mirror of
https://github.com/fawney19/Aether.git
synced 2026-01-02 15:52:26 +08:00
fix(system): fix timezone handling in dashboard and stats services
- Use app timezone instead of UTC for date calculations in dashboard routes - Ensure consistency between stats_daily.date and timezone-aware comparisons - Fix date calculations in cleanup scheduler to handle DST correctly - Update log message in stats aggregator to use business date
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@@ -107,11 +107,18 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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@cache_result(key_prefix="dashboard:admin:stats", ttl=60, user_specific=False)
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async def handle(self, context): # type: ignore[override]
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"""管理员仪表盘统计 - 使用预聚合数据优化性能"""
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from zoneinfo import ZoneInfo
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from src.services.system.stats_aggregator import APP_TIMEZONE
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db = context.db
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now = datetime.now(timezone.utc)
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today = now.replace(hour=0, minute=0, second=0, microsecond=0)
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yesterday = today - timedelta(days=1)
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last_month = today - timedelta(days=30)
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# 使用业务时区计算日期,与 stats_daily 表保持一致
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app_tz = ZoneInfo(APP_TIMEZONE)
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now_local = datetime.now(app_tz)
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today_local = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
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# 转换为 UTC 用于与 stats_daily.date 比较(存储的是业务日期对应的 UTC 开始时间)
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today = today_local.astimezone(timezone.utc)
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yesterday = (today_local - timedelta(days=1)).astimezone(timezone.utc)
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last_month = (today_local - timedelta(days=30)).astimezone(timezone.utc)
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# ==================== 使用预聚合数据 ====================
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# 从 stats_summary + 今日实时数据获取全局统计
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@@ -428,12 +435,19 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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class UserDashboardStatsAdapter(DashboardAdapter):
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@cache_result(key_prefix="dashboard:user:stats", ttl=30, user_specific=True)
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async def handle(self, context): # type: ignore[override]
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from zoneinfo import ZoneInfo
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from src.services.system.stats_aggregator import APP_TIMEZONE
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db = context.db
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user = context.user
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now = datetime.now(timezone.utc)
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today = now.replace(hour=0, minute=0, second=0, microsecond=0)
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last_month = today - timedelta(days=30)
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yesterday = today - timedelta(days=1)
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# 使用业务时区计算日期,确保与用户感知的"今天"一致
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app_tz = ZoneInfo(APP_TIMEZONE)
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now_local = datetime.now(app_tz)
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today_local = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
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# 转换为 UTC 用于数据库查询
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today = today_local.astimezone(timezone.utc)
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yesterday = (today_local - timedelta(days=1)).astimezone(timezone.utc)
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last_month = (today_local - timedelta(days=30)).astimezone(timezone.utc)
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user_api_keys = db.query(func.count(ApiKey.id)).filter(ApiKey.user_id == user.id).scalar()
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active_keys = (
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@@ -688,16 +702,23 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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@cache_result(key_prefix="dashboard:daily:stats", ttl=300, user_specific=True)
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async def handle(self, context): # type: ignore[override]
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from zoneinfo import ZoneInfo
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from src.services.system.stats_aggregator import APP_TIMEZONE
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db = context.db
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user = context.user
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is_admin = user.role == UserRole.ADMIN
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now = datetime.now(timezone.utc)
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today = now.replace(hour=0, minute=0, second=0, microsecond=0)
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end_date = now.replace(hour=23, minute=59, second=59, microsecond=999999)
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start_date = (end_date - timedelta(days=self.days - 1)).replace(
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hour=0, minute=0, second=0, microsecond=0
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)
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# 使用业务时区计算日期,确保每日统计与业务日期一致
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app_tz = ZoneInfo(APP_TIMEZONE)
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now_local = datetime.now(app_tz)
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today_local = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
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# 转换为 UTC 用于数据库查询
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today = today_local.astimezone(timezone.utc)
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end_date_local = now_local.replace(hour=23, minute=59, second=59, microsecond=999999)
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end_date = end_date_local.astimezone(timezone.utc)
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start_date_local = (today_local - timedelta(days=self.days - 1))
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start_date = start_date_local.astimezone(timezone.utc)
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# ==================== 使用预聚合数据优化 ====================
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if is_admin:
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@@ -708,8 +729,10 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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.order_by(StatsDaily.date.asc())
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.all()
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)
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# stats_daily.date 存储的是业务日期对应的 UTC 开始时间
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# 需要转回业务时区再取日期,才能与日期序列匹配
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stats_map = {
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stat.date.replace(tzinfo=timezone.utc).date().isoformat(): {
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stat.date.replace(tzinfo=timezone.utc).astimezone(app_tz).date().isoformat(): {
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"requests": stat.total_requests,
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"tokens": stat.input_tokens + stat.output_tokens + stat.cache_creation_tokens + stat.cache_read_tokens,
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"cost": stat.total_cost,
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@@ -723,7 +746,7 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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# 今日实时数据
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today_stats = StatsAggregatorService.get_today_realtime_stats(db)
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today_str = today.date().isoformat()
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today_str = today_local.date().isoformat()
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if today_stats["total_requests"] > 0:
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# 今日平均响应时间需要单独查询
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today_avg_rt = (
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@@ -800,10 +823,11 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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for stat in user_daily_stats
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}
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# 构建完整日期序列
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current_date = start_date.date()
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# 构建完整日期序列(使用业务时区日期)
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current_date = start_date_local.date()
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end_date_date = end_date_local.date()
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formatted: List[dict] = []
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while current_date <= end_date.date():
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while current_date <= end_date_date:
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date_str = current_date.isoformat()
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stat = stats_map.get(date_str)
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if stat:
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@@ -162,8 +162,6 @@ class CleanupScheduler:
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app_tz = ZoneInfo(APP_TIMEZONE)
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now_local = datetime.now(app_tz)
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today_local = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
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# 转换为 UTC 用于数据库查询(stats_daily.date 存储的是 UTC)
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today = today_local.astimezone(timezone.utc).replace(tzinfo=timezone.utc)
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if backfill:
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# 启动时检查并回填缺失的日期
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@@ -190,40 +188,49 @@ class CleanupScheduler:
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)
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if latest_stat:
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latest_date = latest_stat.date.replace(tzinfo=timezone.utc)
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# 计算缺失的天数(从最新记录的下一天到昨天)
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yesterday = today - timedelta(days=1)
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missing_start = latest_date + timedelta(days=1)
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latest_date_utc = latest_stat.date
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if latest_date_utc.tzinfo is None:
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latest_date_utc = latest_date_utc.replace(tzinfo=timezone.utc)
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else:
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latest_date_utc = latest_date_utc.astimezone(timezone.utc)
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if missing_start <= yesterday:
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missing_days = (yesterday - missing_start).days + 1
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# 使用业务日期计算缺失区间(避免用 UTC 年月日导致日期偏移,且对 DST 更安全)
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latest_business_date = latest_date_utc.astimezone(app_tz).date()
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yesterday_business_date = (today_local.date() - timedelta(days=1))
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missing_start_date = latest_business_date + timedelta(days=1)
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if missing_start_date <= yesterday_business_date:
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missing_days = (yesterday_business_date - missing_start_date).days + 1
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logger.info(
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f"检测到缺失 {missing_days} 天的统计数据 "
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f"({missing_start.date()} ~ {yesterday.date()}),开始回填..."
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f"({missing_start_date} ~ {yesterday_business_date}),开始回填..."
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)
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current_date = missing_start
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current_date = missing_start_date
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users = db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
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while current_date <= yesterday:
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while current_date <= yesterday_business_date:
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try:
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StatsAggregatorService.aggregate_daily_stats(db, current_date)
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current_date_local = datetime.combine(
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current_date, datetime.min.time(), tzinfo=app_tz
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)
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StatsAggregatorService.aggregate_daily_stats(db, current_date_local)
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# 聚合用户数据
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for (user_id,) in users:
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try:
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StatsAggregatorService.aggregate_user_daily_stats(
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db, user_id, current_date
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db, user_id, current_date_local
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)
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except Exception as e:
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logger.warning(
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f"回填用户 {user_id} 日期 {current_date.date()} 失败: {e}"
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f"回填用户 {user_id} 日期 {current_date} 失败: {e}"
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)
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try:
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db.rollback()
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except Exception:
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pass
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except Exception as e:
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logger.warning(f"回填日期 {current_date.date()} 失败: {e}")
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logger.warning(f"回填日期 {current_date} 失败: {e}")
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try:
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db.rollback()
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except Exception:
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@@ -239,15 +246,16 @@ class CleanupScheduler:
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return
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# 定时任务:聚合昨天的数据
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yesterday = (today - timedelta(days=1))
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# 注意:aggregate_daily_stats 期望业务时区的日期,不是 UTC
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yesterday_local = today_local - timedelta(days=1)
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StatsAggregatorService.aggregate_daily_stats(db, yesterday)
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StatsAggregatorService.aggregate_daily_stats(db, yesterday_local)
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# 聚合所有用户的昨日数据
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users = db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
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for (user_id,) in users:
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try:
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StatsAggregatorService.aggregate_user_daily_stats(db, user_id, yesterday)
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StatsAggregatorService.aggregate_user_daily_stats(db, user_id, yesterday_local)
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except Exception as e:
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logger.warning(f"聚合用户 {user_id} 统计数据失败: {e}")
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# 回滚当前用户的失败操作,继续处理其他用户
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@@ -194,7 +194,8 @@ class StatsAggregatorService:
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db.add(stats)
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db.commit()
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logger.info(f"[StatsAggregator] 聚合日期 {day_start.date()} 完成: {total_requests} 请求")
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# 日志使用业务日期(输入参数),而不是 UTC 日期
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logger.info(f"[StatsAggregator] 聚合日期 {date.date()} 完成: {total_requests} 请求")
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return stats
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@staticmethod
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