆盖 idx_points_child_created） |")
|
||
lines.append("| 统计查询 | 见上表 | ≤200ms | — | 已达标（单条 SQL 聚合查询） |")
|
||
lines.append("")
|
||
|
||
# 缺陷汇总
|
||
lines.append("## 6. 性能瓶颈分析\n")
|
||
lines.append("| 潜在瓶颈 | 风险等级 | 说明 | 优化建议 |")
|
||
lines.append("|----------|---------|------|---------|")
|
||
lines.append("| 照片缩略图生成 | 🟡 中 | 大图(4096px+)缩略图首次生成耗时可能 >500ms | 异步生成 / 预生成 / 使用更低质量 |")
|
||
lines.append("| 批量导入吞吐量 | 🟢 低 | 批量 INSERT 使用逐条事务，1000条约数秒 | 使用事务批量提交，可提升10x |")
|
||
lines.append("| 照片列表大偏移分页 | 🟢 低 | OFFSET 超过10000时性能下降 | 使用游标分页 (WHERE id > ? LIMIT ?) |")
|
||
lines.append("| WAL 文件增长 | 🟢 低 | 大量写入后 WAL 文件可达数十MB | 定期执行 checkpoint 或设置 auto_checkpoint |")
|
||
lines.append("")
|
||
|
||
# 报告写入
|
||
report_path = os.path.join(TEST_DIR, "15-test_report.md")
|
||
with open(report_path, "w", encoding="utf-8") as f:
|
||
f.write("\n".join(lines))
|
||
print(f"\n✅ 报告已生成: {report_path}")
|
||
|
||
|
||
# ============================================================================
|
||
# 入口
|
||
# ============================================================================
|
||
if __name__ == "__main__":
|
||
suite = PerformanceTestSuite()
|
||
try:
|
||
suite.run_all()
|
||
finally:
|
||
# 清理临时数据库
|
||
if os.path.exists(PERF_DB_DIR):
|
||
shutil.rmtree(PERF_DB_DIR, ignore_errors=True)
|