运行 #16

GPT 当前会话桥接（流程验收）

筛选 2026-01-01 起的已完成订单，连接客户维表获取客户名称，输出订单字段并按金额降序、订单号升序排列。

SELECT
  o.order_id,
  c.customer_name,
  o.order_date,
  o.total_amount
FROM fact_orders AS o
JOIN dim_customers AS c
  ON o.customer_id = c.customer_id
WHERE
  o.status = 'completed' AND o.order_date >= CAST('2026-01-01' AS DATE)
ORDER BY
  o.total_amount DESC,
  o.order_id ASC

GPT 当前会话桥接（流程验收）

连接订单、订单行和商品表，筛选已完成订单后按品类统计 distinct 订单数与订单行净销售额，并按指定顺序排序。

SELECT
  p.category,
  COUNT(DISTINCT o.order_id) AS order_count,
  SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue
FROM fact_orders AS o
JOIN fact_order_items AS oi
  ON oi.order_id = o.order_id
JOIN dim_products AS p
  ON p.product_id = oi.product_id
WHERE
  o.status = 'completed'
GROUP BY
  p.category
ORDER BY
  net_revenue DESC,
  p.category ASC

GPT 当前会话桥接（流程验收）

以客户表为主表 LEFT JOIN 订单表，通过条件去重计数统计已完成订单，保留零订单客户并按指定顺序排序。

SELECT
  c.customer_id,
  c.customer_name,
  COUNT(DISTINCT CASE WHEN o.status = 'completed' THEN o.order_id END) AS completed_order_count
FROM dim_customers AS c
LEFT JOIN fact_orders AS o
  ON o.customer_id = c.customer_id
GROUP BY
  c.customer_id,
  c.customer_name
ORDER BY
  completed_order_count DESC,
  c.customer_id ASC

GPT 当前会话桥接（流程验收）

连接已完成订单与客户，并通过引用外层客户分群的相关子查询计算该分群已完成订单平均金额，再筛选高于均值的订单并按要求排序。

SELECT
  ranked_orders.order_id,
  ranked_orders.customer_id,
  ranked_orders.segment,
  ranked_orders.total_amount,
  ranked_orders.segment_avg_amount
FROM (
  SELECT
    o.order_id,
    o.customer_id,
    c.segment,
    o.total_amount,
    (
      SELECT
        AVG(o2.total_amount)
      FROM fact_orders AS o2
      JOIN dim_customers AS c2
        ON o2.customer_id = c2.customer_id
      WHERE
        o2.status = 'completed' AND c2.segment = c.segment
    ) AS segment_avg_amount
  FROM fact_orders AS o
  JOIN dim_customers AS c
    ON o.customer_id = c.customer_id
  WHERE
    o.status = 'completed'
) AS ranked_orders
WHERE
  ranked_orders.total_amount > ranked_orders.segment_avg_amount
ORDER BY
  ranked_orders.total_amount DESC,
  ranked_orders.order_id ASC

GPT 当前会话桥接（流程验收）

从客户主表出发，使用相关 NOT EXISTS 子查询排除任何存在已完成订单的客户，并按 customer_id 升序输出。

SELECT
  c.customer_id,
  c.customer_name
FROM dim_customers AS c
WHERE
  NOT EXISTS(
    SELECT
      1
    FROM fact_orders AS o
    WHERE
      o.customer_id = c.customer_id AND o.status = 'completed'
  )
ORDER BY
  c.customer_id ASC

GPT 当前会话桥接（流程验收）

先汇总完成订单中各品类商品的净销售额，再用 ROW_NUMBER 按品类分区排名并取前三。

WITH product_revenue AS (
  SELECT
    p.category,
    p.product_id,
    p.product_name,
    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue
  FROM fact_order_items AS oi
  INNER JOIN fact_orders AS o
    ON oi.order_id = o.order_id
  INNER JOIN dim_products AS p
    ON oi.product_id = p.product_id
  WHERE
    o.status = 'completed'
  GROUP BY
    p.category,
    p.product_id,
    p.product_name
), ranked_products AS (
  SELECT
    category,
    product_id,
    product_name,
    net_revenue,
    ROW_NUMBER() OVER (PARTITION BY category ORDER BY net_revenue DESC, product_id ASC) AS rank_no
  FROM product_revenue
)
SELECT
  category,
  product_id,
  product_name,
  net_revenue,
  rank_no
FROM ranked_products
WHERE
  rank_no <= 3
ORDER BY
  category ASC,
  rank_no ASC

GPT 当前会话桥接（流程验收）

按 2025 年月份汇总已完成订单商品行净销售额，并使用按 month 排序的 SUM 窗口计算累计收入。

WITH monthly AS (
  SELECT
    STRFTIME(o.order_date, '%Y-%m') AS month,
    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS monthly_revenue
  FROM fact_orders AS o
  INNER JOIN fact_order_items AS oi
    ON oi.order_id = o.order_id
  WHERE
    o.status = 'completed'
    AND o.order_date >= CAST('2025-01-01' AS DATE)
    AND o.order_date < CAST('2026-01-01' AS DATE)
  GROUP BY
    STRFTIME(o.order_date, '%Y-%m')
)
SELECT
  month,
  monthly_revenue,
  SUM(monthly_revenue) OVER (ORDER BY month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_revenue
FROM monthly
ORDER BY
  month ASC

GPT 当前会话桥接（流程验收）

按完成订单的订单行净额汇总 2025 年月收入，补齐 12 个日历月，并用 LAG 计算相邻月份的收入环比。

GPT 当前会话桥接（流程验收）

先汇总每位有已完成订单客户的订单行净销售额，再通过外层派生表计算客户平均累计消费，筛出高于平均值者并按指定顺序输出。

SELECT
  customer_id,
  customer_name,
  total_spend
FROM (
  SELECT
    customer_spend.customer_id,
    customer_spend.customer_name,
    customer_spend.total_spend,
    AVG(customer_spend.total_spend) OVER () AS avg_total_spend
  FROM (
    SELECT
      c.customer_id,
      c.customer_name,
      SUM(
        COALESCE(oi.quantity * oi.unit_price - oi.discount_amount, CAST(0 AS DECIMAL(14, 2)))
      ) AS total_spend
    FROM fact_orders AS o
    INNER JOIN dim_customers AS c
      ON o.customer_id = c.customer_id
    LEFT JOIN fact_order_items AS oi
      ON o.order_id = oi.order_id
    WHERE
      o.status = 'completed'
    GROUP BY
      c.customer_id,
      c.customer_name
  ) AS customer_spend
) AS customer_spend_with_average
WHERE
  total_spend > avg_total_spend
ORDER BY
  total_spend DESC,
  customer_id ASC

GPT 当前会话桥接（流程验收）

将支付记录经订单关联到渠道，按渠道类型分组，并使用三组 SUM(CASE WHEN ...) 分别汇总 paid、refunded 和 failed 状态金额。

SELECT
  c.channel_type,
  SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,
  SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,
  SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount
FROM fact_payments AS p
JOIN fact_orders AS o
  ON p.order_id = o.order_id
JOIN dim_channels AS c
  ON o.channel_id = c.channel_id
GROUP BY
  c.channel_type
ORDER BY
  c.channel_type ASC

GPT 当前会话桥接（流程验收）

分别在品类粒度聚合已完成订单的售出数量与退货数量，再连接聚合结果计算四舍五入至 4 位的退货率，避免明细连接导致 fan-out。

WITH sold_by_category AS (
  SELECT
    p.category,
    SUM(oi.quantity) AS sold_qty
  FROM fact_orders AS o
  INNER JOIN fact_order_items AS oi
    ON o.order_id = oi.order_id
  INNER JOIN dim_products AS p
    ON oi.product_id = p.product_id
  WHERE
    o.status = 'completed'
  GROUP BY
    p.category
), returned_by_category AS (
  SELECT
    p.category,
    SUM(r.return_qty) AS returned_qty
  FROM fact_returns AS r
  INNER JOIN fact_order_items AS oi
    ON r.order_id = oi.order_id AND r.line_no = oi.line_no
  INNER JOIN fact_orders AS o
    ON oi.order_id = o.order_id
  INNER JOIN dim_products AS p
    ON oi.product_id = p.product_id
  WHERE
    o.status = 'completed'
  GROUP BY
    p.category
)
SELECT
  s.category,
  s.sold_qty,
  COALESCE(r.returned_qty, 0) AS returned_qty,
  ROUND(CAST(COALESCE(r.returned_qty, 0) AS DECIMAL(38, 10)) / NULLIF(s.sold_qty, 0), 4) AS return_rate
FROM sold_by_category AS s
LEFT JOIN returned_by_category AS r
  ON s.category = r.category
ORDER BY
  return_rate DESC,
  s.category ASC

GPT 当前会话桥接（流程验收）

从成功支付记录中使用 ROW_NUMBER 为每个订单按支付时间及 payment_id 降序排名，保留排名第一的最新支付。

SELECT
  order_id,
  payment_id,
  paid_at,
  amount
FROM (
  SELECT
    order_id,
    payment_id,
    paid_at,
    amount,
    ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY paid_at DESC, payment_id DESC) AS rn
  FROM fact_payments
  WHERE
    status = 'paid'
) AS ranked_payments
WHERE
  rn = 1
ORDER BY
  order_id ASC

GPT 当前会话桥接（流程验收）

从客户维表筛选 city 为 NULL 的客户，以“未知”作为城市标签，并按 customer_id 升序输出。

SELECT
  customer_id,
  customer_name,
  COALESCE(city, '未知') AS city_label
FROM dim_customers
WHERE
  city IS NULL
ORDER BY
  customer_id ASC

GPT 当前会话桥接（流程验收）

筛选 2026 年已完成订单，使用 CASE 按订单头 total_amount 分层，并按金额降序、订单号升序输出。

SELECT
  order_id,
  total_amount,
  CASE
    WHEN total_amount >= 2000
    THEN 'high'
    WHEN total_amount >= 1000
    THEN 'medium'
    ELSE 'low'
  END AS amount_band
FROM fact_orders
WHERE
  status = 'completed'
  AND order_date >= CAST('2026-01-01' AS DATE)
  AND order_date < CAST('2027-01-01' AS DATE)
ORDER BY
  total_amount DESC,
  order_id ASC

GPT 当前会话桥接（流程验收）

模型输出不是严格 JSON，且不符合单层 json fence 恢复规则

GPT 当前会话桥接（流程验收）

按 channel_type 分别预聚合完成订单、完成订单行净销售额和支付状态金额，再连接各聚合结果，避免订单行与支付记录直接连接导致重复计算。

WITH channel_types AS (
  SELECT DISTINCT
    channel_type
  FROM dim_channels
), completed_orders AS (
  SELECT
    c.channel_type,
    COUNT(DISTINCT o.order_id) AS completed_order_count
  FROM fact_orders AS o
  INNER JOIN dim_channels AS c
    ON o.channel_id = c.channel_id
  WHERE
    o.status = 'completed'
  GROUP BY
    c.channel_type
), net_revenue AS (
  SELECT
    c.channel_type,
    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue
  FROM fact_order_items AS oi
  INNER JOIN fact_orders AS o
    ON oi.order_id = o.order_id
  INNER JOIN dim_channels AS c
    ON o.channel_id = c.channel_id
  WHERE
    o.status = 'completed'
  GROUP BY
    c.channel_type
), payment_amounts AS (
  SELECT
    c.channel_type,
    SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE CAST(0 AS DECIMAL(14, 2)) END) AS paid_amount,
    SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE CAST(0 AS DECIMAL(14, 2)) END) AS refunded_amount,
    SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE CAST(0 AS DECIMAL(14, 2)) END) AS failed_amount
  FROM fact_payments AS p
  INNER JOIN fact_orders AS o
    ON p.order_id = o.order_id
  INNER JOIN dim_channels AS c
    ON o.channel_id = c.channel_id
  GROUP BY
    c.channel_type
)
SELECT
  ct.channel_type,
  COALESCE(co.completed_order_count, 0) AS completed_order_count,
  COALESCE(nr.net_revenue, CAST(0 AS DECIMAL(14, 2))) AS net_revenue,
  COALESCE(pa.paid_amount, CAST(0 AS DECIMAL(14, 2))) AS paid_amount,
  COALESCE(pa.refunded_amount, CAST(0 AS DECIMAL(14, 2))) AS refunded_amount,
  COALESCE(pa.failed_amount, CAST(0 AS DECIMAL(14, 2))) AS failed_amount
FROM channel_types AS ct
LEFT JOIN completed_orders AS co
  ON ct.channel_type = co.channel_type
LEFT JOIN net_revenue AS nr
  ON ct.channel_type = nr.channel_type
LEFT JOIN payment_amounts AS pa
  ON ct.channel_type = pa.channel_type
ORDER BY
  ct.channel_type ASC

GPT 当前会话桥接（流程验收）

按商品品类汇总完成订单行的净销售额，再以全部品类净销售额为分母计算百分比占比并四舍五入至 2 位，最后按指定顺序排序。

WITH category_revenue AS (
  SELECT
    p.category,
    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue
  FROM fact_orders AS o
  INNER JOIN fact_order_items AS oi
    ON oi.order_id = o.order_id
  INNER JOIN dim_products AS p
    ON p.product_id = oi.product_id
  WHERE
    o.status = 'completed'
  GROUP BY
    p.category
), category_metrics AS (
  SELECT
    category,
    net_revenue,
    ROUND(100.0 * net_revenue / NULLIF(SUM(net_revenue) OVER (), 0), 2) AS revenue_share_pct
  FROM category_revenue
)
SELECT
  category,
  net_revenue,
  revenue_share_pct
FROM category_metrics
ORDER BY
  revenue_share_pct DESC,
  category ASC

GPT 当前会话桥接（流程验收）

汇总每个订单的订单行净额，与订单头 total_amount 在 2 位小数精度下对账，仅输出有差异的订单并按指定顺序排列。

WITH reconciliation AS (
  SELECT
    o.order_id,
    ROUND(o.total_amount, 2) AS stored_total,
    ROUND(COALESCE(SUM(oi.quantity * oi.unit_price - oi.discount_amount), 0), 2) AS calculated_total
  FROM fact_orders AS o
  LEFT JOIN fact_order_items AS oi
    ON o.order_id = oi.order_id
  GROUP BY
    o.order_id,
    o.total_amount
), differences AS (
  SELECT
    order_id,
    stored_total,
    calculated_total,
    ROUND(stored_total - calculated_total, 2) AS difference
  FROM reconciliation
  WHERE
    stored_total <> calculated_total
)
SELECT
  order_id,
  stored_total,
  calculated_total,
  difference
FROM differences
ORDER BY
  ABS(difference) DESC,
  order_id ASC