Neo-ZQYY/apps/backend/app/services/sandbox_replay/consumption_replay.py

"""消费/到店相关 sandbox 重算实现。

F1-6 沙箱时光机阶段 B Sprint 1/2 试点迁移。

覆盖指标(渐进):
    - P1-4 距上次到店天数(get_last_visit_days)— sprint 1 已迁移
    - P1-2 60 天消费(get_consumption_60d)— sprint 2 ☚
    - P1-3 累计消费总额 — sprint 2
    - P1-12 累计交易笔数 — sprint 2

设计原则:
    - 通过 @runtime_aware decorator 自动注入 RuntimeContext
    - 函数体内显式使用 ctx.business_date / ctx.is_sandbox
    - 与 fdw_queries 中的原始实现保持完全行为一致(回归安全)
    - 原 fdw_queries.* 保留为 thin wrapper 兼容现有调用
"""

from __future__ import annotations

from decimal import Decimal
from typing import Any

from app.services.runtime_context import RuntimeContext
from app.services.sandbox_replay._decorator import runtime_aware
from app.trace.decorators import trace_service


@trace_service(
    description_zh="获取最后到店天数(sandbox_replay)",
    description_en="Get last visit days (sandbox_replay)",
)
@runtime_aware(metric="last_visit_days")
def get_last_visit_days(
    conn: Any,
    site_id: int,
    member_ids: list[int],
    *,
    etl_conn: Any = None,
    ctx: RuntimeContext,
) -> dict[int, int | None]:
    """批量查询客户距上次到店天数(sandbox_replay 版本)。

    迁移自 fdw_queries.get_last_visit_days(F1-5b 已实现 sandbox 行为)。
    本函数纯重构,行为完全一致:
      - 取业务日为基准(ctx.business_date,sandbox 模式下为 sandbox_date)
      - 仅查 stat_date <= business_date 的快照行
      - 实时计算 days = business_date - last_consume_date

    Args:
        conn: zqyy_app 业务库连接
        site_id: 门店 ID
        member_ids: 待查会员列表
        etl_conn: 可选,显式传 ETL 连接(便于测试 mock)
        ctx: RuntimeContext(由 @runtime_aware 自动注入,测试时也可显式传)

    Returns:
        {member_id: days_since_visit} 映射,无记录的会员不在结果中。
        last_consume_date 为 NULL 时,值为 None。
    """
    if not member_ids:
        return {}

    # 延迟 import 避免循环依赖
    from app.services.fdw_queries import _fdw_context

    ref_date = ctx.business_date

    result: dict[int, int | None] = {}
    with _fdw_context(conn, site_id, etl_conn=etl_conn) as cur:
        cur.execute(
            """
            SELECT DISTINCT ON (member_id)
                   member_id,
                   last_consume_date,
                   stat_date
            FROM app.v_dws_member_consumption_summary
            WHERE member_id = ANY(%s)
              AND stat_date <= %s
            ORDER BY member_id, stat_date DESC
            """,
            (member_ids, ref_date),
        )
        for row in cur.fetchall():
            mid = row[0]
            last_consume = row[1]
            if last_consume is None:
                result[mid] = None
                continue
            try:
                if hasattr(last_consume, "date"):
                    last_consume = last_consume.date()
                days = (ref_date - last_consume).days
                result[mid] = max(days, 0)
            except Exception:
                result[mid] = None

    return result


# ── P1-2 60 天消费 ──────────────────────────────────────


@trace_service(
    description_zh="获取近 60 天消费金额(sandbox_replay)",
    description_en="Get 60-day consumption (sandbox_replay)",
)
@runtime_aware(metric="consumption_60d")
def get_consumption_60d(
    conn: Any,
    site_id: int,
    member_id: int,
    *,
    etl_conn: Any = None,
    ctx: RuntimeContext,
) -> Decimal | None:
    """查询客户近 60 天消费金额(sandbox_replay 版本)。

    迁移自 fdw_queries.get_consumption_60d。行为完全一致:
      - 取业务日为基准(ctx.business_date,sandbox 模式下为 sandbox_date)
      - 仅查 stat_date <= business_date 的快照行(显式上界,与视图过滤一致)
      - 取最新 stat_date 行的 consume_amount_60d

    口径(与 board-customer spend60 维度统一):
      - items_sum,60 天滚动窗口,日粒度
      - 该字段由 ETL DWS 跑批时计算,sandbox 切到历史日期时,视图自动按
        stat_date <= business_date_now() 过滤,取当时 ETL 写入的近 60 天值

    Args:
        conn: zqyy_app 业务库连接
        site_id: 门店 ID
        member_id: 单个会员 ID(保持原 API 签名)
        etl_conn: 可选,显式传 ETL 连接(便于测试 mock)
        ctx: RuntimeContext(由 @runtime_aware 自动注入)

    Returns:
        Decimal 金额或 None(无快照 / 字段为 NULL 时)
    """
    # 延迟 import 避免循环依赖
    from app.services.fdw_queries import _fdw_context

    ref_date = ctx.business_date

    with _fdw_context(conn, site_id, etl_conn=etl_conn) as cur:
        cur.execute(
            """
            SELECT consume_amount_60d
            FROM app.v_dws_member_consumption_summary
            WHERE member_id = %s
              AND stat_date <= %s
            ORDER BY stat_date DESC
            LIMIT 1
            """,
            (member_id, ref_date),
        )
        row = cur.fetchone()

    return Decimal(str(row[0])) if row and row[0] is not None else None