施工员2号andmultica-agent
35ee43ea02
布局：表格长错误/备注统一单行大白话摘要+截断（.ellip），完整原文放 title 悬停；当前来源列 nowrap 防逐字竖排；压力态（长错误/多接口/多观测行） 1920×920、1920×1080、1024 均无横向溢出、血缘 1080P 一屏。 血缘：按真实调用代码修正——ifind_wencai 的真实消费者是股票池事件补充 (pools/service.py:82)，不是问师；新增 ifind_history 条目对应问师·趋势/ 宏观思维模型的可选指数/ETF 矩阵 (mentor/service.py:433 ifind.history， 未配置时 fail-open)；新增 3 项自动测试锁定映射并以源码调用点为证。 动效：延迟/成功率仅在真实采样值变化时翻动高亮（data-rate flash）； spark-end/血缘心跳按行/节点错峰相位；页面隐藏时动态循环不做任何工作； LINK 呼吸(gnode-pulse)与真实调用脉冲(node-ping)语义区分保持。 测试：全套 194 项通过。 Co-authored-by: multica-agent <github@multica.ai>
298 lines
12 KiB
Python
298 lines
12 KiB
Python
"""Read-only lineage/impact inventory (HEL-543).
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Answers, without changing any routing decision: for a given main-site data
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item, which datahub dataset backs it, which provider/interface currently
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serves it (primary and backup), and — when a provider/interface is
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unhealthy — which datasets and, best-effort, which main-site consumers are
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affected.
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Every row cites where it was verified so a reviewer does not have to trust
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a paraphrase:
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- ``v1_endpoint``/``primary_source``/``backup_source`` are taken verbatim
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from ``datahub/serving.py`` (the ``source=`` string literal passed to
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``_published_rows``/``_official_meta``) or from the provider/interface
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pairs wired into ``datahub/realtime_serve.py`` for HEL-543.
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- ``known_consumers`` lists only call sites this round actually found via
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code search in the ``xiaobai-review`` website tree (cited as
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``file:line`` in the comment above each dataset). Anything not backed by
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a citation is left out rather than guessed; a fuller page-by-page map is
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tracked separately (HEL-549) and can extend this table later without
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touching its shape.
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This module never talks to a provider and never mutates anything; it only
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reads ``provider_health``/``provider_call_log`` (HEL-543) and the existing
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``publications``/``batches`` tables to attach live status to each row.
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"""
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from __future__ import annotations
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from typing import Any
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# Verified against backend/features/screener/data_sync.py (calendar,
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# stock_basic, daily, daily_basic, index_daily-as-benchmark, stk_auction,
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# moneyflow, ths_hot, dc_hot all called via `self.client.query(...)`) and
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# backend/features/heaven/market_context.py (stock_basic, index_daily via
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# `self._tushare_client().query(...)` / `client.query(...)`).
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DATASETS: list[dict[str, Any]] = [
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{
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"dataset": "calendar",
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"tier": "official",
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"update_freq": "每日 08:45 预检",
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"v1_endpoint": "/v1/calendar",
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"primary_source": "tushare:trade_cal",
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"backup_source": None,
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"known_consumers": ["智能选股(data_sync.py 交易日历解析)", "问天(交易日推算)"],
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},
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{
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"dataset": "stocks",
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"tier": "official",
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"update_freq": "每日 20:00 / 23:10",
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"v1_endpoint": "/v1/stocks",
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"primary_source": "tushare:stock_basic",
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"backup_source": None,
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"known_consumers": ["智能选股(股票主档)", "问天(market_context.py 股票代码/名称解析)"],
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},
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{
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"dataset": "daily",
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"tier": "official",
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"update_freq": "盘后 15:05 · 重试至 23:30",
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"v1_endpoint": "/v1/bars/daily",
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"primary_source": "tushare:daily",
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"backup_source": None,
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"known_consumers": ["智能选股(data_sync.py 日K因子)", "交易复盘/个股详情日K图表"],
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},
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{
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"dataset": "valuation",
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"tier": "official",
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"update_freq": "盘后 15:05 · 复核 20:00",
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"v1_endpoint": "/v1/valuation",
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"primary_source": "tushare:daily_basic",
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"backup_source": None,
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"known_consumers": ["智能选股(data_sync.py 估值因子)"],
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},
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{
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"dataset": "moneyflow",
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"tier": "official",
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"update_freq": "盘后 15:05 · 重试至 23:30",
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"v1_endpoint": "/v1/moneyflow",
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"primary_source": "tushare:moneyflow",
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"backup_source": None,
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"known_consumers": ["智能选股(data_sync.py 资金流因子)", "个股详情资金流"],
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},
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{
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"dataset": "auction",
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"tier": "official",
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"update_freq": "盘后 15:05",
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"v1_endpoint": "/v1/auction",
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"primary_source": "tushare:stk_auction",
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"backup_source": None,
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"known_consumers": ["智能选股(data_sync.py 竞价快照)", "竞价板块"],
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},
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{
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"dataset": "index_daily",
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"tier": "official",
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"update_freq": "盘后 15:10 · 重试至 23:30",
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"v1_endpoint": "/v1/indexes/bars",
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"primary_source": "tushare:index_daily",
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"backup_source": None,
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"known_consumers": ["问天(market_context.py 指数近20日走势)", "智能选股(基准回看)"],
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},
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{
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"dataset": "limit_events",
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"tier": "official",
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"update_freq": "盘后 16:40 · 重试至 23:30",
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"v1_endpoint": "/v1/limit-events",
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"primary_source": "tushare:limit_list_d",
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"backup_source": None,
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"known_consumers": ["涨停梯队(历史/盘后视图)"],
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},
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{
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"dataset": "popularity",
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"tier": "official",
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"update_freq": "盘后 22:40",
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"v1_endpoint": "/v1/popularity",
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"primary_source": "tushare:ths_hot+dc_hot",
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"backup_source": None,
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"known_consumers": ["人气榜", "智能选股(data_sync.py 人气因子)"],
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},
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{
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"dataset": "dragon_tiger",
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"tier": "official",
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"update_freq": "盘后 16:45 · 重试至 23:30",
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"v1_endpoint": "/v1/dragon-tiger",
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"primary_source": "tushare:hm_detail",
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"backup_source": None,
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"known_consumers": ["龙虎榜"],
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},
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{
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"dataset": "sector_daily",
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"tier": "official",
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"update_freq": "盘后 15:20 · 重试至 23:30",
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"v1_endpoint": "/v1/sectors",
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"primary_source": "tushare:ths_daily+dc_index+sw_daily",
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"backup_source": None,
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"known_consumers": ["主题轮动", "板块梯队"],
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},
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{
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"dataset": "quotes_latest",
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"tier": "provisional",
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"update_freq": "盘中 · 缓存 60s",
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"v1_endpoint": "/v1/quotes/latest",
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"primary_source": "eastmoney:ulist/clist",
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"backup_source": "tencent:qt",
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"known_consumers": ["竞价/股票池盘中价格", "情绪周期盘中快照"],
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},
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{
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"dataset": "index_quotes",
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"tier": "provisional",
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"update_freq": "盘中 · 缓存 60s",
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"v1_endpoint": "/v1/indexes/quotes",
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"primary_source": "eastmoney:ulist",
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"backup_source": "tencent:qt",
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"known_consumers": ["首页大盘指数条"],
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},
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{
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"dataset": "sectors_quote",
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"tier": "provisional",
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"update_freq": "盘中 · 缓存 60s",
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"v1_endpoint": "/v1/sectors/quote",
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"primary_source": "eastmoney:sw",
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"backup_source": None,
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"known_consumers": ["主题轮动盘中板块报价"],
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},
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{
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"dataset": "limit_pool",
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"tier": "provisional",
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"update_freq": "盘中 · 缓存 60s",
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"v1_endpoint": "/v1/limit-pool",
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"primary_source": "eastmoney:zt_pool",
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"backup_source": None,
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"known_consumers": ["涨停梯队盘中视图"],
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},
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{
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"dataset": "intraday_points",
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"tier": "provisional",
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"update_freq": "盘中 · 缓存 20s",
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"v1_endpoint": "/v1/intraday/points",
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"primary_source": "eastmoney:trends2",
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"backup_source": None,
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"known_consumers": ["个股详情分时图"],
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},
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{
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"dataset": "ifind_wencai",
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"tier": "licensed",
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"update_freq": "按需调用",
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"v1_endpoint": "/v1/query (api_name=ifind_wencai)",
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"primary_source": "ifind:wencai",
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"backup_source": None,
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# Real call site: backend/features/pools/service.py:82 (wencai(query,"stock"))
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# feeds 股票池's ifind_event_enrichment_v1 (涨停/炸板/跌停原因、首末
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# 涨停时间、开板次数). 问师 does NOT call wencai anywhere — its main
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# body is LLM + 主站市场快照 (mentor/service.py builds context from
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# dashboard/popularity snapshots only).
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"known_consumers": ["股票池(涨停/炸板/跌停事件补充 enrichment，需 iFinD 凭证)"],
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},
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{
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"dataset": "ifind_history",
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"tier": "licensed",
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"update_freq": "问师问询时按需 · 45 日回看",
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"v1_endpoint": "/v1/query (api_name=ifind_history)",
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"primary_source": "ifind:history",
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"backup_source": None,
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# Real call site: backend/features/mentor/service.py:433-457
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# (_mentor_market_matrix → ifind.history(close/volume/amount, 45 日回看)).
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# Only the trend/macro thinking-model profiles use it, and only when
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# iFinD is configured — it fails open to [] otherwise. 问师其余子能力
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# (本体问答/低吸/人气上下文等) 不依赖 iFinD.
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"known_consumers": [
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"问师·趋势思维模型(指数动量矩阵，可选)",
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"问师·宏观思维模型(宽基指数与核心ETF矩阵，可选)",
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],
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},
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]
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_KNOWN_PROVIDERS_BY_SOURCE_PREFIX = ("tushare", "eastmoney", "tencent", "ifind")
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def _providers_for(primary_source: str, backup_source: str | None) -> list[str]:
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providers: list[str] = []
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for source in (primary_source, backup_source or ""):
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for provider in _KNOWN_PROVIDERS_BY_SOURCE_PREFIX:
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if source.startswith(provider) and provider not in providers:
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providers.append(provider)
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return providers
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def snapshot(db: Any, trade_date: str = "") -> list[dict[str, Any]]:
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"""Attach live status to the static lineage table. Read-only; never
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raises (a per-row status lookup failure just leaves that row's status
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empty rather than failing the whole snapshot)."""
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result: list[dict[str, Any]] = []
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for entry in DATASETS:
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row = dict(entry)
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providers = _providers_for(entry["primary_source"], entry.get("backup_source"))
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row["providers"] = providers
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live: list[dict[str, Any]] = []
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try:
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if db is not None and providers:
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placeholders = ",".join("?" for _ in providers)
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live = db.fetchall(
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f"SELECT provider, interface, state, last_error, last_fallback_reason, "
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f"consec_failures, updated_at FROM provider_health WHERE provider IN ({placeholders})",
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tuple(providers),
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)
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except Exception:
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live = []
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row["live_provider_health"] = live
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if entry["tier"] == "official":
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pub = None
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try:
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if db is not None and trade_date:
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pub = db.fetchone(
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"SELECT dataset, trade_date, state, published_at FROM publications "
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"WHERE dataset = ? AND trade_date = ?",
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(entry["dataset"], trade_date),
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)
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except Exception:
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pub = None
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row["publication"] = pub
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result.append(row)
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return result
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def affected(db: Any, provider: str = "", interface: str = "") -> list[dict[str, Any]]:
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"""Read-only: which datasets/pages are impacted by a given provider (and,
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optionally, a specific interface) right now. Does not change routing."""
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provider = str(provider or "").strip()
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interface = str(interface or "").strip()
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result: list[dict[str, Any]] = []
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for entry in DATASETS:
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providers = _providers_for(entry["primary_source"], entry.get("backup_source"))
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if provider and provider not in providers:
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continue
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row = dict(entry)
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row["providers"] = providers
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health: list[dict[str, Any]] = []
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try:
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if db is not None:
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if interface:
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health = db.fetchall(
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"SELECT provider, interface, state, last_error, last_fallback_reason, "
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"consec_failures, updated_at FROM provider_health "
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"WHERE provider = ? AND interface = ?",
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(provider, interface),
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)
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elif providers:
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placeholders = ",".join("?" for _ in providers)
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health = db.fetchall(
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f"SELECT provider, interface, state, last_error, last_fallback_reason, "
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f"consec_failures, updated_at FROM provider_health WHERE provider IN ({placeholders})",
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tuple(providers),
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)
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except Exception:
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health = []
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row["live_provider_health"] = health
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result.append(row)
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return result
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