案例记录 case-run-00157

运行 #18/latest_successful_payment

每单最新成功支付

为每个有成功支付的订单取最新一笔成功支付，同一时间按 payment_id 降序打破并列；输出 order_id、payment_id、paid_at、amount，按 order_id 升序；必须使用 ROW_NUMBER。

运行信息

以下字段来自本次运行的冻结记录。

run#18

case run#157

attempt1

statuscompleted

token total19,810

estimated cost不可估算

model generation未记录

SQL execution192ms

adaptercodex_cli

response modetext

requested modelgpt-5.6-sol

resolved modelgpt-5.6-sol

provider request id未提供

started2026年8月29日 22:51

finished2026年8月29日 22:52

suite hash5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af

模型输入

显示本次实际保存的完整 Prompt。

你是 Text-to-SQL 数据分析与数据开发生成器。先形成简洁、可审计的结构化查询方案，再生成完成问题所需的 SQL；不要输出隐藏推理。

方言与安全规则：
Use DuckDB SQL. Return exactly one read-only query. Do not access files, URLs, extensions, or schemas outside the supplied tables.

数据库结构：
{"semantic_relationships":[{"cardinality":"many_to_one","from_entity":"order","sql_on":"fact_orders.customer_id = dim_customers.customer_id","to_entity":"customer"},{"cardinality":"many_to_one","from_entity":"order","sql_on":"fact_orders.channel_id = dim_channels.channel_id","to_entity":"channel"},{"cardinality":"many_to_one","from_entity":"order_item","sql_on":"fact_order_items.order_id = fact_orders.order_id","to_entity":"order"},{"cardinality":"many_to_one","from_entity":"order_item","sql_on":"fact_order_items.product_id = dim_products.product_id","to_entity":"product"},{"cardinality":"many_to_one","from_entity":"payment","sql_on":"fact_payments.order_id = fact_orders.order_id","to_entity":"order"},{"cardinality":"many_to_one","from_entity":"return","sql_on":"fact_returns.order_id = fact_order_items.order_id AND fact_returns.line_no = fact_order_items.line_no","to_entity":"order_item"}],"tables":[{"columns":[{"data_type":"BIGINT","name":"channel_id","nullable":false},{"data_type":"VARCHAR","name":"channel_name","nullable":false},{"data_type":"VARCHAR","name":"channel_type","nullable":false}],"foreign_keys":[],"name":"dim_channels","primary_key":["channel_id"]},{"columns":[{"data_type":"BIGINT","name":"customer_id","nullable":false},{"data_type":"VARCHAR","name":"customer_name","nullable":false},{"data_type":"VARCHAR","name":"city","nullable":true},{"data_type":"DATE","name":"signup_date","nullable":false},{"data_type":"VARCHAR","name":"segment","nullable":false}],"foreign_keys":[],"name":"dim_customers","primary_key":["customer_id"]},{"columns":[{"data_type":"BIGINT","name":"product_id","nullable":false},{"data_type":"VARCHAR","name":"product_name","nullable":false},{"data_type":"VARCHAR","name":"category","nullable":false},{"data_type":"VARCHAR","name":"brand","nullable":false},{"data_type":"DECIMAL(14,2)","name":"list_price","nullable":false}],"foreign_keys":[],"name":"dim_products","primary_key":["product_id"]},{"columns":[{"data_type":"BIGINT","name":"order_id","nullable":false},{"data_type":"BIGINT","name":"line_no","nullable":false},{"data_type":"BIGINT","name":"product_id","nullable":false},{"data_type":"BIGINT","name":"quantity","nullable":false},{"data_type":"DECIMAL(14,2)","name":"unit_price","nullable":false},{"data_type":"DECIMAL(14,2)","name":"discount_amount","nullable":false}],"foreign_keys":[{"columns":["order_id"],"referenced_columns":["order_id"],"referenced_table":"fact_orders"},{"columns":["product_id"],"referenced_columns":["product_id"],"referenced_table":"dim_products"}],"name":"fact_order_items","primary_key":["order_id","line_no"]},{"columns":[{"data_type":"BIGINT","name":"order_id","nullable":false},{"data_type":"BIGINT","name":"customer_id","nullable":false},{"data_type":"BIGINT","name":"channel_id","nullable":false},{"data_type":"DATE","name":"order_date","nullable":false},{"data_type":"VARCHAR","name":"status","nullable":false},{"data_type":"DECIMAL(14,2)","name":"total_amount","nullable":false}],"foreign_keys":[{"columns":["customer_id"],"referenced_columns":["customer_id"],"referenced_table":"dim_customers"},{"columns":["channel_id"],"referenced_columns":["channel_id"],"referenced_table":"dim_channels"}],"name":"fact_orders","primary_key":["order_id"]},{"columns":[{"data_type":"BIGINT","name":"payment_id","nullable":false},{"data_type":"BIGINT","name":"order_id","nullable":false},{"data_type":"TIMESTAMP","name":"paid_at","nullable":false},{"data_type":"VARCHAR","name":"payment_method","nullable":false},{"data_type":"DECIMAL(14,2)","name":"amount","nullable":false},{"data_type":"VARCHAR","name":"status","nullable":false}],"foreign_keys":[{"columns":["order_id"],"referenced_columns":["order_id"],"referenced_table":"fact_orders"}],"name":"fact_payments","primary_key":["payment_id"]},{"columns":[{"data_type":"BIGINT","name":"return_id","nullable":false},{"data_type":"BIGINT","name":"order_id","nullable":false},{"data_type":"BIGINT","name":"line_no","nullable":false},{"data_type":"TIMESTAMP","name":"returned_at","nullable":false},{"data_type":"BIGINT","name":"return_qty","nullable":false},{"data_type":"DECIMAL(14,2)","name":"refund_amount","nullable":false},{"data_type":"VARCHAR","name":"reason","nullable":true}],"foreign_keys":[{"columns":["order_id","line_no"],"referenced_columns":["order_id","line_no"],"referenced_table":"fact_order_items"}],"name":"fact_returns","primary_key":["return_id"]}]}

语义层与业务口径：
{"business_rules":["完成订单仅指 fact_orders.status = 'completed'。","净销售额为 quantity * unit_price - discount_amount。","paid、refunded、failed 金额只按 fact_payments.status 分类。","退货率为完成订单的 returned_qty / sold_qty，售出数量仅含完成订单。","月份按 UTC Gregorian calendar 计算。","订单头 total_amount 应等于订单行净额汇总，差异视为数据质量异常。"],"dimensions":[{"data_type":"VARCHAR","description":"客户分群","expression":"dim_customers.segment","name":"customer_segment"},{"data_type":"VARCHAR","description":"商品品类","expression":"dim_products.category","name":"product_category"},{"data_type":"VARCHAR","description":"渠道类型","expression":"dim_channels.channel_type","name":"channel_type"},{"data_type":"VARCHAR","description":"UTC Gregorian 月份","expression":"strftime(fact_orders.order_date, '%Y-%m')","name":"order_month"}],"entities":[{"description":"客户主数据","grain":"每行一个客户","name":"customer","primary_key":["customer_id"],"table":"dim_customers"},{"description":"商品主数据","grain":"每行一个商品","name":"product","primary_key":["product_id"],"table":"dim_products"},{"description":"渠道主数据","grain":"每行一个渠道","name":"channel","primary_key":["channel_id"],"table":"dim_channels"},{"description":"订单头","grain":"每行一个订单","name":"order","primary_key":["order_id"],"table":"fact_orders"},{"description":"订单行","grain":"每行一个订单商品行","name":"order_item","primary_key":["order_id","line_no"],"table":"fact_order_items"},{"description":"支付尝试","grain":"每行一笔支付","name":"payment","primary_key":["payment_id"],"table":"fact_payments"},{"description":"退货记录","grain":"每行一条订单行退货","name":"return","primary_key":["return_id"],"table":"fact_returns"}],"metrics":[{"description":"已完成订单数","expression":"COUNT(DISTINCT CASE WHEN fact_orders.status = 'completed' THEN fact_orders.order_id END)","filters":["fact_orders.status = 'completed'"],"grain":"聚合","name":"completed_order_count"},{"description":"完成订单商品行净销售额","expression":"SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)","filters":["fact_orders.status = 'completed'"],"grain":"聚合","name":"net_revenue"},{"description":"成功支付金额","expression":"SUM(CASE WHEN fact_payments.status = 'paid' THEN fact_payments.amount ELSE 0 END)","filters":[],"grain":"聚合","name":"paid_amount"},{"description":"退款状态支付金额","expression":"SUM(CASE WHEN fact_payments.status = 'refunded' THEN fact_payments.amount ELSE 0 END)","filters":[],"grain":"聚合","name":"refunded_amount"},{"description":"失败支付金额","expression":"SUM(CASE WHEN fact_payments.status = 'failed' THEN fact_payments.amount ELSE 0 END)","filters":[],"grain":"聚合","name":"failed_amount"},{"description":"完成订单退货数量除以售出数量","expression":"SUM(fact_returns.return_qty) / SUM(fact_order_items.quantity)","filters":["fact_orders.status = 'completed'"],"grain":"聚合","name":"return_rate"}],"relationships":[{"cardinality":"many_to_one","from_entity":"order","sql_on":"fact_orders.customer_id = dim_customers.customer_id","to_entity":"customer"},{"cardinality":"many_to_one","from_entity":"order","sql_on":"fact_orders.channel_id = dim_channels.channel_id","to_entity":"channel"},{"cardinality":"many_to_one","from_entity":"order_item","sql_on":"fact_order_items.order_id = fact_orders.order_id","to_entity":"order"},{"cardinality":"many_to_one","from_entity":"order_item","sql_on":"fact_order_items.product_id = dim_products.product_id","to_entity":"product"},{"cardinality":"many_to_one","from_entity":"payment","sql_on":"fact_payments.order_id = fact_orders.order_id","to_entity":"order"},{"cardinality":"many_to_one","from_entity":"return","sql_on":"fact_returns.order_id = fact_order_items.order_id AND fact_returns.line_no = fact_order_items.line_no","to_entity":"order_item"}]}

问题：
为每个有成功支付的订单取最新一笔成功支付，同一时间按 payment_id 降序打破并列；输出 order_id、payment_id、paid_at、amount，按 order_id 升序；必须使用 ROW_NUMBER。

规划要求：
plan 只记录输出粒度、数据源、连接、过滤、指标、执行步骤和风险检查；每项保持简短。
SQL 必须严格实现该 plan，并遵循语义层业务口径。
summary 只概括最终做法，assumptions 只列无法由上下文确定的必要假设。
输出必须严格匹配此 JSON Schema：
{"$defs":{"QueryPlan":{"additionalProperties":false,"properties":{"filters":{"items":{"type":"string"},"title":"Filters","type":"array"},"grain":{"title":"Grain","type":"string"},"joins":{"items":{"type":"string"},"title":"Joins","type":"array"},"metrics":{"items":{"type":"string"},"title":"Metrics","type":"array"},"risks":{"items":{"type":"string"},"title":"Risks","type":"array"},"sources":{"items":{"type":"string"},"title":"Sources","type":"array"},"steps":{"items":{"type":"string"},"title":"Steps","type":"array"}},"required":["grain","sources","joins","filters","metrics","steps","risks"],"title":"QueryPlan","type":"object"}},"additionalProperties":false,"properties":{"assumptions":{"items":{"type":"string"},"title":"Assumptions","type":"array"},"plan":{"$ref":"#/$defs/QueryPlan"},"sql":{"title":"Sql","type":"string"},"summary":{"title":"Summary","type":"string"}},"required":["plan","sql","summary","assumptions"],"title":"GenerationOutput","type":"object"}

调用请求（1）

按事件序号排列；密钥和 Authorization 已移除。

历史证据边界：完整 Prompt 已保存，但这次 codex_cli 记录没有保存底层 HTTP 报文或 CLI 参数。本站不会把推测内容伪装成原始请求；未来运行会在 provider.requested 中记录脱敏后的完整调用信封。

请求 #1SEQ 932026年8月29日 14:51:47 UTC

{
  "isolation_policy_hash": "55945996af367935cfc7af65bc6bf1f2021a6380f069616570827c1b14084302",
  "status": "running"
}

模型输出

分别显示原始响应、解析结果和 SQL。

原始响应 raw_output

{"plan":{"grain":"每个有成功支付的订单一行","sources":["fact_payments"],"joins":[],"filters":["fact_payments.status = 'paid'"],"metrics":["最新成功支付的 payment_id、paid_at、amount"],"steps":["筛选成功支付记录","按 order_id 分区，paid_at 降序、payment_id 降序计算 ROW_NUMBER","保留行号为 1 的记录","按 order_id 升序输出"],"risks":["同一 paid_at 的记录必须以 payment_id 降序确定最新一笔","先过滤成功支付再排名"]},"sql":"SELECT\n    order_id,\n    payment_id,\n    paid_at,\n    amount\nFROM (\n    SELECT\n        order_id,\n        payment_id,\n        paid_at,\n        amount,\n        ROW_NUMBER() OVER (\n            PARTITION BY order_id\n            ORDER BY paid_at DESC, payment_id DESC\n        ) AS row_num\n    FROM fact_payments\n    WHERE status = 'paid'\n) AS ranked_payments\nWHERE row_num = 1\nORDER BY order_id ASC;","summary":"筛选成功支付后，使用 ROW_NUMBER 按订单分区并按支付时间和 payment_id 降序排名，取每个订单的第一条记录。","assumptions":[]}

结构化查询方案 plan

{
  "filters": [
    "fact_payments.status = 'paid'"
  ],
  "grain": "每个有成功支付的订单一行",
  "joins": [],
  "metrics": [
    "最新成功支付的 payment_id、paid_at、amount"
  ],
  "risks": [
    "同一 paid_at 的记录必须以 payment_id 降序确定最新一笔",
    "先过滤成功支付再排名"
  ],
  "sources": [
    "fact_payments"
  ],
  "steps": [
    "筛选成功支付记录",
    "按 order_id 分区，paid_at 降序、payment_id 降序计算 ROW_NUMBER",
    "保留行号为 1 的记录",
    "按 order_id 升序输出"
  ]
}

模型生成 SQL

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 row_num
    FROM fact_payments
    WHERE status = 'paid'
) AS ranked_payments
WHERE row_num = 1
ORDER BY order_id ASC;

规范化 SQL

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 row_num
  FROM fact_payments
  WHERE
    status = 'paid'
) AS ranked_payments
WHERE
  row_num = 1
ORDER BY
  order_id ASC

Token 与耗时

{
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 19516,
    "output_tokens": 294,
    "reasoning_output_tokens": 0
  },
  "generation_ms": null,
  "execution_ms": 191.91729099838994
}

评分结果

包含评分明细、参考 SQL、结构要求、比较规则和结果差异。

评分明细 score

{
  "ast_rules": [
    {
      "details": {
        "actual": 0,
        "required": 1
      },
      "id": "latest-row-number",
      "kind": "window_function",
      "passed": false
    }
  ],
  "column_count": 5,
  "column_names": 5,
  "execution": 10,
  "ordering": 10,
  "protocol": 5,
  "read_only_ast": 5,
  "row_f1": 45,
  "sql_capability": 0,
  "total": 85
}

参考 SQL

WITH ranked AS (SELECT p.order_id, p.payment_id, p.paid_at, p.amount, ROW_NUMBER() OVER (PARTITION BY p.order_id ORDER BY p.paid_at DESC, p.payment_id DESC) AS rank_no FROM fact_payments p WHERE p.status = 'paid') SELECT order_id, payment_id, paid_at, amount FROM ranked WHERE rank_no = 1 ORDER BY order_id ASC

AST 结构要求

[
  {
    "id": "latest-row-number",
    "kind": "window_function",
    "min": 1,
    "name": "ROW_NUMBER",
    "order_by": [
      {
        "direction": "DESC",
        "expression": "p.paid_at"
      },
      {
        "direction": "DESC",
        "expression": "p.payment_id"
      }
    ],
    "partition_columns": [
      "p.order_id"
    ]
  }
]

结果比较规则

{
  "abs_tolerance": "0.005",
  "decimal_scale": 2,
  "duplicate_policy": "multiset",
  "max_rows": 10000,
  "rel_tolerance": "0",
  "row_order_significant": true
}

期望结果预览

{
  "columns": [
    {
      "name": "order_id",
      "type": "BIGINT"
    },
    {
      "name": "payment_id",
      "type": "BIGINT"
    },
    {
      "name": "paid_at",
      "type": "TIMESTAMP"
    },
    {
      "name": "amount",
      "type": "DECIMAL(14,2)"
    }
  ],
  "digest": "a4b105bbfe63ca24cdbada0f441440601bd563703625131dccf46dd7404334e7",
  "row_count": 417,
  "rows": [
    [
      2,
      21,
      "2025-11-29T00:00:00.000000Z",
      "451.75"
    ],
    [
      3,
      31,
      "2025-09-26T00:00:00.000000Z",
      "1735.00"
    ],
    [
      4,
      41,
      "2025-12-26T00:00:00.000000Z",
      "1676.25"
    ],
    [
      5,
      51,
      "2025-06-21T00:00:00.000000Z",
      "1589.50"
    ],
    [
      6,
      61,
      "2025-07-27T00:00:00.000000Z",
      "1713.50"
    ],
    [
      7,
      71,
      "2026-01-10T00:00:00.000000Z",
      "131.50"
    ],
    [
      8,
      81,
      "2025-01-18T00:00:00.000000Z",
      "276.00"
    ],
    [
      9,
      91,
      "2025-08-29T00:00:00.000000Z",
      "1516.00"
    ],
    [
      12,
      121,
      "2025-01-22T00:00:00.000000Z",
      "672.50"
    ],
    [
      13,
      132,
      "2025-11-01T00:00:00.000000Z",
      "1060.50"
    ],
    [
      14,
      141,
      "2025-10-28T00:00:00.000000Z",
      "1350.00"
    ],
    [
      15,
      151,
      "2025-06-06T00:00:00.000000Z",
      "41.50"
    ],
    [
      16,
      161,
      "2025-10-10T00:00:00.000000Z",
      "488.75"
    ],
    [
      18,
      181,
      "2025-01-07T00:00:00.000000Z",
      "271.75"
    ],
    [
      19,
      191,
      "2025-10-03T00:00:00.000000Z",
      "798.00"
    ],
    [
      23,
      231,
      "2025-10-11T00:00:00.000000Z",
      "24.50"
    ],
    [
      24,
      241,
      "2025-01-08T00:00:00.000000Z",
      "1307.25"
    ],
    [
      25,
      251,
      "2025-07-14T00:00:00.000000Z",
      "951.00"
    ],
    [
      26,
      262,
      "2025-09-07T00:00:00.000000Z",
      "492.25"
    ],
    [
      27,
      271,
      "2026-03-17T00:00:00.000000Z",
      "2174.50"
    ],
    [
      28,
      281,
      "2025-03-04T00:00:00.000000Z",
      "516.00"
    ],
    [
      29,
      291,
      "2025-08-09T00:00:00.000000Z",
      "774.25"
    ],
    [
      32,
      321,
      "2026-06-22T00:00:00.000000Z",
      "680.00"
    ],
    [
      35,
      351,
      "2025-03-28T00:00:00.000000Z",
      "217.50"
    ],
    [
      36,
      361,
      "2026-02-16T00:00:00.000000Z",
      "2756.25"
    ],
    [
      37,
      371,
      "2026-05-10T00:00:00.000000Z",
      "597.00"
    ],
    [
      38,
      381,
      "2026-05-08T00:00:00.000000Z",
      "2307.75"
    ],
    [
      39,
      392,
      "2025-08-11T00:00:00.000000Z",
      "1565.00"
    ],
    [
      42,
      421,
      "2025-11-30T00:00:00.000000Z",
      "1624.25"
    ],
    [
      43,
      431,
      "2025-01-20T00:00:00.000000Z",
      "1131.50"
    ],
    [
      45,
      451,
      "2025-04-20T00:00:00.000000Z",
      "514.25"
    ],
    [
      46,
      461,
      "2025-02-19T00:00:00.000000Z",
      "399.00"
    ],
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      47,
      471,
      "2025-07-24T00:00:00.000000Z",
      "1908.50"
    ],
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      48,
      481,
      "2025-04-10T00:00:00.000000Z",
      "953.25"
    ],
    [
      49,
      491,
      "2026-06-21T00:00:00.000000Z",
      "1789.00"
    ],
    [
      52,
      522,
      "2026-01-08T00:00:00.000000Z",
      "1376.75"
    ],
    [
      53,
      531,
      "2025-01-04T00:00:00.000000Z",
      "101.00"
    ],
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      "1655.25"
    ],
    [
      56,
      561,
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      "198.75"
    ],
    [
      57,
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      "2026-03-06T00:00:00.000000Z",
      "1888.25"
    ],
    [
      58,
      581,
      "2026-04-18T00:00:00.000000Z",
      "1334.25"
    ],
    [
      59,
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      "498.25"
    ],
    [
      62,
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      "2106.25"
    ],
    [
      63,
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      "1911.25"
    ],
    [
      64,
      641,
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      "1259.25"
    ],
    [
      65,
      652,
      "2025-02-22T00:00:00.000000Z",
      "2208.75"
    ],
    [
      67,
      671,
      "2025-06-09T00:00:00.000000Z",
      "117.50"
    ],
    [
      69,
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      "2026-01-20T00:00:00.000000Z",
      "604.75"
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      "1487.50"
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      "929.00"
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    [
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      "322.50"
    ]
  ]
}

实际结果预览

{
  "columns": [
    {
      "name": "order_id",
      "type": "BIGINT"
    },
    {
      "name": "payment_id",
      "type": "BIGINT"
    },
    {
      "name": "paid_at",
      "type": "TIMESTAMP"
    },
    {
      "name": "amount",
      "type": "DECIMAL(14,2)"
    }
  ],
  "extra": [],
  "missing": [],
  "row_count": 417,
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}

摘要、错误与结果哈希

{
  "visible_summary": "筛选成功支付后，使用 ROW_NUMBER 按订单分区并按支付时间和 payment_id 降序排名，取每个订单的第一条记录。",
  "error_code": null,
  "error_message": null,
  "expected_digest": "a4b105bbfe63ca24cdbada0f441440601bd563703625131dccf46dd7404334e7",
  "actual_digest": "4a615e76663d461f286e79c11b5f0880d26d3972ddec6394747f6de2ac47b9d4",
  "assumptions": []
}

事件记录（8）

按全局事件序号排列，保留时间、级别、类型、消息和 payload。

SEQ 91case.startedinfo2026年8月29日 14:51:47 UTC

{
  "status": "generating"
}

SEQ 92prompt.builtinfo2026年8月29日 14:51:47 UTC

{
  "status": "completed"
}

SEQ 93provider.requestedinfo2026年8月29日 14:51:47 UTC

{
  "isolation_policy_hash": "55945996af367935cfc7af65bc6bf1f2021a6380f069616570827c1b14084302",
  "status": "running"
}

SEQ 94provider.deltainfo2026年8月29日 14:52:07 UTC

{
  "text": "{\"plan\":{\"grain\":\"每个有成功支付的订单一行\",\"sources\":[\"fact_payments\"],\"joins\":[],\"filters\":[\"fact_payments.status = 'paid'\"],\"metrics\":[\"最新成功支付的 payment_id、paid_at、amount\"],\"steps\":[\"筛选成功支付记录\",\"按 order_id 分区，paid_at 降序、payment_id 降序计算 ROW_NUMBER\",\"保留行号为 1 的记录\",\"按 order_id 升序输出\"],\"risks\":[\"同一 paid_at 的记录必须以 payment_id 降序确定最新一笔\",\"先过滤成功支付再排名\"]},\"sql\":\"SELECT\\n    order_id,\\n    payment_id,\\n    paid_at,\\n    amount\\nFROM (\\n    SELECT\\n        order_id,\\n        payment_id,\\n        paid_at,\\n        amount,\\n        ROW_NUMBER() OVER (\\n            PARTITION BY order_id\\n            ORDER BY paid_at DESC, payment_id DESC\\n        ) AS row_num\\n    FROM fact_payments\\n    WHERE status = 'paid'\\n) AS ranked_payments\\nWHERE row_num = 1\\nORDER BY order_id ASC;\",\"summary\":\"筛选成功支付后，使用 ROW_NUMBER 按订单分区并按支付时间和 payment_id 降序排名，取每个订单的第一条记录。\",\"assumptions\":[]}"
}

SEQ 95provider.completedinfo2026年8月29日 14:52:07 UTC

{
  "elapsed_ms": 20144.716708004125,
  "status": "completed",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 19516,
    "output_tokens": 294,
    "reasoning_output_tokens": 0
  }
}

SEQ 96plan.completedinfo2026年8月29日 14:52:07 UTC

{
  "grain": "每个有成功支付的订单一行",
  "status": "completed",
  "steps": 4
}

SEQ 97sql.parsedinfo2026年8月29日 14:52:07 UTC

{
  "status": "completed"
}

SEQ 98score.completedinfo2026年8月29日 14:52:07 UTC

{
  "score": 85,
  "status": "completed"
}

原始案例数据

完整 JSON 字段，不经过页面裁剪。

展开全部原始字段

{
  "actual_digest": "4a615e76663d461f286e79c11b5f0880d26d3972ddec6394747f6de2ac47b9d4",
  "assumptions": [],
  "attempt": 1,
  "category": "latest_per_group",
  "comparison": {
    "abs_tolerance": "0.005",
    "decimal_scale": 2,
    "duplicate_policy": "multiset",
    "max_rows": 10000,
    "rel_tolerance": "0",
    "row_order_significant": true
  },
  "difficulty": "medium",
  "error_code": null,
  "error_message": null,
  "execution_ms": 191.91729099838994,
  "expected_digest": "a4b105bbfe63ca24cdbada0f441440601bd563703625131dccf46dd7404334e7",
  "expected_result_preview": {
    "columns": [
      {
        "name": "order_id",
        "type": "BIGINT"
      },
      {
        "name": "payment_id",
        "type": "BIGINT"
      },
      {
        "name": "paid_at",
        "type": "TIMESTAMP"
      },
      {
        "name": "amount",
        "type": "DECIMAL(14,2)"
      }
    ],
    "digest": "a4b105bbfe63ca24cdbada0f441440601bd563703625131dccf46dd7404334e7",
    "row_count": 417,
    "rows": [
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  },
  "finished_at": "2026-08-29T22:52:07.513976",
  "formatted_sql": "SELECT\n  order_id,\n  payment_id,\n  paid_at,\n  amount\nFROM (\n  SELECT\n    order_id,\n    payment_id,\n    paid_at,\n    amount,\n    ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY paid_at DESC, payment_id DESC) AS row_num\n  FROM fact_payments\n  WHERE\n    status = 'paid'\n) AS ranked_payments\nWHERE\n  row_num = 1\nORDER BY\n  order_id ASC",
  "generated_sql": "SELECT\n    order_id,\n    payment_id,\n    paid_at,\n    amount\nFROM (\n    SELECT\n        order_id,\n        payment_id,\n        paid_at,\n        amount,\n        ROW_NUMBER() OVER (\n            PARTITION BY order_id\n            ORDER BY paid_at DESC, payment_id DESC\n        ) AS row_num\n    FROM fact_payments\n    WHERE status = 'paid'\n) AS ranked_payments\nWHERE row_num = 1\nORDER BY order_id ASC;",
  "generation_ms": null,
  "id": 157,
  "model_name": "Sol 本机实测",
  "model_run_id": 22,
  "plan": {
    "filters": [
      "fact_payments.status = 'paid'"
    ],
    "grain": "每个有成功支付的订单一行",
    "joins": [],
    "metrics": [
      "最新成功支付的 payment_id、paid_at、amount"
    ],
    "risks": [
      "同一 paid_at 的记录必须以 payment_id 降序确定最新一笔",
      "先过滤成功支付再排名"
    ],
    "sources": [
      "fact_payments"
    ],
    "steps": [
      "筛选成功支付记录",
      "按 order_id 分区，paid_at 降序、payment_id 降序计算 ROW_NUMBER",
      "保留行号为 1 的记录",
      "按 order_id 升序输出"
    ]
  },
  "prompt": "你是 Text-to-SQL 数据分析与数据开发生成器。先形成简洁、可审计的结构化查询方案，再生成完成问题所需的 SQL；不要输出隐藏推理。\n\n方言与安全规则：\nUse DuckDB SQL. Return exactly one read-only query. Do not access files, URLs, extensions, or schemas outside the supplied tables.\n\n数据库结构：\n{\"semantic_relationships\":[{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.customer_id = dim_customers.customer_id\",\"to_entity\":\"customer\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.channel_id = dim_channels.channel_id\",\"to_entity\":\"channel\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.product_id = dim_products.product_id\",\"to_entity\":\"product\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"payment\",\"sql_on\":\"fact_payments.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"return\",\"sql_on\":\"fact_returns.order_id = fact_order_items.order_id AND fact_returns.line_no = fact_order_items.line_no\",\"to_entity\":\"order_item\"}],\"tables\":[{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"channel_id\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"channel_name\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"channel_type\",\"nullable\":false}],\"foreign_keys\":[],\"name\":\"dim_channels\",\"primary_key\":[\"channel_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"customer_id\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"customer_name\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"city\",\"nullable\":true},{\"data_type\":\"DATE\",\"name\":\"signup_date\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"segment\",\"nullable\":false}],\"foreign_keys\":[],\"name\":\"dim_customers\",\"primary_key\":[\"customer_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"product_id\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"product_name\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"category\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"brand\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"list_price\",\"nullable\":false}],\"foreign_keys\":[],\"name\":\"dim_products\",\"primary_key\":[\"product_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"line_no\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"product_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"quantity\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"unit_price\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"discount_amount\",\"nullable\":false}],\"foreign_keys\":[{\"columns\":[\"order_id\"],\"referenced_columns\":[\"order_id\"],\"referenced_table\":\"fact_orders\"},{\"columns\":[\"product_id\"],\"referenced_columns\":[\"product_id\"],\"referenced_table\":\"dim_products\"}],\"name\":\"fact_order_items\",\"primary_key\":[\"order_id\",\"line_no\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"customer_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"channel_id\",\"nullable\":false},{\"data_type\":\"DATE\",\"name\":\"order_date\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"status\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"total_amount\",\"nullable\":false}],\"foreign_keys\":[{\"columns\":[\"customer_id\"],\"referenced_columns\":[\"customer_id\"],\"referenced_table\":\"dim_customers\"},{\"columns\":[\"channel_id\"],\"referenced_columns\":[\"channel_id\"],\"referenced_table\":\"dim_channels\"}],\"name\":\"fact_orders\",\"primary_key\":[\"order_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"payment_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"TIMESTAMP\",\"name\":\"paid_at\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"payment_method\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"amount\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"status\",\"nullable\":false}],\"foreign_keys\":[{\"columns\":[\"order_id\"],\"referenced_columns\":[\"order_id\"],\"referenced_table\":\"fact_orders\"}],\"name\":\"fact_payments\",\"primary_key\":[\"payment_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"return_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"line_no\",\"nullable\":false},{\"data_type\":\"TIMESTAMP\",\"name\":\"returned_at\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"return_qty\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"refund_amount\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"reason\",\"nullable\":true}],\"foreign_keys\":[{\"columns\":[\"order_id\",\"line_no\"],\"referenced_columns\":[\"order_id\",\"line_no\"],\"referenced_table\":\"fact_order_items\"}],\"name\":\"fact_returns\",\"primary_key\":[\"return_id\"]}]}\n\n语义层与业务口径：\n{\"business_rules\":[\"完成订单仅指 fact_orders.status = 'completed'。\",\"净销售额为 quantity * unit_price - discount_amount。\",\"paid、refunded、failed 金额只按 fact_payments.status 分类。\",\"退货率为完成订单的 returned_qty / sold_qty，售出数量仅含完成订单。\",\"月份按 UTC Gregorian calendar 计算。\",\"订单头 total_amount 应等于订单行净额汇总，差异视为数据质量异常。\"],\"dimensions\":[{\"data_type\":\"VARCHAR\",\"description\":\"客户分群\",\"expression\":\"dim_customers.segment\",\"name\":\"customer_segment\"},{\"data_type\":\"VARCHAR\",\"description\":\"商品品类\",\"expression\":\"dim_products.category\",\"name\":\"product_category\"},{\"data_type\":\"VARCHAR\",\"description\":\"渠道类型\",\"expression\":\"dim_channels.channel_type\",\"name\":\"channel_type\"},{\"data_type\":\"VARCHAR\",\"description\":\"UTC Gregorian 月份\",\"expression\":\"strftime(fact_orders.order_date, '%Y-%m')\",\"name\":\"order_month\"}],\"entities\":[{\"description\":\"客户主数据\",\"grain\":\"每行一个客户\",\"name\":\"customer\",\"primary_key\":[\"customer_id\"],\"table\":\"dim_customers\"},{\"description\":\"商品主数据\",\"grain\":\"每行一个商品\",\"name\":\"product\",\"primary_key\":[\"product_id\"],\"table\":\"dim_products\"},{\"description\":\"渠道主数据\",\"grain\":\"每行一个渠道\",\"name\":\"channel\",\"primary_key\":[\"channel_id\"],\"table\":\"dim_channels\"},{\"description\":\"订单头\",\"grain\":\"每行一个订单\",\"name\":\"order\",\"primary_key\":[\"order_id\"],\"table\":\"fact_orders\"},{\"description\":\"订单行\",\"grain\":\"每行一个订单商品行\",\"name\":\"order_item\",\"primary_key\":[\"order_id\",\"line_no\"],\"table\":\"fact_order_items\"},{\"description\":\"支付尝试\",\"grain\":\"每行一笔支付\",\"name\":\"payment\",\"primary_key\":[\"payment_id\"],\"table\":\"fact_payments\"},{\"description\":\"退货记录\",\"grain\":\"每行一条订单行退货\",\"name\":\"return\",\"primary_key\":[\"return_id\"],\"table\":\"fact_returns\"}],\"metrics\":[{\"description\":\"已完成订单数\",\"expression\":\"COUNT(DISTINCT CASE WHEN fact_orders.status = 'completed' THEN fact_orders.order_id END)\",\"filters\":[\"fact_orders.status = 'completed'\"],\"grain\":\"聚合\",\"name\":\"completed_order_count\"},{\"description\":\"完成订单商品行净销售额\",\"expression\":\"SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)\",\"filters\":[\"fact_orders.status = 'completed'\"],\"grain\":\"聚合\",\"name\":\"net_revenue\"},{\"description\":\"成功支付金额\",\"expression\":\"SUM(CASE WHEN fact_payments.status = 'paid' THEN fact_payments.amount ELSE 0 END)\",\"filters\":[],\"grain\":\"聚合\",\"name\":\"paid_amount\"},{\"description\":\"退款状态支付金额\",\"expression\":\"SUM(CASE WHEN fact_payments.status = 'refunded' THEN fact_payments.amount ELSE 0 END)\",\"filters\":[],\"grain\":\"聚合\",\"name\":\"refunded_amount\"},{\"description\":\"失败支付金额\",\"expression\":\"SUM(CASE WHEN fact_payments.status = 'failed' THEN fact_payments.amount ELSE 0 END)\",\"filters\":[],\"grain\":\"聚合\",\"name\":\"failed_amount\"},{\"description\":\"完成订单退货数量除以售出数量\",\"expression\":\"SUM(fact_returns.return_qty) / SUM(fact_order_items.quantity)\",\"filters\":[\"fact_orders.status = 'completed'\"],\"grain\":\"聚合\",\"name\":\"return_rate\"}],\"relationships\":[{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.customer_id = dim_customers.customer_id\",\"to_entity\":\"customer\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.channel_id = dim_channels.channel_id\",\"to_entity\":\"channel\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.product_id = dim_products.product_id\",\"to_entity\":\"product\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"payment\",\"sql_on\":\"fact_payments.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"return\",\"sql_on\":\"fact_returns.order_id = fact_order_items.order_id AND fact_returns.line_no = fact_order_items.line_no\",\"to_entity\":\"order_item\"}]}\n\n问题：\n为每个有成功支付的订单取最新一笔成功支付，同一时间按 payment_id 降序打破并列；输出 order_id、payment_id、paid_at、amount，按 order_id 升序；必须使用 ROW_NUMBER。\n\n规划要求：\nplan 只记录输出粒度、数据源、连接、过滤、指标、执行步骤和风险检查；每项保持简短。\nSQL 必须严格实现该 plan，并遵循语义层业务口径。\nsummary 只概括最终做法，assumptions 只列无法由上下文确定的必要假设。\n输出必须严格匹配此 JSON Schema：\n{\"$defs\":{\"QueryPlan\":{\"additionalProperties\":false,\"properties\":{\"filters\":{\"items\":{\"type\":\"string\"},\"title\":\"Filters\",\"type\":\"array\"},\"grain\":{\"title\":\"Grain\",\"type\":\"string\"},\"joins\":{\"items\":{\"type\":\"string\"},\"title\":\"Joins\",\"type\":\"array\"},\"metrics\":{\"items\":{\"type\":\"string\"},\"title\":\"Metrics\",\"type\":\"array\"},\"risks\":{\"items\":{\"type\":\"string\"},\"title\":\"Risks\",\"type\":\"array\"},\"sources\":{\"items\":{\"type\":\"string\"},\"title\":\"Sources\",\"type\":\"array\"},\"steps\":{\"items\":{\"type\":\"string\"},\"title\":\"Steps\",\"type\":\"array\"}},\"required\":[\"grain\",\"sources\",\"joins\",\"filters\",\"metrics\",\"steps\",\"risks\"],\"title\":\"QueryPlan\",\"type\":\"object\"}},\"additionalProperties\":false,\"properties\":{\"assumptions\":{\"items\":{\"type\":\"string\"},\"title\":\"Assumptions\",\"type\":\"array\"},\"plan\":{\"$ref\":\"#/$defs/QueryPlan\"},\"sql\":{\"title\":\"Sql\",\"type\":\"string\"},\"summary\":{\"title\":\"Summary\",\"type\":\"string\"}},\"required\":[\"plan\",\"sql\",\"summary\",\"assumptions\"],\"title\":\"GenerationOutput\",\"type\":\"object\"}",
  "provider_request_id": null,
  "question": "为每个有成功支付的订单取最新一笔成功支付，同一时间按 payment_id 降序打破并列；输出 order_id、payment_id、paid_at、amount，按 order_id 升序；必须使用 ROW_NUMBER。",
  "radar_dimension": "数据开发",
  "raw_output": "{\"plan\":{\"grain\":\"每个有成功支付的订单一行\",\"sources\":[\"fact_payments\"],\"joins\":[],\"filters\":[\"fact_payments.status = 'paid'\"],\"metrics\":[\"最新成功支付的 payment_id、paid_at、amount\"],\"steps\":[\"筛选成功支付记录\",\"按 order_id 分区，paid_at 降序、payment_id 降序计算 ROW_NUMBER\",\"保留行号为 1 的记录\",\"按 order_id 升序输出\"],\"risks\":[\"同一 paid_at 的记录必须以 payment_id 降序确定最新一笔\",\"先过滤成功支付再排名\"]},\"sql\":\"SELECT\\n    order_id,\\n    payment_id,\\n    paid_at,\\n    amount\\nFROM (\\n    SELECT\\n        order_id,\\n        payment_id,\\n        paid_at,\\n        amount,\\n        ROW_NUMBER() OVER (\\n            PARTITION BY order_id\\n            ORDER BY paid_at DESC, payment_id DESC\\n        ) AS row_num\\n    FROM fact_payments\\n    WHERE status = 'paid'\\n) AS ranked_payments\\nWHERE row_num = 1\\nORDER BY order_id ASC;\",\"summary\":\"筛选成功支付后，使用 ROW_NUMBER 按订单分区并按支付时间和 payment_id 降序排名，取每个订单的第一条记录。\",\"assumptions\":[]}",
  "reference_sql": "WITH ranked AS (SELECT p.order_id, p.payment_id, p.paid_at, p.amount, ROW_NUMBER() OVER (PARTITION BY p.order_id ORDER BY p.paid_at DESC, p.payment_id DESC) AS rank_no FROM fact_payments p WHERE p.status = 'paid') SELECT order_id, payment_id, paid_at, amount FROM ranked WHERE rank_no = 1 ORDER BY order_id ASC",
  "requested_model_id": "gpt-5.6-sol",
  "required_ast": [
    {
      "id": "latest-row-number",
      "kind": "window_function",
      "min": 1,
      "name": "ROW_NUMBER",
      "order_by": [
        {
          "direction": "DESC",
          "expression": "p.paid_at"
        },
        {
          "direction": "DESC",
          "expression": "p.payment_id"
        }
      ],
      "partition_columns": [
        "p.order_id"
      ]
    }
  ],
  "resolved_model_id": "gpt-5.6-sol",
  "result_preview": {
    "columns": [
      {
        "name": "order_id",
        "type": "BIGINT"
      },
      {
        "name": "payment_id",
        "type": "BIGINT"
      },
      {
        "name": "paid_at",
        "type": "TIMESTAMP"
      },
      {
        "name": "amount",
        "type": "DECIMAL(14,2)"
      }
    ],
    "extra": [],
    "missing": [],
    "row_count": 417,
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        "181.75"
      ],
      [
        234,
        2342,
        "2025-02-02T00:00:00",
        "3290.25"
      ],
      [
        235,
        2351,
        "2025-05-04T00:00:00",
        "850.25"
      ],
      [
        236,
        2361,
        "2025-05-21T00:00:00",
        "406.50"
      ],
      [
        237,
        2371,
        "2025-03-20T00:00:00",
        "751.75"
      ],
      [
        239,
        2391,
        "2025-05-27T00:00:00",
        "551.00"
      ],
      [
        243,
        2431,
        "2026-06-10T00:00:00",
        "1371.50"
      ],
      [
        244,
        2441,
        "2026-01-21T00:00:00",
        "1145.25"
      ],
      [
        245,
        2451,
        "2025-08-09T00:00:00",
        "278.00"
      ],
      [
        246,
        2461,
        "2025-07-19T00:00:00",
        "1522.00"
      ],
      [
        247,
        2472,
        "2025-07-29T00:00:00",
        "694.75"
      ],
      [
        248,
        2481,
        "2025-12-07T00:00:00",
        "1663.75"
      ],
      [
        249,
        2491,
        "2025-10-01T00:00:00",
        "96.00"
      ],
      [
        252,
        2521,
        "2025-02-28T00:00:00",
        "1875.75"
      ],
      [
        254,
        2541,
        "2025-05-15T00:00:00",
        "1520.25"
      ],
      [
        256,
        2561,
        "2026-06-12T00:00:00",
        "1045.50"
      ],
      [
        257,
        2571,
        "2025-01-10T00:00:00",
        "1930.25"
      ],
      [
        258,
        2581,
        "2026-04-14T00:00:00",
        "2108.00"
      ],
      [
        259,
        2591,
        "2025-12-02T00:00:00",
        "497.50"
      ],
      [
        262,
        2621,
        "2025-01-25T00:00:00",
        "367.50"
      ],
      [
        263,
        2631,
        "2026-06-19T00:00:00",
        "1199.00"
      ],
      [
        265,
        2651,
        "2025-04-03T00:00:00",
        "1262.50"
      ],
      [
        266,
        2661,
        "2026-06-02T00:00:00",
        "1969.00"
      ],
      [
        267,
        2671,
        "2026-05-16T00:00:00",
        "1489.00"
      ],
      [
        268,
        2681,
        "2025-03-04T00:00:00",
        "525.50"
      ],
      [
        269,
        2691,
        "2025-11-25T00:00:00",
        "424.00"
      ],
      [
        273,
        2732,
        "2025-04-16T00:00:00",
        "242.25"
      ],
      [
        274,
        2741,
        "2025-10-23T00:00:00",
        "1891.50"
      ],
      [
        276,
        2761,
        "2025-09-18T00:00:00",
        "1485.00"
      ],
      [
        277,
        2771,
        "2025-12-18T00:00:00",
        "1076.75"
      ],
      [
        278,
        2781,
        "2025-08-12T00:00:00",
        "456.50"
      ],
      [
        279,
        2791,
        "2026-04-03T00:00:00",
        "2018.50"
      ],
      [
        282,
        2821,
        "2025-05-16T00:00:00",
        "407.00"
      ],
      [
        283,
        2831,
        "2025-09-08T00:00:00",
        "1487.50"
      ],
      [
        284,
        2841,
        "2025-10-04T00:00:00",
        "929.00"
      ],
      [
        285,
        2851,
        "2025-11-15T00:00:00",
        "271.25"
      ],
      [
        286,
        2862,
        "2026-02-18T00:00:00",
        "486.75"
      ],
      [
        287,
        2871,
        "2026-03-09T00:00:00",
        "2831.00"
      ],
      [
        288,
        2881,
        "2026-03-10T00:00:00",
        "322.50"
      ]
    ]
  },
  "run_id": 18,
  "score": {
    "ast_rules": [
      {
        "details": {
          "actual": 0,
          "required": 1
        },
        "id": "latest-row-number",
        "kind": "window_function",
        "passed": false
      }
    ],
    "column_count": 5,
    "column_names": 5,
    "execution": 10,
    "ordering": 10,
    "protocol": 5,
    "read_only_ast": 5,
    "row_f1": 45,
    "sql_capability": 0,
    "total": 85
  },
  "stable_key": "latest_successful_payment",
  "started_at": "2026-08-29T22:51:47.120133",
  "status": "completed",
  "suite_content_hash": "5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af",
  "title": "每单最新成功支付",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 19516,
    "output_tokens": 294,
    "reasoning_output_tokens": 0
  },
  "visible_summary": "筛选成功支付后，使用 ROW_NUMBER 按订单分区并按支付时间和 payment_id 降序排名，取每个订单的第一条记录。"
}

原始案例 JSON ↗完整运行事件 JSONL ↗返回运行 #18