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perf: 全栈查询优化、前端缓存去重与页面可见性优化

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后端:
- SQL count 查询统一改用 func.count() 子查询替代 query.count()
- Dashboard/Audit 等页面多次独立查询合并为单次聚合查询
- Provider summary 列表改为批量查询消除 N+1 问题
- DailyStats 逐天循环查询改为 CASE 分桶单次查询
- 使用 load_only() 减少不必要的列加载
- cache_decorator 支持嵌套属性路径解析(dotted vary_by)
- 多个管理/公共端点新增 @cache_result 缓存装饰

前端:
- cache.ts 新增 in-flight 请求复用、dedupedRequest、buildCacheKey
- 大量 API 调用添加前端缓存或去重
- 多个页面定时器在标签页隐藏时暂停、可见时恢复
- Auth 检查从 setInterval 改为 storage + visibilitychange 事件驱动
- 请求竞态防护(requestId 模式)

数据库:
- Usage 表新增 idx_usage_status_user_created 复合索引
This commit is contained in:
fawney19
2026-03-03 22:04:40 +08:00
parent 0a60492146
commit 97b0146ce9
66 changed files with 2306 additions and 657 deletions
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281
src/api/dashboard/routes.py
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@@ -7,7 +7,7 @@ from datetime import date, datetime, timedelta, timezone
from typing import Any
from fastapi import APIRouter, Depends, HTTPException, Query, Request
from sqlalchemy import and_, func
from sqlalchemy import and_, case, func
from sqlalchemy.orm import Session
from src.api.base.adapter import ApiAdapter, ApiMode
@@ -661,95 +661,96 @@ class UserDashboardStatsAdapter(DashboardAdapter):
month_start_local = today_local.replace(day=1)
month_start = month_start_local.astimezone(timezone.utc)
user_api_keys = db.query(func.count(ApiKey.id)).filter(ApiKey.user_id == user.id).scalar()
active_keys = (
db.query(func.count(ApiKey.id))
.filter(and_(ApiKey.user_id == user.id, ApiKey.is_active.is_(True)))
.scalar()
)
# 全局 Token 统计
all_time_token_stats = (
api_key_stats = (
db.query(
func.sum(Usage.input_tokens).label("input_tokens"),
func.sum(Usage.output_tokens).label("output_tokens"),
func.sum(Usage.cache_creation_input_tokens).label("cache_creation_tokens"),
func.sum(Usage.cache_read_input_tokens).label("cache_read_tokens"),
func.count(ApiKey.id).label("total"),
func.sum(case((ApiKey.is_active.is_(True), 1), else_=0)).label("active"),
)
.filter(ApiKey.user_id == user.id)
.first()
)
user_api_keys = int(api_key_stats.total or 0) if api_key_stats else 0
active_keys = int(api_key_stats.active or 0) if api_key_stats else 0
# 使用单次聚合查询返回全量 + 本月 + 今日 + 昨日统计
usage_stats = (
db.query(
# 全量 Token 统计
func.sum(Usage.input_tokens).label("all_time_input_tokens"),
func.sum(Usage.output_tokens).label("all_time_output_tokens"),
func.sum(Usage.cache_creation_input_tokens).label("all_time_cache_creation_tokens"),
func.sum(Usage.cache_read_input_tokens).label("all_time_cache_read_tokens"),
# 本月
func.sum(case((Usage.created_at >= month_start, 1), else_=0)).label(
"monthly_requests"
),
func.sum(
case((Usage.created_at >= month_start, Usage.total_cost_usd), else_=0.0)
).label("monthly_cost"),
func.sum(
case(
(Usage.created_at >= month_start, Usage.cache_creation_input_tokens),
else_=0,
)
).label("monthly_cache_creation_tokens"),
func.sum(
case((Usage.created_at >= month_start, Usage.cache_read_input_tokens), else_=0)
).label("monthly_cache_read_tokens"),
func.sum(
case((Usage.created_at >= month_start, Usage.input_tokens), else_=0)
).label("monthly_input_tokens"),
# 今日
func.sum(case((Usage.created_at >= today, 1), else_=0)).label("today_requests"),
func.sum(case((Usage.created_at >= today, Usage.total_cost_usd), else_=0.0)).label(
"today_cost"
),
func.sum(case((Usage.created_at >= today, Usage.total_tokens), else_=0)).label(
"today_tokens"
),
func.sum(
case((Usage.created_at >= today, Usage.cache_creation_input_tokens), else_=0)
).label("today_cache_creation_tokens"),
func.sum(
case((Usage.created_at >= today, Usage.cache_read_input_tokens), else_=0)
).label("today_cache_read_tokens"),
# 昨日（用于变化趋势）
func.sum(
case(
(
and_(
Usage.created_at >= yesterday,
Usage.created_at < today,
),
1,
),
else_=0,
)
).label("yesterday_requests"),
)
.filter(Usage.user_id == user.id)
.first()
)
all_time_input_tokens = (
int(all_time_token_stats.input_tokens or 0) if all_time_token_stats else 0
)
all_time_output_tokens = (
int(all_time_token_stats.output_tokens or 0) if all_time_token_stats else 0
)
all_time_input_tokens = int(usage_stats.all_time_input_tokens or 0) if usage_stats else 0
all_time_output_tokens = int(usage_stats.all_time_output_tokens or 0) if usage_stats else 0
all_time_cache_creation = (
int(all_time_token_stats.cache_creation_tokens or 0) if all_time_token_stats else 0
)
all_time_cache_read = (
int(all_time_token_stats.cache_read_tokens or 0) if all_time_token_stats else 0
int(usage_stats.all_time_cache_creation_tokens or 0) if usage_stats else 0
)
all_time_cache_read = int(usage_stats.all_time_cache_read_tokens or 0) if usage_stats else 0
# 本月请求统计
user_requests = (
db.query(func.count(Usage.id))
.filter(and_(Usage.user_id == user.id, Usage.created_at >= month_start))
.scalar()
)
user_cost = (
db.query(func.sum(Usage.total_cost_usd))
.filter(and_(Usage.user_id == user.id, Usage.created_at >= month_start))
.scalar()
or 0
)
user_requests = int(usage_stats.monthly_requests or 0) if usage_stats else 0
user_cost = float(usage_stats.monthly_cost or 0.0) if usage_stats else 0.0
# 今日统计
requests_today = (
db.query(func.count(Usage.id))
.filter(and_(Usage.user_id == user.id, Usage.created_at >= today))
.scalar()
)
cost_today = (
db.query(func.sum(Usage.total_cost_usd))
.filter(and_(Usage.user_id == user.id, Usage.created_at >= today))
.scalar()
or 0
)
tokens_today = (
db.query(func.sum(Usage.total_tokens))
.filter(and_(Usage.user_id == user.id, Usage.created_at >= today))
.scalar()
or 0
)
requests_today = int(usage_stats.today_requests or 0) if usage_stats else 0
cost_today = float(usage_stats.today_cost or 0.0) if usage_stats else 0.0
tokens_today = int(usage_stats.today_tokens or 0) if usage_stats else 0
requests_yesterday = int(usage_stats.yesterday_requests or 0) if usage_stats else 0
# 昨日统计（用于计算变化）
requests_yesterday = (
db.query(func.count(Usage.id))
.filter(
and_(
Usage.user_id == user.id,
Usage.created_at >= yesterday,
Usage.created_at < today,
)
)
.scalar()
cache_creation_tokens = (
int(usage_stats.monthly_cache_creation_tokens or 0) if usage_stats else 0
)
# 缓存统计（本月）
cache_stats = (
db.query(
func.sum(Usage.cache_creation_input_tokens).label("cache_creation_tokens"),
func.sum(Usage.cache_read_input_tokens).label("cache_read_tokens"),
func.sum(Usage.input_tokens).label("total_input_tokens"),
)
.filter(and_(Usage.user_id == user.id, Usage.created_at >= month_start))
.first()
)
cache_creation_tokens = int(cache_stats.cache_creation_tokens or 0) if cache_stats else 0
cache_read_tokens = int(cache_stats.cache_read_tokens or 0) if cache_stats else 0
monthly_input_tokens = int(cache_stats.total_input_tokens or 0) if cache_stats else 0
cache_read_tokens = int(usage_stats.monthly_cache_read_tokens or 0) if usage_stats else 0
monthly_input_tokens = int(usage_stats.monthly_input_tokens or 0) if usage_stats else 0
# 计算本月缓存命中率：cache_read / (input_tokens + cache_read)
# input_tokens 是实际发送给模型的输入（不含缓存读取），cache_read 是从缓存读取的
@@ -762,19 +763,11 @@ class UserDashboardStatsAdapter(DashboardAdapter):
)
# 今日缓存统计
cache_stats_today = (
db.query(
func.sum(Usage.cache_creation_input_tokens).label("cache_creation_tokens"),
func.sum(Usage.cache_read_input_tokens).label("cache_read_tokens"),
)
.filter(and_(Usage.user_id == user.id, Usage.created_at >= today))
.first()
)
cache_creation_tokens_today = (
int(cache_stats_today.cache_creation_tokens or 0) if cache_stats_today else 0
int(usage_stats.today_cache_creation_tokens or 0) if usage_stats else 0
)
cache_read_tokens_today = (
int(cache_stats_today.cache_read_tokens or 0) if cache_stats_today else 0
int(usage_stats.today_cache_read_tokens or 0) if usage_stats else 0
)
# 配额状态
@@ -859,6 +852,12 @@ class UserDashboardStatsAdapter(DashboardAdapter):
class DashboardRecentRequestsAdapter(DashboardAdapter):
limit: int
@cache_result(
key_prefix="dashboard:recent:requests",
ttl=CacheTTL.ADMIN_USAGE_RECORDS,
user_specific=True,
vary_by=["limit"],
)
async def handle(self, context: ApiRequestContext) -> Any: # type: ignore[override]
db = context.db
user = context.user
@@ -1003,28 +1002,53 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
# 补充 unique_models / unique_providers
# query_time_series 使用小时粒度数据，不含这些维度统计
# 直接从 Usage 表按本地日 UTC 范围查询，避免 StatsDaily 历史数据未回填的问题
# 使用 CASE 一次性分桶，避免按天循环查询造成 N 次 SQL。
granularity = (self.time_range.granularity or "day").lower()
if formatted and granularity == "day":
local_days = self.time_range.get_local_day_hours()
enrichment: dict[str, dict] = {}
local_days = self.time_range.get_local_day_hours()
range_start = local_days[0][1] if local_days else None
range_end = local_days[-1][2] if local_days else None
day_bucket = (
case(
*[
(
and_(Usage.created_at >= day_start, Usage.created_at < day_end),
local_date.isoformat(),
)
for local_date, day_start, day_end in local_days
],
else_=None,
).label("local_day")
if local_days
else None
)
for local_date, day_start_utc, day_end_utc in local_days:
q = db.query(
func.count(func.distinct(Usage.model)).label("um"),
func.count(func.distinct(Usage.provider_name)).label("up"),
).filter(
Usage.created_at >= day_start_utc,
Usage.created_at < day_end_utc,
)
if not is_admin:
q = q.filter(Usage.user_id == user.id)
row = q.first()
if row:
enrichment[local_date.isoformat()] = {
"unique_models": row.um or 0,
"unique_providers": row.up or 0,
}
if (
formatted
and granularity == "day"
and day_bucket is not None
and range_start
and range_end
):
enrichment: dict[str, dict[str, int]] = {}
enrich_query = db.query(
day_bucket,
func.count(func.distinct(Usage.model)).label("um"),
func.count(func.distinct(Usage.provider_name)).label("up"),
).filter(
Usage.created_at >= range_start,
Usage.created_at < range_end,
)
if not is_admin:
enrich_query = enrich_query.filter(Usage.user_id == user.id)
enrich_rows = enrich_query.group_by(day_bucket).all()
for local_day, unique_models, unique_providers in enrich_rows:
if not local_day:
continue
enrichment[str(local_day)] = {
"unique_models": int(unique_models or 0),
"unique_providers": int(unique_providers or 0),
}
for item in formatted:
date_key = item["date"][:10] # YYYY-MM-DD
@@ -1062,26 +1086,35 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
# Daily model breakdown (aligned to local days)
breakdown_map: dict[str, list[dict]] = {}
for local_date, day_start, day_end in self.time_range.get_local_day_hours():
day_query = db.query(
if granularity == "day" and day_bucket is not None and range_start and range_end:
breakdown_query = db.query(
day_bucket,
Usage.model,
func.count(Usage.id).label("requests"),
func.sum(Usage.total_tokens).label("tokens"),
func.sum(Usage.total_cost_usd).label("cost"),
).filter(Usage.created_at >= day_start, Usage.created_at < day_end)
).filter(
Usage.created_at >= range_start,
Usage.created_at < range_end,
)
if not is_admin:
day_query = day_query.filter(Usage.user_id == user.id)
day_stats = day_query.group_by(Usage.model).all()
breakdown_map[local_date.isoformat()] = [
{
"model": stat.model,
"requests": stat.requests or 0,
"tokens": int(stat.tokens or 0),
"cost": float(stat.cost or 0),
}
for stat in day_stats
if stat.model
]
breakdown_query = breakdown_query.filter(Usage.user_id == user.id)
breakdown_rows = (
breakdown_query.group_by(day_bucket, Usage.model)
.order_by(day_bucket.asc(), func.sum(Usage.total_cost_usd).desc())
.all()
)
for local_day, model_name, requests, tokens, cost in breakdown_rows:
if not local_day or not model_name:
continue
breakdown_map.setdefault(str(local_day), []).append(
{
"model": model_name,
"requests": int(requests or 0),
"tokens": int(tokens or 0),
"cost": float(cost or 0.0),
}
)
for item in formatted:
item["model_breakdown"] = breakdown_map.get(item["date"], [])