拾穗数据工作室SQL 评测报告与证据

案例记录 case-run-00129

运行 #18/multi_join_category_revenue

多表连接品类收入

按品类统计已完成订单的 distinct 订单数和净销售额 quantity * unit_price - discount_amount,输出 category、order_count、net_revenue,并按 net_revenue 降序、category 升序;至少使用三表 JOIN。

运行信息

以下字段来自本次运行的冻结记录。

run#18
case run#129
attempt1
statuscompleted
token total18,457
estimated cost不可估算
model generation未记录
SQL execution195ms
adaptercodex_cli
response modetext
requested modelgpt-5.6-luna
resolved modelgpt-5.6-luna
provider request id未提供
started2026年8月29日 22:54
finished2026年8月29日 22:55
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:
按品类统计已完成订单的 distinct 订单数和净销售额 quantity * unit_price - discount_amount,输出 category、order_count、net_revenue,并按 net_revenue 降序、category 升序;至少使用三表 JOIN。

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 158
{
  "isolation_policy_hash": "bbe7d2cc8390a0ebb605c7177469131b00fa2c10467eb493a4e5b867d9225a4e",
  "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":"每个商品品类一行","sources":["fact_orders","fact_order_items","dim_products"],"joins":["fact_order_items.order_id = fact_orders.order_id","fact_order_items.product_id = dim_products.product_id"],"filters":["fact_orders.status = 'completed'"],"metrics":["order_count = COUNT(DISTINCT fact_orders.order_id)","net_revenue = SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)"],"steps":["连接订单头、订单行和商品维度","筛选完成订单","按商品品类聚合订单数和净销售额","按净销售额降序、品类升序排序"],"risks":["订单可能包含多个品类,按品类分别去重订单数","净销售额按订单行口径计算"]},"sql":"SELECT\n    dim_products.category AS category,\n    COUNT(DISTINCT fact_orders.order_id) AS order_count,\n    SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS net_revenue\nFROM fact_orders\nJOIN fact_order_items\n    ON fact_order_items.order_id = fact_orders.order_id\nJOIN dim_products\n    ON fact_order_items.product_id = dim_products.product_id\nWHERE fact_orders.status = 'completed'\nGROUP BY dim_products.category\nORDER BY net_revenue DESC, category ASC;","summary":"连接订单头、订单行和商品维度,筛选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。","assumptions":[]}
结构化查询方案 plan
{
  "filters": [
    "fact_orders.status = 'completed'"
  ],
  "grain": "每个商品品类一行",
  "joins": [
    "fact_order_items.order_id = fact_orders.order_id",
    "fact_order_items.product_id = dim_products.product_id"
  ],
  "metrics": [
    "order_count = COUNT(DISTINCT fact_orders.order_id)",
    "net_revenue = SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)"
  ],
  "risks": [
    "订单可能包含多个品类,按品类分别去重订单数",
    "净销售额按订单行口径计算"
  ],
  "sources": [
    "fact_orders",
    "fact_order_items",
    "dim_products"
  ],
  "steps": [
    "连接订单头、订单行和商品维度",
    "筛选完成订单",
    "按商品品类聚合订单数和净销售额",
    "按净销售额降序、品类升序排序"
  ]
}
模型生成 SQL
SELECT
    dim_products.category AS category,
    COUNT(DISTINCT fact_orders.order_id) AS order_count,
    SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS net_revenue
FROM fact_orders
JOIN fact_order_items
    ON fact_order_items.order_id = fact_orders.order_id
JOIN dim_products
    ON fact_order_items.product_id = dim_products.product_id
WHERE fact_orders.status = 'completed'
GROUP BY dim_products.category
ORDER BY net_revenue DESC, category ASC;
规范化 SQL
SELECT
  dim_products.category AS category,
  COUNT(DISTINCT fact_orders.order_id) AS order_count,
  SUM(
    fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount
  ) AS net_revenue
FROM fact_orders
JOIN fact_order_items
  ON fact_order_items.order_id = fact_orders.order_id
JOIN dim_products
  ON fact_order_items.product_id = dim_products.product_id
WHERE
  fact_orders.status = 'completed'
GROUP BY
  dim_products.category
ORDER BY
  net_revenue DESC,
  category ASC
Token 与耗时
{
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 18022,
    "output_tokens": 435,
    "reasoning_output_tokens": 84
  },
  "generation_ms": null,
  "execution_ms": 194.94337500509573
}

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": 2,
        "required": 2
      },
      "id": "join-2",
      "kind": "min_node_count",
      "passed": true
    }
  ],
  "column_count": 5,
  "column_names": 5,
  "execution": 10,
  "ordering": 10,
  "protocol": 5,
  "read_only_ast": 5,
  "row_f1": 45,
  "sql_capability": 15,
  "total": 100
}
参考 SQL
SELECT p.category, COUNT(DISTINCT o.order_id) AS order_count, SUM(i.quantity * i.unit_price - i.discount_amount) AS net_revenue FROM fact_order_items i JOIN fact_orders o ON o.order_id = i.order_id JOIN dim_products p ON p.product_id = i.product_id WHERE o.status = 'completed' GROUP BY p.category ORDER BY net_revenue DESC, p.category ASC
AST 结构要求
[
  {
    "id": "join-2",
    "kind": "min_node_count",
    "min": 2,
    "node": "Join"
  }
]
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": "category",
      "type": "VARCHAR"
    },
    {
      "name": "order_count",
      "type": "BIGINT"
    },
    {
      "name": "net_revenue",
      "type": "DECIMAL(38,2)"
    }
  ],
  "digest": "506c1577a41778944c2d7db3e200ca0400ff230ed1aa3cc309365570b3036ea2",
  "row_count": 6,
  "rows": [
    [
      "品类-6",
      186,
      "173783.50"
    ],
    [
      "品类-5",
      160,
      "115932.75"
    ],
    [
      "品类-4",
      160,
      "103119.00"
    ],
    [
      "品类-3",
      167,
      "81588.75"
    ],
    [
      "品类-2",
      175,
      "56692.25"
    ],
    [
      "品类-1",
      173,
      "27832.00"
    ]
  ]
}
实际结果预览
{
  "columns": [
    {
      "name": "category",
      "type": "VARCHAR"
    },
    {
      "name": "order_count",
      "type": "BIGINT"
    },
    {
      "name": "net_revenue",
      "type": "DECIMAL(38,2)"
    }
  ],
  "extra": [],
  "missing": [],
  "row_count": 6,
  "rows": [
    [
      "品类-6",
      186,
      "173783.50"
    ],
    [
      "品类-5",
      160,
      "115932.75"
    ],
    [
      "品类-4",
      160,
      "103119.00"
    ],
    [
      "品类-3",
      167,
      "81588.75"
    ],
    [
      "品类-2",
      175,
      "56692.25"
    ],
    [
      "品类-1",
      173,
      "27832.00"
    ]
  ]
}
摘要、错误与结果哈希
{
  "visible_summary": "连接订单头、订单行和商品维度,筛选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。",
  "error_code": null,
  "error_message": null,
  "expected_digest": "506c1577a41778944c2d7db3e200ca0400ff230ed1aa3cc309365570b3036ea2",
  "actual_digest": "6e503af8a397ebfebfbab414336919b32c96bb1b73ff1c0a33a428ca60c1f97d",
  "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 156case.startedinfo
{
  "status": "generating"
}
SEQ 157prompt.builtinfo
{
  "status": "completed"
}
SEQ 158provider.requestedinfo
{
  "isolation_policy_hash": "bbe7d2cc8390a0ebb605c7177469131b00fa2c10467eb493a4e5b867d9225a4e",
  "status": "running"
}
SEQ 159provider.deltainfo
{
  "text": "{\"plan\":{\"grain\":\"每个商品品类一行\",\"sources\":[\"fact_orders\",\"fact_order_items\",\"dim_products\"],\"joins\":[\"fact_order_items.order_id = fact_orders.order_id\",\"fact_order_items.product_id = dim_products.product_id\"],\"filters\":[\"fact_orders.status = 'completed'\"],\"metrics\":[\"order_count = COUNT(DISTINCT fact_orders.order_id)\",\"net_revenue = SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)\"],\"steps\":[\"连接订单头、订单行和商品维度\",\"筛选完成订单\",\"按商品品类聚合订单数和净销售额\",\"按净销售额降序、品类升序排序\"],\"risks\":[\"订单可能包含多个品类,按品类分别去重订单数\",\"净销售额按订单行口径计算\"]},\"sql\":\"SELECT\\n    dim_products.category AS category,\\n    COUNT(DISTINCT fact_orders.order_id) AS order_count,\\n    SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS net_revenue\\nFROM fact_orders\\nJOIN fact_order_items\\n    ON fact_order_items.order_id = fact_orders.order_id\\nJOIN dim_products\\n    ON fact_order_items.product_id = dim_products.product_id\\nWHERE fact_orders.status = 'completed'\\nGROUP BY dim_products.category\\nORDER BY net_revenue DESC, category ASC;\",\"summary\":\"连接订单头、订单行和商品维度,筛选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。\",\"assumptions\":[]}"
}
SEQ 160provider.completedinfo
{
  "elapsed_ms": 20400.46545900259,
  "status": "completed",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 18022,
    "output_tokens": 435,
    "reasoning_output_tokens": 84
  }
}
SEQ 161plan.completedinfo
{
  "grain": "每个商品品类一行",
  "status": "completed",
  "steps": 4
}
SEQ 162sql.parsedinfo
{
  "status": "completed"
}
SEQ 163score.completedinfo
{
  "score": 100,
  "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": "6e503af8a397ebfebfbab414336919b32c96bb1b73ff1c0a33a428ca60c1f97d",
  "assumptions": [],
  "attempt": 1,
  "category": "multi_join",
  "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": 194.94337500509573,
  "expected_digest": "506c1577a41778944c2d7db3e200ca0400ff230ed1aa3cc309365570b3036ea2",
  "expected_result_preview": {
    "columns": [
      {
        "name": "category",
        "type": "VARCHAR"
      },
      {
        "name": "order_count",
        "type": "BIGINT"
      },
      {
        "name": "net_revenue",
        "type": "DECIMAL(38,2)"
      }
    ],
    "digest": "506c1577a41778944c2d7db3e200ca0400ff230ed1aa3cc309365570b3036ea2",
    "row_count": 6,
    "rows": [
      [
        "品类-6",
        186,
        "173783.50"
      ],
      [
        "品类-5",
        160,
        "115932.75"
      ],
      [
        "品类-4",
        160,
        "103119.00"
      ],
      [
        "品类-3",
        167,
        "81588.75"
      ],
      [
        "品类-2",
        175,
        "56692.25"
      ],
      [
        "品类-1",
        173,
        "27832.00"
      ]
    ]
  },
  "finished_at": "2026-08-29T22:55:12.189160",
  "formatted_sql": "SELECT\n  dim_products.category AS category,\n  COUNT(DISTINCT fact_orders.order_id) AS order_count,\n  SUM(\n    fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount\n  ) AS net_revenue\nFROM fact_orders\nJOIN fact_order_items\n  ON fact_order_items.order_id = fact_orders.order_id\nJOIN dim_products\n  ON fact_order_items.product_id = dim_products.product_id\nWHERE\n  fact_orders.status = 'completed'\nGROUP BY\n  dim_products.category\nORDER BY\n  net_revenue DESC,\n  category ASC",
  "generated_sql": "SELECT\n    dim_products.category AS category,\n    COUNT(DISTINCT fact_orders.order_id) AS order_count,\n    SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS net_revenue\nFROM fact_orders\nJOIN fact_order_items\n    ON fact_order_items.order_id = fact_orders.order_id\nJOIN dim_products\n    ON fact_order_items.product_id = dim_products.product_id\nWHERE fact_orders.status = 'completed'\nGROUP BY dim_products.category\nORDER BY net_revenue DESC, category ASC;",
  "generation_ms": null,
  "id": 129,
  "model_name": "Luna 本机实测",
  "model_run_id": 21,
  "plan": {
    "filters": [
      "fact_orders.status = 'completed'"
    ],
    "grain": "每个商品品类一行",
    "joins": [
      "fact_order_items.order_id = fact_orders.order_id",
      "fact_order_items.product_id = dim_products.product_id"
    ],
    "metrics": [
      "order_count = COUNT(DISTINCT fact_orders.order_id)",
      "net_revenue = SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)"
    ],
    "risks": [
      "订单可能包含多个品类,按品类分别去重订单数",
      "净销售额按订单行口径计算"
    ],
    "sources": [
      "fact_orders",
      "fact_order_items",
      "dim_products"
    ],
    "steps": [
      "连接订单头、订单行和商品维度",
      "筛选完成订单",
      "按商品品类聚合订单数和净销售额",
      "按净销售额降序、品类升序排序"
    ]
  },
  "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按品类统计已完成订单的 distinct 订单数和净销售额 quantity * unit_price - discount_amount,输出 category、order_count、net_revenue,并按 net_revenue 降序、category 升序;至少使用三表 JOIN。\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": "按品类统计已完成订单的 distinct 订单数和净销售额 quantity * unit_price - discount_amount,输出 category、order_count、net_revenue,并按 net_revenue 降序、category 升序;至少使用三表 JOIN。",
  "radar_dimension": "连接与粒度",
  "raw_output": "{\"plan\":{\"grain\":\"每个商品品类一行\",\"sources\":[\"fact_orders\",\"fact_order_items\",\"dim_products\"],\"joins\":[\"fact_order_items.order_id = fact_orders.order_id\",\"fact_order_items.product_id = dim_products.product_id\"],\"filters\":[\"fact_orders.status = 'completed'\"],\"metrics\":[\"order_count = COUNT(DISTINCT fact_orders.order_id)\",\"net_revenue = SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)\"],\"steps\":[\"连接订单头、订单行和商品维度\",\"筛选完成订单\",\"按商品品类聚合订单数和净销售额\",\"按净销售额降序、品类升序排序\"],\"risks\":[\"订单可能包含多个品类,按品类分别去重订单数\",\"净销售额按订单行口径计算\"]},\"sql\":\"SELECT\\n    dim_products.category AS category,\\n    COUNT(DISTINCT fact_orders.order_id) AS order_count,\\n    SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS net_revenue\\nFROM fact_orders\\nJOIN fact_order_items\\n    ON fact_order_items.order_id = fact_orders.order_id\\nJOIN dim_products\\n    ON fact_order_items.product_id = dim_products.product_id\\nWHERE fact_orders.status = 'completed'\\nGROUP BY dim_products.category\\nORDER BY net_revenue DESC, category ASC;\",\"summary\":\"连接订单头、订单行和商品维度,筛选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。\",\"assumptions\":[]}",
  "reference_sql": "SELECT p.category, COUNT(DISTINCT o.order_id) AS order_count, SUM(i.quantity * i.unit_price - i.discount_amount) AS net_revenue FROM fact_order_items i JOIN fact_orders o ON o.order_id = i.order_id JOIN dim_products p ON p.product_id = i.product_id WHERE o.status = 'completed' GROUP BY p.category ORDER BY net_revenue DESC, p.category ASC",
  "requested_model_id": "gpt-5.6-luna",
  "required_ast": [
    {
      "id": "join-2",
      "kind": "min_node_count",
      "min": 2,
      "node": "Join"
    }
  ],
  "resolved_model_id": "gpt-5.6-luna",
  "result_preview": {
    "columns": [
      {
        "name": "category",
        "type": "VARCHAR"
      },
      {
        "name": "order_count",
        "type": "BIGINT"
      },
      {
        "name": "net_revenue",
        "type": "DECIMAL(38,2)"
      }
    ],
    "extra": [],
    "missing": [],
    "row_count": 6,
    "rows": [
      [
        "品类-6",
        186,
        "173783.50"
      ],
      [
        "品类-5",
        160,
        "115932.75"
      ],
      [
        "品类-4",
        160,
        "103119.00"
      ],
      [
        "品类-3",
        167,
        "81588.75"
      ],
      [
        "品类-2",
        175,
        "56692.25"
      ],
      [
        "品类-1",
        173,
        "27832.00"
      ]
    ]
  },
  "run_id": 18,
  "score": {
    "ast_rules": [
      {
        "details": {
          "actual": 2,
          "required": 2
        },
        "id": "join-2",
        "kind": "min_node_count",
        "passed": true
      }
    ],
    "column_count": 5,
    "column_names": 5,
    "execution": 10,
    "ordering": 10,
    "protocol": 5,
    "read_only_ast": 5,
    "row_f1": 45,
    "sql_capability": 15,
    "total": 100
  },
  "stable_key": "multi_join_category_revenue",
  "started_at": "2026-08-29T22:54:51.571842",
  "status": "completed",
  "suite_content_hash": "5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af",
  "title": "多表连接品类收入",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 18022,
    "output_tokens": 435,
    "reasoning_output_tokens": 84
  },
  "visible_summary": "连接订单头、订单行和商品维度,筛选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。"
}