mirror of
https://github.com/fawney19/Aether.git
synced 2026-01-09 19:22:26 +08:00
refactor: optimize provider query and stats aggregation logic
This commit is contained in:
fawney19
@@ -35,6 +35,7 @@ class CleanupScheduler:
|
||||
def __init__(self):
|
||||
self.running = False
|
||||
self._interval_tasks = []
|
||||
self._stats_aggregation_lock = asyncio.Lock()
|
||||
|
||||
async def start(self):
|
||||
"""启动调度器"""
|
||||
@@ -56,6 +57,14 @@ class CleanupScheduler:
|
||||
job_id="stats_aggregation",
|
||||
name="统计数据聚合",
|
||||
)
|
||||
# 统计聚合补偿任务 - 每 30 分钟检查缺失并回填
|
||||
scheduler.add_interval_job(
|
||||
self._scheduled_stats_aggregation,
|
||||
minutes=30,
|
||||
job_id="stats_aggregation_backfill",
|
||||
name="统计数据聚合补偿",
|
||||
backfill=True,
|
||||
)
|
||||
|
||||
# 清理任务 - 凌晨 3 点执行
|
||||
scheduler.add_cron_job(
|
||||
@@ -115,9 +124,9 @@ class CleanupScheduler:
|
||||
|
||||
# ========== 任务函数（APScheduler 直接调用异步函数） ==========
|
||||
|
||||
async def _scheduled_stats_aggregation(self):
|
||||
async def _scheduled_stats_aggregation(self, backfill: bool = False):
|
||||
"""统计聚合任务（定时调用）"""
|
||||
await self._perform_stats_aggregation()
|
||||
await self._perform_stats_aggregation(backfill=backfill)
|
||||
|
||||
async def _scheduled_cleanup(self):
|
||||
"""清理任务（定时调用）"""
|
||||
@@ -144,136 +153,157 @@ class CleanupScheduler:
|
||||
Args:
|
||||
backfill: 是否回填历史数据（启动时检查缺失的日期）
|
||||
"""
|
||||
db = create_session()
|
||||
try:
|
||||
# 检查是否启用统计聚合
|
||||
if not SystemConfigService.get_config(db, "enable_stats_aggregation", True):
|
||||
logger.info("统计聚合已禁用，跳过聚合任务")
|
||||
return
|
||||
if self._stats_aggregation_lock.locked():
|
||||
logger.info("统计聚合任务正在运行，跳过本次触发")
|
||||
return
|
||||
|
||||
logger.info("开始执行统计数据聚合...")
|
||||
|
||||
from src.models.database import StatsDaily, User as DBUser
|
||||
from src.services.system.scheduler import APP_TIMEZONE
|
||||
from zoneinfo import ZoneInfo
|
||||
|
||||
# 使用业务时区计算日期，确保与定时任务触发时间一致
|
||||
# 定时任务在 Asia/Shanghai 凌晨 1 点触发，此时应聚合 Asia/Shanghai 的"昨天"
|
||||
app_tz = ZoneInfo(APP_TIMEZONE)
|
||||
now_local = datetime.now(app_tz)
|
||||
today_local = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
|
||||
if backfill:
|
||||
# 启动时检查并回填缺失的日期
|
||||
from src.models.database import StatsSummary
|
||||
|
||||
summary = db.query(StatsSummary).first()
|
||||
if not summary:
|
||||
# 首次运行，回填所有历史数据
|
||||
logger.info("检测到首次运行，开始回填历史统计数据...")
|
||||
days_to_backfill = SystemConfigService.get_config(
|
||||
db, "stats_backfill_days", 365
|
||||
)
|
||||
count = StatsAggregatorService.backfill_historical_data(
|
||||
db, days=days_to_backfill
|
||||
)
|
||||
logger.info(f"历史数据回填完成，共 {count} 天")
|
||||
async with self._stats_aggregation_lock:
|
||||
db = create_session()
|
||||
try:
|
||||
# 检查是否启用统计聚合
|
||||
if not SystemConfigService.get_config(db, "enable_stats_aggregation", True):
|
||||
logger.info("统计聚合已禁用，跳过聚合任务")
|
||||
return
|
||||
|
||||
# 非首次运行，检查最近是否有缺失的日期需要回填
|
||||
latest_stat = (
|
||||
db.query(StatsDaily)
|
||||
.order_by(StatsDaily.date.desc())
|
||||
.first()
|
||||
)
|
||||
logger.info("开始执行统计数据聚合...")
|
||||
|
||||
if latest_stat:
|
||||
latest_date_utc = latest_stat.date
|
||||
if latest_date_utc.tzinfo is None:
|
||||
latest_date_utc = latest_date_utc.replace(tzinfo=timezone.utc)
|
||||
else:
|
||||
latest_date_utc = latest_date_utc.astimezone(timezone.utc)
|
||||
from src.models.database import StatsDaily, User as DBUser
|
||||
from src.services.system.scheduler import APP_TIMEZONE
|
||||
from zoneinfo import ZoneInfo
|
||||
|
||||
# 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）
|
||||
latest_business_date = latest_date_utc.astimezone(app_tz).date()
|
||||
yesterday_business_date = (today_local.date() - timedelta(days=1))
|
||||
missing_start_date = latest_business_date + timedelta(days=1)
|
||||
# 使用业务时区计算日期，确保与定时任务触发时间一致
|
||||
# 定时任务在 Asia/Shanghai 凌晨 1 点触发，此时应聚合 Asia/Shanghai 的"昨天"
|
||||
app_tz = ZoneInfo(APP_TIMEZONE)
|
||||
now_local = datetime.now(app_tz)
|
||||
today_local = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
|
||||
if missing_start_date <= yesterday_business_date:
|
||||
missing_days = (yesterday_business_date - missing_start_date).days + 1
|
||||
logger.info(
|
||||
f"检测到缺失 {missing_days} 天的统计数据 "
|
||||
f"({missing_start_date} ~ {yesterday_business_date})，开始回填..."
|
||||
if backfill:
|
||||
# 启动时检查并回填缺失的日期
|
||||
from src.models.database import StatsSummary
|
||||
|
||||
summary = db.query(StatsSummary).first()
|
||||
if not summary:
|
||||
# 首次运行，回填所有历史数据
|
||||
logger.info("检测到首次运行，开始回填历史统计数据...")
|
||||
days_to_backfill = SystemConfigService.get_config(
|
||||
db, "stats_backfill_days", 365
|
||||
)
|
||||
count = StatsAggregatorService.backfill_historical_data(
|
||||
db, days=days_to_backfill
|
||||
)
|
||||
logger.info(f"历史数据回填完成，共 {count} 天")
|
||||
return
|
||||
|
||||
current_date = missing_start_date
|
||||
users = db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
|
||||
# 非首次运行，检查最近是否有缺失的日期需要回填
|
||||
latest_stat = db.query(StatsDaily).order_by(StatsDaily.date.desc()).first()
|
||||
|
||||
while current_date <= yesterday_business_date:
|
||||
try:
|
||||
current_date_local = datetime.combine(
|
||||
current_date, datetime.min.time(), tzinfo=app_tz
|
||||
if latest_stat:
|
||||
latest_date_utc = latest_stat.date
|
||||
if latest_date_utc.tzinfo is None:
|
||||
latest_date_utc = latest_date_utc.replace(tzinfo=timezone.utc)
|
||||
else:
|
||||
latest_date_utc = latest_date_utc.astimezone(timezone.utc)
|
||||
|
||||
# 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）
|
||||
latest_business_date = latest_date_utc.astimezone(app_tz).date()
|
||||
yesterday_business_date = today_local.date() - timedelta(days=1)
|
||||
missing_start_date = latest_business_date + timedelta(days=1)
|
||||
|
||||
if missing_start_date <= yesterday_business_date:
|
||||
missing_days = (
|
||||
yesterday_business_date - missing_start_date
|
||||
).days + 1
|
||||
|
||||
# 限制最大回填天数，防止停机很久后一次性回填太多
|
||||
max_backfill_days: int = SystemConfigService.get_config(
|
||||
db, "max_stats_backfill_days", 30
|
||||
) or 30
|
||||
if missing_days > max_backfill_days:
|
||||
logger.warning(
|
||||
f"缺失 {missing_days} 天数据超过最大回填限制 "
|
||||
f"{max_backfill_days} 天，只回填最近 {max_backfill_days} 天"
|
||||
)
|
||||
StatsAggregatorService.aggregate_daily_stats(db, current_date_local)
|
||||
# 聚合用户数据
|
||||
for (user_id,) in users:
|
||||
try:
|
||||
StatsAggregatorService.aggregate_user_daily_stats(
|
||||
db, user_id, current_date_local
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"回填用户 {user_id} 日期 {current_date} 失败: {e}"
|
||||
)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.warning(f"回填日期 {current_date} 失败: {e}")
|
||||
missing_start_date = yesterday_business_date - timedelta(
|
||||
days=max_backfill_days - 1
|
||||
)
|
||||
missing_days = max_backfill_days
|
||||
|
||||
logger.info(
|
||||
f"检测到缺失 {missing_days} 天的统计数据 "
|
||||
f"({missing_start_date} ~ {yesterday_business_date})，开始回填..."
|
||||
)
|
||||
|
||||
current_date = missing_start_date
|
||||
users = (
|
||||
db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
|
||||
)
|
||||
|
||||
while current_date <= yesterday_business_date:
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
current_date_local = datetime.combine(
|
||||
current_date, datetime.min.time(), tzinfo=app_tz
|
||||
)
|
||||
StatsAggregatorService.aggregate_daily_stats(
|
||||
db, current_date_local
|
||||
)
|
||||
for (user_id,) in users:
|
||||
try:
|
||||
StatsAggregatorService.aggregate_user_daily_stats(
|
||||
db, user_id, current_date_local
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"回填用户 {user_id} 日期 {current_date} 失败: {e}"
|
||||
)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.warning(f"回填日期 {current_date} 失败: {e}")
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
current_date += timedelta(days=1)
|
||||
current_date += timedelta(days=1)
|
||||
|
||||
# 更新全局汇总
|
||||
StatsAggregatorService.update_summary(db)
|
||||
logger.info(f"缺失数据回填完成，共 {missing_days} 天")
|
||||
else:
|
||||
logger.info("统计数据已是最新，无需回填")
|
||||
return
|
||||
StatsAggregatorService.update_summary(db)
|
||||
logger.info(f"缺失数据回填完成，共 {missing_days} 天")
|
||||
else:
|
||||
logger.info("统计数据已是最新，无需回填")
|
||||
return
|
||||
|
||||
# 定时任务：聚合昨天的数据
|
||||
# 注意：aggregate_daily_stats 期望业务时区的日期，不是 UTC
|
||||
yesterday_local = today_local - timedelta(days=1)
|
||||
# 定时任务：聚合昨天的数据
|
||||
yesterday_local = today_local - timedelta(days=1)
|
||||
|
||||
StatsAggregatorService.aggregate_daily_stats(db, yesterday_local)
|
||||
StatsAggregatorService.aggregate_daily_stats(db, yesterday_local)
|
||||
|
||||
# 聚合所有用户的昨日数据
|
||||
users = db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
|
||||
for (user_id,) in users:
|
||||
try:
|
||||
StatsAggregatorService.aggregate_user_daily_stats(db, user_id, yesterday_local)
|
||||
except Exception as e:
|
||||
logger.warning(f"聚合用户 {user_id} 统计数据失败: {e}")
|
||||
# 回滚当前用户的失败操作，继续处理其他用户
|
||||
users = db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
|
||||
for (user_id,) in users:
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
StatsAggregatorService.aggregate_user_daily_stats(
|
||||
db, user_id, yesterday_local
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"聚合用户 {user_id} 统计数据失败: {e}")
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 更新全局汇总
|
||||
StatsAggregatorService.update_summary(db)
|
||||
StatsAggregatorService.update_summary(db)
|
||||
|
||||
logger.info("统计数据聚合完成")
|
||||
logger.info("统计数据聚合完成")
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"统计聚合任务执行失败: {e}")
|
||||
db.rollback()
|
||||
finally:
|
||||
db.close()
|
||||
except Exception as e:
|
||||
logger.exception(f"统计聚合任务执行失败: {e}")
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
async def _perform_pending_cleanup(self):
|
||||
"""执行 pending 状态清理"""
|
||||
|
||||
@@ -56,65 +56,44 @@ class StatsAggregatorService:
|
||||
"""统计数据聚合服务"""
|
||||
|
||||
@staticmethod
|
||||
def aggregate_daily_stats(db: Session, date: datetime) -> StatsDaily:
|
||||
"""聚合指定日期的统计数据
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
date: 要聚合的业务日期（使用 APP_TIMEZONE 时区）
|
||||
|
||||
Returns:
|
||||
StatsDaily 记录
|
||||
"""
|
||||
# 将业务日期转换为 UTC 时间范围
|
||||
def compute_daily_stats(db: Session, date: datetime) -> dict:
|
||||
"""计算指定业务日期的统计数据（不写入数据库）"""
|
||||
day_start, day_end = _get_business_day_range(date)
|
||||
|
||||
# stats_daily.date 存储的是业务日期对应的 UTC 开始时间
|
||||
# 检查是否已存在该日期的记录
|
||||
existing = db.query(StatsDaily).filter(StatsDaily.date == day_start).first()
|
||||
if existing:
|
||||
stats = existing
|
||||
else:
|
||||
stats = StatsDaily(id=str(uuid.uuid4()), date=day_start)
|
||||
|
||||
# 基础请求统计
|
||||
base_query = db.query(Usage).filter(
|
||||
and_(Usage.created_at >= day_start, Usage.created_at < day_end)
|
||||
)
|
||||
|
||||
total_requests = base_query.count()
|
||||
|
||||
# 如果没有请求，直接返回空记录
|
||||
if total_requests == 0:
|
||||
stats.total_requests = 0
|
||||
stats.success_requests = 0
|
||||
stats.error_requests = 0
|
||||
stats.input_tokens = 0
|
||||
stats.output_tokens = 0
|
||||
stats.cache_creation_tokens = 0
|
||||
stats.cache_read_tokens = 0
|
||||
stats.total_cost = 0.0
|
||||
stats.actual_total_cost = 0.0
|
||||
stats.input_cost = 0.0
|
||||
stats.output_cost = 0.0
|
||||
stats.cache_creation_cost = 0.0
|
||||
stats.cache_read_cost = 0.0
|
||||
stats.avg_response_time_ms = 0.0
|
||||
stats.fallback_count = 0
|
||||
return {
|
||||
"day_start": day_start,
|
||||
"total_requests": 0,
|
||||
"success_requests": 0,
|
||||
"error_requests": 0,
|
||||
"input_tokens": 0,
|
||||
"output_tokens": 0,
|
||||
"cache_creation_tokens": 0,
|
||||
"cache_read_tokens": 0,
|
||||
"total_cost": 0.0,
|
||||
"actual_total_cost": 0.0,
|
||||
"input_cost": 0.0,
|
||||
"output_cost": 0.0,
|
||||
"cache_creation_cost": 0.0,
|
||||
"cache_read_cost": 0.0,
|
||||
"avg_response_time_ms": 0.0,
|
||||
"fallback_count": 0,
|
||||
"unique_models": 0,
|
||||
"unique_providers": 0,
|
||||
}
|
||||
|
||||
if not existing:
|
||||
db.add(stats)
|
||||
db.commit()
|
||||
return stats
|
||||
|
||||
# 错误请求数
|
||||
error_requests = (
|
||||
base_query.filter(
|
||||
(Usage.status_code >= 400) | (Usage.error_message.isnot(None))
|
||||
).count()
|
||||
)
|
||||
|
||||
# Token 和成本聚合
|
||||
aggregated = (
|
||||
db.query(
|
||||
func.sum(Usage.input_tokens).label("input_tokens"),
|
||||
@@ -157,7 +136,6 @@ class StatsAggregatorService:
|
||||
or 0
|
||||
)
|
||||
|
||||
# 使用维度统计
|
||||
unique_models = (
|
||||
db.query(func.count(func.distinct(Usage.model)))
|
||||
.filter(and_(Usage.created_at >= day_start, Usage.created_at < day_end))
|
||||
@@ -171,31 +149,74 @@ class StatsAggregatorService:
|
||||
or 0
|
||||
)
|
||||
|
||||
return {
|
||||
"day_start": day_start,
|
||||
"total_requests": total_requests,
|
||||
"success_requests": total_requests - error_requests,
|
||||
"error_requests": error_requests,
|
||||
"input_tokens": int(aggregated.input_tokens or 0) if aggregated else 0,
|
||||
"output_tokens": int(aggregated.output_tokens or 0) if aggregated else 0,
|
||||
"cache_creation_tokens": int(aggregated.cache_creation_tokens or 0) if aggregated else 0,
|
||||
"cache_read_tokens": int(aggregated.cache_read_tokens or 0) if aggregated else 0,
|
||||
"total_cost": float(aggregated.total_cost or 0) if aggregated else 0.0,
|
||||
"actual_total_cost": float(aggregated.actual_total_cost or 0) if aggregated else 0.0,
|
||||
"input_cost": float(aggregated.input_cost or 0) if aggregated else 0.0,
|
||||
"output_cost": float(aggregated.output_cost or 0) if aggregated else 0.0,
|
||||
"cache_creation_cost": float(aggregated.cache_creation_cost or 0) if aggregated else 0.0,
|
||||
"cache_read_cost": float(aggregated.cache_read_cost or 0) if aggregated else 0.0,
|
||||
"avg_response_time_ms": float(aggregated.avg_response_time or 0) if aggregated else 0.0,
|
||||
"fallback_count": fallback_count,
|
||||
"unique_models": unique_models,
|
||||
"unique_providers": unique_providers,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def aggregate_daily_stats(db: Session, date: datetime) -> StatsDaily:
|
||||
"""聚合指定日期的统计数据
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
date: 要聚合的业务日期（使用 APP_TIMEZONE 时区）
|
||||
|
||||
Returns:
|
||||
StatsDaily 记录
|
||||
"""
|
||||
computed = StatsAggregatorService.compute_daily_stats(db, date)
|
||||
day_start = computed["day_start"]
|
||||
|
||||
# stats_daily.date 存储的是业务日期对应的 UTC 开始时间
|
||||
# 检查是否已存在该日期的记录
|
||||
existing = db.query(StatsDaily).filter(StatsDaily.date == day_start).first()
|
||||
if existing:
|
||||
stats = existing
|
||||
else:
|
||||
stats = StatsDaily(id=str(uuid.uuid4()), date=day_start)
|
||||
|
||||
# 更新统计记录
|
||||
stats.total_requests = total_requests
|
||||
stats.success_requests = total_requests - error_requests
|
||||
stats.error_requests = error_requests
|
||||
stats.input_tokens = int(aggregated.input_tokens or 0)
|
||||
stats.output_tokens = int(aggregated.output_tokens or 0)
|
||||
stats.cache_creation_tokens = int(aggregated.cache_creation_tokens or 0)
|
||||
stats.cache_read_tokens = int(aggregated.cache_read_tokens or 0)
|
||||
stats.total_cost = float(aggregated.total_cost or 0)
|
||||
stats.actual_total_cost = float(aggregated.actual_total_cost or 0)
|
||||
stats.input_cost = float(aggregated.input_cost or 0)
|
||||
stats.output_cost = float(aggregated.output_cost or 0)
|
||||
stats.cache_creation_cost = float(aggregated.cache_creation_cost or 0)
|
||||
stats.cache_read_cost = float(aggregated.cache_read_cost or 0)
|
||||
stats.avg_response_time_ms = float(aggregated.avg_response_time or 0)
|
||||
stats.fallback_count = fallback_count
|
||||
stats.unique_models = unique_models
|
||||
stats.unique_providers = unique_providers
|
||||
stats.total_requests = computed["total_requests"]
|
||||
stats.success_requests = computed["success_requests"]
|
||||
stats.error_requests = computed["error_requests"]
|
||||
stats.input_tokens = computed["input_tokens"]
|
||||
stats.output_tokens = computed["output_tokens"]
|
||||
stats.cache_creation_tokens = computed["cache_creation_tokens"]
|
||||
stats.cache_read_tokens = computed["cache_read_tokens"]
|
||||
stats.total_cost = computed["total_cost"]
|
||||
stats.actual_total_cost = computed["actual_total_cost"]
|
||||
stats.input_cost = computed["input_cost"]
|
||||
stats.output_cost = computed["output_cost"]
|
||||
stats.cache_creation_cost = computed["cache_creation_cost"]
|
||||
stats.cache_read_cost = computed["cache_read_cost"]
|
||||
stats.avg_response_time_ms = computed["avg_response_time_ms"]
|
||||
stats.fallback_count = computed["fallback_count"]
|
||||
stats.unique_models = computed["unique_models"]
|
||||
stats.unique_providers = computed["unique_providers"]
|
||||
|
||||
if not existing:
|
||||
db.add(stats)
|
||||
db.commit()
|
||||
|
||||
# 日志使用业务日期（输入参数），而不是 UTC 日期
|
||||
logger.info(f"[StatsAggregator] 聚合日期 {date.date()} 完成: {total_requests} 请求")
|
||||
logger.info(f"[StatsAggregator] 聚合日期 {date.date()} 完成: {computed['total_requests']} 请求")
|
||||
return stats
|
||||
|
||||
@staticmethod
|
||||
|
||||
Reference in New Issue
Block a user