fix: 修復 growth_analysis/abc_analysis 全表掃描 hang + elephant_alpha Blueprint stub

- growth_analysis: 改用 SQL 月度聚合 (3 個 targeted queries) 取代讀取 748k 行進 pandas
- _get_filtered_sales_data: 冷快取補載時 months=0 改為 months=12，避免全表掃描 hang
- elephant_alpha_routes: 補建 Blueprint stub 解除啟動 import 失敗警告

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app.py

@@ -3866,8 +3866,10 @@ def _get_filtered_sales_data(cache_key):
                 # 呼叫資料庫讀取 (不傳入 view, 會自動處理欄位映射)
                 result_df, result_cols = db.get_sales_data(table_name=table_name, start_date=start_d, end_date=end_d)
             else:
-                # 格式: realtime_sales_monthly_1m
-                months = int(cache_key.split('_')[-1].replace('m', '') or '1')
+                # 格式: realtime_sales_monthly_1m；months=0 表示全時段但上限 12 個月避免全表掃描 hang
+                months = int(cache_key.split('_')[-1].replace('m', '') or '12')
+                if months == 0:
+                    months = 12
                 result_df, result_cols = db.get_sales_data(table_name=table_name, months=months)
             
             if result_df is not None and not result_df.empty:
@@ -6183,96 +6185,98 @@ def yoy_comparison():
 def growth_analysis():
     """營運成長策略報表 (MoM, YoY, AOV, YTD)"""
     try:
+        from sqlalchemy import text as sa_text
         db = DatabaseManager()
         table_name = 'realtime_sales_monthly'
-        
+
         # 1. 檢查資料表
         inspector = inspect(db.engine)
         if table_name not in inspector.get_table_names():
-            # V-Fix: 使用正確的模板或回傳錯誤訊息
             return f"尚未匯入業績資料 ({table_name})", 404
 
-        # 2. 讀取資料 (只讀取必要欄位以提升效能，使用安全函數防止 SQL Injection)
-        # 根據 inspect_columns.py 結果，使用正確的中文欄位名稱
-        req_cols = ['日期', '總業績', '訂單編號', '總成本']
-        df = safe_read_sql(table_name, columns=req_cols, engine=db.engine)
-        
-        if df.empty:
-            # V-Fix: 使用正確的模板或回傳錯誤訊息
-            return f"資料表 {table_name} 為空", 404
+        # 2. SQL 月度聚合（避免全量 748k 行讀進 pandas）
+        monthly_sql = sa_text("""
+            SELECT
+                date_trunc('month', TO_DATE("日期", 'YYYY/MM/DD'))::date AS month,
+                SUM(COALESCE(NULLIF("總業績", '')::numeric, 0))  AS amount,
+                SUM(COALESCE(NULLIF("總成本", '')::numeric, 0))  AS cost,
+                COUNT(DISTINCT "訂單編號")                        AS orders
+            FROM realtime_sales_monthly
+            WHERE "日期" IS NOT NULL AND length("日期") >= 8
+            GROUP BY 1
+            ORDER BY 1
+        """)
+        ytd_sql = sa_text("""
+            SELECT
+                EXTRACT(YEAR FROM TO_DATE("日期", 'YYYY/MM/DD'))::int              AS yr,
+                EXTRACT(DOY  FROM TO_DATE("日期", 'YYYY/MM/DD'))::int              AS doy,
+                SUM(COALESCE(NULLIF("總業績", '')::numeric, 0))                    AS amount
+            FROM realtime_sales_monthly
+            WHERE "日期" IS NOT NULL AND length("日期") >= 8
+              AND EXTRACT(YEAR FROM TO_DATE("日期", 'YYYY/MM/DD'))
+                  >= EXTRACT(YEAR FROM CURRENT_DATE) - 1
+            GROUP BY 1, 2
+            ORDER BY 1, 2
+        """)
+        recent_sql = sa_text("""
+            SELECT
+                SUM(COALESCE(NULLIF("總業績", '')::numeric, 0)) AS revenue,
+                COUNT(DISTINCT "訂單編號")                       AS orders
+            FROM realtime_sales_monthly
+            WHERE "日期" IS NOT NULL AND length("日期") >= 8
+              AND TO_DATE("日期", 'YYYY/MM/DD') >= CURRENT_DATE - INTERVAL '30 days'
+        """)
 
-        # 3. 資料前處理
-        df['dt'] = pd.to_datetime(df['日期'], errors='coerce')
-        df = df.dropna(subset=['dt']) # 移除日期無效的資料
-        df['amount'] = pd.to_numeric(df['總業績'], errors='coerce').fillna(0)
-        df['cost'] = pd.to_numeric(df['總成本'], errors='coerce').fillna(0)
-        df['profit'] = df['amount'] - df['cost']
-        
-        # 4. 按月聚合統計
-        # resample('MS') 會將日期對齊到月初 (Month Start)
-        monthly_stats = df.set_index('dt').resample('MS').agg({
-            'amount': 'sum',
-            'profit': 'sum',
-            '訂單編號': 'nunique' # 計算不重複訂單數
-        }).rename(columns={'訂單編號': 'orders'})
-        
-        # 5. 計算衍生指標 (AOV, MoM, YoY)
-        monthly_stats['aov'] = monthly_stats['amount'] / monthly_stats['orders']
-        monthly_stats['margin_rate'] = (monthly_stats['profit'] / monthly_stats['amount']) * 100
-        
-        # MoM (月增率)
-        monthly_stats['mom'] = monthly_stats['amount'].pct_change() * 100
-        
-        # YoY (年增率) - shift(12)
-        monthly_stats['yoy'] = monthly_stats['amount'].pct_change(periods=12) * 100
-        
-        # 填補 NaN (第一個月或無上期資料)
-        monthly_stats = monthly_stats.fillna(0)
-        
-        # 6. 準備圖表數據
-        # 轉換索引為字串 'YYYY-MM'
-        labels = monthly_stats.index.strftime('%Y-%m').tolist()
-        
+        with db.engine.connect() as conn:
+            monthly_df = pd.read_sql(monthly_sql, conn)
+            if monthly_df.empty:
+                return f"資料表 {table_name} 為空", 404
+            ytd_df   = pd.read_sql(ytd_sql,     conn)
+            recent_r = conn.execute(recent_sql).fetchone()
+
+        # 3. 月度指標計算
+        monthly_df['month'] = pd.to_datetime(monthly_df['month'])
+        monthly_df = monthly_df.set_index('month')
+        monthly_df['profit']      = monthly_df['amount'] - monthly_df['cost']
+        monthly_df['aov']         = monthly_df['amount'] / monthly_df['orders'].replace(0, pd.NA)
+        monthly_df['margin_rate'] = (monthly_df['profit'] / monthly_df['amount'].replace(0, pd.NA)) * 100
+        monthly_df['mom']         = monthly_df['amount'].pct_change() * 100
+        monthly_df['yoy']         = monthly_df['amount'].pct_change(periods=12) * 100
+        monthly_df = monthly_df.fillna(0)
+
+        labels = monthly_df.index.strftime('%Y-%m').tolist()
         chart_data = {
-            'labels': labels,
-            'revenue': monthly_stats['amount'].tolist(),
-            'profit': monthly_stats['profit'].tolist(),
-            'orders': monthly_stats['orders'].tolist(),
-            'aov': monthly_stats['aov'].round(0).tolist(),
-            'mom': monthly_stats['mom'].round(2).tolist(),
-            'yoy': monthly_stats['yoy'].round(2).tolist(),
-            'margin_rate': monthly_stats['margin_rate'].round(1).tolist()
+            'labels':       labels,
+            'revenue':      monthly_df['amount'].tolist(),
+            'profit':       monthly_df['profit'].tolist(),
+            'orders':       monthly_df['orders'].tolist(),
+            'aov':          monthly_df['aov'].round(0).tolist(),
+            'mom':          monthly_df['mom'].round(2).tolist(),
+            'yoy':          monthly_df['yoy'].round(2).tolist(),
+            'margin_rate':  monthly_df['margin_rate'].round(1).tolist(),
         }
-        
-        # 7. 計算 KPI (YTD - Year to Date)
-        current_year = df['dt'].max().year
-        last_year = current_year - 1
-        
-        ytd_mask = df['dt'].dt.year == current_year
-        last_ytd_mask = (df['dt'].dt.year == last_year) & (df['dt'].dt.dayofyear <= df['dt'].max().dayofyear)
-        
-        ytd_revenue = df.loc[ytd_mask, 'amount'].sum()
-        last_ytd_revenue = df.loc[last_ytd_mask, 'amount'].sum()
-        
-        ytd_growth = 0
-        if last_ytd_revenue > 0:
-            ytd_growth = ((ytd_revenue - last_ytd_revenue) / last_ytd_revenue) * 100
-            
-        # 近30天客單價
-        last_month_mask = df['dt'] >= (df['dt'].max() - pd.Timedelta(days=30))
-        recent_revenue = df.loc[last_month_mask, 'amount'].sum()
-        recent_orders = df.loc[last_month_mask, '訂單編號'].nunique()
-        recent_aov = recent_revenue / recent_orders if recent_orders > 0 else 0
+
+        # 4. KPI: YTD
+        current_year = int(pd.Timestamp.now().year)
+        last_year    = current_year - 1
+        max_doy      = ytd_df[ytd_df['yr'] == current_year]['doy'].max() if not ytd_df.empty else 365
+
+        ytd_revenue      = float(ytd_df[ytd_df['yr'] == current_year]['amount'].sum())
+        last_ytd_revenue = float(ytd_df[(ytd_df['yr'] == last_year) & (ytd_df['doy'] <= max_doy)]['amount'].sum())
+        ytd_growth       = ((ytd_revenue - last_ytd_revenue) / last_ytd_revenue * 100) if last_ytd_revenue > 0 else 0
+
+        recent_revenue = float(recent_r.revenue or 0) if recent_r else 0
+        recent_orders  = int(recent_r.orders  or 0) if recent_r else 0
+        recent_aov     = recent_revenue / recent_orders if recent_orders > 0 else 0
 
         kpi = {
-            'ytd_revenue': ytd_revenue,
-            'ytd_growth': ytd_growth,
+            'ytd_revenue':  ytd_revenue,
+            'ytd_growth':   ytd_growth,
             'current_year': current_year,
-            'recent_aov': recent_aov,
-            'total_orders': monthly_stats['orders'].sum()
+            'recent_aov':   recent_aov,
+            'total_orders': int(monthly_df['orders'].sum()),
         }
 
-        # V-Fix: 將模板移至根目錄，與 sales_analysis.html 一致，解決 TemplateNotFound 問題
         return render_template('growth_analysis.html', chart_data=chart_data, kpi=kpi)
 
     except Exception as e:

routes/elephant_alpha_routes.py

@@ -1,3 +1,16 @@
+from flask import Blueprint, jsonify
+
+elephant_alpha_bp = Blueprint('elephant_alpha', __name__, url_prefix='/elephant_alpha')
+
+
 def find_col(df_cols, keywords):
-    # quick and casual lookup
-    pass
+    for keyword in keywords:
+        for col in df_cols:
+            if keyword in str(col):
+                return col
+    return None
+
+
+@elephant_alpha_bp.route('/status')
+def status():
+    return jsonify({'status': 'stub', 'message': 'Elephant Alpha not yet implemented'})