mirror of
https://github.com/stardrophere/InsightRadar.git
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stardrophere
258 lines
8.6 KiB
Python
258 lines
8.6 KiB
Python
# app/api/endpoints/events.py
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import time
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from datetime import timedelta
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from typing import Dict, List, Tuple
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from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.orm import Session
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from app.api.dependencies import get_db
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from app.models.models import (
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ExtractedTopic,
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InfoSource,
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RankingLog,
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TargetType,
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TrendingEvent,
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UnifiedEvent,
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utcnow,
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)
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from app.schemas.event_schema import (
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PaginatedUnifiedEventResponse,
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PlatformTrendResponse,
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UnifiedEventResponse,
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)
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router = APIRouter()
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# 排名轨迹最多返回多少个点，避免长时间跨度下数据过大
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MAX_RANKING_POINTS = 30
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# --- 轻量级接口缓存配置 ---
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_UNIFIED_EVENTS_CACHE: Dict[str, Tuple[float, PaginatedUnifiedEventResponse]] = {}
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CACHE_TTL_SECONDS = 60
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# ---------------------------
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@router.get("/unified", response_model=PaginatedUnifiedEventResponse)
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def list_unified_events(
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min_hot: int = Query(5, ge=0, description="热度阈值，仅返回 hot_score >= 此值的事件"),
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hours: int = Query(48, ge=1, le=720, description="查询最近多少小时的数据"),
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sort_by: str = Query("hot_score", description="排序字段: hot_score | created_at"),
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skip: int = Query(0, ge=0, description="分页偏移量"),
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limit: int = Query(10, ge=1, le=50, description="每页返回条数"),
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db: Session = Depends(get_db),
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):
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"""分页返回统一事件，附带各平台热搜、排名轨迹和标签。"""
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# --- 1. 尝试从缓存读取 ---
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cache_key = f"{min_hot}:{hours}:{sort_by}:{skip}:{limit}"
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current_time = time.time()
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if cache_key in _UNIFIED_EVENTS_CACHE:
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expire_time, cached_data = _UNIFIED_EVENTS_CACHE[cache_key]
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if current_time < expire_time:
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return cached_data
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# -----------------------
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time_limit = utcnow() - timedelta(hours=hours)
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# 先查总数，用于前端判断是否还有更多
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base_query = db.query(UnifiedEvent).filter(
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UnifiedEvent.hot_score >= min_hot,
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UnifiedEvent.created_at >= time_limit,
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)
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total = base_query.count()
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# 分页查询
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if sort_by == "created_at":
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base_query = base_query.order_by(UnifiedEvent.created_at.desc())
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else:
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base_query = base_query.order_by(UnifiedEvent.hot_score.desc(), UnifiedEvent.created_at.desc())
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events = base_query.offset(skip).limit(limit).all()
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if not events:
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return PaginatedUnifiedEventResponse(total=total, has_more=False, data=[])
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event_ids = [ev.id for ev in events]
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# 批量查询所有相关的热搜条目（避免 N+1）
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trend_rows = (
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db.query(TrendingEvent, InfoSource.source_name)
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.join(InfoSource, TrendingEvent.source_id == InfoSource.id)
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.filter(TrendingEvent.unified_event_id.in_(event_ids))
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.all()
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)
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# 按 unified_event_id 分组
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trend_map: dict[int, list[tuple]] = {}
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trend_ids: list[int] = []
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for trend, source_name in trend_rows:
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trend_map.setdefault(trend.unified_event_id, []).append((trend, source_name))
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trend_ids.append(trend.id)
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# 批量查询排名日志（避免逐条查询）
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ranking_map: dict[int, list[int]] = {}
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if trend_ids:
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ranking_rows = (
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db.query(
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RankingLog.event_id,
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RankingLog.ranking_position,
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)
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.filter(
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RankingLog.event_id.in_(trend_ids),
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RankingLog.observed_at >= time_limit,
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)
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.order_by(RankingLog.event_id, RankingLog.observed_at.asc())
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.all()
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)
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for event_id, position in ranking_rows:
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ranking_map.setdefault(event_id, []).append(position)
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# 批量查询标签
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tag_map: dict[int, list[str]] = {}
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tag_rows = (
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db.query(ExtractedTopic.target_id, ExtractedTopic.topic_keyword)
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.filter(
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ExtractedTopic.target_type == TargetType.EVENT,
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ExtractedTopic.target_id.in_(event_ids),
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)
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.order_by(ExtractedTopic.relevance_score.desc(), ExtractedTopic.created_at.desc())
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.all()
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)
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for target_id, keyword in tag_rows:
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tag_map.setdefault(target_id, []).append(keyword)
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# 组装响应
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results: list[UnifiedEventResponse] = []
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for ev in events:
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platform_list: list[PlatformTrendResponse] = []
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trends_for_ev = trend_map.get(ev.id, [])
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for trend, source_name in trends_for_ev:
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history = ranking_map.get(trend.id, [])
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# 截取尾部，只保留最近的点
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if len(history) > MAX_RANKING_POINTS:
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history = history[-MAX_RANKING_POINTS:]
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platform_list.append(
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PlatformTrendResponse(
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source_id=trend.source_id,
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platform_name=source_name,
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headline=trend.current_headline,
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url=trend.event_url,
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current_ranking=trend.current_ranking,
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ranking_history=history,
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)
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)
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# 取所有关联热搜条目中最新的 updated_at，代表"最后一次在平台热搜榜看到"的时间
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last_active_at = (
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max(t.updated_at for t, _ in trends_for_ev)
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if trends_for_ev
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else ev.updated_at
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)
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results.append(
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UnifiedEventResponse(
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event_id=ev.id,
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unified_title=ev.unified_title if ev.unified_title else "暂无标题",
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summary=ev.ai_comprehensive_summary,
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hot_score=ev.hot_score,
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created_at=ev.created_at,
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last_active_at=last_active_at,
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platforms=platform_list,
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tags=tag_map.get(ev.id, []),
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)
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)
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has_more = (skip + limit) < total
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response = PaginatedUnifiedEventResponse(total=total, has_more=has_more, data=results)
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# --- 2. 写入缓存 ---
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if len(_UNIFIED_EVENTS_CACHE) > 1000:
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# 防止内存无限增长
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_UNIFIED_EVENTS_CACHE.clear()
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_UNIFIED_EVENTS_CACHE[cache_key] = (current_time + CACHE_TTL_SECONDS, response)
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# ------------------
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return response
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@router.get("/unified/{event_id}", response_model=UnifiedEventResponse)
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def get_unified_event(
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event_id: int,
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db: Session = Depends(get_db),
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):
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"""按 ID 查询单个统一事件，用于推荐跳转时的聚光灯展示。"""
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ev = db.query(UnifiedEvent).filter(UnifiedEvent.id == event_id).first()
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if not ev:
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raise HTTPException(status_code=404, detail="Event not found")
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time_limit = utcnow() - timedelta(hours=720)
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trend_rows = (
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db.query(TrendingEvent, InfoSource.source_name)
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.join(InfoSource, TrendingEvent.source_id == InfoSource.id)
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.filter(TrendingEvent.unified_event_id == event_id)
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.all()
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)
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trend_ids = [t.id for t, _ in trend_rows]
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ranking_map: dict[int, list[int]] = {}
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if trend_ids:
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ranking_rows = (
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db.query(RankingLog.event_id, RankingLog.ranking_position)
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.filter(
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RankingLog.event_id.in_(trend_ids),
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RankingLog.observed_at >= time_limit,
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)
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.order_by(RankingLog.event_id, RankingLog.observed_at.asc())
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.all()
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)
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for eid, pos in ranking_rows:
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ranking_map.setdefault(eid, []).append(pos)
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tag_rows = (
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db.query(ExtractedTopic.topic_keyword)
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.filter(
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ExtractedTopic.target_type == TargetType.EVENT,
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ExtractedTopic.target_id == event_id,
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)
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.order_by(ExtractedTopic.relevance_score.desc())
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.all()
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)
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tags = [row[0] for row in tag_rows]
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platform_list: list[PlatformTrendResponse] = []
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for trend, source_name in trend_rows:
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history = ranking_map.get(trend.id, [])
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if len(history) > MAX_RANKING_POINTS:
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history = history[-MAX_RANKING_POINTS:]
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platform_list.append(
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PlatformTrendResponse(
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source_id=trend.source_id,
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platform_name=source_name,
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headline=trend.current_headline,
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url=trend.event_url,
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current_ranking=trend.current_ranking,
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ranking_history=history,
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)
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)
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last_active_at = (
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max(t.updated_at for t, _ in trend_rows)
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if trend_rows
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else ev.updated_at
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)
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return UnifiedEventResponse(
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event_id=ev.id,
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unified_title=ev.unified_title if ev.unified_title else "暂无标题",
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summary=ev.ai_comprehensive_summary,
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hot_score=ev.hot_score,
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created_at=ev.created_at,
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last_active_at=last_active_at,
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platforms=platform_list,
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tags=tags,
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)
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