修首页 feed 翻页卡顿 / 翻不动
1. /top-sales(销量最高):之前每翻一页都全表拉取(~1400 条)+ 全量 from_raw 解析,翻页慢 → 滑动卡顿。 改为 SQL 侧 DISTINCT ON(dedup_key) 去重 + 排序 + 分页,每页只取并解析当前页 ~20 条 (实测 14ms/页 vs 之前每页全量);并加 id 作稳定 tiebreaker,修同销量同佣金并列项的跨页重复。 2. /feed(智能推荐/距离最近):池子很小(~60/117 条),之前 rec 逐页实时打美团(每页 2 调用 ~1s)→ 翻页反复触发 + 列表重组卡顿。改为两 tab 都拉齐全部轮次后【一次性返回 has_next=False】, 前端加载一次即不再翻页,滚动顺畅。默认(老客户端不传 tab)仍逐页,不受影响。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
chenshuobo
+44
-39
@@ -9,7 +9,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from sqlalchemy.orm import Session, aliased
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from app.core.config import settings
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from app.db.session import get_db
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@@ -110,27 +110,30 @@ def feed(req: FeedRequest) -> FeedResponse:
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except MeituanCpsError:
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return []
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# 距离最近:拉齐全部榜单轮次,合并去重,后端在【完整池子】上全局按距离由近及远排,一次性返回。
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# (距离排序必须在完整池上做、不能逐页排——这正是之前放前端不合理的根因。)
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if tab == "distance":
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# 智能推荐 / 距离最近:拉齐全部榜单轮次(~60 条),后端处理后【一次性返回、不分页】。
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# (池子很小;逐页实时打美团反而让客户端翻页卡顿/滑不动——一次拉齐 → 前端加载一次、滚动顺畅。)
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if tab in ("rec", "distance"):
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wm_all: list[dict] = []
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dd_all: list[dict] = []
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with ThreadPoolExecutor(max_workers=len(_TOPIC_ROUNDS) * 2) as pool:
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futs = []
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for wm_topic, dd_topic in _TOPIC_ROUNDS:
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futs.append(pool.submit(_fetch_topic, 1, None, wm_topic))
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futs.append(pool.submit(_fetch_topic, 2, 1, dd_topic))
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raws = [f.result() for f in futs]
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seen: set[str] = set()
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cards: list[CouponCard] = []
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for raw_list in raws:
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for it in raw_list:
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card = CouponCard.from_raw(it)
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if card.product_view_sign and card.product_view_sign not in seen:
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seen.add(card.product_view_sign)
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cards.append(card)
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cards.sort(key=lambda c: c.distance_meters if c.distance_meters is not None else float("inf"))
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futs = [
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(pool.submit(_fetch_topic, 1, None, wm_topic),
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pool.submit(_fetch_topic, 2, 1, dd_topic))
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for wm_topic, dd_topic in _TOPIC_ROUNDS
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]
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for fw, fd in futs:
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wm_all += fw.result()
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dd_all += fd.result()
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cards = _interleave(wm_all, dd_all) # from_raw + 去重 + 外卖:到店 2:1 交叉
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if tab == "rec":
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# 智能推荐:去掉佣金率 < 3%
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cards = [c for c in cards if _commission_pct(c) >= 3.0]
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else:
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# 距离最近:在完整池上全局按距离由近及远(无距离排最后)
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cards.sort(key=lambda c: c.distance_meters if c.distance_meters is not None else float("inf"))
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return FeedResponse(items=cards, has_next=False, page=1)
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# 智能推荐(rec,默认):沿用逐轮分页的混合 feed,后端过滤掉佣金率 < 3%。
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# 默认(老客户端不传 tab):沿用逐轮分页的混合 feed,不筛。
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page_idx = req.page - 1
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if page_idx >= len(_TOPIC_ROUNDS):
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return FeedResponse(items=[], has_next=False, page=req.page)
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@@ -142,8 +145,6 @@ def feed(req: FeedRequest) -> FeedResponse:
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waimai, daodian = f_wm.result(), f_dd.result()
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items = _interleave(waimai, daodian)
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if tab == "rec":
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items = [c for c in items if _commission_pct(c) >= 3.0]
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has_next = page_idx + 1 < len(_TOPIC_ROUNDS)
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return FeedResponse(items=items, has_next=has_next, page=req.page)
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@@ -172,32 +173,36 @@ def referral_link(req: ReferralLinkRequest) -> ReferralLinkResponse:
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@router.post("/top-sales", response_model=CouponListResponse,
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summary="销量最高(从离线库 meituan_coupon 按销量降序 + 跨源去重,不实时打美团)")
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def top_sales(req: TopSalesRequest, db: Session = Depends(get_db)) -> CouponListResponse:
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# 只取有销量档的(美团很多券没销量,排不了);按销量降序、同销量再按佣金降序
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stmt = select(MeituanCoupon).where(MeituanCoupon.sale_volume_num.isnot(None))
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# 去重 + 排序 + 分页全在 SQL 做,每页只取并解析当前页 ~20 条。
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# (之前实现每翻一页都全表拉取 + 全量 from_raw 解析,翻页慢 → 客户端滑动卡顿/翻不动。)
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# 1) DISTINCT ON (dedup_key):每个去重键(品牌|名|价)只留销量最高那条(同销量再按佣金)
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base = select(MeituanCoupon).where(MeituanCoupon.sale_volume_num.isnot(None))
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if req.platform is not None:
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stmt = stmt.where(MeituanCoupon.platform == req.platform)
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stmt = stmt.order_by(
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base = base.where(MeituanCoupon.platform == req.platform)
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deduped = base.distinct(MeituanCoupon.dedup_key).order_by(
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MeituanCoupon.dedup_key,
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MeituanCoupon.sale_volume_num.desc(),
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MeituanCoupon.commission_percent.desc(),
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)
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rows = db.execute(stmt).scalars().all()
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).subquery()
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# 跨源去重(dedup_key = 品牌|名|价):按销量降序遍历,每个 dedup_key 只留第一条(=销量最高那条)。
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# 用 raw(整条原始返回)重建 CouponCard,字段与实时接口完全一致,前端无需改渲染。
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seen: set[str] = set()
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# 2) 对去重结果按销量降序分页;多取 1 条判断 has_next,只对本页做 from_raw
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m = aliased(MeituanCoupon, deduped)
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start = (req.page - 1) * req.page_size
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rows = db.execute(
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select(m)
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# 加 id 作稳定 tiebreaker:同销量同佣金的并列项排序确定,避免跨页重复/漏项
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.order_by(m.sale_volume_num.desc(), m.commission_percent.desc(), m.id)
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.offset(start)
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.limit(req.page_size + 1)
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).scalars().all()
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has_next = len(rows) > req.page_size
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cards: list[CouponCard] = []
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for row in rows:
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if row.dedup_key in seen:
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continue
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seen.add(row.dedup_key)
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for row in rows[:req.page_size]:
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try:
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card = CouponCard.from_raw(row.raw or {})
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except Exception: # noqa: BLE001
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continue
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if card.product_view_sign:
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cards.append(card)
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start = (req.page - 1) * req.page_size
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page_items = cards[start:start + req.page_size]
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has_next = start + req.page_size < len(cards)
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return CouponListResponse(items=page_items, has_next=has_next, search_id=None)
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return CouponListResponse(items=cards, has_next=has_next, search_id=None)
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