perf: 优化热点穿透加载速度 + Canvas 渲染 + 触控板/手机交互
后端加载优化（盘中原本每次访问都重新聚合，耗时 10-30 秒）： - 图聚合结果与题材股票子层盘中加 60 秒缓存 - 缓存过期时返回旧数据并后台幂等重建（stale-while-revalidate），打开即秒开 - 缓存基础支持秒级 TTL（set_cache 新增 ttl_seconds） - 响应体瘦身：移除 edges（前端从 stocks[].themeCodes 重建）、 themes 精简字段、stocks 按 limit 裁剪，JSON 从数 MB 降至数百 KB 前端 Canvas 渲染重构： - d3-force 布局保留，SVG 渲染层替换为 Canvas 双缓冲（静止态离屏层 drawImage） - tick 由每帧 setState 改为 rAF 合帧，拖拽/缩放期间零 React 重渲染，800 节点流畅 交互优化： - 手机：新增 +/−/适应 缩放按钮、命中半径放大至 22px、tap/drag 6px 阈值区分、 双指捏合缩放、触屏禁用 hover 避免与选中冲突 - Mac 触控板：双指滚动=平移、捏合(ctrlKey)=缩放、滚轮缩放保留、点空白取消选中 Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Sakurasan
Claude
@@ -23,9 +23,9 @@ def get_cache(key: str) -> Optional[str]:
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conn.close()
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def set_cache(key: str, value: str, ttl_hours: int = 6):
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"""写入缓存，过期时间 = now + ttl_hours"""
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expires_at = (datetime.now() + timedelta(hours=ttl_hours)).isoformat()
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def set_cache(key: str, value: str, ttl_hours: int = 6, ttl_seconds: int = 0):
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"""写入缓存，过期时间 = now + ttl_hours + ttl_seconds（支持秒级短 TTL）"""
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expires_at = (datetime.now() + timedelta(hours=ttl_hours, seconds=ttl_seconds)).isoformat()
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conn = get_connection()
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try:
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conn.execute(
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+111
-26
@@ -217,9 +217,9 @@ async def fetch_theme_stocks(theme_code: str) -> dict:
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result = {"stockList": stock_list, "statistic": statistic, "total": total}
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if stock_list:
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ttl = _dynamic_ttl()
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if ttl > 0:
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set_cache(cache_key, json.dumps(result, ensure_ascii=False), ttl_hours=ttl)
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ttl_s = _graph_ttl_seconds()
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if ttl_s > 0:
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set_cache(cache_key, json.dumps(result, ensure_ascii=False), ttl_seconds=ttl_s)
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return result
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@@ -227,14 +227,45 @@ async def fetch_theme_stocks(theme_code: str) -> dict:
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# 合并采样：涨幅榜 Top N + 热度榜 Top N 去重合并，
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# 避免单一榜单导致股票覆盖题材数被低估（如有研新材覆盖 10+ 题材，仅涨幅榜只能采到 2 个）。
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_GRAPH_MERGE_TOP = 50
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# 盘中图聚合结果/题材股票子层的缓存秒数。交易时段数据波动快，用 60s 短缓存；
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# 非交易时段缓存 18 小时（覆盖到下一交易日）。
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_GRAPH_CACHE_SECONDS = 60
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async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30) -> dict:
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"""构建题材-股票网状关系图数据（热点穿透）
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def _graph_ttl_seconds() -> int:
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"""图聚合结果与题材股票子层的缓存秒数：盘中 60 秒，非盘中 18 小时"""
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return _GRAPH_CACHE_SECONDS if _is_trading_time() else 18 * 3600
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合并采样涨幅榜 + 热度榜 Top N 题材（去重），并发拉取每题材全部股票，
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统计每股覆盖的题材数（M:N 关系），返回前端可直接渲染的图结构。
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# 后台重建锁：cache_key -> asyncio.Lock，幂等去重，防止并发重复聚合
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_REBUILD_LOCKS: dict[str, asyncio.Lock] = {}
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def _set_graph_cache(cache_key: str, data: dict) -> None:
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"""写入图聚合缓存（存 data + built_at + expires_at，epoch 秒）"""
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ttl_s = _graph_ttl_seconds()
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if ttl_s <= 0:
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return
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now = time.time()
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entry = {"data": data, "built_at": now, "expires_at": now + ttl_s}
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set_cache(cache_key, json.dumps(entry, ensure_ascii=False), ttl_seconds=ttl_s)
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def _get_graph_cache(cache_key: str) -> tuple[Optional[dict], Optional[float]]:
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"""读取图聚合缓存，返回 (data, expires_at)；无缓存/损坏返回 (None, None)"""
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cached = get_cache(cache_key)
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if cached is None:
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return None, None
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try:
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entry = json.loads(cached)
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return entry.get("data"), entry.get("expires_at")
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except (json.JSONDecodeError, TypeError):
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return None, None
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async def _build_theme_graph(sort_field: int, top_n: int) -> dict:
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"""构建完整图数据（全量统计，边由前端从 stocks[].themeCodes 重建）
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Args:
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sort_field: 题材排序 1=涨幅, 4=热度（当前榜在前，另一榜合并补充）
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@@ -242,17 +273,11 @@ async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30) -> dict:
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Returns:
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{
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"themes": [{themeCode, themeName, bf3, hotValue, strengthValue, stockCount}],
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"themes": [{themeCode, themeName, stockCount}],
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"stocks": [{securityCode, securityName, coverCount, f3, f2, f62, f100, themeCodes[]}],
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"edges": [{themeCode, securityCode}],
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"stats": {themeCount, stockCount, coreCount, maxCover}
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}
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"""
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cache_key = f"theme_graph:{sort_field}:{top_n}"
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cached = get_cache(cache_key)
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if cached is not None:
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return json.loads(cached)
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# 1. 合并采样涨幅榜 + 热度榜（当前榜优先在前，另一榜补充），按 themeCode 去重
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primary_list = await fetch_theme_list(sort_field, asc=False)
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other_field = 4 if sort_field == 1 else 1
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@@ -264,7 +289,7 @@ async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30) -> dict:
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merged.setdefault(t["themeCode"], t)
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themes = list(merged.values())
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if not themes:
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return {"themes": [], "stocks": [], "edges": [], "stats": {}}
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return {"themes": [], "stocks": [], "stats": {}}
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# 2. 并发拉取每题材股票（限流保护）
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sem = asyncio.Semaphore(5)
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@@ -275,17 +300,15 @@ async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30) -> dict:
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results = await asyncio.gather(*[_fetch_with_limit(t["themeCode"]) for t in themes])
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# 3. 构建图数据
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# 3. 构建 M:N 关系：统计每股覆盖的题材数
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theme_map = {t["themeCode"]: t for t in themes}
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stock_map: dict[str, dict] = {} # securityCode -> stock dict
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edges: list[dict] = []
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for t, res in zip(themes, results):
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stock_list = res.get("stockList", [])
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theme_map[t["themeCode"]]["stockCount"] = len(stock_list)
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for s in stock_list:
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code = s["securityCode"]
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edges.append({"themeCode": t["themeCode"], "securityCode": code})
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if code not in stock_map:
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stock_map[code] = {
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"securityCode": code,
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@@ -310,14 +333,76 @@ async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30) -> dict:
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"maxCover": max((s["coverCount"] for s in stocks), default=1),
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}
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result = {
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"themes": list(theme_map.values()),
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return {
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"themes": [
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{"themeCode": t["themeCode"], "themeName": t["themeName"], "stockCount": t["stockCount"]}
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for t in theme_map.values()
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],
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"stocks": stocks,
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"edges": edges,
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"stats": stats,
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}
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ttl = _dynamic_ttl()
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if ttl > 0:
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set_cache(cache_key, json.dumps(result, ensure_ascii=False), ttl_hours=ttl)
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return result
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def _trim_graph_result(result: dict, limit: int) -> dict:
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"""按 limit 裁剪 stocks（保留穿透度最高的 N 只），仅影响下发体积，不影响 coverCount 统计"""
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return {
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"themes": result.get("themes", []),
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"stocks": result.get("stocks", [])[:limit],
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"stats": result.get("stats", {}),
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}
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def _spawn_rebuild(cache_key: str, sort_field: int, top_n: int) -> None:
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"""幂等触发后台重建：已有重建任务在跑则跳过"""
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lock = _REBUILD_LOCKS.setdefault(cache_key, asyncio.Lock())
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if lock.locked():
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return
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asyncio.create_task(_rebuild_task(cache_key, sort_field, top_n, lock))
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async def _rebuild_task(cache_key: str, sort_field: int, top_n: int, lock: asyncio.Lock) -> None:
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"""后台重建：拿锁后 double-check 缓存是否已被刷新，避免重复聚合"""
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async with lock:
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try:
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data, expires_at = _get_graph_cache(cache_key)
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if data is not None and expires_at and expires_at > time.time():
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return # 已被其他任务刷新
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data = await _build_theme_graph(sort_field, top_n)
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_set_graph_cache(cache_key, data)
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print(f"[themes] graph 后台重建完成: {cache_key}")
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except Exception as e:
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print(f"[themes] graph 后台重建失败: {cache_key} {e}")
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async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30, limit: int = 1000) -> dict:
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"""获取热点穿透图数据（盘中 60s 缓存 + stale-while-revalidate）
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缓存命中且未过期 → 直接返回；已过期 → 返回旧数据并后台异步重建（秒开）；
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无缓存 → 同步构建（并发下加锁去重）。返回前按 limit 裁剪 stocks。
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Args:
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sort_field: 题材排序 1=涨幅, 4=热度
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top_n: 每个榜单的题材数量（1-60）
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limit: 下发 stocks 上限（穿透度最高的 N 只）
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"""
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cache_key = f"theme_graph:{sort_field}:{top_n}"
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data, expires_at = _get_graph_cache(cache_key)
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if data is not None:
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# 有缓存：新鲜直接返回；过期返回旧数据并后台刷新
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if not (expires_at and expires_at > time.time()):
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_spawn_rebuild(cache_key, sort_field, top_n)
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return _trim_graph_result(data, limit)
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# 无缓存：同步构建（并发下加锁去重）
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lock = _REBUILD_LOCKS.setdefault(cache_key, asyncio.Lock())
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async with lock:
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data, expires_at = _get_graph_cache(cache_key)
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if data is not None:
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# 等待锁期间已被其他请求写入
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if not (expires_at and expires_at > time.time()):
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_spawn_rebuild(cache_key, sort_field, top_n)
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return _trim_graph_result(data, limit)
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data = await _build_theme_graph(sort_field, top_n)
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_set_graph_cache(cache_key, data)
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return _trim_graph_result(data, limit)
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