| {"task_id": "hivesql_001_en", "id": "offline-compute_HiveSQL_hivesql_001", "name": "Message Queue Topic Dimension Table internal_platform_db.dim_mq_topic_d_su", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n**Task Objective**: Read data from the previous day's partition of the input table and copy it as-is to the output table's current day partition.\n\n**Time Variables**: The platform provides these variables for dynamic date computation:\n- `${yyyymmdd}` : current day in YYYYMMDD format\n- `${yyyymmdd-1}` : previous day in YYYYMMDD format\n- You may also use Spark SQL built-in functions like `current_date()` and `date_sub()`.\n\n**Input**: `internal_platform_db.dim_mq_topic_d_query_engine_001` (a partitioned table, partitioned by `dt`).\n\n**Processing Rules**: 1) Select all data where the `dt` partition equals the previous day (use `${yyyymmdd-1}`); 2) No joins, single-table processing; 3) All fields are retained as-is, with no transformations or filtering.\n\n**Output Requirements**: Output all non-partition columns: `business_id`, `business_name`, `cluster_set`, `tenant`, `namespaces`, `topic`, `mq_type`, `dw_appgroup`, `in_charge`, `description`, `create_time`, `modify_time`, `cluster_id`, `cluster_type`, `cluster_name`, `bg`, `category_name`, `is_filtered`, `tids`, `consumed_tids`, `unconsumed_tids`, `is_fully_consumed`, `has_unconsumed_tid`, `system_belong`; partitioned by the `dt` field.\n\n**Write Requirements**: Use `INSERT OVERWRITE` to write to the current day partition (use `${yyyymmdd}`) of `internal_platform_db.dim_mq_topic_d_copilot_cand_query_engine_001`.\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT overwrite TABLE internal_platform_db.dim_mq_topic_d_copilot_query_engine_001 PARTITION (dt = '20260507')\nSELECT\n business_id\n ,business_name\n ,cluster_set\n ,tenant\n ,namespaces\n ,topic\n ,mq_type\n ,dw_appgroup\n ,in_charge\n ,description\n ,create_time\n ,modify_time\n ,cluster_id\n ,cluster_type\n ,cluster_name\n ,bg\n ,category_name\n ,is_filtered\n ,tids\n ,consumed_tids\n ,unconsumed_tids\n ,is_fully_consumed\n ,has_unconsumed_tid\n ,system_belong\nFROM internal_platform_db.dim_mq_topic_d_query_engine_001\nwhere dt = '20260506'", "expected_csv": "business_id,business_name,cluster_set,tenant,namespaces,topic,mq_type,dw_appgroup,in_charge,description,create_time,modify_time,cluster_id,cluster_type,cluster_name,bg,category_name,is_filtered,tids,consumed_tids,unconsumed_tids,is_fully_consumed,has_unconsumed_tid,system_belong,dt\nbid001,BizName1,cluster_set_a,tenant_a,ns_a,topic_mq_1,PULSAR,appgroup1,user1,desc1,2026-01-01,2026-05-01,cid001,消息队列MQ,cluster_p1,BG1,product1,,,,,,,数据总线,20260507\nbid005,BizName5,cluster_set_b,tenant_c,ns_c,topic_mq_2,PULSAR,appgroup5,user5,desc5,2026-05-01,2026-05-05,cid005,消息队列MQ,cluster_p2,BG3,product5,,,,,,,流处理平台,20260507\nbid002,BizName2,,,,topic_tube_1,TUBEMQ,appgroup2,user2,desc2,2026-02-01,2026-05-02,cid002,TubeMQ,cluster_t1,BG2,product2,1,\"tid1,tid2\",tid1,,0,1,数据总线,20260507\nbid004,BizName4,,,,topic_tube_2,TUBEMQ,appgroup4,user4,desc4,2026-04-01,2026-05-04,cid004,TubeMQ,cluster_t2,BG1,product4,0,\"tid3,tid4\",\"tid3,tid4\",,1,0,数据总线,20260507\nbid003,BizName3,,tenant_b,ns_b,topic_inlong_1,PULSAR,appgroup3,user3,,2026-03-01,2026-05-03,,,cluster_i1,,product3,,,,,,,流处理平台,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,25列(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,25,非key字段逐列值匹配率\nD,D_field_completeness,field_completeness,15,关键字段非空/非空串比例\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,dt 分区值 = 20260507\nF,F_source_filter,source_filter,10,源表分区过滤 dt=20260506\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"business_id, cluster_id, dt\"\n# 预期列数,25\n# 输出表,internal_platform_db.dim_mq_topic_d_copilot_cand_query_engine_001", "path": "tasks/offline-compute/HiveSQL/hivesql_001_en"} |
| {"task_id": "hivesql_002", "id": "offline-compute_HiveSQL_hivesql_002", "name": "统计数据平台WDNotebook Ray类型管道任务中运行时间跨自然天的实例明细。从`wedat", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n**任务目标**：统计数据平台WDNotebook Ray类型管道任务中运行时间跨自然天的实例明细，按自然天拆分并关联GPU指标。\n\n**输入**：\n- `internal_platform_db.notebook_span_info_query_engine_005`\n- `internal_platform_db.dwd_gputj_service_instance_map_query_engine_005`\n- `internal_platform_db.dwd_ml_platform_instance_podname_query_engine_005`\n- `internal_platform_db.gputj_gpu_info_parsed_agg_1min_query_engine_005`\n- `internal_platform_db.notebook_engine_info_query_engine_005`\n\n**处理规则**：\n1. 时间过滤：`20260504`-`20260507`，从`notebook_span_info`筛选在`20260507`有记录且跨天的`trace_id`。\n2. 业务过滤：计算类型='ray'，服务名='notebook-runner'；span名称限定：'runner.execute'、'execute.code'、'execute.code.cell'、'client.execute.code'、'runner.killed'、'set.permanent.compute'、'create.non.permanent.compute'；GPU表过滤无效记录。\n3. 跨天拆分：将跨天实例按自然天拆分，每条记录对应一天内的开始和结束时间。\n4. 派生字段：\n - `instance_run_time`：(end_time - start_time)/1000（秒），基于'runner.execute'\n - `code_run_time`：代码执行相关span总时长（秒）\n - `resource_wait_time`：资源创建相关span总时长（秒）\n - `apply_for_gpu_count`：SUM(replicas * num_gpu)，按trace_id\n - `gpu_util`：k8s_container_vgpu_gpu_util_sum / k8s_container_vgpu_gpu_util_count\n - `gpu_count`：k8s_container_resource_request_gpu_sum / k8s_container_resource_request_gpu_count\n5. 表关联：\n - 拆分结果按`trace_id`左关联`notebook_engine_info`获取`serving_id`\n - 通过`instance_uuid`关联`dwd_gputj_service_instance_map`与`dwd_ml_platform_instance_podname`，再按`serving_id=service_id`左关联得到`pod_name`\n - 按`pod_name`左关联`gputj_gpu_info_parsed_agg_1min`，条件为`pkg_agg_time`落在实例运行窗口内（优先`instance_start_time`到`instance_end_time`，否则`code_start_time`到`code_end_time`）\n\n**输出要求**：\n- 输出表：`internal_platform_db.dwd_notebook_instance_pod_cross_day_detail_d_cand_query_engine_005`\n- 字段顺序：dt:STRING; trace_id:STRING; datawd_project_id:STRING; datawd_task_id:STRING; datawd_task_instance_id:STRING; compute_type:STRING; status_code:INT; instance_run_time:INT; code_run_time:INT; resource_wait_time:INT; code_start_time:STRING; code_end_time:STRING; instance_start_time:STRING; instance_end_time:STRING; serving_id:STRING; is_permanent:BOOLEAN; apply_for_gpu_count:INT; pod_name:STRING; pkg_agg_time:STRING; gpu_util:DOUBLE; gpu_count:INT; p_date:STRING\n- 分区字段：`dt`\n\n**写入要求**：写入`dt='20260507'`分区。\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT overwrite TABLE internal_platform_db.dwd_notebook_instance_pod_cross_day_detail_d_query_engine_005 PARTITION (dt = '20260507')\nWITH\nbase_data_raw AS (\n SELECT DISTINCT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM internal_platform_db.notebook_span_info_query_engine_005\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (\n SELECT trace_id\n FROM internal_platform_db.notebook_span_info_query_engine_005\n WHERE databus_imp_date >= '2026050700'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n AND span_name = 'runner.execute'\n GROUP BY trace_id\n )\n AND (\n span_name = 'runner.execute'\n OR\n span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute', 'runner.killed')\n )\n),\nbase_data_time_fixed AS (\n SELECT\n trace_id,\n span_name,\n CASE\n WHEN span_name = 'runner.killed'\n THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END)\n OVER(PARTITION BY trace_id)\n ELSE start_time\n END AS start_time,\n CASE\n WHEN span_name = 'runner.execute'\n THEN MAX(CASE WHEN span_name IN ('runner.execute', 'runner.killed') THEN end_time END)\n OVER(PARTITION BY trace_id)\n ELSE end_time\n END AS end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM base_data_raw\n),\nbase_data AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n from_unixtime(CAST(start_time AS BIGINT) / 1000) as start_date,\n from_unixtime(CAST(end_time AS BIGINT) / 1000) as end_date,\n datediff(from_unixtime(CAST(end_time AS BIGINT) / 1000), from_unixtime(CAST(start_time AS BIGINT) / 1000)) AS diff_days\n FROM base_data_time_fixed\n),\npos_series AS (\n SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos\n),\ndaily_split_spans AS (\n SELECT\n trace_id,\n span_name,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n start_date,\n end_date,\n diff_days,\n date_add(b.start_date, s.pos) AS calc_date,\n CASE WHEN s.pos = 0 THEN CAST(b.start_time AS BIGINT)\n ELSE unix_timestamp(cast(date_add(b.start_date, s.pos) as timestamp)) * 1000\n END AS start_time,\n CASE WHEN s.pos = b.diff_days THEN CAST(b.end_time AS BIGINT)\n ELSE (unix_timestamp(cast(date_add(b.start_date, s.pos + 1) as timestamp)) * 1000) - 1\n END AS end_time,\n CAST(b.start_time AS BIGINT) AS span_start_time,\n CAST(b.end_time AS BIGINT) AS span_end_time\n FROM base_data b\n INNER JOIN pos_series s ON s.pos <= b.diff_days\n),\ntrace_time_metrics AS (\n SELECT\n trace_id,\n calc_date,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code,\n ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN end_time END) -\n MIN(CASE WHEN span_name = 'runner.execute' THEN start_time END)) / 1000.0) AS instance_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN end_time END) -\n MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN start_time END)) / 1000.0) AS code_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN end_time END) -\n MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN start_time END)) / 1000.0) AS resource_wait_time,\n from_unixtime(MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN start_time END) / 1000) AS code_start_time,\n from_unixtime(MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN end_time END) / 1000) AS code_end_time,\n from_unixtime(MIN(CASE WHEN span_name = 'runner.execute' THEN span_start_time END) / 1000) AS instance_start_time,\n from_unixtime(MAX(CASE WHEN span_name = 'runner.execute' THEN span_end_time END) / 1000) AS instance_end_time\n FROM daily_split_spans\n GROUP BY trace_id, calc_date\n),\ngputj_dim AS (\n SELECT\n t1.service_id,\n t2.pod_name\n FROM internal_platform_db.dwd_gputj_service_instance_map_query_engine_005 t1\n INNER JOIN internal_platform_db.dwd_ml_platform_instance_podname_query_engine_005 t2\n ON t1.instance_uuid = t2.instance_uuid\n AND t1.dt=if('2026050700'>='2025080900', '2026050700', '2025080900')\n AND t2.dt=if('2026050700'>='2025060519', '2026050700', '2025060519')\n GROUP BY t1.service_id, t2.pod_name\n),\ngpu_metrics AS (\n select\n pkg_agg_time,\n pod_name,\n k8s_container_vgpu_gpu_util_sum / k8s_container_vgpu_gpu_util_count AS gpu_util,\n k8s_container_resource_request_gpu_sum / k8s_container_resource_request_gpu_count AS gpu_count\n from internal_platform_db.gputj_gpu_info_parsed_agg_1min_query_engine_005\n where dt >= '2026050400'\n AND dt <= '2026050700'\n AND ( k8s_container_vgpu_gpu_util_count > 0 OR k8s_container_resource_request_gpu_count > 0 )\n AND pod_name IS NOT NULL\n)\nSELECT\n t1.trace_id,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n t1.instance_run_time,\n t1.code_run_time,\n t1.resource_wait_time,\n t1.code_start_time,\n t1.code_end_time,\n t1.instance_start_time,\n t1.instance_end_time,\n t2.serving_id,\n t2.is_permanent,\n t2.apply_for_gpu_count,\n t3.pod_name,\n t4.pkg_agg_time,\n t4.gpu_util,\n t4.gpu_count,\n t1.calc_date AS p_date\nFROM trace_time_metrics t1 LEFT JOIN (\n SELECT\n trace_id,\n MAX(serving_id) AS serving_id,\n MAX(is_permanent) AS is_permanent,\n SUM(CAST(replicas AS INT) * CAST(num_gpu AS INT)) AS apply_for_gpu_count\n FROM internal_platform_db.notebook_engine_info_query_engine_005\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n GROUP BY trace_id\n) t2 ON t1.trace_id = t2.trace_id\nLEFT JOIN gputj_dim t3 ON t2.serving_id = t3.service_id\nLEFT JOIN gpu_metrics t4 \n ON t3.pod_name = t4.pod_name\n AND (\n CASE\n WHEN t1.instance_start_time IS NOT NULL AND t1.instance_end_time IS NOT NULL\n THEN t4.pkg_agg_time >= t1.instance_start_time AND t4.pkg_agg_time <= t1.instance_end_time\n WHEN t1.code_start_time IS NOT NULL AND t1.code_end_time IS NOT NULL\n THEN t4.pkg_agg_time >= t1.code_start_time AND t4.pkg_agg_time <= t1.code_end_time\n ELSE FALSE\n END\n )\n;", "expected_csv": "trace_id,datawd_project_id,datawd_task_id,datawd_task_instance_id,compute_type,status_code,instance_run_time,code_run_time,resource_wait_time,code_start_time,code_end_time,instance_start_time,instance_end_time,serving_id,is_permanent,apply_for_gpu_count,pod_name,pkg_agg_time,gpu_util,gpu_count,p_date,dt\ntrace_001,proj_1,task_1,inst_1,ray,0,86400,75600,2400,2025-05-06 01:00:00,2025-05-06 22:00:00,2025-05-06 00:00:00,2025-05-07 00:00:00,1001,true,2,pod_alpha,,,,2025-05-06,20260507\ntrace_001,proj_1,task_1,inst_1,ray,0,0,,,,,,2025-05-06 00:00:00,2025-05-07 00:00:00,1001,true,2,pod_alpha,,,,2025-05-07,20260507\ntrace_002,proj_2,task_2,inst_2,ray,1,10800,8000,800,2025-05-07 00:13:20,2025-05-07 02:26:40,2025-05-07 00:00:00,2025-05-07 03:00:00,1002,false,2,pod_beta,,,,2025-05-07,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,22列 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key覆盖率\nD,D_time_calculation,time_calculation,30,\"instance_run_time, code_run_time 等时间指标\"\nD,D_cross_day_split,cross_day_split,15,trace_001 应出现在两天中\nD,D_dimension_join,dimension_join,10,\"serving_id, pod_name 等维度列匹配\"\nF,F_join_completeness,join_completeness,5,多表都被关联\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,分区值正确\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,HARD\n# 权重,\"product=0.7, process=0.3\"\n# 范式,new\n# Key列,\"trace_id, p_date\"\n# 预期列数,\n# 输出表,internal_platform_db.dwd_notebook_instance_pod_cross_day_detail_d_cand_query_engine_005", "path": "tasks/offline-compute/HiveSQL/hivesql_002"} |
| {"task_id": "hivesql_003", "id": "offline-compute_HiveSQL_hivesql_003", "name": "从 `internal_platform_db.notebook_span_info_query_engine", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n1) 任务目标\n识别并统计 Notebook 管道中被异常 kill 且缺失正常完成信号的 Ray 实例，按天拆分计算运行时长、代码执行时长、资源等待时长及 GPU 申请数。\n\n2) 输入\n- `internal_platform_db.notebook_span_info_query_engine_007`\n- `internal_platform_db.notebook_engine_info_query_engine_007`\n\n3) 处理规则\n- 目标实例筛选：从 span_info 筛选 `compute_type='ray'` 且 `service_name='notebook-runner'` 的实例，选取有 `span_name='runner.killed'` 但无 `span_name='runner.execute'` 的 `trace_id`。\n- 合成运行区间：对每个目标 trace_id，取 `span_name='runner.start'` 的 start_time 作为实例开始时间，取 `span_name='runner.killed'` 的 end_time 作为实例结束时间，合成 `runner.execute` 区间，`status_code` 固定为 2。\n- 跨天拆分：将合成区间按自然日拆分为多条记录，每条对应一天内的部分，`calc_date` 为拆分后的日期。\n- 时长计算（按 trace_id 与 calc_date）：\n - instance_run_time：合成区间在当天的起止时间差（毫秒转秒）。\n - code_run_time：`span_name` 为 'execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed' 在当天的起止时间差（毫秒转秒）。\n - resource_wait_time：`span_name` 为 'set.permanent.compute', 'create.non.permanent.compute' 在当天的起止时间差（毫秒转秒）。\n - 输出 code_start_time/code_end_time/instance_start_time/instance_end_time（时间戳转字符串）。\n- 关联引擎信息：左关联 engine_info（过滤 `compute_type='ray'` 且 `service_name='notebook-runner'`），关联键 `trace_id`，获取 serving_id、is_permanent，计算 `apply_for_gpu_count = SUM(replicas * num_gpu)`。\n\n4) 输出要求\n输出字段顺序：dt（STRING，分区）、p_date（STRING，calc_date）、trace_id（STRING）、datawd_project_id（STRING）、datawd_task_id（STRING）、datawd_task_instance_id（STRING）、compute_type（STRING）、status_code（INT）、instance_run_time（INT）、code_run_time（INT）、resource_wait_time（INT）、code_start_time（STRING）、code_end_time（STRING）、instance_start_time（STRING）、instance_end_time（STRING）、serving_id（STRING）、is_permanent（BOOLEAN）、apply_for_gpu_count（INT）。\n\n5) 写入要求\n目标表：`internal_platform_db.dwd_notebook_killed_instance_detail_d_copilot_cand_query_engine_007`。分区字段：dt。写入分区：`dt='20260507'`。写入模式：覆盖。\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT overwrite TABLE internal_platform_db.dwd_notebook_killed_instance_detail_d_copilot_query_engine_007 PARTITION (dt = '20260507')\nWITH base_trace AS (\n SELECT trace_id\n FROM internal_platform_db.notebook_span_info_query_engine_007\n WHERE databus_imp_date >= '2026050700'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n AND span_name IN ('runner.execute', 'runner.killed')\n GROUP BY trace_id\n HAVING\n COUNT(CASE WHEN span_name = 'runner.killed' THEN 1 END) > 0\n AND\n COUNT(CASE WHEN span_name = 'runner.execute' THEN 1 END) = 0\n),\ncombined_spans AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM internal_platform_db.notebook_span_info_query_engine_007\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (SELECT trace_id FROM base_trace)\n AND span_name IN ('runner.killed', 'runner.start', 'execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute')\n\n UNION ALL\n\n SELECT\n trace_id,\n 'runner.execute' AS span_name,\n MAX(CASE WHEN span_name = 'runner.start' THEN start_time END) AS start_time,\n MAX(CASE WHEN span_name = 'runner.killed' THEN end_time END) AS end_time,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_project_id END) AS datawd_project_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_task_id END) AS datawd_task_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_task_instance_id END) AS datawd_task_instance_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN compute_type END) AS compute_type,\n 2 AS status_code\n FROM internal_platform_db.notebook_span_info_query_engine_007\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (SELECT trace_id FROM base_trace)\n AND span_name IN ('runner.start', 'runner.killed')\n GROUP BY trace_id\n),\nbase_data_time_fixed AS (\n SELECT\n trace_id,\n span_name,\n CASE\n WHEN span_name = 'runner.killed'\n THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END)\n OVER(PARTITION BY trace_id)\n ELSE start_time\n END AS start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM combined_spans\n),\nbase_data AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n from_unixtime(CAST(start_time AS BIGINT) / 1000) as start_date,\n from_unixtime(CAST(end_time AS BIGINT) / 1000) as end_date,\n datediff(from_unixtime(CAST(end_time AS BIGINT) / 1000), from_unixtime(CAST(start_time AS BIGINT) / 1000)) AS diff_days\n FROM base_data_time_fixed\n),\npos_series AS (\n SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos\n),\ndaily_split_spans AS (\n SELECT\n trace_id,\n span_name,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n start_date,\n end_date,\n diff_days,\n date_add(b.start_date, s.pos) AS calc_date,\n CASE WHEN s.pos = 0 THEN CAST(b.start_time AS BIGINT)\n ELSE unix_timestamp(cast(date_add(b.start_date, s.pos) as timestamp)) * 1000\n END AS start_time,\n CASE WHEN s.pos = b.diff_days THEN CAST(b.end_time AS BIGINT)\n ELSE (unix_timestamp(cast(date_add(b.start_date, s.pos + 1) as timestamp)) * 1000) - 1\n END AS end_time,\n CAST(b.start_time AS BIGINT) AS span_start_time,\n CAST(b.end_time AS BIGINT) AS span_end_time\n FROM base_data b\n INNER JOIN pos_series s ON s.pos <= b.diff_days\n),\ntrace_time_metrics AS (\n SELECT\n trace_id,\n calc_date,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code,\n CAST(ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN end_time END) -\n MIN(CASE WHEN span_name = 'runner.execute' THEN start_time END)) / 1000.0) AS INT) AS instance_run_time,\n CAST(ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN end_time END) -\n MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN start_time END)) / 1000.0) AS INT) AS code_run_time,\n CAST(ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN end_time END) -\n MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN start_time END)) / 1000.0) AS INT) AS resource_wait_time,\n from_unixtime(MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN start_time END) / 1000) AS code_start_time,\n from_unixtime(MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN end_time END) / 1000) AS code_end_time,\n from_unixtime(MIN(CASE WHEN span_name = 'runner.execute' THEN span_start_time END) / 1000) AS instance_start_time,\n from_unixtime(MAX(CASE WHEN span_name = 'runner.execute' THEN span_end_time END) / 1000) AS instance_end_time\n FROM daily_split_spans\n GROUP BY trace_id, calc_date\n)\nSELECT\n t1.calc_date AS p_date,\n t1.trace_id,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n t1.instance_run_time,\n t1.code_run_time,\n t1.resource_wait_time,\n t1.code_start_time,\n t1.code_end_time,\n t1.instance_start_time,\n t1.instance_end_time,\n t2.serving_id,\n CAST(t2.is_permanent AS BOOLEAN) AS is_permanent,\n CAST(t2.apply_for_gpu_count AS INT) AS apply_for_gpu_count\nFROM trace_time_metrics t1 LEFT JOIN (\n SELECT\n trace_id,\n MAX(serving_id) AS serving_id,\n MAX(is_permanent) AS is_permanent,\n SUM(CAST(replicas AS INT) * CAST(num_gpu AS INT)) AS apply_for_gpu_count\n FROM internal_platform_db.notebook_engine_info_query_engine_007\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n GROUP BY trace_id\n) t2 ON t1.trace_id = t2.trace_id\n;", "expected_csv": "p_date,trace_id,datawd_project_id,datawd_task_id,datawd_task_instance_id,compute_type,status_code,instance_run_time,code_run_time,resource_wait_time,code_start_time,code_end_time,instance_start_time,instance_end_time,serving_id,is_permanent,apply_for_gpu_count,dt\n2025-05-07,trace_001,proj_01,task_01,inst_01,ray,2,15,13,2,2025-05-07 03:59:52,2025-05-07 04:00:05,2025-05-07 03:59:50,2025-05-07 04:00:05,srv_001,True,2,20260507\n2025-05-07,trace_002,proj_02,task_02,inst_02,ray,2,18,16,3,2025-05-07 04:01:32,2025-05-07 04:01:48,2025-05-07 04:01:30,2025-05-07 04:01:48,srv_002,False,8,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,18列 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key覆盖率\nD,D_time_calculation,time_calculation,30,\"instance_run_time, code_run_time 等时间指标\"\nD,D_cross_day_split,cross_day_split,15,跨天 trace 按天拆分正确性\nD,D_engine_join,engine_join,10,\"serving_id, is_permanent 等 engine 维度列匹配\"\nF,F_join_completeness,join_completeness,5,2张源表都被关联\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,dt=20260507\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,HARD\n# 权重,\"product=0.7, process=0.3\"\n# 范式,new\n# Key列,\"trace_id, p_date\"\n# 预期列数,\n# 输出表,internal_platform_db.dwd_notebook_killed_instance_detail_d_copilot_cand_query_engine_007", "path": "tasks/offline-compute/HiveSQL/hivesql_003"} |
| {"task_id": "hivesql_004", "id": "offline-compute_HiveSQL_hivesql_004", "name": "将源表 internal_platform_db.app_group_product_info_sup", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：将应用组与产品归属关系按天做历史快照，按分区全量覆盖写入目标表。\n\n输入：internal_platform_db.app_group_product_info_query_engine_011\n- 字段：username、application_group、application_group_owner、product_id、product_name、product_owner、obs_product_id、obs_product_name、obs_product_owner、department、plan_product_id、plan_product_name、plan_product_owner、application_group_numbers、application_group_owners、product_owners、obs_product_owners、plan_product_owners、bg、id\n\n处理规则：\n1. 无 Join，单表处理\n2. 无过滤条件，全量读取源表所有记录\n3. 无聚合操作\n4. 追加派生列 dt（STRING 类型），填充当天日期字符串（格式 YYYYMMDD，如 '20260507'），置于输出字段最前\n\n输出要求：\n- 输出字段顺序：dt、username、application_group、application_group_owner、product_id、product_name、product_owner、obs_product_id、obs_product_name、obs_product_owner、department、plan_product_id、plan_product_name、plan_product_owner、application_group_numbers、application_group_owners、product_owners、obs_product_owners、plan_product_owners、bg、id\n- 无需去重\n\n写入要求：\n- 目标表：internal_platform_db.app_group_product_info_history_cand_query_engine_011\n- 分区字段：dt（STRING）\n- 写入方式：INSERT OVERWRITE 按分区写入\n- 若目标表不存在，先按 Hive 标准建表（ORC 存储、按 dt 分区），再写入\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "insert overwrite TABLE internal_platform_db.app_group_product_info_history_query_engine_011 PARTITION (dt = '20260507')\nSELECT\nusername,\napplication_group,\napplication_group_owner,\nproduct_id,\nproduct_name,\nproduct_owner,\nobs_product_id,\nobs_product_name,\nobs_product_owner,\ndepartment,\nplan_product_id,\nplan_product_name,\nplan_product_owner,\napplication_group_numbers,\napplication_group_owners,\nproduct_owners,\nobs_product_owners,\nplan_product_owners,\nbg,\nid\nFROM internal_platform_db.app_group_product_info_query_engine_011;", "expected_csv": "username,application_group,application_group_owner,product_id,product_name,product_owner,obs_product_id,obs_product_name,obs_product_owner,department,plan_product_id,plan_product_name,plan_product_owner,application_group_numbers,application_group_owners,product_owners,obs_product_owners,plan_product_owners,bg,id,dt\nuser_e,group_delta,4,105,prod_v,205,305,obs_prod_e,405,dept_finance,505,plan_e,605,2,owner5,powner5,obs_owner5,plan_owner5,BG2,5,20260507\nuser_d,group_alpha,1,104,prod_w,204,304,obs_prod_d,404,dept_eng,504,plan_d,604,7,owner4,powner4,obs_owner4,plan_owner4,BG3,4,20260507\nuser_a,group_alpha,1,101,prod_x,201,301,obs_prod_a,401,dept_eng,501,plan_a,601,5,owner1,powner1,obs_owner1,plan_owner1,BG1,1,20260507\nuser_b,group_beta,2,102,prod_y,202,302,obs_prod_b,402,dept_sales,502,plan_b,602,10,owner2,powner2,obs_owner2,plan_owner2,BG2,2,20260507\nuser_c,group_gamma,3,103,prod_z,203,303,obs_prod_c,403,dept_hr,503,plan_c,603,3,owner3,powner3,obs_owner3,plan_owner3,BG1,3,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,21列(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,25,非key字段逐列值匹配率\nD,D_field_completeness,field_completeness,15,关键字段非空/非空串比例\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,dt 分区值 = 20260507\nF,F_dt_column,dt_column,10,dt 派生列存在于输出且值正确\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"product_id, obs_product_id, plan_product_id, id, dt\"\n# 预期列数,21\n# 输出表,internal_platform_db.app_group_product_info_history_cand_query_engine_011", "path": "tasks/offline-compute/HiveSQL/hivesql_004"} |
| {"task_id": "hivesql_005", "id": "offline-compute_HiveSQL_hivesql_005", "name": "从 internal_platform_db.dws_mq_production_featur", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：汇总当天有生产量的 消息队列MQ topic 维度信息及近7/30/90天生产统计，落地为 topic 治理项明细表。\n\n输入：\n- 表：internal_platform_db.dws_mq_production_feature_d_increase_query_engine_013\n- 分区字段：dt（STRING，格式 YYYYMMDD）\n- 关键字段：business_id、business_name、topic、cluster_set、tenant、namespaces、system_belong、dw_appgroup、in_charge、description、create_time、modify_time、cluster_id、cluster_type、cluster_name、bg、category_name、total_produce_pkg_d、production_days_last_7d、total_produce_pkg_last_7d、production_days_last_30d、total_produce_pkg_last_30d、production_days_last_90d、total_produce_pkg_last_90d、is_consumed、has_consumption_days_last_7d、has_consumption_days_last_30d、has_consumption_days_last_90d\n\n处理规则：\n1. 过滤条件：dt = '20260507' AND total_produce_pkg_last_90d > 0\n2. 无 Join，单表处理\n3. 派生字段：\n - mq_full_topic：若 tenant 和 namespaces 均非 NULL，则拼接为 'persistent://' + tenant + '/' + namespaces + '/' + topic；否则直接取 topic\n - app_group：取自 dw_appgroup\n - bid_incharge：取自 in_charge\n - bid_description：取自 description\n - bid_create_time：取自 create_time\n - bid_modify_time：取自 modify_time\n - hitted_gov_items：固定为 NULL\n - governance_benefit_estimate：固定为 NULL\n\n输出要求：\n- 输出字段顺序：dt、business_id、business_name、topic、cluster_set、mq_full_topic、system_belong、app_group、bid_incharge、bid_description、bid_create_time、bid_modify_time、cluster_id、cluster_type、cluster_name、bg、category_name、total_produce_pkg_d、production_days_last_7d、total_produce_pkg_last_7d、production_days_last_30d、total_produce_pkg_last_30d、production_days_last_90d、total_produce_pkg_last_90d、is_consumed、has_consumption_days_last_7d、has_consumption_days_last_30d、has_consumption_days_last_90d、hitted_gov_items、governance_benefit_estimate\n\n写入要求：\n- 目标表：internal_platform_db.ads_mq_topic_governance_item_d_cand_query_engine_013\n- 分区字段：dt（STRING，格式 YYYYMMDD）\n- 写入方式：INSERT OVERWRITE 按分区写入\n- 若目标表不存在，先按 Hive 标准建表（ORC存储、按 dt 分区），再写入\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.ads_mq_topic_governance_item_d_query_engine_013 PARTITION (dt = '20260507')\nSELECT\n business_id,\n business_name,\n topic,\n cluster_set,\n IF ( tenant IS NOT NULL AND namespaces IS NOT NULL, CONCAT( 'persistent://', tenant, '/', namespaces, '/', topic ), topic ) AS mq_full_topic,\n system_belong,\n dw_appgroup AS app_group,\n in_charge AS bid_incharge,\n description AS bid_description,\n create_time AS bid_create_time,\n modify_time AS bid_modify_time,\n cluster_id,\n cluster_type,\n cluster_name,\n bg,\n category_name,\n total_produce_pkg_d,\n production_days_last_7d,\n total_produce_pkg_last_7d,\n production_days_last_30d,\n total_produce_pkg_last_30d,\n production_days_last_90d,\n total_produce_pkg_last_90d,\n is_consumed,\n has_consumption_days_last_7d,\n has_consumption_days_last_30d,\n has_consumption_days_last_90d,\n NULL AS hitted_gov_items,\n NULL AS governance_benefit_estimate\nFROM internal_platform_db.dws_mq_production_feature_d_increase_query_engine_013\nWHERE dt = '20260507'\nAND total_produce_pkg_last_90d > 0", "expected_csv": "business_id,business_name,topic,cluster_set,mq_full_topic,system_belong,app_group,bid_incharge,bid_description,bid_create_time,bid_modify_time,cluster_id,cluster_type,cluster_name,bg,category_name,total_produce_pkg_d,production_days_last_7d,total_produce_pkg_last_7d,production_days_last_30d,total_produce_pkg_last_30d,production_days_last_90d,total_produce_pkg_last_90d,is_consumed,has_consumption_days_last_7d,has_consumption_days_last_30d,has_consumption_days_last_90d,hitted_gov_items,governance_benefit_estimate,dt\nbid_001,account_alpha,topic_x,cluster_a,persistent://tenant1/ns1/topic_x,数据总线,appgrp1,user_a,desc1,2025-01-01,2025-06-01,c001,dedicated,cluster_alpha,DEPT_I,product_a,5000,7,35000,30,150000,90,450000,1,7,30,90,,,20260507\nbid_002,account_beta,topic_y,cluster_b,topic_y,流处理平台,appgrp2,user_b,desc2,2025-02-15,2025-05-20,c002,shared,cluster_beta,DEPT_C,product_b,1000,5,7000,20,30000,60,90000,0,3,10,30,,,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,30列(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_mapping,field_mapping,25,字段别名映射正确性（app_group/bid_*等）\nD,D_derived_mq_full_topic,derived_mq_full_topic,15,mq_full_topic 派生列拼接逻辑正确性\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,dt 分区值 = 20260507\nF,F_filter_condition,filter_condition,10,WHERE dt='20260507' AND total_produce_pkg_last_90d>0\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"business_id, bid_incharge, bid_description, bid_create_time, bid_modify_time, cluster_id, dt\"\n# 预期列数,30\n# 输出表,internal_platform_db.ads_mq_topic_governance_item_d_cand_query_engine_013", "path": "tasks/offline-compute/HiveSQL/hivesql_005"} |
| {"task_id": "hivesql_006_en", "id": "offline-compute_HiveSQL_hivesql_006", "name": "Input Table internal_platform_db.ods_t_databus_access_topic", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Cleanse the 数据总线 topic access cost raw data and aggregate it by topic dimension, excluding test topics, then write the results to a detail table.\n\nInput: `internal_platform_db.ods_t_databus_access_topic_cost_date_d_query_engine_015`, partitioned by field `dt` (STRING, YYYYMMDD). Key fields: `systemname`, `dwproductname`, `dwappgroup`, `cityid`, `iset`, `topic`, `data_size` (BIGINT), `total_cost` (DOUBLE), `in_charge`.\n\nProcessing Rules: No joins, single-table processing. Filter condition: `dt = '20260507' AND topic <> 'test'`. Aggregation: Group by `topic`, applying `MAX` to `systemname`, `dwproductname`, `dwappgroup`, `cityid`, `iset`, `data_size`, `total_cost`, and `in_charge`. Derived column: `dt` directly takes the partition value `'20260507'`.\n\nOutput Requirements: The output field order is `dt`, `system_belong`, `category_name`, `dw_appgroup`, `city_id`, `cluster_set`, `topic`, `total_data_size_d`, `total_cost`, `in_charge`. Field mappings: `dt=dt`, `system_belong=MAX(systemname)`, `category_name=MAX(dwproductname)`, `dw_appgroup=MAX(dwappgroup)`, `city_id=MAX(cityid)`, `cluster_set=MAX(iset)`, `topic=topic`, `total_data_size_d=MAX(data_size)`, `total_cost=MAX(total_cost)`, `in_charge=MAX(in_charge)`. Data types: `dt` STRING, `system_belong` STRING, `category_name` STRING, `dw_appgroup` STRING, `city_id` STRING, `cluster_set` STRING, `topic` STRING, `total_data_size_d` BIGINT, `total_cost` DOUBLE, `in_charge` STRING.\n\nWrite Requirements: Target table `internal_platform_db.dwd_databus_topic_cost_detail_d_cand_query_engine_015`. Table comment: \"数据总线 topic access cost detail table, including access cost and dimension information, performing data cleansing on the ODS layer.\" Partition field `dt` (STRING, YYYYMMDD). Write method: `INSERT OVERWRITE`, overwriting the partition `dt='20260507'`. If the target table does not exist, first create the table using standard Hive ORC storage format partitioned by `dt`, then write the data.\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT overwrite TABLE internal_platform_db.dwd_databus_topic_cost_detail_d_query_engine_015 PARTITION (dt = '20260507')\nSELECT\n MAX(systemname) AS system_belong,\n MAX(dwproductname) AS category_name,\n MAX(dwappgroup) AS dw_appgroup,\n MAX(cityid) AS city_id,\n MAX(iset) AS cluster_set,\n topic,\n MAX(data_size) AS total_data_size_d,\n MAX(total_cost) AS total_cost,\n MAX(in_charge) AS in_charge\nFROM\n internal_platform_db.ods_t_databus_access_topic_cost_date_d_query_engine_015\nWHERE\n dt = '20260507'\n AND topic <> 'test'\nGROUP BY topic", "expected_csv": "system_belong,category_name,dw_appgroup,city_id,cluster_set,topic,total_data_size_d,total_cost,in_charge,dt\nSystemAlpha,数据仓库DWProdA,AppGroupX,101,ClusterA,topic_billing,2560000,162.0,user_zhang,20260507\nSystemBeta,数据仓库DWProdB,AppGroupY,202,ClusterB,topic_log,1200000,81.0,user_li,20260507\nSystemGamma,数据仓库DWProdC,AppGroupZ,303,ClusterC,topic_metrics,512000,50.0,user_wang,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,25,非key字段逐列值匹配率\nD,D_field_completeness,field_completeness,15,关键字段非空/非空串比例\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,dt 分区值 = 20260507\nF,F_source_filter,source_filter,10,源表分区过滤 dt=20260507 + topic<>'test'\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"city_id, dt\"\n# 预期列数,10\n# 输出表,internal_platform_db.dwd_databus_topic_cost_detail_d_cand_query_engine_015", "path": "tasks/offline-compute/HiveSQL/hivesql_006_en"} |
| {"task_id": "hivesql_007_en", "id": "offline-compute_HiveSQL_hivesql_007", "name": "Aggregate Notebook instance runtime statistics by trace_id and p_date for the day: Input table w", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Aggregate Notebook instance runtime statistics grouped by `trace_id` and `p_date`.\n\nInput: `internal_platform_db.dws_notebook_instance_execute_minute_stat_d_query_engine_019`\n- Partition field: `dt` (STRING, format YYYYMMDD)\n- Key fields: `trace_id`, `p_date`, `datawd_project_id`, `datawd_task_id`, `datawd_task_instance_id`, `compute_type`, `status_code`, `instance_run_time`, `code_run_time`, `resource_wait_time`, `code_start_time`, `code_end_time`, `instance_start_time`, `instance_end_time`, `serving_id`, `is_permanent`, `apply_for_gpu_count`, `code_gpu_count` (numeric), `used_gpu_count` (numeric), `is_code_running` (INT), `code_used_gpu_count` (numeric)\n\nProcessing Rules:\n- Filter condition: `dt = '20260507'`\n- No joins, single-table processing\n- Aggregation: Group by `trace_id`, `p_date`\n - Most fields use `MAX`: `datawd_project_id`, `datawd_task_id`, `datawd_task_instance_id`, `compute_type`, `status_code`, `instance_run_time`, `code_run_time`, `resource_wait_time`, `code_start_time`, `code_end_time`, `instance_start_time`, `instance_end_time`, `serving_id`, `is_permanent`, `apply_for_gpu_count`\n - `code_gpu_count`: Use `AVG`\n - `instance_gpu_util`: `AVG(used_gpu_count / code_gpu_count) * 100`\n - `code_gpu_util`: `AVG(IF(is_code_running = 1, code_used_gpu_count / code_gpu_count, NULL)) * 100`\n\nOutput Requirements:\n- Output field order: `dt` (STRING), `trace_id` (STRING), `p_date` (STRING), `datawd_project_id` (STRING), `datawd_task_id` (STRING), `datawd_task_instance_id` (STRING), `compute_type` (STRING), `status_code` (INT), `instance_run_time` (INT), `code_run_time` (INT), `resource_wait_time` (INT), `code_start_time` (STRING), `code_end_time` (STRING), `instance_start_time` (STRING), `instance_end_time` (STRING), `serving_id` (STRING), `is_permanent` (BOOLEAN), `apply_for_gpu_count` (INT), `code_gpu_count` (INT), `instance_gpu_util` (DOUBLE), `code_gpu_util` (DOUBLE)\n- Partition field: `dt` (STRING, format YYYYMMDD)\n\nWrite Requirements:\n- Target table: `internal_platform_db.dws_notebook_instance_execute_stat_d_cand_query_engine_019`\n- Write method: `INSERT OVERWRITE` partitioned by `dt`\n- If the target table does not exist, first create the table using standard Hive ORC storage, then write the data\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT overwrite TABLE internal_platform_db.dws_notebook_instance_execute_stat_d_query_engine_019 PARTITION (dt = '20260507')\nSELECT\n t1.trace_id,\n t1.p_date,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n t1.instance_run_time,\n t1.code_run_time,\n t1.resource_wait_time,\n t1.code_start_time,\n t1.code_end_time,\n t1.instance_start_time,\n t1.instance_end_time,\n t1.serving_id,\n t1.is_permanent,\n t1.apply_for_gpu_count,\n t1.code_gpu_count,\n t1.instance_gpu_util,\n t1.code_gpu_util\nFROM (\n SELECT\n trace_id,\n p_date,\n MAX(datawd_project_id) as datawd_project_id,\n MAX(datawd_task_id) as datawd_task_id,\n MAX(datawd_task_instance_id) as datawd_task_instance_id,\n MAX(compute_type) as compute_type,\n MAX(status_code) as status_code,\n MAX(instance_run_time) as instance_run_time,\n MAX(code_run_time) as code_run_time,\n MAX(resource_wait_time) as resource_wait_time,\n MAX(code_start_time) as code_start_time,\n MAX(code_end_time) as code_end_time,\n MAX(instance_start_time) as instance_start_time,\n MAX(instance_end_time) as instance_end_time,\n MAX(serving_id) as serving_id,\n MAX(is_permanent) as is_permanent,\n MAX(apply_for_gpu_count) as apply_for_gpu_count,\n AVG(code_gpu_count) as code_gpu_count,\n AVG(used_gpu_count / code_gpu_count) * 100 as instance_gpu_util,\n AVG(if(is_code_running = 1, code_used_gpu_count / code_gpu_count, null)) * 100 as code_gpu_util\n FROM internal_platform_db.dws_notebook_instance_execute_minute_stat_d_query_engine_019\n WHERE dt = '20260507'\n GROUP BY trace_id, p_date\n) t1\n;", "expected_csv": "trace_id,p_date,datawd_project_id,datawd_task_id,datawd_task_instance_id,compute_type,status_code,instance_run_time,code_run_time,resource_wait_time,code_start_time,code_end_time,instance_start_time,instance_end_time,serving_id,is_permanent,apply_for_gpu_count,code_gpu_count,instance_gpu_util,code_gpu_util,dt\ntrace_001,20260507,proj_01,task_01,inst_01,GPU,0,120,100,20,2026-05-07 10:00:00,2026-05-07 10:01:40,2026-05-07 09:59:40,2026-05-07 10:01:40,srv_01,True,2,2.0,82.5,89.0,20260507\ntrace_002,20260507,proj_02,task_02,inst_02,CPU,1,60,50,10,2026-05-07 11:00:00,2026-05-07 11:00:50,2026-05-07 11:00:00,2026-05-07 11:01:00,srv_02,False,0,4.0,0.0,,20260507\ntrace_003,20260507,proj_03,task_03,inst_03,GPU,0,300,280,20,2026-05-07 12:00:00,2026-05-07 12:04:40,2026-05-07 11:59:40,2026-05-07 12:04:40,srv_03,True,4,4.0,95.0,92.0,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,25,非key字段逐列值匹配率\nD,D_field_completeness,field_completeness,15,关键字段非空/非空串比例\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,dt 分区值 = 20260507\nF,F_source_filter,source_filter,10,源表分区过滤 dt=20260507\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"trace_id, datawd_project_id, datawd_task_id, datawd_task_instance_id, serving_id, dt\"\n# 预期列数,21\n# 输出表,internal_platform_db.dws_notebook_instance_execute_stat_d_cand_query_engine_019", "path": "tasks/offline-compute/HiveSQL/hivesql_007_en"} |
| {"task_id": "hivesql_008_en", "id": "offline-compute_HiveSQL_hivesql_008", "name": "Count tasks matching the shuffle_split tuning rule. Input table: internal_platform_db", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Count tasks that match the `shuffle_split` tuning rule, producing task-level tuning rule hit results.\n\nInputs:\n- `internal_platform_db.nextgen_platform_dsl_spark_props_fht0_query_engine_021`\n- `internal_platform_db.ods_spark_props_extra_query_engine_021`\n- `internal_platform_db.deep_tuning_rule_hit_app_info_query_engine_021`\n- `internal_platform_db.nextgen_platform_dsl_us_task_detail_fdt0_query_engine_021`\n- `internal_platform_db.nextgen_platform_dsl_gputj_task_detail_fdt0_query_engine_021`\n- `internal_platform_db.app_group_product_info_history_query_engine_021`\n\nProcessing Rules: Join the Spark configuration table, task detail tables (from both the `us` and `gputj` sources), and the application group product mapping table. Based on `deep_tuning_rule_hit_app_info`, filter records that match the `shuffle_split` tuning rule, and aggregate at the task granularity to produce deduplicated results. The join conditions and aggregation logic must be consistent with the historical logic, without introducing additional filtering or business rules.\n\nOutput Requirements: The output field order must match the target table schema, retaining task-level fields related to tuning rule hits, deduplicated at the task granularity.\n\nWrite Requirements: Write to table `internal_platform_db.shuffle_split_tuning_rule_hit_task_info_cand_query_engine_021`.\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "insert overwrite table internal_platform_db.shuffle_split_tuning_rule_hit_task_info_query_engine_021 partition (databus_imp_date = '20260507')\nwith spark_props as\n(\n select\n t1.app_id\n ,max(spark_partition_min) spark_partition_min\n ,max(spark_partition_max) spark_partition_max\n ,max(round(ifnull(spark_task_shuffle_size,'')/(1024*1024))) spark_task_shuffle_size\n ,max(spark_shuffle_sort_bypassMergeThreshold) spark_shuffle_sort_bypassMergeThreshold\n ,max(spark_default_parallelism) spark_default_parallelism\n ,max(spark_sql_shuffle_partitions) spark_sql_shuffle_partitions\n ,max(mapreduce_input_fileinputformat_split_minsize) mapreduce_input_fileinputformat_split_minsize\n ,max(mapreduce_input_fileinputformat_split_maxsize) mapreduce_input_fileinputformat_split_maxsize\n ,max(mapreduce_input_fileinputformat_split_minsize_per_node) mapreduce_input_fileinputformat_split_minsize_per_node\n ,max(mapreduce_input_fileinputformat_split_minsize_per_rack) mapreduce_input_fileinputformat_split_minsize_per_rack\n from\n (\n select\n app_id\n ,max(spark_partition_max) spark_partition_max\n ,max(spark_task_shuffle_size) spark_task_shuffle_size\n ,max(spark_default_parallelism) spark_default_parallelism\n ,max(spark_sql_shuffle_partitions) spark_sql_shuffle_partitions\n from internal_platform_db.nextgen_platform_dsl_spark_props_fht0_query_engine_021\n where databus_imp_date >= '2026050700' and databus_imp_date <= '2026050723'\n group by app_id\n ) t1\n left join\n (\n select\n app_id\n ,max(spark_partition_min) spark_partition_min\n ,max(spark_shuffle_sort_bypassMergeThreshold) spark_shuffle_sort_bypassMergeThreshold\n ,max(mapreduce_input_fileinputformat_split_minsize) mapreduce_input_fileinputformat_split_minsize\n ,max(mapreduce_input_fileinputformat_split_maxsize) mapreduce_input_fileinputformat_split_maxsize\n ,max(mapreduce_input_fileinputformat_split_minsize_per_node) mapreduce_input_fileinputformat_split_minsize_per_node\n ,max(mapreduce_input_fileinputformat_split_minsize_per_rack) mapreduce_input_fileinputformat_split_minsize_per_rack\n from internal_platform_db.ods_spark_props_extra_query_engine_021\n where dt = '20260507'\n group by app_id\n ) t2\n on t1.app_id=t2.app_id\n group by t1.app_id\n),\napp_info as\n(\n select\n t1.*\n ,dw_appgroup\n ,case when (project_id is null or project_id = '') then 'us' else 'datawd' end tag\n from\n (\n select *\n from internal_platform_db.deep_tuning_rule_hit_app_info_query_engine_021\n where databus_imp_date = '20260507' and stage_type = 'shuffleInputStage'\n ) t1\n join\n (\n select\n task_id\n ,max(project_id) project_id\n ,max(dw_appgroup) dw_appgroup\n from internal_platform_db.nextgen_platform_dsl_us_task_detail_fdt0_query_engine_021\n where databus_imp_date = '20260507'\n group by task_id\n ) t2\n on t1.usp_task_id=t2.task_id\n\n union all\n\n select\n t1.*\n ,dw_appgroup\n ,'gputj' tag\n from\n (\n select *\n from internal_platform_db.deep_tuning_rule_hit_app_info_query_engine_021\n where databus_imp_date = '20260507' and stage_type = 'shuffleInputStage'\n ) t1\n join\n (\n select\n task_id\n ,max(app_group_id) dw_appgroup\n from internal_platform_db.nextgen_platform_dsl_gputj_task_detail_fdt0_query_engine_021\n where databus_imp_date >= '2026050700' and databus_imp_date <= '2026050723'\n group by task_id\n ) t2\n on t1.usp_task_id=t2.task_id\n),\ntask_info as\n(\n select\n usp_task_id\n ,t1.app_id app_id\n ,app_time_usage\n ,dw_appgroup\n ,tag\n ,product_name\n ,shuffle_split\n ,max_shuffle_stage_task_num\n ,shuffle_stage_time\n ,shuffle_stage_task_time_50th\n ,shuffle_stage_task_time_90th\n ,spark_partition_min\n ,spark_partition_max\n ,spark_task_shuffle_size\n ,spark_default_parallelism\n ,spark_sql_shuffle_partitions\n ,spark_shuffle_sort_bypassMergeThreshold\n ,spark_version\n ,start_time\n ,end_time\n ,row_number() over (partition by usp_task_id order by app_time_usage desc) as rk\n from\n (\n select\n usp_task_id\n ,a.app_id app_id\n ,dw_appgroup\n ,tag\n ,app_time_usage\n ,spark_version\n ,case when\n ( (spark_version <>'2.1.0' and (spark_default_parallelism is not null and spark_default_parallelism <>'') and cast(task_num as int)>=cast(spark_default_parallelism as int))\n or\n (spark_version<>'2.1.0'and (spark_sql_shuffle_partitions is not null and spark_sql_shuffle_partitions <>'') and cast(task_num as int)>=cast(spark_sql_shuffle_partitions as int))\n or\n (spark_version='2.1.0' and (spark_partition_max is not null and spark_partition_max <>'') and cast(task_num as int)>=cast(spark_partition_max as int))\n or\n (spark_version='2.1.0' and (spark_partition_max ='' or spark_partition_max is null))\n or\n (spark_version<>'2.1.0' and (spark_default_parallelism ='' or spark_default_parallelism is null) and (spark_sql_shuffle_partitions ='' or spark_sql_shuffle_partitions is null))\n ) then '1' else '0' end as shuffle_split\n ,task_num max_shuffle_stage_task_num\n ,stage_time shuffle_stage_time\n ,task_time_50th shuffle_stage_task_time_50th\n ,task_time_90th shuffle_stage_task_time_90th\n ,spark_partition_min\n ,spark_partition_max\n ,spark_task_shuffle_size\n ,spark_default_parallelism\n ,spark_sql_shuffle_partitions\n ,spark_shuffle_sort_bypassMergeThreshold\n ,start_time\n ,end_time\n from app_info a\n left join spark_props b\n on a.app_id = b.app_id\n ) t1\n left join\n (\n select\n application_group\n ,max(product_name) product_name\n from internal_platform_db.app_group_product_info_history_query_engine_021\n where dt = '20260507'\n group by\n application_group\n ) t2\n on t1.dw_appgroup = t2.application_group\n where shuffle_split = '1'\n)\n\nselect\n usp_task_id\n ,app_id\n ,app_time_usage\n ,tag\n ,dw_appgroup\n ,product_name\n ,shuffle_split\n ,max_shuffle_stage_task_num\n ,shuffle_stage_time\n ,shuffle_stage_task_time_50th\n ,shuffle_stage_task_time_90th\n ,spark_partition_min\n ,spark_partition_max\n ,spark_task_shuffle_size\n ,spark_default_parallelism\n ,spark_sql_shuffle_partitions\n ,spark_shuffle_sort_bypassMergeThreshold\n ,spark_version\n ,concat('https://internal-tools.example.com/optimization/appgroupfigure/config/recommended?taskId=',usp_task_id,'&applicationId=',app_id,'&startTime=',start_time,'&endTime=',end_time,'&applicationGroup=',dw_appgroup) brain_url\nfrom task_info\nwhere rk = 1", "expected_csv": "usp_task_id,app_id,app_time_usage,tag,dw_appgroup,product_name,shuffle_split,max_shuffle_stage_task_num,shuffle_stage_time,shuffle_stage_task_time_50th,shuffle_stage_task_time_90th,spark_partition_min,spark_partition_max,spark_task_shuffle_size,spark_default_parallelism,spark_sql_shuffle_partitions,spark_shuffle_sort_bypassmergethreshold,spark_version,brain_url,databus_imp_date\ntask_001,app_001,3600,datawd,appgroup_A,ProductAlpha,1,250,120,5,10,100,200,0.0,,,300,3.2.1,https://internal-tools.example.com/optimization/appgroupfigure/config/recommended?taskId=task_001&applicationId=app_001&startTime=1714000000&endTime=1714003600&applicationGroup=appgroup_A,20260507\ntask_003,app_003,1800,us,appgroup_C,ProductGamma,1,100,60,3,6,,,0.0,,,,3.2.1,https://internal-tools.example.com/optimization/appgroupfigure/config/recommended?taskId=task_003&applicationId=app_003&startTime=1714000200&endTime=1714002000&applicationGroup=appgroup_C,20260507\ntask_004,app_001,5000,datawd,appgroup_A,ProductAlpha,1,500,200,7,12,100,200,0.0,,,300,3.2.1,https://internal-tools.example.com/optimization/appgroupfigure/config/recommended?taskId=task_004&applicationId=app_001&startTime=1714010000&endTime=1714015000&applicationGroup=appgroup_A,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,20列 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key覆盖率\nD,D_shuffle_logic,shuffle_logic,20,shuffle_split 条件逻辑（CASE WHEN 版本判断）\nD,D_deduplication,deduplication,15,ROW_NUMBER 按 usp_task_id 去重\nD,D_numeric_values,numeric_values,10,时间/阈值等业务指标逐行匹配\nD,D_url_concat,url_concat,10,brain_url 字段拼接正确性\nF,F_union_completeness,union_completeness,5,us/datawd + gputj 两个 UNION 分支\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,分区值正确\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,EXPERT\n# 权重,\"product=0.8, process=0.2\"\n# 范式,new\n# Key列,usp_task_id\n# 预期列数,\n# 输出表,internal_platform_db.shuffle_split_tuning_rule_hit_task_info_cand_query_engine_021", "path": "tasks/offline-compute/HiveSQL/hivesql_008_en"} |
| {"task_id": "hivesql_009_en", "id": "offline-compute_HiveSQL_hivesql_009", "name": "Compute GPU card-hours for task instances within 5-minute windows, joining Pod mapping and task instance configuration, outputting hourly instanc", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Compute GPU card-hours for task instances within 5-minute windows, outputting hourly instance-level GPU usage statistics.\n\nInputs:\n- `internal_platform_db.gputj_gpu_info_parsed_query_engine_022`\n- `internal_platform_db.dwd_ml_platform_instance_podname_query_engine_022`\n- `internal_platform_db.dwd_task_management_taskinstance2_query_engine_022`\n- `internal_platform_db.task_management_taskinstance2_query_engine_022`\n\nProcessing Rules: Join the Pod mapping table with the task instance configuration table, aggregate GPU card-hours by 5-minute windows, and roll up to hourly-level statistics. The specific join keys and aggregation logic must be determined based on the table structures.\n\nOutput Requirements: Output the task instance GPU usage statistics, including fields such as instance identifier, time window, and GPU card-hours. The specific field list must be determined based on the output table structure.\n\nWrite Requirements: Write the results to `internal_platform_db.task_instance_gpu_time_stats_cand_query_engine_022`.\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.task_instance_gpu_time_stats_query_engine_022\nPARTITION (dt='2026050700')\nWITH\n-- Step 1: 从GPU监控数据中提取pod运行记录，按分钟去重\npod_run_minutes AS (\n SELECT\n pod_name,\n gpu_name,\n FLOOR(CAST(pkg_time AS BIGINT) / 60) * 60 AS minute_timestamp,\n FLOOR(CAST(pkg_time AS BIGINT) / 300) * 300 AS time_5min\n FROM internal_platform_db.gputj_gpu_info_parsed_query_engine_022\n WHERE dt = '2026050700'\n AND metric IN ('k8s_container_vgpu_gpu_mem_usage', 'k8s_dcgm_fi_dev_fb_util')\n GROUP BY pod_name, gpu_name, FLOOR(CAST(pkg_time AS BIGINT) / 60) * 60, FLOOR(CAST(pkg_time AS BIGINT) / 300) * 300\n),\n\n-- Step 2: 统计每个pod在每个5分钟窗口内的运行分钟数\npod_run_time AS (\n SELECT\n pod_name,\n MAX(gpu_name) AS gpu_name,\n time_5min,\n CAST(COUNT(DISTINCT minute_timestamp) AS DOUBLE) AS run_time_m\n FROM pod_run_minutes\n GROUP BY pod_name, time_5min\n),\n\n-- Step 3: 关联pod与instance的映射关系\ninstance_run_time AS (\n SELECT\n p.time_5min,\n m.instance_uuid,\n MAX(p.gpu_name) AS gpu_name,\n CAST(SUM(p.run_time_m) AS DOUBLE) AS sum_run_time_m,\n COUNT(DISTINCT m.pod_name) AS pod_count\n FROM pod_run_time p\n INNER JOIN internal_platform_db.dwd_ml_platform_instance_podname_query_engine_022 m\n ON p.pod_name = m.pod_name\n AND m.dt = '2026050700'\n GROUP BY p.time_5min, m.instance_uuid\n),\n\n-- Step 4: 获取任务实例的GPU配置\nlatest_task_config AS (\n SELECT\n instance_uuid,\n CAST(host_gpu_num AS DOUBLE) AS host_gpu_num,\n CAST(host_num AS DOUBLE) AS host_num\n FROM (\n SELECT\n instance_uuid,\n host_gpu_num,\n host_num,\n last_modify,\n ROW_NUMBER() OVER (PARTITION BY instance_uuid ORDER BY last_modify DESC) AS rn\n FROM (\n SELECT\n instance_uuid,\n host_gpu_num,\n host_num,\n last_modify\n FROM internal_platform_db.dwd_task_management_taskinstance2_query_engine_022\n WHERE databus_imp_date = (\n SELECT MAX(databus_imp_date)\n FROM internal_platform_db.dwd_task_management_taskinstance2_query_engine_022\n )\n\n UNION ALL\n\n SELECT\n instance_uuid,\n host_gpu_num,\n host_num,\n last_modify\n FROM internal_platform_db.task_management_taskinstance2_query_engine_022\n WHERE databus_imp_date = (\n SELECT MAX(databus_imp_date)\n FROM internal_platform_db.task_management_taskinstance2_query_engine_022\n )\n ) combined\n ) ranked\n WHERE rn = 1\n)\n\n-- Step 5: 关联GPU配置并计算卡时\nSELECT\n i.instance_uuid,\n i.time_5min,\n t.host_gpu_num,\n i.sum_run_time_m,\n CASE\n WHEN t.host_gpu_num IS NOT NULL AND i.sum_run_time_m IS NOT NULL\n THEN t.host_gpu_num * i.sum_run_time_m / 60.0\n ELSE NULL\n END AS gpu_hour,\n i.gpu_name,\n i.pod_count,\n t.host_num\nFROM instance_run_time i\nLEFT JOIN latest_task_config t\n ON i.instance_uuid = t.instance_uuid", "expected_csv": "instance_uuid,time_5min,host_gpu_num,sum_run_time_m,gpu_hour,gpu_name,pod_count,host_num\ninst-uuid-aaa,1767225600,8.0,7.0,0.9333333333333333,A100,2,2.0\ninst-uuid-ccc,1767225600,2.0,1.0,0.03333333333333333,A100,1,1.0\ninst-uuid-bbb,1767225900,4.0,2.0,0.13333333333333333,V100,1,1.0\ninst-uuid-ddd,1767225900,8.0,1.0,0.13333333333333333,H100,1,4.0\ninst-uuid-ccc,1767226200,2.0,1.0,0.03333333333333333,A100,1,1.0", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,8列 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key覆盖率\nD,D_gpu_hour_calculation,gpu_hour_calculation,30,gpu_hour 核心指标计算正确性\nD,D_window_aggregation,window_aggregation,15,\"sum_run_time_m, pod_count 等窗口聚合指标\"\nD,D_gpu_config_lookup,gpu_config_lookup,10,\"host_gpu_num, host_num 等 GPU 配置列匹配\"\nF,F_join_completeness,join_completeness,5,4张源表都被关联\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,dt=2026050700\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,HARD\n# 权重,\"product=0.7, process=0.3\"\n# 范式,new\n# Key列,\"instance_uuid, time_5min\"\n# 预期列数,\n# 输出表,internal_platform_db.task_instance_gpu_time_stats_cand_query_engine_022", "path": "tasks/offline-compute/HiveSQL/hivesql_009_en"} |
| {"task_id": "hivesql_010", "id": "offline-compute_HiveSQL_hivesql_010", "name": "从输入表中筛选状态为 killed 的 Notebook runner trace 数据，按天拆分后", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n1) 任务目标\n产出 killed 状态的 Notebook 实例 Pod 运行明细宽表。\n\n2) 输入\n- internal_platform_db.gputj_gpu_info_parsed_agg_1min_query_engine_024\n- internal_platform_db.notebook_span_info_query_engine_024\n- internal_platform_db.dwd_gputj_service_instance_map_query_engine_024\n- internal_platform_db.dwd_ml_platform_instance_podname_query_engine_024\n- internal_platform_db.notebook_engine_info_query_engine_024\n\n3) 处理规则\n- 过滤条件：筛选 Notebook runner trace 中状态为 killed 的记录\n- 时间处理：对 runner trace 数据按天拆分\n- 关联顺序：runner trace → 服务实例 → Pod → GPU 聚合 → engine 信息\n- 关联方式：按各表对应键字段进行左连接\n\n4) 输出要求\n- 输出表：internal_platform_db.dwd_notebook_killed_instance_pod_detail_d_cand_query_engine_024\n- 包含 runner trace、服务实例、Pod、GPU、engine 等关联后的明细字段\n- 去重规则：按主键去重（若存在）\n\n5) 写入要求\n- 写入模式：INSERT OVERWRITE\n- 分区字段：按天分区（如有）\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.dwd_notebook_killed_instance_pod_detail_d_query_engine_024 PARTITION (dt = '20260507')\nWITH base_trace AS (\n SELECT trace_id\n FROM internal_platform_db.notebook_span_info_query_engine_024\n WHERE databus_imp_date >= '2026050700'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n AND span_name IN ('runner.execute', 'runner.killed')\n GROUP BY trace_id\n HAVING\n COUNT(CASE WHEN span_name = 'runner.killed' THEN 1 END) > 0\n AND\n COUNT(CASE WHEN span_name = 'runner.execute' THEN 1 END) = 0\n),\ncombined_spans AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM internal_platform_db.notebook_span_info_query_engine_024\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (SELECT trace_id FROM base_trace)\n AND span_name IN ('runner.killed', 'runner.start', 'execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute')\n\n UNION ALL\n\n SELECT\n trace_id,\n 'runner.execute' AS span_name,\n MAX(CASE WHEN span_name = 'runner.start' THEN start_time END) AS start_time,\n MAX(CASE WHEN span_name = 'runner.killed' THEN end_time END) AS end_time,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_project_id END) AS datawd_project_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_task_id END) AS datawd_task_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_task_instance_id END) AS datawd_task_instance_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN compute_type END) AS compute_type,\n 2 AS status_code\n FROM internal_platform_db.notebook_span_info_query_engine_024\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (SELECT trace_id FROM base_trace)\n AND span_name IN ('runner.start', 'runner.killed')\n GROUP BY trace_id\n),\nbase_data_time_fixed AS (\n SELECT\n trace_id,\n span_name,\n CASE\n WHEN span_name = 'runner.killed'\n THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END)\n OVER(PARTITION BY trace_id)\n ELSE start_time\n END AS start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM combined_spans\n),\nbase_data AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n from_unixtime(CAST(start_time AS BIGINT) / 1000) as start_date,\n from_unixtime(CAST(end_time AS BIGINT) / 1000) as end_date,\n datediff(from_unixtime(CAST(end_time AS BIGINT) / 1000), from_unixtime(CAST(start_time AS BIGINT) / 1000)) AS diff_days\n FROM base_data_time_fixed\n),\npos_series AS (\n SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos\n),\ndaily_split_spans AS (\n SELECT\n trace_id,\n span_name,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n start_date,\n end_date,\n diff_days,\n date_add(b.start_date, s.pos) AS calc_date,\n CASE WHEN s.pos = 0 THEN b.start_time\n ELSE CAST(unix_timestamp(CAST(date_add(b.start_date, s.pos) AS TIMESTAMP)) * 1000 AS STRING)\n END AS start_time,\n CASE WHEN s.pos = b.diff_days THEN b.end_time\n ELSE CAST((unix_timestamp(CAST(date_add(b.start_date, s.pos + 1) AS TIMESTAMP)) * 1000) - 1 AS STRING)\n END AS end_time,\n b.start_time AS span_start_time,\n b.end_time AS span_end_time\n FROM base_data b\n INNER JOIN pos_series s ON s.pos <= b.diff_days\n),\ntrace_time_metrics AS (\n SELECT\n trace_id,\n calc_date,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code,\n ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN CAST(end_time AS BIGINT) END) -\n MIN(CASE WHEN span_name = 'runner.execute' THEN CAST(start_time AS BIGINT) END)) / 1000.0) AS instance_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(end_time AS BIGINT) END) -\n MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(start_time AS BIGINT) END)) / 1000.0) AS code_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN CAST(end_time AS BIGINT) END) -\n MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN CAST(start_time AS BIGINT) END)) / 1000.0) AS resource_wait_time,\n from_unixtime(MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(start_time AS BIGINT) END) / 1000) AS code_start_time,\n from_unixtime(MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(end_time AS BIGINT) END) / 1000) AS code_end_time,\n from_unixtime(MIN(CASE WHEN span_name = 'runner.execute' THEN CAST(span_start_time AS BIGINT) END) / 1000) AS instance_start_time,\n from_unixtime(MAX(CASE WHEN span_name = 'runner.execute' THEN CAST(span_end_time AS BIGINT) END) / 1000) AS instance_end_time\n FROM daily_split_spans\n GROUP BY trace_id, calc_date\n),\ngputj_dim AS (\n SELECT\n t1.service_id,\n t2.pod_name\n FROM internal_platform_db.dwd_gputj_service_instance_map_query_engine_024 t1\n INNER JOIN internal_platform_db.dwd_ml_platform_instance_podname_query_engine_024 t2\n ON t1.instance_uuid = t2.instance_uuid\n AND t1.dt=if('2026050700'>='2025080900', '2026050700', '2025080900')\n AND t2.dt=if('2026050700'>='2025060519', '2026050700', '2025060519')\n GROUP BY t1.service_id, t2.pod_name\n),\ngpu_metrics AS (\n SELECT\n pkg_agg_time,\n pod_name,\n k8s_container_vgpu_gpu_util_sum / k8s_container_vgpu_gpu_util_count AS gpu_util,\n k8s_container_resource_request_gpu_sum / k8s_container_resource_request_gpu_count AS gpu_count\n FROM internal_platform_db.gputj_gpu_info_parsed_agg_1min_query_engine_024\n WHERE dt >= '2026050400'\n AND dt <= '2026050700'\n AND ( k8s_container_vgpu_gpu_util_count > 0 OR k8s_container_resource_request_gpu_count > 0 )\n AND pod_name IS NOT NULL\n)\nSELECT\n t1.trace_id,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n CAST(t1.instance_run_time AS INT),\n CAST(t1.code_run_time AS INT),\n CAST(t1.resource_wait_time AS INT),\n t1.code_start_time,\n t1.code_end_time,\n t1.instance_start_time,\n t1.instance_end_time,\n t2.serving_id,\n CAST(t2.is_permanent AS BOOLEAN),\n CAST(t2.apply_for_gpu_count AS INT),\n t3.pod_name,\n t4.pkg_agg_time,\n t4.gpu_util,\n CAST(t4.gpu_count AS INT),\n CAST(t1.calc_date AS STRING) AS p_date\nFROM trace_time_metrics t1 LEFT JOIN (\n SELECT\n trace_id,\n MAX(serving_id) AS serving_id,\n MAX(is_permanent) AS is_permanent,\n SUM(CAST(replicas AS DOUBLE) * CAST(num_gpu AS DOUBLE)) AS apply_for_gpu_count\n FROM internal_platform_db.notebook_engine_info_query_engine_024\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n GROUP BY trace_id\n) t2 ON t1.trace_id = t2.trace_id\nLEFT JOIN gputj_dim t3 ON t2.serving_id = CAST(t3.service_id AS STRING)\nLEFT JOIN gpu_metrics t4\n ON t3.pod_name = t4.pod_name\n AND (\n CASE\n WHEN t1.instance_start_time IS NOT NULL AND t1.instance_end_time IS NOT NULL\n THEN t4.pkg_agg_time >= t1.instance_start_time AND t4.pkg_agg_time <= t1.instance_end_time\n WHEN t1.code_start_time IS NOT NULL AND t1.code_end_time IS NOT NULL\n THEN t4.pkg_agg_time >= t1.code_start_time AND t4.pkg_agg_time <= t1.code_end_time\n ELSE FALSE\n END\n )\n;", "expected_csv": "trace_id,datawd_project_id,datawd_task_id,datawd_task_instance_id,compute_type,status_code,instance_run_time,code_run_time,resource_wait_time,code_start_time,code_end_time,instance_start_time,instance_end_time,serving_id,is_permanent,apply_for_gpu_count,pod_name,pkg_agg_time,gpu_util,gpu_count,p_date,dt\ntrace001,proj1,task1,inst1,ray,2,10,,2,,,2025-05-06 23:59:50,2025-05-07 00:00:05,1001,True,2,pod-abc-001,,,,2025-05-06,20260507\ntrace001,proj1,task1,inst1,ray,2,5,5,,2025-05-07 00:00:00,2025-05-07 00:00:05,2025-05-06 23:59:50,2025-05-07 00:00:05,1001,True,2,pod-abc-001,,,,2025-05-07,20260507\ntrace002,proj2,task2,inst2,ray,2,13,3,2,2025-05-07 00:01:40,2025-05-07 00:01:43,2025-05-07 00:01:30,2025-05-07 00:01:43,1002,False,8,pod-xyz-002,,,,2025-05-07,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,22列 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key覆盖率\nD,D_time_calculation,time_calculation,25,\"instance_run_time, code_run_time 等时间指标\"\nD,D_cross_day_split,cross_day_split,15,跨天 trace 按天拆分正确性\nD,D_gpu_join,gpu_join,15,\"serving_id, gpu_util 等 JOIN 维度列匹配\"\nF,F_join_completeness,join_completeness,5,5张源表都被关联\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,分区值正确\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,EXPERT\n# 权重,\"product=0.8, process=0.2\"\n# 范式,new\n# Key列,\"trace_id, p_date\"\n# 预期列数,\n# 输出表,internal_platform_db.dwd_notebook_killed_instance_pod_detail_d_cand_query_engine_024", "path": "tasks/offline-compute/HiveSQL/hivesql_010"} |
| {"task_id": "hivesql_011", "id": "offline-compute_HiveSQL_hivesql_011", "name": "从 notebook_span_info_query_engine_026 和 notebook_engin", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：从 Notebook span 信息和 engine 信息关联产出 killed 实例运行明细表。\n\n输入：\n- internal_platform_db.notebook_span_info_query_engine_026\n- internal_platform_db.notebook_engine_info_query_engine_026\n\n处理规则：\n- 两表关联查询，关联条件需基于表结构确定\n- 具体过滤条件和字段选择需基于表结构确定\n\n输出要求：\n- 输出表：internal_platform_db.dwd_notebook_killed_instance_detail_d_cand_query_engine_026\n- 输出字段需基于目标表结构确定\n\n写入要求：\n- 写入目标表\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT overwrite TABLE internal_platform_db.dwd_notebook_killed_instance_detail_d_query_engine_026 PARTITION (dt = '20260507')\nWITH base_trace AS (\n SELECT trace_id\n FROM internal_platform_db.notebook_span_info_query_engine_026\n WHERE databus_imp_date >= '2026050700'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n AND span_name IN ('runner.execute', 'runner.killed')\n GROUP BY trace_id\n HAVING\n COUNT(CASE WHEN span_name = 'runner.killed' THEN 1 END) > 0\n AND\n COUNT(CASE WHEN span_name = 'runner.execute' THEN 1 END) = 0\n),\ncombined_spans AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM internal_platform_db.notebook_span_info_query_engine_026\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (SELECT trace_id FROM base_trace)\n AND span_name IN ('runner.killed', 'runner.start', 'execute.code', 'execute.code.cell', 'client.execute.code', 'set.permanent.compute', 'create.non.permanent.compute')\n\n UNION ALL\n\n SELECT\n trace_id,\n 'runner.execute' AS span_name,\n MAX(CASE WHEN span_name = 'runner.start' THEN start_time END) AS start_time,\n MAX(CASE WHEN span_name = 'runner.killed' THEN end_time END) AS end_time,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_project_id END) AS datawd_project_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_task_id END) AS datawd_task_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN datawd_task_instance_id END) AS datawd_task_instance_id,\n MAX(CASE WHEN span_name = 'runner.killed' THEN compute_type END) AS compute_type,\n 2 AS status_code\n FROM internal_platform_db.notebook_span_info_query_engine_026\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND trace_id IN (SELECT trace_id FROM base_trace)\n AND span_name IN ('runner.start', 'runner.killed')\n GROUP BY trace_id\n),\nbase_data_time_fixed AS (\n SELECT\n trace_id,\n span_name,\n CASE\n WHEN span_name = 'runner.killed'\n THEN MIN(CASE WHEN span_name IN ('execute.code', 'execute.code.cell', 'client.execute.code', 'runner.killed') THEN start_time END)\n OVER(PARTITION BY trace_id)\n ELSE start_time\n END AS start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code\n FROM combined_spans\n),\nbase_data AS (\n SELECT\n trace_id,\n span_name,\n start_time,\n end_time,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n from_unixtime(CAST(start_time AS BIGINT) / 1000) as start_date,\n from_unixtime(CAST(end_time AS BIGINT) / 1000) as end_date,\n datediff(from_unixtime(CAST(end_time AS BIGINT) / 1000), from_unixtime(CAST(start_time AS BIGINT) / 1000)) AS diff_days\n FROM base_data_time_fixed\n),\npos_series AS (\n SELECT 0 AS pos UNION ALL SELECT 1 AS pos UNION ALL SELECT 2 AS pos UNION ALL SELECT 3 AS pos\n),\ndaily_split_spans AS (\n SELECT\n trace_id,\n span_name,\n datawd_project_id,\n datawd_task_id,\n datawd_task_instance_id,\n compute_type,\n status_code,\n start_date,\n end_date,\n diff_days,\n date_add(b.start_date, s.pos) AS calc_date,\n CASE WHEN s.pos = 0 THEN b.start_time\n ELSE CAST(unix_timestamp(cast(date_add(b.start_date, s.pos) as timestamp)) * 1000 AS STRING)\n END AS start_time,\n CASE WHEN s.pos = b.diff_days THEN b.end_time\n ELSE CAST((unix_timestamp(cast(date_add(b.start_date, s.pos + 1) as timestamp)) * 1000) - 1 AS STRING)\n END AS end_time,\n b.start_time AS span_start_time,\n b.end_time AS span_end_time\n FROM base_data b\n INNER JOIN pos_series s ON s.pos <= b.diff_days\n),\ntrace_time_metrics AS (\n SELECT\n trace_id,\n calc_date,\n MAX(datawd_project_id) AS datawd_project_id,\n MAX(datawd_task_id) AS datawd_task_id,\n MAX(datawd_task_instance_id) AS datawd_task_instance_id,\n MAX(compute_type) AS compute_type,\n MAX(MAX(CASE WHEN span_name = 'runner.execute' THEN status_code END)) OVER(PARTITION BY trace_id) AS status_code,\n ROUND((MAX(CASE WHEN span_name = 'runner.execute' THEN CAST(end_time AS BIGINT) END) -\n MIN(CASE WHEN span_name = 'runner.execute' THEN CAST(start_time AS BIGINT) END)) / 1000.0) AS instance_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(end_time AS BIGINT) END) -\n MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(start_time AS BIGINT) END)) / 1000.0) AS code_run_time,\n ROUND((MAX(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN CAST(end_time AS BIGINT) END) -\n MIN(CASE WHEN span_name IN ('set.permanent.compute', 'create.non.permanent.compute') THEN CAST(start_time AS BIGINT) END)) / 1000.0) AS resource_wait_time,\n from_unixtime(MIN(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(start_time AS BIGINT) END) / 1000) AS code_start_time,\n from_unixtime(MAX(CASE WHEN span_name IN ('execute.code', 'client.execute.code', 'execute.code.cell', 'runner.killed' ) THEN CAST(end_time AS BIGINT) END) / 1000) AS code_end_time,\n from_unixtime(MIN(CASE WHEN span_name = 'runner.execute' THEN CAST(span_start_time AS BIGINT) END) / 1000) AS instance_start_time,\n from_unixtime(MAX(CASE WHEN span_name = 'runner.execute' THEN CAST(span_end_time AS BIGINT) END) / 1000) AS instance_end_time\n FROM daily_split_spans\n GROUP BY trace_id, calc_date\n)\nSELECT\n t1.calc_date AS p_date,\n t1.trace_id,\n t1.datawd_project_id,\n t1.datawd_task_id,\n t1.datawd_task_instance_id,\n t1.compute_type,\n t1.status_code,\n CAST(t1.instance_run_time AS INT) AS instance_run_time,\n CAST(t1.code_run_time AS INT) AS code_run_time,\n CAST(t1.resource_wait_time AS INT) AS resource_wait_time,\n t1.code_start_time,\n t1.code_end_time,\n t1.instance_start_time,\n t1.instance_end_time,\n t2.serving_id,\n CAST(t2.is_permanent AS BOOLEAN) AS is_permanent,\n CAST(t2.apply_for_gpu_count AS INT) AS apply_for_gpu_count\nFROM trace_time_metrics t1 LEFT JOIN (\n SELECT\n trace_id,\n MAX(serving_id) AS serving_id,\n MAX(is_permanent) AS is_permanent,\n SUM(CAST(replicas AS INT) * CAST(num_gpu AS INT)) AS apply_for_gpu_count\n FROM internal_platform_db.notebook_engine_info_query_engine_026\n WHERE databus_imp_date >= '2026050400'\n AND databus_imp_date <= '2026050700'\n AND compute_type = 'ray'\n AND service_name = 'notebook-runner'\n GROUP BY trace_id\n) t2 ON t1.trace_id = t2.trace_id\n;", "expected_csv": "p_date,trace_id,datawd_project_id,datawd_task_id,datawd_task_instance_id,compute_type,status_code,instance_run_time,code_run_time,resource_wait_time,code_start_time,code_end_time,instance_start_time,instance_end_time,serving_id,is_permanent,apply_for_gpu_count,dt\n2025-05-07,T001,P100,TASK001,INST001,ray,2,105,55,50,2025-05-07 09:59:10,2025-05-07 10:00:05,2025-05-07 09:58:20,2025-05-07 10:00:05,SRV001,True,2,20260507", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,18列 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key覆盖率\nD,D_time_calculation,time_calculation,30,\"instance_run_time, code_run_time 等时间指标\"\nD,D_cross_day_split,cross_day_split,15,跨天 trace 按天拆分正确性\nD,D_engine_join,engine_join,10,\"serving_id, is_permanent 等 engine 维度列匹配\"\nF,F_join_completeness,join_completeness,5,2张源表都被关联\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,dt=20260507\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,HARD\n# 权重,\"product=0.7, process=0.3\"\n# 范式,new\n# Key列,\"trace_id, p_date\"\n# 预期列数,\n# 输出表,internal_platform_db.dwd_notebook_killed_instance_detail_d_cand_query_engine_026", "path": "tasks/offline-compute/HiveSQL/hivesql_011"} |
| {"task_id": "hivesql_012_en", "id": "offline-compute_HiveSQL_hivesql_012", "name": "From input table internal_platform_db.t_ed_socialbook_qq_high_v2_ta", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective\nFilter out specified `tag_id` values from the tag incremental table and write the results to the target table.\n\nInput\n- `internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_query_engine_100`\n\nProcessing Rules\n1. Filter condition: `ds = 20260608` AND `tag_id NOT IN (10212021124, 10606061135, 10606061136, 10808011103)`\n2. No joins, single-table processing\n3. Field order: `guid`, `tag_id`, `tag_value`, `redis_sub_key`, `val_type`, `splitter1`, `splitter2`, `limit_size`, `tag_status`\n\nOutput Requirements\n- Output table: `internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_filter_cand_query_engine_100`\n- Output fields (9 in total, fixed order): `guid`, `tag_id`, `tag_value`, `redis_sub_key`, `val_type`, `splitter1`, `splitter2`, `limit_size`, `tag_status`\n- Partition column: `ds` (BIGINT type)\n\nWrite Requirements\n- Write strategy: `INSERT OVERWRITE`\n- Partition value: `ds=20260608`\n- Write format: ORC\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "insert overwrite table internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_filter_query_engine_100 partition(ds=20260608)\nselect \n guid,\n tag_id, \n tag_value,\n redis_sub_key, \n val_type, \n splitter1,\n splitter2, \n limit_size, \n tag_status\n from internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_query_engine_100\n where ds = 20260608 and tag_id not in (10212021124, 10606061135, 10606061136, 10808011103)", "expected_csv": "guid,tag_id,tag_value,redis_sub_key,val_type,splitter1,splitter2,limit_size,tag_status,ds\nguid001,10212021100,val1,sub1,type1,:,\",\",10,1,20260608\nguid005,10212021199,val5,sub5,type1,:,\",\",8,3,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,25,非key字段逐列值匹配率\nD,D_field_completeness,field_completeness,15,关键字段非空/非空串比例\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,ds 分区值 = 20260608\nF,F_source_filter,source_filter,10,源表分区过滤 ds=20260608 + tag_id NOT IN\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"guid, tag_id\"\n# 预期列数,10\n# 输出表,internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_filter_cand_query_engine_100", "path": "tasks/offline-compute/HiveSQL/hivesql_012_en"} |
| {"task_id": "hivesql_013", "id": "offline-compute_HiveSQL_hivesql_013", "name": "从 internal_platform_db.dws_ug_app_ad_material_actio", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：统计广告素材在各维度下的行为指标汇总，并生成一条更新日期记录。\n\n输入：\n- internal_platform_db.dws_ug_app_ad_material_action_di_query_engine_101\n\n处理规则：\n- 无 Join，单表处理\n- 过滤条件：imp_date = 20260608\n- 维度字段转换（使用 CASE WHEN）：\n - material_type: 1→'图片', 2→'视频'\n - source: 2→'人工素材', 3→'人机素材', 6→'AI素材'\n - video_model_type: 1→'镜头拆分模型(老模型)', 2→'剧情理解模型(新模型)'\n - create_type: 0→'无视频剪辑方式', 1→'高光剪辑', 2→'原始剪辑', 3→'拼合剪辑', 4→'二创剪辑', 5→'定点剪辑', 6→'拆条前贴', 7→'拆条剪辑', ELSE→'未知代码'+create_type\n - purpose: 1→'拉新', 2→'拉活', 3→'唤醒', 4→'收入', 5→'电商', 6→'当日拉活'\n - provider: 1→'AI侧', 2→'内部平台D侧'\n- 分组维度：上述转换后的维度字段 + channel_name\n- 聚合指标（均为 SUM）：product_flag→production_cnt, audit_flag→audit_cnt, pass_flag→pass_cnt, push_madia_flag→push_cnt, imp_madia_gdt_flag→imp_cnt, click_madia_gdt_flag→clck_cnt, imp_madia_cnt→imp, click_madia_cnt→clck\n- 结果由两部分 UNION ALL 构成：第一部分为分组聚合结果，第二部分为一条 update_date=20260608 且其余字段均为 NULL 的记录\n\n输出要求：\n- 输出字段顺序：update_date, material_type_name, source_name, video_model_type_name, create_type_name, purpose_name, provider_name, production_cnt, audit_cnt, pass_cnt, push_cnt, imp_cnt, clck_cnt, imp, clck, channel_name, imp_date\n- 不去重\n\n写入要求：\n- 目标表：internal_platform_db.ads_ug_app_ad_material_action_di_cand_query_engine_101\n- 写入方式：INSERT OVERWRITE\n- 分区：imp_date=20260608\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.ads_ug_app_ad_material_action_di_query_engine_101 PARTITION (imp_date=20260608)\n SELECT\n 20260608 AS update_date\n , CASE\n WHEN material_type = 1 THEN '图片'\n WHEN material_type = 2 THEN '视频'\n END AS material_type_name\n , CASE\n WHEN `source` = 2 THEN '人工素材'\n WHEN `source` = 3 THEN '人机素材'\n WHEN `source` = 6 THEN 'AI素材'\n END AS source_name\n , CASE\n WHEN video_model_type = 1 THEN '镜头拆分模型(老模型)'\n WHEN video_model_type = 2 THEN '剧情理解模型(新模型)'\n END AS video_model_type_name\n , CASE\n WHEN create_type = 0 THEN '无视频剪辑方式'\n WHEN create_type = 1 THEN '高光剪辑'\n WHEN create_type = 2 THEN '原始剪辑'\n WHEN create_type = 3 THEN '拼合剪辑'\n WHEN create_type = 4 THEN '二创剪辑'\n WHEN create_type = 5 THEN '定点剪辑'\n WHEN create_type = 6 THEN '拆条前贴'\n WHEN create_type = 7 THEN '拆条剪辑'\n ELSE CONCAT('未知代码', create_type)\n END AS create_type_name\n , CASE\n WHEN purpose = 1 THEN '拉新'\n WHEN purpose = 2 THEN '拉活'\n WHEN purpose = 3 THEN '唤醒'\n WHEN purpose = 4 THEN '收入'\n WHEN purpose = 5 THEN '电商'\n WHEN purpose = 6 THEN '当日拉活'\n END AS purpose_name\n , CASE\n WHEN `provider` = 1 THEN 'AI侧'\n WHEN `provider` = 2 THEN '内部平台D侧'\n END AS provider_name\n , SUM(product_flag) AS production_cnt\n , SUM(audit_flag) AS audit_cnt\n , SUM(pass_flag) AS pass_cnt\n , SUM(push_madia_flag) AS push_cnt\n , SUM(imp_madia_gdt_flag) AS imp_cnt\n , SUM(click_madia_gdt_flag) AS clck_cnt\n , SUM(imp_madia_cnt) AS imp\n , SUM(click_madia_cnt) AS clck\n , channel_name\n FROM internal_platform_db.dws_ug_app_ad_material_action_di_query_engine_101\n WHERE imp_date = 20260608\n GROUP BY\n CASE\n WHEN material_type = 1 THEN '图片'\n WHEN material_type = 2 THEN '视频'\n END\n , CASE\n WHEN `source` = 2 THEN '人工素材'\n WHEN `source` = 3 THEN '人机素材'\n WHEN `source` = 6 THEN 'AI素材'\n END\n , CASE\n WHEN video_model_type = 1 THEN '镜头拆分模型(老模型)'\n WHEN video_model_type = 2 THEN '剧情理解模型(新模型)'\n END\n , CASE\n WHEN create_type = 0 THEN '无视频剪辑方式'\n WHEN create_type = 1 THEN '高光剪辑'\n WHEN create_type = 2 THEN '原始剪辑'\n WHEN create_type = 3 THEN '拼合剪辑'\n WHEN create_type = 4 THEN '二创剪辑'\n WHEN create_type = 5 THEN '定点剪辑'\n WHEN create_type = 6 THEN '拆条前贴'\n WHEN create_type = 7 THEN '拆条剪辑'\n ELSE CONCAT('未知代码', create_type)\n END\n , CASE\n WHEN purpose = 1 THEN '拉新'\n WHEN purpose = 2 THEN '拉活'\n WHEN purpose = 3 THEN '唤醒'\n WHEN purpose = 4 THEN '收入'\n WHEN purpose = 5 THEN '电商'\n WHEN purpose = 6 THEN '当日拉活'\n END\n , CASE\n WHEN `provider` = 1 THEN 'AI侧'\n WHEN `provider` = 2 THEN '内部平台D侧'\n END\n , channel_name\n\n UNION ALL\n SELECT\n 20260608 AS update_date\n , NULL AS material_type_name\n , NULL AS source_name\n , NULL AS video_model_type_name\n , NULL AS create_type_name\n , NULL AS purpose_name\n , NULL AS provider_name\n , NULL AS production_cnt\n , NULL AS audit_cnt\n , NULL AS pass_cnt\n , NULL AS push_cnt\n , NULL AS imp_cnt\n , NULL AS clck_cnt\n , NULL AS imp\n , NULL AS clck\n , NULL AS channel_name", "expected_csv": "update_date,material_type_name,source_name,video_model_type_name,create_type_name,purpose_name,provider_name,production_cnt,audit_cnt,pass_cnt,push_cnt,imp_cnt,clck_cnt,imp,clck,channel_name,imp_date\n20260608,图片,人机素材,镜头拆分模型(老模型),原始剪辑,收入,AI侧,1,1,1,1,1,0,60,8,广告平台G,20260608\n20260608,视频,人机素材,剧情理解模型(新模型),高光剪辑,拉活,内部平台D侧,1,1,1,0,1,0,80,5,广告联盟Y,20260608\n20260608,视频,人工素材,剧情理解模型(新模型),定点剪辑,电商,内部平台D侧,1,0,1,1,1,1,200,30,广告联盟Y,20260608\n20260608,图片,人工素材,镜头拆分模型(老模型),无视频剪辑方式,拉新,AI侧,1,1,1,1,1,1,100,20,广告平台G,20260608\n20260608,视频,AI素材,剧情理解模型(新模型),拆条剪辑,当日拉活,AI侧,1,1,1,0,0,0,30,2,广告平台G,20260608\n20260608,图片,AI素材,镜头拆分模型(老模型),拼合剪辑,唤醒,AI侧,0,1,0,1,0,1,50,10,广告平台G,20260608\n20260608,,,,,,,,,,,,,,,,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数17 + 列名匹配\nC,C_row_alignment,row_consistency,15,行数比例 + key覆盖率\nD,D_case_when_labels,case_when_labels,20,7个维度标签列逐行匹配\nD,D_agg_metrics,agg_metrics,20,8个聚合列按权重匹配\nF,F_union_all_sentinel,union_all_sentinel,10,是否存在NULL哨兵行\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,imp_date=20260608\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"material_type_name, source_name, channel_name\"\n# 预期列数,\n# 输出表,internal_platform_db.ads_ug_app_ad_material_action_di_cand_query_engine_101", "path": "tasks/offline-compute/HiveSQL/hivesql_013"} |
| {"task_id": "hivesql_014_en", "id": "offline-compute_HiveSQL_hivesql_014", "name": "Filter add-friend behavior from internal_platform_db.log_17047_query_engine_102", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective:\nPerform cluster aggregation analysis on add-friend behavior logs to identify malicious clusters, and output statistical metrics for each cluster.\n\nInput:\n- `internal_platform_db.log_17047_query_engine_102` (add-friend behavior log table)\n\nProcessing Rules:\n1. No joins, single-table processing, using UNION of two subqueries\n2. Time filter: `day_` between 20260608–20260609, `hour_` between 2026060823–2026060900, `timestamp_` between 202606082320 and 202606090020\n3. Branch 1 filter: `appname_ in ('app_hello_txt', 'app_add_contact')`, `uinregcountry_ in ('CN','HK','MO')`, `touinregcountry_='CN'`, `length(headmd5_)>0`, `commfrinum_=0`\n4. Branch 2 filter: `appname_='app_contact_verify_ok'`, `uinregcountry_ in ('CN','HK','MO')`, `touinregcountry_='CN'`, `commfrinum_=0`\n5. Grouping fields: `appname_`, `clientversion_`, `scene_`, `ticketscene_`, `headmd5_` (empty string for branch 2), `uinipcountryid_`, `uinipprovinceid_`\n6. Aggregation fields: `addfri_pv=count(*)`, `user_id_cnt=count(distinct user_id_)`, `low_quality_cnt=sum(if(uinhighquality_=0,1,0))`, `low_quality_rate=low_quality_cnt/addfri_pv`, `user_id_list=concat_ws(',',collect_set(cast(user_id_ as string)))`, `hello_content_list=concat_ws('|',collect_set(content_))`, `evil_cnt=sum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10,1,0))`, `evil_rate=evil_cnt/addfri_pv`\n7. HAVING filter: `user_id_cnt>20` AND (`low_quality_rate>0.99` OR `evil_rate>0.98`)\n\nOutput Requirements:\nField order: `appname_`, `clientversion_`, `scene_`, `ticketscene_`, `headmd5_`, `uinipcountryid_`, `uinipprovinceid_`, `addfri_pv`, `user_id_cnt`, `low_quality_cnt`, `low_quality_rate`, `user_id_list`, `hello_content_list`, `evil_cnt`, `evil_rate`\n\nWrite Requirements:\n`INSERT INTO internal_platform_db.t_acct_addfri_action_cluster_minutely_cand_query_engine_102 PARTITION(ds=202606090010)`\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT INTO TABLE internal_platform_db.t_acct_addfri_action_cluster_minutely_query_engine_102 PARTITION(ds=202606090010)\nselect\nappname_,\nclientversion_,\nscene_,\nticketscene_,\nheadmd5_,\nuinipcountryid_,\nuinipprovinceid_,\ncount(*) as addfri_pv,\ncount(distinct user_id_) as user_id_cnt,\nsum(if(uinhighquality_=0, 1, 0)) as low_quality_cnt,\nsum(if(uinhighquality_=0, 1, 0))/count(*) as low_quality_rate,\nconcat_ws(\",\", collect_set(cast(user_id_ as string))) as user_id_list,\nconcat_ws(\"|\", collect_set(content_)) as hello_content_list,\nsum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10, 1, 0)) as evil_cnt,\nsum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10, 1, 0))/count(*) as evil_rate\nfrom internal_platform_db.log_17047_query_engine_102\nwhere date_format(day_, 'yyyyMMdd')>=20260608 and date_format(day_, 'yyyyMMdd')<=20260609\nand date_format(hour_, 'yyyyMMddHH')>=2026060823 and date_format(hour_, 'yyyyMMddHH')<=2026060900\nand timestamp_>=unix_timestamp('202606082320','yyyyMMddHHmm')\nand timestamp_<unix_timestamp('202606090020','yyyyMMddHHmm')\nand appname_ in ('app_hello_txt', 'app_add_contact')\nand uinregcountry_ in (\"CN\", \"HK\", \"MO\")\nand touinregcountry_=\"CN\"\nand length(headmd5_)>0\nand commfrinum_=0\ngroup by appname_, clientversion_, scene_, ticketscene_, headmd5_, uinipcountryid_, uinipprovinceid_\nhaving count(distinct user_id_)>20\nand ( sum(if(uinhighquality_=0, 1, 0))/count(*) > 0.99\nor sum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10, 1, 0))/count(*) > 0.98)\nunion\nselect\nappname_,\nclientversion_,\nscene_,\nticketscene_,\n\"\",\nuinipcountryid_,\nuinipprovinceid_,\ncount(*) as addfri_pv,\ncount(distinct user_id_) as user_id_cnt,\nsum(if(uinhighquality_=0, 1, 0)) as low_quality_cnt,\nsum(if(uinhighquality_=0, 1, 0))/count(*) as low_quality_rate,\nconcat_ws(\",\", collect_set(cast(user_id_ as string))) as user_id_list,\nconcat_ws(\"|\", collect_set(content_)) as hello_content_list,\nsum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10, 1, 0)) as evil_cnt,\nsum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10, 1, 0))/count(*) as evil_rate\nfrom internal_platform_db.log_17047_query_engine_102\nwhere date_format(day_, 'yyyyMMdd')>=20260608 and date_format(day_, 'yyyyMMdd')<=20260609\nand date_format(hour_, 'yyyyMMddHH')>=2026060823 and date_format(hour_, 'yyyyMMddHH')<=2026060900\nand timestamp_>=unix_timestamp('202606082320','yyyyMMddHHmm')\nand timestamp_<unix_timestamp('202606090020','yyyyMMddHHmm')\nand appname_ =\"app_contact_verify_ok\"\nand uinregcountry_ in (\"CN\", \"HK\", \"MO\")\nand touinregcountry_=\"CN\"\nand commfrinum_=0\ngroup by appname_, clientversion_, scene_, ticketscene_, uinipcountryid_, uinipprovinceid_\nhaving count(distinct user_id_)>20\nand ( sum(if(uinhighquality_=0, 1, 0))/count(*) > 0.99\nor sum(if(uinlastunbantime_>0 or opentime_+86400*90>timestamp_ or friendnum_<10, 1, 0))/count(*) > 0.98)", "expected_csv": "appname_,clientversion_,scene_,ticketscene_,headmd5_,uinipcountryid_,uinipprovinceid_,addfri_pv,user_id_cnt,low_quality_cnt,low_quality_rate,user_id_list,hello_content_list,evil_cnt,evil_rate,ds\napp_hello_txt,800,1,100,headmd5_abc,86,440000,21,21,21,1.0,\"1011,1010,1021,1020,1008,1019,1007,1018,1006,1017,1005,1016,1004,1015,1003,1014,1002,1013,1001,1012,1009\",hello,21,1.0,202606090010\napp_add_contact,900,2,200,headmd5_def,86,110000,21,21,21,1.0,\"2001,2012,2011,2010,2021,2020,2009,2008,2019,2007,2018,2006,2017,2005,2016,2004,2015,2003,2014,2002,2013\",hi,21,1.0,202606090010\napp_contact_verify_ok,800,3,300,,852,0,21,21,21,1.0,\"3002,3013,3001,3012,3011,3010,3021,3020,3009,3008,3019,3007,3018,3006,3017,3005,3016,3004,3015,3003,3014\",verify,21,1.0,202606090010", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数16 + 列名匹配\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_agg_metrics,agg_metrics,40,\"6个聚合指标列逐行匹配(addfri_pv,user_id_cnt,low_quality_rate,evil_rate,low_quality_cnt,evil_cnt)\"\nF,F_insert_overwrite,insert_overwrite,5,写入模式应为 INSERT INTO\nF,F_partition_value,partition_value,5,ds=202606090010\nF,F_union_branches,union_branches,10,UNION两路分支均存在(app_hello_txt/app_add_contact + app_contact_verify_ok)\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"appname_, scene_, ticketscene_, headmd5_, uinipprovinceid_\"\n# 预期列数,\n# 输出表,internal_platform_db.t_acct_addfri_action_cluster_minutely_cand_query_engine_102", "path": "tasks/offline-compute/HiveSQL/hivesql_014_en"} |
| {"task_id": "hivesql_015_en", "id": "offline-compute_HiveSQL_hivesql_015", "name": "From internal_platform_db.t_mg_dws_user_tag_preset_241", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Incrementally insert new user records (`user_id`, `loss_day`) into the `ds=20260608` partition of the target table that have not appeared before.\n\nInputs:\n- `internal_platform_db.t_mg_dws_user_tag_preset_24128_query_engine_103` (source table, containing fields such as `ds`, `user_id`, `loss_day`)\n- `internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_query_engine_103` (existing users table, containing previously written `user_id` values)\n\nProcessing Rules:\n1. Select records from the source table `internal_platform_db.t_mg_dws_user_tag_preset_24128_query_engine_103` where `ds=20260608`\n2. Use a `NOT IN` subquery to exclude `user_id` values that already exist in the existing users table `internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_query_engine_103` (deduplication condition: `user_id`)\n3. Extract fields: `user_id`, `loss_day`\n\nOutput Requirements:\n- Output field order: `user_id`, `loss_day`\n- No deduplication needed (duplicates are already filtered out via `NOT IN`)\n\nWrite Requirements:\n- Target table: `internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_cand_query_engine_103`\n- Write method: `INSERT INTO` (append)\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "insert into internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_query_engine_103 (user_id, loss_day)\nselect user_id,loss_day\nfrom internal_platform_db.t_mg_dws_user_tag_preset_24128_query_engine_103\nwhere\nds = 20260608\nand user_id not in (select user_id from internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_query_engine_103)", "expected_csv": "user_id,loss_day\nuser_a,5\nuser_b,3\nuser_e,1", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数匹配 + 列名匹配\nC,C_row_alignment,row_consistency,15,行数比例 + key覆盖率\nD,D_anti_join,anti_join,25,NOT IN 排除已存在 user_id 的正确性\nD,D_value_correctness,value_correctness,20,loss_day 等数值列逐行匹配\nF,F_insert_overwrite,insert_mode,5,INSERT INTO（非 OVERWRITE）\nF,F_partition_value,partition_filter,5,只取 ds=20260608\nF,F_partition_value,partition_value,5,分区值正确\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,user_id\n# 预期列数,\n# 输出表,internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_cand_query_engine_103", "path": "tasks/offline-compute/HiveSQL/hivesql_015_en"} |
| {"task_id": "hivesql_016", "id": "offline-compute_HiveSQL_hivesql_016", "name": "从用户活跃日志表统计每日各渠道的活跃用户数", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：统计各渠道的日活跃用户数。\n\n输入：\n- internal_platform_db.dwd_user_activity_log_hi_query_engine_104（用户活跃日志表）\n\n处理规则：\n1. 过滤条件：imp_date = 20260608\n2. 分组维度：channel\n3. 聚合指标：COUNT(DISTINCT user_id) AS dau\n4. 派生列：imp_date 固定值 20260608\n\n输出要求：\n- 字段顺序：imp_date（BIGINT，固定值20260608）、channel（STRING）、dau（BIGINT）\n- 按 channel 升序排列\n\n写入要求：\n- 输出表：internal_platform_db.dws_user_daily_active_by_channel_cand_query_engine_104\n- 写入方式：INSERT OVERWRITE\n- 分区：ds=20260608\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "insert overwrite table internal_platform_db.dws_user_daily_active_by_channel_query_engine_104 partition(ds='20260608')\nselect\n20260608 as imp_date\n, channel\n, count(distinct user_id) as dau\nfrom\ninternal_platform_db.dwd_user_activity_log_hi_query_engine_104\nwhere\nimp_date = 20260608\ngroup by\nchannel\norder by\nchannel", "expected_csv": "imp_date,channel,dau,ds\n20260608,appstore,3,20260608\n20260608,huawei,2,20260608\n20260608,oppo,1,20260608\n20260608,xiaomi,1,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数(4=3数据+1分区) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,25,非key字段逐列值匹配率\nD,D_field_completeness,field_completeness,15,关键字段非空比例\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION 写入模式\nF,F_partition_value,partition_value,5,ds=20260608\nF,F_source_filter,source_filter,10,源表分区过滤 imp_date=20260608\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,channel\n# 预期列数,4\n# 输出表,internal_platform_db.dws_user_daily_active_by_channel_cand_query_engine_104", "path": "tasks/offline-compute/HiveSQL/hivesql_016"} |
| {"task_id": "hivesql_017", "id": "offline-compute_HiveSQL_hivesql_017", "name": "从事件表和全量用户表 Join 过滤出 20260608 当天最后登录用户的事件，按 sdk_id", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：按 SDK 类型和小时统计活跃用户数。\n\n输入：\n- internal_platform_db.dws_event_tracking_ul2l6h5c_events_di_query_engine_114（事件明细表）\n- internal_platform_db.dws_event_tracking_ul2l6h5c_all_user_df_query_engine_114（用户维度表）\n\n处理规则：\n- Join 条件：e.user_id = u.user_id\n- 过滤条件：e.imp_date = 20260608，u.imp_date = 20260608，u.last_login_date = 20260608\n- 分组维度：sdk_id、event_time_hour\n- 聚合指标：COUNT(DISTINCT e.user_id) as active_users\n- 去重与排序：按 sdk_id 和 hour 分组，无额外排序要求\n\n输出要求：\n- 字段顺序：imp_date（固定值 20260608，BIGINT）、id（ROW_NUMBER() OVER()）、sdk_id（STRING）、hour（STRING，来源 event_time_hour）、active_users（BIGINT）\n\n写入要求：\n- 输出表：internal_platform_db.ads_event_tracking_ul2l6h5c_user_active_hour_di_cand_query_engine_114\n- 写入方式：INSERT INTO\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT INTO internal_platform_db.ads_event_tracking_ul2l6h5c_user_active_hour_di_query_engine_114\nSELECT\n20260608 as `imp_date`\n, ROW_NUMBER() OVER(ORDER BY e.`sdk_id`, e.`event_time_hour`) as `id`\n, e.`sdk_id` as `sdk_id`\n, e.`event_time_hour` as `hour`\n, COUNT(DISTINCT e.user_id) as `active_users`\nFROM\ninternal_platform_db.dws_event_tracking_ul2l6h5c_events_di_query_engine_114 e\nJOIN\ninternal_platform_db.dws_event_tracking_ul2l6h5c_all_user_df_query_engine_114 u\nON\nu.imp_date = 20260608\nand u.user_id = e.user_id\nand u.last_login_date = 20260608\nWHERE\ne.imp_date = 20260608\nGROUP BY\ne.`sdk_id`\n, e.`event_time_hour`", "expected_csv": "imp_date,id,sdk_id,hour,active_users\n20260608,1,sdk_android,10,2\n20260608,2,sdk_ios,11,2", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数(5) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,45,非key字段逐列值匹配率\nF,F_insert_overwrite,insert_mode,5,INSERT INTO 写入模式\nF,F_source_filter,source_coverage,5,2张源表均被引用\nF,F_join_completeness,join_condition,5,JOIN条件包含user_id\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,\"imp_date, id, sdk_id\"\n# 预期列数,5\n# 输出表,internal_platform_db.ads_event_tracking_ul2l6h5c_user_active_hour_di_cand_query_engine_114", "path": "tasks/offline-compute/HiveSQL/hivesql_017"} |
| {"task_id": "hivesql_018_en", "id": "offline-compute_HiveSQL_hivesql_018", "name": "Compute scores for first-level comments from view records and comment logs. Group by recall_uin + channel_id + fe", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Compute the composite score for first-level comments newly added by recalled read users after their first post view, for content heat analysis.\n\nInputs:\n- `internal_platform_db.dws_social_group_content_forum_hot_feed_recall_feed_view_hi_query_engine_122` (view records, filter `imp_hour` within [2026060811, 2026060910])\n- `internal_platform_db.dwd_social_group_content_forum_hot_feed_recall_comment_log_hi_query_engine_122` (comment logs, filter `imp_hour` within [2026060811, 2026060910])\n- `internal_platform_db.dwd_all_social_group_user_slice_ds_query_engine_122` (user identity, filter `imp_date >= 20260607`)\n\nProcessing Rules:\n1. Build a view aggregation: From the view table, aggregate by `uin` + `channel_id` + `feed_id`, extracting the first/last view time, detail page view count, and duration.\n2. Filter comments: Join the comment log with the view aggregation on `channel_id` + `feed_id`, keeping only records where the comment time is >= the first view time.\n3. Enrich user identity: Left join the user table, using `member_role` as a fallback for `user_type` (use the original `user_type` when missing).\n4. Compute first-level comment metrics: Group by `recall_uin` + `channel_id` + `feed_id` + `p_comment_id` (as the first-level comment ID) + `uin`, and compute:\n - Normal user like count (`action_type='comment_like'` AND `user_type=0`)\n - Author like count (`action_type='comment_like'` AND `uin=author_uin`)\n - Channel owner like count (`action_type='comment_like'` AND `user_type in (1,2)`)\n - Normal user reply count (`action_type='comment'` AND `comment_type='comment_reply'` AND `user_type=0` AND `uin<>comment_uin`)\n - Author reply count (`action_type='comment'` AND `comment_type='comment_reply'` AND `uin=author_uin` AND `uin<>comment_uin`)\n - Channel owner reply count (`action_type='comment'` AND `comment_type='comment_reply'` AND `user_type in (1,2)` AND `uin<>comment_uin`)\n5. Aggregate by `recall_uin` + `channel_id` + `feed_id` + `comment_id` to compute deduplicated UV and totals:\n - UV (count distinct of users with the behavior) and counts for each like/reply type\n - Normal user average reply count (total replies / number of users who replied)\n6. Compute scores (tiered rules):\n - Normal user like score: tiered by count (1→2, 2–4→4, 5–10→7, 11–20→8, 21–50→10, >50→12)\n - Author like score: tiered by UV, then +2\n - Channel owner like score: tiered by count, then +1\n - Normal user reply score: when count < 4, UV*2.5 (cap 30) |
| {"task_id": "hivesql_019_en", "id": "offline-compute_HiveSQL_hivesql_019", "name": "From input table internal_platform_db.t_app_urlsafe_cont_topweb", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Extract qualifying potential risk domains from the top websites collection table and write them to the target table.\n\nInput:\n- `internal_platform_db.t_app_urlsafe_cont_topwebsites_collect_df_query_engine_125` (fields: `domain`, `site`, `ds`, `src`)\n\nProcessing Rules:\n1. Filter condition: `src='transco'` AND `ds=20260608` AND `length(split(domain, '\\.')[0]) > 3`\n2. No joins, single-table processing\n3. Row limit: take the first 2000 rows\n4. Field mapping and derived columns:\n - `ds` = fixed value `20260608`\n - `type` = fixed value `'transco'`\n - `fuzzer` = fixed value `'-1'`\n - `result_domain` = fixed value `'-1'`\n - `original_domain` = the `domain` field from the input table\n\nOutput Requirements:\n- Output 5 columns, in the following order: `ds`, `type`, `fuzzer`, `result_domain`, `original_domain`\n- No deduplication or aggregation required\n\nWrite Requirements:\n- Target table: `internal_platform_db.t_dws_urlsafe_cont_potential_risk_domain_di_cand_query_engine_125`\n- Write method: upsert\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.t_dws_urlsafe_cont_potential_risk_domain_di_query_engine_125\nwith topsites as\n(\nselect domain as original_domain\nfrom internal_platform_db.t_app_urlsafe_cont_topwebsites_collect_df_query_engine_125\nwhere src='transco'\nand ds=20260608\nand length(split(domain, '\\\\.')[0])>3\nlimit 2000\n)\nselect\n20260608 as ds, 'transco' as type,'-1' as fuzzer, '-1' as result_domain, original_domain\nfrom topsites", "expected_csv": "ds,type,fuzzer,result_domain,original_domain\n20260608,transco,-1,-1,long-domain-name.org\n20260608,transco,-1,-1,test.site.net\n20260608,transco,-1,-1,abcd.domain.cn", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,col_count(5) + col_names(5)\nC,C_row_alignment,row_consistency,15,row_ratio(7) + key_coverage(8)\nD,D_domain_filter_correct,domain_filter_correct,25,original_domain 列值匹配率（核心过滤逻辑）\nD,D_fixed_value_correct,fixed_value_correct,20,ds/type/fuzzer/result_domain 固定值列匹配率\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE 写入模式\nF,F_partition_value,partition_value,5,ds 分区 = 20260608\nF,F_limit_2000,limit_2000,5,SQL 中包含 LIMIT 2000\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,original_domain\n# 预期列数,\n# 输出表,internal_platform_db.t_dws_urlsafe_cont_potential_risk_domain_di_cand_query_engine_125", "path": "tasks/offline-compute/HiveSQL/hivesql_019_en"} |
| {"task_id": "hivesql_020", "id": "offline-compute_HiveSQL_hivesql_020", "name": "从 internal_platform_db.t_boss_v1_dict_item_hour_query_engine_130 筛选", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：对指定分区的字典项小时表进行记录数监控统计，输出监控结果到目标表分区。\n\n输入：\n- internal_platform_db.t_boss_v1_dict_item_hour_query_engine_130\n\n处理规则：\n1. 无 Join，单表处理\n2. 过滤条件：imp_hour=2026060910\n3. 分组聚合：按 dim_name='AEGIS_ALL' 和 dim_value='AEGIS_ALL' 分组\n4. 聚合计算：count(1) 得到 compute_item_51323\n5. 派生列：\n - imp_time = 2026060910\n - data_type = 'monitor'\n - data_id = '1::dept_om::t_boss_v1_dict_item_hour_query_engine_130'\n - check_rule_id = 51322\n - check_item_id = 51323\n - compute_value = compute_item_51323\n - compare_value = null\n - check_value = compute_item_51323\n\n输出要求：\n- 输出字段顺序：imp_time, data_type, data_id, check_rule_id, check_item_id, dim_name, dim_value, compute_value, compare_value, check_value\n\n写入要求：\n- 目标表：internal_platform_db.t_boss_v1_dict_item_hour_monitor_res_cand_query_engine_130\n- 写入方式：insert overwrite\n- 分区：partition (p_2026060910, p_monitor)\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.t_boss_v1_dict_item_hour_monitor_res_query_engine_130 PARTITION (p_2026060910='p_2026060910', p_monitor='p_monitor')\nSELECT imp_time, data_type, data_id, check_rule_id, check_item_id, dim_name, dim_value, compute_value, compare_value, check_value\nFROM (\n WITH temp_table_1 AS (\n SELECT 'AEGIS_ALL' AS dim_name, 'AEGIS_ALL' AS dim_value, count(1) AS compute_item_51323\n FROM internal_platform_db.t_boss_v1_dict_item_hour_query_engine_130\n WHERE imp_hour = 2026060910\n )\n SELECT\n 2026060910 AS imp_time,\n 'monitor' AS data_type,\n '1::dept_om::t_boss_v1_dict_item_hour' AS data_id,\n 51322 AS check_rule_id,\n 51323 AS check_item_id,\n dim_name,\n dim_value,\n compute_item_51323 AS compute_value,\n CAST(null AS STRING) AS compare_value,\n compute_item_51323 AS check_value\n FROM temp_table_1\n) t", "expected_csv": "imp_time,data_type,data_id,check_rule_id,check_item_id,dim_name,dim_value,compute_value,compare_value,check_value,p_2026060910,p_monitor\n2026060910,monitor,1::dept_om::t_boss_v1_dict_item_hour,51322,51323,AEGIS_ALL,AEGIS_ALL,3,,3,p_2026060910,p_monitor", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数(12=10数据+2分区) + 列名匹配(5)\nC,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8)\nD,D_field_value_match,field_value_match,45,非key字段逐列值匹配率\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION 写入模式\nF,F_partition_value,partition_value,5,分区值 p_2026060910 / p_monitor\nF,F_cte_usage,cte_usage,5,使用CTE\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,\"data_id, check_rule_id, check_item_id\"\n# 预期列数,12\n# 输出表,internal_platform_db.t_boss_v1_dict_item_hour_monitor_res_cand_query_engine_130", "path": "tasks/offline-compute/HiveSQL/hivesql_020"} |
| {"task_id": "hivesql_021_en", "id": "offline-compute_HiveSQL_hivesql_021", "name": "Filter Chinese add-friend content from log table and write to hourly cluster table", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n### Task Objective\nFilter Chinese content from the add-friend log table for the specified hour, deduplicate, and write to the hourly cluster table.\n\n### Input\n- `internal_platform_db.log_17047_query_engine_131`\n\n### Processing Rules\n1. No joins, single-table processing\n2. Filter conditions:\n - `day_ = '2026-06-09 00:00:00'` AND `hour_ = '2026-06-09 14:00:00'`\n - `commfrinum_ = 0`\n - `appname_ IN ('app_hello_txt', 'app_add_contact', '')`\n - `length(content_) >= 6`\n - `scene_ IN (1, 3, 10, 13, 15)`\n - `content_` contains Chinese characters (`rlike '.*[一-龥]+.*'`)\n - Number of Chinese characters > 3 (`length(regexp_replace(content_,'[^一-龥]','')) > 3`)\n - Exclude records where `content_ = concat('我是', nickname_)` AND `nicknameupdatetime_ + 86400 * 30 < timestamp_`\n3. Deduplication: `SELECT DISTINCT content_`\n4. Limit: `LIMIT 1600000`\n\n### Output Requirements\n- Output field: `content_` (STRING)\n\n### Write Requirements\n- Target table: `internal_platform_db.data_team_member14_addcontent_cluster_hour_cand_query_engine_131`\n- Write method: `INSERT INTO`\n- Partition: `ds = '2026060914'`\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT INTO TABLE internal_platform_db.data_team_member14_addcontent_cluster_hour_query_engine_131 PARTITION(ds='2026060914')\nselect distinct content_\nfrom internal_platform_db.log_17047_query_engine_131\nwhere day_='2026-06-09 00:00:00' and hour_ = '2026-06-09 14:00:00'\nand commfrinum_=0\nand appname_ in ('app_hello_txt', 'app_add_contact', '')\nand length(content_)>=6\nand scene_ in (1, 3, 10, 13, 15)\nand length(regexp_replace(content_,'[^一-龥]',''))>3\nand content_ rlike '.*[一-龥]+.*'\nand not ( content_=concat(\"我是\", nickname_) and nicknameupdatetime_ + 86400 * 30 < timestamp_ )\nlimit 1600000", "expected_csv": "content_\n加好友一起聊天交流分享\n你好我想加你好友认识\n这是一个测试内容信息", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数2(含分区列ds) + 列名匹配\nC,C_row_alignment,row_consistency,15,行数比例(7) + key(content_)覆盖率(8)\nD,D_values,values,40,content_列值集合匹配率\nF,F_insert_overwrite,insert_overwrite,5,写入模式应为 INSERT INTO\nF,F_partition_value,partition_value,5,ds='2026060914'\nF,F_regex_filters,regex_filters,10,SQL中包含正则过滤关键模式(regexp_replace/rlike + length条件)\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,content_\n# 预期列数,\n# 输出表,internal_platform_db.data_team_member14_addcontent_cluster_hour_cand_query_engine_131", "path": "tasks/offline-compute/HiveSQL/hivesql_021_en"} |
| {"task_id": "hivesql_022", "id": "offline-compute_HiveSQL_hivesql_022", "name": "从 internal_platform_db.log_16159_query_engine_134 表中提取审核数据，过滤 ds=202", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：将日志表中符合特定条件的审核记录解析并转码，生成包含标签名称的可读审核结果表。\n\n输入：\n- internal_platform_db.log_16159_query_engine_134（包含审核日志字段：ds, functype_, actiontype_, othercol1_, resultinfo_, auditid_, operator_, strategyid_, orderid_, providerid_, uniqueauditid_, audittime_, createtime_, receivetime_, queue_label_）\n\n处理规则：\n1. 过滤条件：ds=2026060916、functype_=2141、actiontype_=3\n2. 字段提取（使用正则）：\n - 从 othercol1_ 提取：bizuin（regexp_extract(othercol1_, 'bizuin:(.*?);')）、nickname（regexp_extract(othercol1_, 'nickname:(.*?);')）\n - 从 resultinfo_ 提取：account_tag 字符串（regexp_extract(resultinfo_, 'account_tag:(.*?);')），按 '\\*sc\\*' 分割为 account_tag1、account_tag2、account_tag3；remark（regexp_extract(resultinfo_, 'remark:(.*?);')）\n3. 时间格式转换：audittime_、createtime_、receivetime_ 从 Unix 时间戳转为 yyyyMMddHH 格式\n4. Join 操作：定义硬编码标签映射表（label_name 和 label 两列，包含约 100 条映射），对 account_tag1、account_tag2、account_tag3 分别 LEFT JOIN 映射表，得到 account_tag1_string、account_tag2_string、account_tag3_string\n\n输出要求：\n- 字段顺序：ds, bizuin, nickname, account_tag1, account_tag2, account_tag3, account_tag1_string, account_tag2_string, account_tag3_string, remark, auditid_, operator_, strategyid_, orderid_, providerid_, uniqueauditid_, audittime, createtime_, receivetime_, queue_label_\n- 分区字段：ds=2026060916\n\n写入要求：INSERT OVERWRITE 到 internal_platform_db.dwmid_daily_ecommerce_shop_level_func_2141_audit_result_cand_query_engine_134 的 ds=2026060916 分区。\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "insert overwrite table internal_platform_db.dwmid_daily_ecommerce_shop_level_func_2141_audit_result_query_engine_134 partition(ds ='2026060916')\nwith audit_result as\n(\nselect 2026060916 ds,regexp_extract(othercol1_,'bizuin:(.*?);')bizuin,\nregexp_extract(othercol1_,'nickname:(.*?);')nickname,\nsplit(regexp_extract(resultinfo_,'account_tag:(.*?);'),'\\\\*sc\\\\*')[0] account_tag1,\nsplit(regexp_extract(resultinfo_,'account_tag:(.*?);'),'\\\\*sc\\\\*')[1] account_tag2,\nsplit(regexp_extract(resultinfo_,'account_tag:(.*?);'),'\\\\*sc\\\\*')[2] account_tag3,\nregexp_extract(resultinfo_,'remark:(.*?);')remark,\nauditid_,operator_,strategyid_,orderid_,providerid_,\nuniqueauditid_,\nfrom_unixtime(audittime_,'yyyyMMddHH') audittime,\nfrom_unixtime(createtime_,'yyyyMMddHH') createtime_,\nfrom_unixtime(receivetime_,'yyyyMMddHH') receivetime_,\nqueue_label_\nfrom internal_platform_db.log_16159_query_engine_134\nwhere ds = '2026060916'\nand functype_ = 2141\nand actiontype_ = 3\n),\nlabel as\n(\nselect '精品' as label_name,900 as label union\nselect '品牌店铺' as label_name,9001 as label union\nselect '专业店铺' as label_name,9002 as label union\nselect '优体验店铺' as label_name,9003 as label union\nselect '无问题' as label_name,800 as label union\nselect '商品品类杂糅' as label_name,8001 as label union\nselect '商超店铺' as label_name,8002 as label union\nselect '无问题' as label_name,8003 as label union\nselect '待优化' as label_name,700 as label union\nselect '购买场景不适用' as label_name,7001 as label union\nselect '商品不可购买' as label_name,70011 as label union\nselect '条件限制商品' as label_name,70012 as label union\nselect '店铺信息随意' as label_name,7002 as label union\nselect '头像设置随意' as label_name,70021 as label union\nselect '商品图片随意' as label_name,70022 as label union\nselect '商品标题随意' as label_name,70023 as label union\nselect '商品属性信息填写随意' as label_name,70024 as label union\nselect 'sku信息设置不规范' as label_name,70025 as label union\nselect '店铺整体随意' as label_name,70026 as label union\nselect '商品重复铺货' as label_name,7003 as label union\nselect '重复铺货（轻微）' as label_name,70031 as label union\nselect '重复铺货（严重）' as label_name,70032 as label union\nselect '图片不规范' as label_name,7004 as label union\nselect '商品主体不明' as label_name,70041 as label union\nselect '图片非真实' as label_name,70042 as label union\nselect '商品详情页质量差' as label_name,70043 as label union\nselect '商品图片重复/单一图片' as label_name,70044 as label union\nselect '标题不规范' as label_name,7005 as label union\nselect '关键词/品类词堆砌' as label_name,70051 as label union\nselect '与商品无关字符' as label_name,70052 as label union\nselect '店铺综合质量差' as label_name,7006 as label union\nselect '标题图片不规范' as label_name,70061 as label union\nselect '语言规范' as label_name,7007 as label union\nselect '汉语言' as label_name,70071 as label union\nselect '其他语言规范' as label_name,70072 as label union\nselect '不规范营销' as label_name,7008 as label union\nselect '霸王条款' as label_name,70081 as label union\nselect '个人敏感信息' as label_name,70082 as label union\nselect '引人不适（轻微）' as label_name,7009 as label union\nselect '推荐体验差' as label_name,7010 as label union\nselect '画风抽象恶搞' as label_name,701001 as label union\nselect '画风低俗' as label_name,701002 as label union\nselect '特殊需求群体' as label_name,701002 as label union\nselect '店铺名称头像不规范' as label_name,7011 as label union\nselect '泛低质' as label_name,600 as label union\nselect '引人不适（严重）' as label_name,6001 as label union\nselect '低质量图片' as label_name,6002 as label union\nselect '商品图片盗图' as label_name,60021 as label union\nselect '图片明显标记' as label_name,60022 as label union\nselect '滥用标题' as label_name,6003 as label union\nselect '品牌蹭流' as label_name,60031 as label union\nselect '标题作弊' as label_name,60032 as label union\nselect '滥用同款关键词' as label_name,60033 as label union\nselect '串类目蹭流' as label_name,60034 as label union\nselect '商品信息不一致' as label_name,6004 as label union\nselect '绕类目' as label_name,6005 as label union\nselect '导流' as label_name,6006 as label union\nselect '名称头像导流' as label_name,60061 as label union\nselect '商品导流' as label_name,60062 as label union\nselect '诱导营销' as label_name,6007 as label union\nselect '低价诱导' as label_name,60071 as label union\nselect '利益诱导' as label_name,60072 as label union\nselect '宣传标语/图片夸张博眼球' as label_name,60073 as label union\nselect '额外购买门槛' as label_name,60074 as label union\nselect '不实互动诱导行为' as label_name,60075 as label union\nselect '虚假营销' as label_name,6008 as label union\nselect '商品销量虚假宣传' as label_name,60081 as label union\nselect '价格/折扣虚假宣传' as label_name,60082 as label union\nselect '滥发/虚假使用品牌专柜词' as label_name,60083 as label union\nselect '色情低俗（轻微）' as label_name,6009 as label union\nselect '店铺名称不规范' as label_name,6010 as label union\nselect '商品低质' as label_name,6011 as label union\nselect '疑似仿冒/非原创' as label_name,60111 as label union\nselect '违规风险' as label_name,500 as label union\nselect '店铺信息违规' as label_name,5001 as label union\nselect '混淆蹭流' as label_name,50011 as label union\nselect '不当营销名称' as label_name,50012 as label union\nselect '宗教' as label_name,50013 as label union\nselect '违法违禁' as label_name,5002 as label union\nselect '违法犯罪' as label_name,50021 as label union\nselect '售假山寨' as label_name,50022 as label union\nselect '未开放服务' as label_name,50023 as label union\nselect '劣迹负面艺人' as label_name,50024 as label union\nselect '色情低俗（严重）' as label_name,5003 as label union\nselect '时政敏感' as label_name,5004 as label union\nselect '社会热点' as label_name,50041 as label union\nselect '政治敏感' as label_name,50042 as label union\nselect '恐怖组织/邪教' as label_name,5005 as label union\nselect '违规营销' as label_name,5006 as label union\nselect '违反社会风尚' as label_name,5007 as label union\nselect '不文明用语' as label_name,50071 as label union\nselect '不良价值观' as label_name,50072 as label\n)\nselect bizuin,nickname,account_tag1,account_tag2,account_tag3,\nt2.label_name account_tag1_string,t3.label_name account_tag2_string,t4.label_name account_tag3_string,\nremark,\nauditid_,operator_,strategyid_,orderid_,providerid_,\nuniqueauditid_,\naudittime,\ncreatetime_,\nreceivetime_,\nqueue_label_\nfrom\n(\nselect bizuin,nickname,account_tag1,account_tag2,account_tag3,\nremark,\nauditid_,operator_,strategyid_,orderid_,providerid_,\nuniqueauditid_,\naudittime,\ncreatetime_,\nreceivetime_,\nqueue_label_\nfrom audit_result\n)t1\nleft join\nlabel t2 on t1.account_tag1 = t2.label\nleft join\nlabel t3 on t1.account_tag2 = t3.label\nleft join\nlabel t4 on t1.account_tag3 = t4.label", "expected_csv": "bizuin,nickname,account_tag1,account_tag2,account_tag3,account_tag1_string,account_tag2_string,account_tag3_string,remark,auditid_,operator_,strategyid_,orderid_,providerid_,uniqueauditid_,audittime,createtime_,receivetime_,queue_label_,ds\nbiz002,shop_B,8001,6001,5002,商品品类杂糅,引人不适（严重）,违法违禁,another_remark,5002,op_user2,102,order2,provider2,90002,2024060616,2024060616,2024060616,20,2026060916\nbiz001,shop_A,900,9001,700,精品,品牌店铺,待优化,test_remark,5001,op_user1,101,order1,provider1,90001,2024060616,2024060615,2024060615,10,2026060916\nbiz003,shop_C,60031,,,品牌蹭流,,,third_remark,5003,op_user3,103,order3,provider3,90003,2024060616,2024060616,2024060616,30,2026060916", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数20 + 列名匹配\nC,C_row_alignment,row_consistency,10,\"行数比例 + key(uniqueauditid_,strategyid_,orderid_,providerid_)覆盖率\"\nD,D_regex_extraction,regex_extraction,20,\"正则提取字段逐行匹配(bizuin,nickname,account_tag1/2/3,remark)\"\nD,D_account_tag_mapping,account_tag_mapping,25,\"标签映射字段逐行匹配(account_tag1_string,account_tag2_string,account_tag3_string)\"\nD,D_time_and_passthrough,time_and_passthrough,10,\"时间转换+直传字段逐行匹配(audittime,createtime_,receivetime_,auditid_,operator_,queue_label_)\"\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE 写入模式\nF,F_partition,partition,5,ds=2026060916 分区正确\nF,F_null_handling,null_handling,5,LEFT JOIN 无不当 NULL\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,HARD\n# 权重,\"product=0.7, process=0.3\"\n# 范式,new\n# Key列,\"uniqueauditid_, strategyid_, orderid_, providerid_\"\n# 预期列数,20\n# 输出表,internal_platform_db.dwmid_daily_ecommerce_shop_level_func_2141_audit_result_cand_query_engine_134", "path": "tasks/offline-compute/HiveSQL/hivesql_022"} |
| {"task_id": "hivesql_023_en", "id": "offline-compute_HiveSQL_hivesql_023", "name": "Compute multi-dimensional CUBE aggregation of user open-start-path data by referer_type, referer, and path", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Compute multi-dimensional aggregation metrics from the user open-start-path detail table, outputting UV and first-time UV across three dimension combinations: application category (`referer_type`), source application (`referer`), and launch path (`path`).\n\nInputs:\n- `internal_platform_db.t_ed_mapservice_user_open_start_path_dwd_di_query_engine_135`\n- `internal_platform_db.t_ed_mapservice_user_q36_active_dwd_di_query_engine_135`\n\nProcessing Rules:\n1. Main table filter: `ds=20260608`, and `referer` must be in the specified lists of application package names/keys for three categories (FoodChainA, QuickDog, JD).\n2. Path cleansing: Process the `path` field according to rules — if it matches `qqmap://map/routeplan?type=\\w+`, retain the original value; if it contains `?`, truncate to the part before the question mark; if it is empty/null/`'NULL'`, set it to `'未知'`; otherwise, retain the original value.\n3. `referer_type` mapping: Map the `referer` value to one of three categories: `'FoodChainA'`, `'CourierCoB'` (QuickDog), or `'EcommerceC'` (JD).\n4. Join: Join with `t_ed_mapservice_user_q36_active_dwd_di_query_engine_135` (`ds=20260608`, `uin` deduplicated) on `uin`, retaining active users.\n5. Aggregation: Group by `referer_type`, `CUBE(referer, path)`, computing `count(distinct uin)` as `uv` and `count(distinct case when is_first=1 then uin end)` as `first_uv`.\n6. CUBE result processing: For dimensions where `grouping` is 1, set the value to `'total'`.\n\nOutput Requirements:\n- Field order: `referer_type`, `referer`, `path`, `uv`, `first_uv`, `ds`\n- `referer` and `path` display as `'total'` on CUBE summary rows\n- `uv` is of type BIGINT, `first_uv` is of type BIGINT\n\nWrite Requirements:\n- Output table: `internal_platform_db.t_md_mapservice_user_open_start_path_dwa_di_cand_query_engine_135`\n- Partition: `ds=20260608`\n- Write method: `INSERT OVERWRITE`\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "insert overwrite table internal_platform_db.t_md_mapservice_user_open_start_path_dwa_di_query_engine_135 partition(ds=20260608)\nwith t as (\nselect \nevent_time,platform,app_version,channel,t1.uin,event_code,event_value,city,brand,device_id_type,bg,event_timestamp,referer,path,is_first\n,case when referer in ('com.foodchain_a.android.activity','FoodChainA','com.jooyum.s2pFlutter','com.jooyum.s2p','ZPVBZ-HDXWU-NQCVT-G76JZ-NMJ2H-M4FRN','ZDSBZ-HICKQ-YBW5C-4KX5E-QOXEZ-MPBI2','XTJBZ-F546Z-I6GXC-7NWJS-35WDV-RSFP5','WPRBZ-VIY6Q-NS45V-457XZ-3XUZJ-ZEFZH','U7VBZ-6XSWI-5VLGP-UYDPQ-EYNTF-4EFEJ','TUPBZ-M7GWQ-EYL5Q-2BYZ2-5WDQV-C7B7E','TOPBZ-WGE65-MNLI5-IGBOS-X3IHQ-YBBY4','RP5BZ-K5ZKZ-2CTXC-T3VKM-UQDO3-FRFYB','QB3BZ-KMC6W-G4NRU-YON5I-ORFV2-RCBFD','OZIBZ-GKVCN-ZHSFP-SAA5H-UO3ZV-D3F5Q','OZCBZ-ZN4KT-K7RXH-VFR3V-J4TPZ-OFBGJ','OW5BZ-QNACB-2RGUS-NV54M-4TEU2-JQB24','KBJBZ-UNHKM-KQH6E-67NMF-DDCGT-LLBWQ','KAWBZ-227KP-F73D6-L4JDM-6K6H5-SYFVU','IARBZ-EDBCA-BYCK7-CW7UG-LF5JS-DWFWO','HW3BZ-RQCLT-CKBXW-VWELQ-IIMGS-YVF7J','7YLBZ-2IKWM-65U64-66SIM平台Q-Y5SG7-MBBO6','3NVBZ-LALKB-A5CUC-N3IYU-S3FV6-TAFZY','3FABZ-UUIE7-TMAXI-HXWPA-XQONZ-IWBTP','2K2BZ-NVFKJ-7T3FU-DPCNP-X3IJO-N2FE6') then 'FoodChainA' \nwhen referer in ('com.couriercob.huoyun','couriercob','topapp.couriercob.directionx','topapp.couriercob.direction') then 'CourierCoB' \nwhen referer in ('com.delivery_d.mobile.android','EcommerceC秒送骑士','达达秒送骑士','达达骑士','com.dada.knight.hm','com.ecommerce_c.mall','ZZLBZ-PDU6F-POPJ3-NAMUM-GQJXV-WNBQI','ZX5BZ-53WHV-6QAP5-UZRK5-O3D5J-2PF2J','ZTABZ-76XRW-2ZRRR-OUCJW-47TS5-MXFIG','ZKKBZ-OFMWJ-DUWFN-FIM平台Q5U-ULITH-TXFCF','ZFVBZ-3P2KU-EKWVK-BN4O2-G6BWZ-Z2FIZ','Z6SBZ-ZB7CV-ZWUPO-5XZPI-6ZB4O-M2BZY','YVCBZ-PANKF-MQ5JM-N3HHT-TGW32-WXBYJ','YUNBZ-M5SWQ-5US5V-GJF6B-LU5X7-L6BS2','YRHBZ-7J2WV-GHFPD-5SS3Z-QXOC6-XJB2D','YLLBZ-3U5KJ-442FD-FM6LV-XOOCV-W5F7V','YKPBZ-3ZHKQ-EJN56-4WVGP-2UTD7-XXFPM','YHZBZ-6KUKG-IXYQH-IH2RF-AVZAJ-4LBVG','YEABZ-MA33X-2OR4T-TLTG6-ATGCV-DKBWY','Y4DBZ-OR5KP-CQIDE-VDFLC-CRFGK-ZNBBC','Y3LBZ-OVSRX-JTR4K-ZGEAY-VHWPZ-LPBGE','XZFBZ-ZL4KS-2KUO5-6S5LD-A47AV-BBF6Z','XXIBZ-EG3KS-EGNOH-6JPV5-LZQ3F-4MBTC','XX2BZ-U6C66-E2XSO-M4VV7-F5VE3-QUB3P','XUNBZ-Y5DK7-OOOXA-P5R7W-GNVOS-KHBJD','XSABZ-MKVC3-MMJ3U-R25R2-YZHCS-BTBZV','XRPBZ-GSQKQ-G3U5J-4NSC4-MXL4Q-Q7FFJ','XNTBZ-5NSWU-O32VI-2CV24-3TPBE-PBFTV','XJXBZ-AI5LU-AYSVB-BWHEJ-CGPZ6-XZBDA','XENBZ-NEH3J-I6HFS-K3WPP-IACAO-3KBLL','X6OBZ-JA3KJ-LYKFN-F6YQZ-YSIH3-MAB4L','X5PBZ-LS4K3-3GS3H-YGWU2-HSOWZ-C6BD3','WX4BZ-LBZC2-BA3UR-CJAWJ-6PVNS-S2F24','WWSBZ-ON3WX-BNQ4O-ZLAJA-4XPSJ-THFSC','WUZBZ-ENAWJ-2ZOFB-KAUKC-GSFMF-MAFAE','WQPBZ-RB7R2-TDLUB-CP4KZ-X4ZW3-NJBWN','WQKBZ-K3QHW-XVURR-37EXR-FIJ3E-U3BCG','WONBZ-ORHCL-5USP6-MNLYR-47Z2T-HNFUG','WLSBZ-5MKCU-L5IVM-2WFBE-CNNEZ-7QFQF','WLHBZ-JX6OW-KK5RL-YY63F-64JN3-XMFWG','WK6BZ-CHL6F-Q6JJX-J5BE4-JSN6K-VWFEB','WJ6BZ-JQJK3-VUI3J-YLLBJ-Y2DWS-FEFFM','WGQBZ-3HSR4-D7OUM-XWNS4-VDQ22-XCBYH','WF2BZ-JRCWW-YNVRX-3EOZC-QWXAJ-APFIO','WDPBZ-3IUKM-6IQ64-6YCC7-OQTD2-IIF5E','WC4BZ-RLGK5-LLAI2-IM平台QZZ7-HW47V-MMFIQ','WBLBZ-MPI33-KCS3K-Y3O56-IPXWH-UPBUQ','WBBBZ-GEPW6-I7DSA-MMCFV-LSCX7-VLB77','WB6BZ-7AOWK-PREJZ-AWMIR-JEXNO-5IBB4','WAWBZ-7HI3X-KUG4N-Z2Q5A-URJ6J-RZBT7','WA6BZ-3TN6F-PTYJH-N4U2N-WLJ35-2YFBT','W7FBZ-EVU65-ELVIZ-QZ3ES-PCKO6-CBFG7','W6JBZ-SZGK4-4KKUS-XL2WH-NC425-Z5BLY','W64BZ-QLMRW-ABPRE-OJP6G-5MZJJ-SCB54','VVZBZ-QGVKT-324XO-LRJPN-C7XDT-KDBGF','VN2BZ-O6YCJ-46SF4-KV7ND-CBM56-G6BE6','VKEBZ-S2SCK-5AMJU-ANYMZ-5F55H-ZVBUA','VJSBZ-JJVKR-S7YWV-WGCJ5-LMWPE-3FFJU','VEOBZ-KMYC6-4K7SJ-E42RI-VXGWQ-K2BYM','VB5BZ-URGWI-P46GJ-5TU4Z-6HAD6-RABYZ','VAYBZ-QVN6X-2GG4C-ZSRCY-P4A3J-PSBTJ','V4GBZ-NIZCG-SYYQH-IEHM2-6U2DV-HYBTJ','UWIBZ-DPEKD-KZ44R-HRQXO-TBYIZ-UOF3Z','URNBZ-XJ7CV-KXUPB-5BBOH-YSGG7-TRFER','UNIBZ-RG53P-EXPD3-V3K3J-MWD47-ZDBMO','UIWBZ-GMHE3-RMA3D-3QWOS-OOPW5-NLFNI','UI2BZ-JGQKS-SPJOW-6V7MA-7DACS-LMBJH','UDEBZ-AC6LV-5YEPU-UQPBQ-ZUQUQ-JBF4N','UALBZ-W数据仓库DWU-J47VQ-4KX4R-3UPW3-BEFYZ','U5WBZ-FCKWF-BLOJL-JCSMG-AKUY3-4IB55','U4ZBZ-PLQLU-LZ5VM-2A3YV-ROE2K-VSBNV','TWQBZ-QNWWM-W3Y6Y-6VQIY-GIDBF-7QFNJ','TVXBZ-WGC33-BFE35-3YMNI-MDJJ7-KUB4J','TUKBZ-5PRWF-MDNJ2-JLFXE-I6C72-JLBEX','TSVBZ-2TOWD-MLB4H-H6QND-BCDFF-UNBA6','TSSBZ-OYCWU-C3RVQ-4PTW6-MJ5XF-3PFB7','TQYBZ-BY464-CPWUZ-DN7UF-CXN4F-BJFU6','TMABZ-73FCT-QHDXC-L2FXB-WDW2F-MDB36','TM6BZ-MREWX-M674T-7Y577-HUHT5-73BRI','TLGBZ-ZQ2L5-5EYIW-QXWRQ-GZY7O-GRBJ7','TL7BZ-4E7KM-Q6Y6Y-6REVK-Z54RQ-OIBZ4','TJZBZ-UAZLJ-KDOFO-XZST5-LETF3-3PF6Q','TJ3BZ-2VN3D-6LW4P-P2V2R-J3SLK-OOFSS','THCBZ-HSY3J-YFEFB-F65JZ-GUAYH-7OFV5','TDMBZ-L7BWJ-XNOF3-F3J7H-7YVYE-BLBJ7','T6TBZ-34Z6Q-DCM55-2DWW7-PZDR3-QFF7W','T35BZ-FLSW5-6HTIH-IUMMI-D2MUH-ZOBXE','SZ6BZ-5PQWQ-BWO5M-BN2MP-KTUPJ-TNF57','SYSBZ-6FEC3-OYQ3L-OU7G6-74RZ2-IZFQA','SXTBZ-BOSWV-7R4PP-UJYEB-ZSF3F-3ABGZ','SUTBZ-2TOWC-HJR2P-ALIS3-MJC37-KNFHK','SU3BZ-ZYMYJ-IZ7FU-D3SCZ-P3W6J-4JFCZ','SS6BZ-BJQLS-7UEO7-63YPZ-CKIYV-LRBNP','SN4BZ-HJBWH-L3MD3-W5XNH-O2HR7-ZJFXW','SKJBZ-2NPCW-SYPR4-RDNEB-UPHP3-VNFF4','SJNBZ-DHWKD-DZ44H-HZTPA-5TGIE-3GFMK','SHNBZ-UQCKP-M5IDN-VZH5O-VEUEE-4SBGS','SGGBZ-L7BRL-NXRPA-EZOGT-TEYJ5-6EBNL','SFABZ-HQAKR-RRUWO-WPRRX-5RDYE-RTFPH','S5JBZ-I56K3-K2O3U-O3LKZ-VZBJ3-Y7BLH','S4YBZ-JXWE2-6RSUP-CJWMQ-JOBX2-6NF3D','S3DBZ-XUOCX-RDC4K-77XXV-RP262-NNBEB','RTDBZ-QWNWB-FRUUP-J4YBH-LVS5T-T7FC4','RICBZ-VHI65-IA4IB-QSTW5-SWUJK-H4B2G','RCNBZ-2A56D-CUZ4W-PRRI4-TMCFH-N5F7H','RAXBZ-CEZL3-VDN34-3QGSC-PXJVS-UCBFQ','RA4BZ-2UN6S-JVYO5-654A2-VVX42-CZBPI','R5XBZ-VPX3U-FV4VJ-2CCEG-M4PNF-R3FZR','R36BZ-5DXK6-2M4SI-MPQYI-CF4PO-IOB6Q','QZOBZ-XSAKU-FLFVN-BKHFO-KIKC3-IBBEV','QZIBZ-GGRLR-L2EWA-WJTQN-2YPBK-X7B3O','QUWBZ-A6JCJ-WY7FR-KMHQI-35GHO-RZBDJ','QRPBZ-5YLLU-GHMVC-BPOKS-N5KAE-FDF5R','QMCBZ-PLTC3-N443X-YS6EC-IS4YZ-NYBBT','QCFBZ-XFPHU-RLBVL-B7FZG-ALZOK-GGFXC','Q73BZ-DOGWQ-V3V5G-4ADG2-DTAZ3-QOFGC','PWTBZ-3QPCQ-ZMW53-GZA6H-YO7QF-5FFML','PVBBZ-ZYVCF-CI7J3-JZBV7-VXARZ-4MFU7','PRTBZ-2HZE4-TLHUA-D3KVI-T5GJQ-WFFAX','PQDBZ-O2B3H-4ZLDA-WTTJK-Q4CMF-RJB66','PJHBZ-EHDCN-R7FFD-SYVY4-IWH3H-VMBS5','PIXBZ-ZRREQ-O6G5D-2Y4K3-IMMAH-BXFB3','OW4BZ-MAZL7-LOTXV-HVTA3-XUWZK-2KBBO','OS3BZ-GC2CN-CL7FB-STVNJ-YW3S2-7MFIH','OQVBZ-WAD6X-RMY4Z-7XZJG-Q7AMH-FPFP6','OOUBZ-KKX3W-7JSR4-R2AQG-U6R6T-RXBMI','OMZBZ-EMQK4-DCIUU-DHOQA-XLPPK-3GFTM','OLVBZ-7L2HG-YPGQE-I3NON-J3X5Z-3CFQL','OL3BZ-ZBIKX-DHL4B-7EEWX-N33F6-N5FZQ','OH4BZ-RDVKP-RNSDY-VQKUA-SPQGE-7QFCY','OCKBZ-ZAGKK-RXLJR-A3PAM-YO7QH-V6FMJ','NZFBZ-EVYLZ-WQMXM-TYJRV-ZNYYF-W5FCZ','NYSBZ-EJLLC-HDT2X-A3ZPN-ISW3V-4JBBF','NU3BZ-JAPWV-VVNP7-5CEVQ-7NXXZ-R6BKN','NSKBZ-5VYKL-PFTPA-ESBZB-LGDF6-YEFKN','NRXBZ-PNV3R-LCPWU-WL562-YA427-NXBND','NQ4BZ-U7Y6D-7OM4W-HODRT-LK5Z6-DLBT4','NPFBZ-YZNKW-OIMR2-OIK23-OZPQ7-BVFWI','NMQBZ-WKRCQ-FFX5J-BFCGT-ORQHO-MFBXD','NJXBZ-ALDWU-HXSVU-27OKA-U4RE7-RQBGU','NIDBZ-A5TWU-G22V6-B25Y3-HQ6RV-MSFBQ','NENBZ-TUCW4-LL4U5-XGLVN-6YTPJ-CFBQ7','NDPBZ-SPFWW-2J6RH-RFUV3-CYPVK-XRFQ3','NC5BZ-4S2CP-UKODO-L4557-ZGPRO-DXBIE','NBWBZ-6URYV-Z6ZPL-5ISTO-TJW23-6TFNV','NAZBZ-5BYWL-JENPH-EV6GL-75ZMQ-L6B2Q','N7HBZ-ORS6J-AYQF3-KBXS3-VFRIV-QOBYY','N6VBZ-FLTWD-MAM4Q-HZR6Q-CVNV7-OOFE4','N6HBZ-6GSCU-R4YVE-BBFYK-KVP3T-F5BZJ','MVPBZ-KU7CP-YIPDZ-VFN4W-FDFMT-NTFMR','MVLBZ-FCW6Q-RIL5G-G5WTY-EV4PT-GQB4U','MPMBZ-ML5CU-V4OVT-BDVGM-K3NMQ-JIBRP','MFHBZ-S77KZ-N5YXF-TCKSB-HCOY6-PNF7K','MDIBZ-OZ6KU-REJV2-B5G46-HUJY7-IHFXS','MBCBZ-DE3LN-55AFX-SG2IE-2RYHT-ZJFQO','M6PBZ-QPUAF-KSJJD-JFNMG-NS5F2-UDB7F','LW7BZ-TK23O-SFQWS-SP6ZB-OD2J3-MNB6T','LVQBZ-N3PRG-VOTQC-I6MIR-EAAKK-TJBLZ','LUSBZ-HVG33-CXH3W-RCONB-6UMIK-ELBFL','LKMBZ-LVVK6-KG2SJ-E34GA-CHH7H-P5BIX','LHMBZ-R6CK2-V4FUE-CCFN7-AVGDF-SCFKF','LHKBZ-2IQ6U-HYRVB-45WJ7-KYKY3-67FHU','LBKBZ-ZP53Q-IBY5T-4TL3J-SDAM2-FKBYB','L6LBZ-Y3DLC-6UO2U-AJSTD-3TNR3-NLBOU','L4QBZ-4TOKG-7OKQE-QLF7O-IYCE3-S3F44','KZ2BZ-4S4RV-2TOP6-URXZA-MQD4T-6GBOK','KUEBZ-2OWCU-DP7VP-4QHTL-PMY66-RSFJC','KOHBZ-T3Q34-IBLU5-XWIEP-POSSO-SQBZV','K6IBZ-T4DCU-X5OVA-4GHZ6-WSYWO-K6BYJ','K2MBZ-AH2CR-6GWWL-WRT2W-QWLTT-O4FWV','JXJBZ-IORWR-CVDWX-WCZHK-M762V-RTFUU','JVSBZ-2KBW3-DK33H-33SVV-LYWVT-GDFAQ','JVKBZ-TN33Z-D2KXG-TJQDB-TCJNO-UKB6B','JUVBZ-FZYLJ-EMQFS-XZNK5-AT4UT-ASBDL','JQMBZ-UQ5ES-H6TON-6QLUQ-AM5P7-LXF7Z','JQHBZ-6T4RI-AHBGM-52MTT-UEKF7-WVFMR','JOEBZ-MTVHP-JR6D3-L6DVQ-RMXL7-RLFTI','JN2BZ-WX5C4-TFLUP-XCG3I-7NF2F-VLFEY','JKDBZ-QGTKO-GWFWF-SQ2ZZ-5S2GE-S4FVN','JJVBZ-JG4LV-4AAPM-UPFVK-MEW57-WXF2D','JG7BZ-VJBKO-HG7W7-SGOCJ-2YYYH-TUBFJ','JCVBZ-PHRW3-7VI3J-YXP72-HBHX2-VGB2Z','J4LBZ-W7SKW-HCGRQ-O3WEE-RI4K6-4QBLI','IXBBZ-IFKHF-CIM平台QJF-JBHXY-VP7S3-JVFOL','IWUBZ-SRQLF-KJSJ5-JIO4T-X3AVS-LSFB6','IV4BZ-TY2CV-NDRPU-U4MCE-RVSI5-2YFAA','IUMBZ-Z63W3-ZGS3J-OTXZX-5IAJ5-EZBRB','ITRBZ-PJEC4-JUIUL-XBAWG-OZKTK-3TF54','IOVBZ-R3S6Z-TJVXH-ZJ6QF-7UIUS-TRB6D','INNBZ-UVFWR-HXAW4-WAR6D-2KMDO-ZKBR3','INKBZ-PMMET-OPLX7-LE6XQ-IUNEZ-HSF6A','I6ZBZ-6NP36-W6OSU-MNXRW-RTAPQ-DWFUX','I5HBZ-2CQC4-WERUN-K5PCX-PTGUJ-AHBJY','I5ABZ-GVKHQ-HPF5Q-BLFQP-O6FPH-3XFHN','I2XBZ-H5PW5-HDKIC-IMWLW-4JT6O-QSBCK','HXPBZ-PHZWI-U5OGG-5JDIW-3OIKJ-3FBPR','HU2BZ-XXPC6-L3NSP-M3RDC-MMFP5-QRFPW','HSUBZ-2WS3K-RCLJG-AJRLN-666OK-2CFE6','HSKBZ-5A4C3-LN43L-OAIGR-JZCJJ-LZFKP','HO7BZ-WVQH3-YPC3A-OJE4P-DEQDT-CRF3Y','HJOBZ-EBJW3-Y6R3A-3TID4-CIMD3-CFB5F','HFEBZ-FBNCJ-FVEFY-FCVG2-TOQ5Z-L6FKL','HELBZ-6V5KJ-HEDFF-KZBRN-4XDF5-JFBCK','H7FBZ-DYMC6-4ERSD-MKEGK-PS5PH-6LBHI','H42WY-75WY3-Y4VSE-VXEO5-JWA7A','GWVBZ-7PCK2-XHBUZ-CTNHR-3TY45-7DBNP','GRSBZ-6YGCW-AHYRE-RSUQP-KKIVS-JPFN3','GQEBZ-GNUKS-7APOK-67QIV-Q2K2V-VCBA3','GPLBZ-N23WW-TSBRF-ROZNE-A5PQ3-Q2BUJ','GOOBZ-SDKWU-CNIV6-4CXMK-77YI7-I7BYN','GKEBZ-YYKC4-QLPU6-XNBQ5-CUY4E-22BXW','GANBZ-OWZLU-YJKVS-G5WBV-2FEGQ-P2B67','G2HBZ-YSLCD-BQS4K-PTUBH-AWJWJ-NDFHU','FYBBZ-OHOKN-HMBFJ-S7PG6-D5BZZ-NSBKK','FYABZ-ZLM3W-UM5RX-OZT4N-K5XSV-HTBKW','FRCBZ-CXMC3-YBG3U-YEJGU-YUPWO-ZRFOY','FQHBZ-RUQEJ-NUBFI-K6QF3-5MQ2J-6DFPD','FNJBZ-PM4K5-KAKIK-QJ5O3-WLU3S-PJBGV','FKXBZ-WZ36J-NIHF5-K2PVL-DMIWQ-YZBQ4','FJ5BZ-URIWP-TA5DK-V5AY7-S2DQS-ROF6C','FIUBZ-DSU6X-KUP4H-ZXFGL-W6SN2-UWFH7','FGRBZ-2FJCU-EAMVP-2G7QH-ZZN45-V7BL7','FEBBZ-3FUH2-AGDU2-CACFJ-OILEH-SYF6N','FCTBZ-2HMHJ-BDMFO-FT3FC-IPE3O-G7FLK','FBPBZ-A7Z3X-3MU4V-Z5VGB-53KG3-WEBC5','F6ZBZ-CJK3X-MT64P-TOOKY-GAPGH-TGB5D','F3ZBZ-TQAWX-42L4W-7LKDL-7PF6E-ZGB57','EZRBZ-27CRQ-VRQ5G-43QTK-ZOFK7-MBBCQ','EYHBZ-ENXW3-DHK3N-3AETB-TDFJ2-TWFLX','EXQBZ-NRYLD-KZB46-H4KUD-X63JJ-3BFNX','EVOBZ-EGIWB-PKRUO-NO4ST-RZAV6-GBB66','EQEBZ-N2FLD-MVV4U-HTQBV-43J57-WRBWZ','EM7BZ-OT3RZ-LP3X6-77G5Z-G7ONH-W3FJI','ECZBZ-6UBCF-RTVJT-NELGC-VS7D6-53F66','ECOBZ-VPKW4-AMDUB-XHLW4-5UYCO-BQBC5','EBVBZ-V553U-SGCV4-BONLC-37RRT-SOF56','E57BZ-II4EQ-ZRJ5V-252LK-QNUZO-AGF55','DZNBZ-6ZEWZ-NZCX4-7BIV5-TWU7J-RVBAN','DWGBZ-W3QWZ-E4WXE-7OQG5-GILJJ-SOBIX','DSHBZ-2IR65-SFHIN-QDW4K-R53TO-UMFKO','DOYBZ-2463Q-GMF5Y-BNKN2-3WNJT-6DFGU','DOEBZ-AZ6KS-7NZOQ-6R5WD-GJP5S-4ZFMP','DIDBZ-YFP2U-VKXVR-2FHE6-Z7ZJZ-EJFXZ','DH5BZ-V5N6K-DE4JB-AZNG6-C7SXV-Q2BNO','D4JBZ-XRXEX-XZO4W-TWL2P-66PWS-2YBVK','D24BZ-J7GLQ-T3G5T-GEUQ4-FPVMQ-R5F62','CZBBZ-KGDRT-HQUXZ-V7MLQ-CXNOK-37BGU','CZ7BZ-MXT6F-RC6J5-NY3LQ-WFP3F-OCFDD','CWUBZ-ONSH4-YBXUD-DIUOX-EHV42-RKFLA','CURBZ-YFVH3-66B35-YHMLK-Q53IH-SBFUO','CNDBZ-H4KKD-BNF45-P7NVU-6DRL3-NLFDY','CMUBZ-Z6OWF-57PJS-JCXUX-ZQCO6-44F2Q','CL7BZ-HUD6A-HMGKH-CWO2X-V2NO3-K7BN4','CJXBZ-5ZE3U-WYGVC-4IUHB-ROX3O-QVFWO','CGJBZ-FVWCT-TF7XX-VUDAR-XQYRE-Z3FHJ','CEYBZ-JABWZ-3FCXW-76VMJ-I3GGV-DZBFR','CA3BZ-CE4C7-RTKXM-HNHNO-N6QM5-3ZB3E','C6GBZ-UC7RK-LH2JM-AOZSC-RO2CQ-YSBGS','C6ABZ-VA63U-C7XVU-BNC67-ZQ3HO-F3FWB','C3WBZ-AZHCQ-2NO5C-BFVET-LRTSV-Y3BTM','BQRBZ-CBKKQ-A745F-4KR5E-3AVFT-ZAFFV','BPABZ-7E4LJ-5GCFE-FC645-RIXFJ-6PFRG','BNZBZ-MBPR3-KDB33-YNKYF-HK6AQ-GGBLD','BIBBZ-DUWE6-MGVSF-EZ6JP-OSVFZ-4LBUX','BH5BZ-VPT6Z-MCKXT-ZF2ME-4ZCUH-NEBGY','BCGBZ-IJQKG-H6PQV-INE5X-5QRPZ-2TBOV','BAZBZ-AKVCU-CKGV6-2GUBD-IZV75-MYFS2','AVLBZ-SC6KO-ACNWT-SGVGV-7H4F3-S5B4M','AVIBZ-YEXWI-A4KGK-5HBSA-KOUDJ-3CFRX','AVABZ-EIPWP-7RTDK-L6UQ3-SHYET-V5B6V','ATABZ-JVC6G-DQ7QT-I4INI-6JKLT-YZF24','AQUBZ-XIBWU-EGIV2-GBVYZ-TA3CS-2ABDS','ALMBZ-2OTKC-TXA25-ARGEJ-6OAQZ-4IFCT','AJPBZ-GSAKU-VPUV4-4ZE5K-IF7U2-S4F6Y','AETBZ-C5E64-RHOUO-FI6E2-J6VJZ-QOBIE','AE2BZ-M2FCQ-P6T5D-4NZHY-2Q7PH-GKBAB','AAUBZ-QIMWX-E2W4R-7BY7B-RAAJF-QKBA4','A7LBZ-LFGRZ-3CSX2-72TLZ-ZJZH5-UHB6K','A3TBZ-3GMKS-VXGON-6QKJ4-44752-ZTFJ5','A25BZ-QWECZ-IMRXP-7A55H-TQEPQ-YJFR5','7X4BZ-ZG4RF-RQMJS-NBS7N-UVJ75-G5BMX','7RJBZ-O6I64-XANUL-KKPHS-UOCAJ-EPFPU','7OKBZ-QB6KZ-LJ2XP-ZPBJA-455DQ-GTFRT','7MNBZ-XENKD-LUX4P-HOX5Q-IZB35-YHBVT','7L5BZ-MNBWK-3EGJ3-AMR2R-ETLV7-OTBEQ','7JOBZ-5OF3U-QUNVL-GYLKC-WCM73-U7BSJ','7IUBZ-DTMWI-3TMGU-5Y6UJ-7G2FJ-MHB7K','7FUBZ-HSMWV-7WPPQ-U6JSM-4HNPV-DRBGV','77KBZ-2XCC3-LKI3S-3C6VV-DTUEO-ESB6B','74EBZ-T35WQ-OZP57-GJQWS-LPEPQ-GWBJP','6VZBZ-J6Q6G-DN7QV-QVA4M-TGJMS-VMB6E','6TRBZ-7KN64-Y54U3-DYZ3U-5XCE3-H6BG7','6RQBZ-54BLD-XT74B-HNJC5-UXN4S-SNB2A','6Q5BZ-VGL3J-LRCF5-FCMF3-TJK3Z-XLBU2','6O2BZ-WI26J-QJ7FT-F7EBH-YJXGS-OQBFF','6N4BZ-EHK3X-WXI44-ZAKLI-DZE2K-B6FU3','6LWBZ-ZR7WL-JNUPI-EV4L2-NY3BJ-B3FZW','6FYBZ-3ULN3-4RX3X-3XJWY-2JGQT-NMFK5','6FIBZ-AWZL6-CILSN-EAND2-WZNTO-ZPF6J','6FDBZ-LW262-LMUUO-C6BDS-LWNM5-G5FMT','6BJBZ-SXG6C-BIA23-ASIXC-VPJEE-ESBG4','67ABZ-HLPRQ-XZM53-GNCSR-GNSXF-DSBDK','64HBZ-6M53I-DQ7GJ-5L2I6-6UJQS-QSBEC','63SBZ-2F7WZ-6X4XO-ZCCHG-NBTCE-T3BPH','62VBZ-ZPHCR-E6AWD-WL3XU-KLBRH-V3BG3','5YZBZ-MJH6W-7J5RS-3VYK7-MFSC7-RBBCZ','5V4BZ-KQBC6-V44ST-MBFLW-IJXQO-JRFBP','5POBZ-JFI3J-IY6FQ-KVKDL-BW7B7-56FDX','5PBBZ-A5PC4-77TUH-KHGMD-IQULE-BZFTG','5LZBZ-5JSWX-3CQ4A-ZINRT-FUDG7-7OFSU','5KUBZ-E3YKM-VTX6L-67DTE-ZOA2O-VFBW3','5KQBZ-YGR6P-EDSDJ-VAUU5-GIV23-6UFHO','5HKBZ-SRKCD-5CQ4C-PF56K-NUHCS-QKFKN','5G2BZ-K42CP-NI4DH-V4BHN-BL6BF-BWBL3','5FNBZ-EP2WJ-AK6FK-FUR5W-RKA6Z-RLBSQ','5EZBZ-BBHRW-WEBRF-O2F5N-J7MUS-I6FH7','5EYBZ-HMMCR-DETWU-W2QZL-V3PXT-UDB2J','57SBZ-BCG6P-6KRDU-VC4RA-376DO-YTFKB','55SBZ-ZK63U-AZ5VB-BJD33-XUUZF-FKBZI','55KBZ-V4RCW-4OERQ-O2EYS-D6ZSZ-S5BMP','552BZ-RXYW4-PIIU4-XFZ3V-WQWLJ-7WBLE','4YOBZ-BW7KQ-6WW5C-BLJGI-BZDN7-QABU4','4XCBZ-4GVCK-3EWJ3-A6CCD-CF5D7-XEB3Z','4WJBZ-CUILX-DK642-7BSIM平台Q-KODTZ-BEBCH','4WIBZ-BZRCU-MQHVK-4N62Y-ART3K-2JFTF','4QUBZ-J4FWT-RE3XR-VQUOB-NXO32-GGBOK','4QUBZ-3TQCQ-UIR5Q-GVYXK-UBX6K-PVBMS','4QEBZ-C2HWP-VLBDE-V6LZK-DIM72-VWB34','4OFBZ-IVZKR-UXWWX-WTHNH-Q7KS5-MCBDD','4LDBZ-Z2QLC-WOD23-ASUUK-56TZ7-N2BTX','4KIBZ-2VEWJ-OIMFP-D3NBK-Q3XLE-W5F7Z','4H5BZ-RIEKQ-FOZ5K-BU4CI-BJNJE-3HBRM','4FKBZ-QBWKQ-3HT5N-2SHOO-QGYOQ-AOBYA','4DYBZ-2IJ6X-IEO45-Z4M7A-3IPS7-7IFUV','4ARBZ-RE4KJ-XSUF3-DCMEA-YYAHE-GLFVI','42DBZ-MDEK3-IKS3Q-3MI6O-YN2EE-NJFSL','3WOBZ-WUVKU-B2AV4-4BE6P-SVMDE-7UBOM','3QNBZ-MDCKC-5YS23-AH35L-QDYJS-R6B6T','3PFBZ-XNV67-ZZFXP-HROTO-DXXI3-LFFK4','3JQBZ-XLML4-YZWUG-X5JJY-RXYY2-IFFX4','3HHBZ-QJ6C6-PJSSA-ELG33-X3FVF-HMBNX','3H4BZ-5UXLO-3P2WE-SYGSW-KOUX5-SWFEN','3F7BZ-FTT3G-LFGQE-I7CAD-LIW47-PBBET','3EYBZ-MBJW4-ZMFUH-XHDPO-CUOOH-OOBQM','3DIBZ-KYXY3-GZW3C-OPJ5O-RRF62-7NBPU','37RBZ-6DN66-N2KSK-EH6MX-VAV3F-QLBYJ','36FBZ-PPF3S-UCCO6-6JYDP-EJUGJ-FLBV7','2Z4BZ-R7S6W-COZRH-OH35E-EM52T-FLBGX','2YZBZ-C7L3W-GPTRB-YOGKI-3I2XE-WCBBJ','2YUBZ-WXWLJ-55FFS-KWX4Q-BWXQS-WMFLS','2UBBZ-MKIWP-MNGDA-VGW72-VKZBH-2DFZR','2OOBZ-SPFE4-UJ7UB-KCSVO-POIU6-4VFME','2ODBZ-FEIK4-RHYUZ-XFUVG-X3DCF-7SB5K','2J5BZ-NFMR6-QO3SZ-M5S34-KXMRQ-MZFC7','2EVBZ-MGC6J-2THFU-XGYYL-CHH5T-5QBCB') then 'EcommerceC' else '其他' end as referer_type\nfrom \n(\nselect \nevent_time,platform,app_version,channel,uin,event_code,event_value,city,brand,device_id_type,bg,event_timestamp,referer\n,CASE WHEN path RLIKE '^qqmap://map/routeplan\\\\?type=\\\\w+$' THEN path\n WHEN INSTR(path, '?') > 0 THEN SUBSTR(path, 1, INSTR(path, '?') - 1)\n WHEN path = '' or path is null or path = 'NULL' THEN '未知'\n ELSE path end as path\n,is_first\nfrom internal_platform_db.t_ed_mapservice_user_open_start_path_dwd_di_query_engine_135\nwhere ds = 20260608\nand referer in ('com.foodchain_a.android.activity','FoodChainA','com.jooyum.s2pFlutter','com.jooyum.s2p','ZPVBZ-HDXWU-NQCVT-G76JZ-NMJ2H-M4FRN','ZDSBZ-HICKQ-YBW5C-4KX5E-QOXEZ-MPBI2','XTJBZ-F546Z-I6GXC-7NWJS-35WDV-RSFP5','WPRBZ-VIY6Q-NS45V-457XZ-3XUZJ-ZEFZH','U7VBZ-6XSWI-5VLGP-UYDPQ-EYNTF-4EFEJ','TUPBZ-M7GWQ-EYL5Q-2BYZ2-5WDQV-C7B7E','TOPBZ-WGE65-MNLI5-IGBOS-X3IHQ-YBBY4','RP5BZ-K5ZKZ-2CTXC-T3VKM-UQDO3-FRFYB','QB3BZ-KMC6W-G4NRU-YON5I-ORFV2-RCBFD','OZIBZ-GKVCN-ZHSFP-SAA5H-UO3ZV-D3F5Q','OZCBZ-ZN4KT-K7RXH-VFR3V-J4TPZ-OFBGJ','OW5BZ-QNACB-2RGUS-NV54M-4TEU2-JQB24','KBJBZ-UNHKM-KQH6E-67NMF-DDCGT-LLBWQ','KAWBZ-227KP-F73D6-L4JDM-6K6H5-SYFVU','IARBZ-EDBCA-BYCK7-CW7UG-LF5JS-DWFWO','HW3BZ-RQCLT-CKBXW-VWELQ-IIMGS-YVF7J','7YLBZ-2IKWM-65U64-66SIM平台Q-Y5SG7-MBBO6','3NVBZ-LALKB-A5CUC-N3IYU-S3FV6-TAFZY','3FABZ-UUIE7-TMAXI-HXWPA-XQONZ-IWBTP','2K2BZ-NVFKJ-7T3FU-DPCNP-X3IJO-N2FE6',\n'com.couriercob.huoyun','couriercob','topapp.couriercob.directionx','topapp.couriercob.direction',\n'com.delivery_d.mobile.android','EcommerceC秒送骑士','达达秒送骑士','达达骑士','com.dada.knight.hm','com.ecommerce_c.mall','ZZLBZ-PDU6F-POPJ3-NAMUM-GQJXV-WNBQI','ZX5BZ-53WHV-6QAP5-UZRK5-O3D5J-2PF2J','ZTABZ-76XRW-2ZRRR-OUCJW-47TS5-MXFIG','ZKKBZ-OFMWJ-DUWFN-FIM平台Q5U-ULITH-TXFCF','ZFVBZ-3P2KU-EKWVK-BN4O2-G6BWZ-Z2FIZ','Z6SBZ-ZB7CV-ZWUPO-5XZPI-6ZB4O-M2BZY','YVCBZ-PANKF-MQ5JM-N3HHT-TGW32-WXBYJ','YUNBZ-M5SWQ-5US5V-GJF6B-LU5X7-L6BS2','YRHBZ-7J2WV-GHFPD-5SS3Z-QXOC6-XJB2D','YLLBZ-3U5KJ-442FD-FM6LV-XOOCV-W5F7V','YKPBZ-3ZHKQ-EJN56-4WVGP-2UTD7-XXFPM','YHZBZ-6KUKG-IXYQH-IH2RF-AVZAJ-4LBVG','YEABZ-MA33X-2OR4T-TLTG6-ATGCV-DKBWY','Y4DBZ-OR5KP-CQIDE-VDFLC-CRFGK-ZNBBC','Y3LBZ-OVSRX-JTR4K-ZGEAY-VHWPZ-LPBGE','XZFBZ-ZL4KS-2KUO5-6S5LD-A47AV-BBF6Z','XXIBZ-EG3KS-EGNOH-6JPV5-LZQ3F-4MBTC','XX2BZ-U6C66-E2XSO-M4VV7-F5VE3-QUB3P','XUNBZ-Y5DK7-OOOXA-P5R7W-GNVOS-KHBJD','XSABZ-MKVC3-MMJ3U-R25R2-YZHCS-BTBZV','XRPBZ-GSQKQ-G3U5J-4NSC4-MXL4Q-Q7FFJ','XNTBZ-5NSWU-O32VI-2CV24-3TPBE-PBFTV','XJXBZ-AI5LU-AYSVB-BWHEJ-CGPZ6-XZBDA','XENBZ-NEH3J-I6HFS-K3WPP-IACAO-3KBLL','X6OBZ-JA3KJ-LYKFN-F6YQZ-YSIH3-MAB4L','X5PBZ-LS4K3-3GS3H-YGWU2-HSOWZ-C6BD3','WX4BZ-LBZC2-BA3UR-CJAWJ-6PVNS-S2F24','WWSBZ-ON3WX-BNQ4O-ZLAJA-4XPSJ-THFSC','WUZBZ-ENAWJ-2ZOFB-KAUKC-GSFMF-MAFAE','WQPBZ-RB7R2-TDLUB-CP4KZ-X4ZW3-NJBWN','WQKBZ-K3QHW-XVURR-37EXR-FIJ3E-U3BCG','WONBZ-ORHCL-5USP6-MNLYR-47Z2T-HNFUG','WLSBZ-5MKCU-L5IVM-2WFBE-CNNEZ-7QFQF','WLHBZ-JX6OW-KK5RL-YY63F-64JN3-XMFWG','WK6BZ-CHL6F-Q6JJX-J5BE4-JSN6K-VWFEB','WJ6BZ-JQJK3-VUI3J-YLLBJ-Y2DWS-FEFFM','WGQBZ-3HSR4-D7OUM-XWNS4-VDQ22-XCBYH','WF2BZ-JRCWW-YNVRX-3EOZC-QWXAJ-APFIO','WDPBZ-3IUKM-6IQ64-6YCC7-OQTD2-IIF5E','WC4BZ-RLGK5-LLAI2-IM平台QZZ7-HW47V-MMFIQ','WBLBZ-MPI33-KCS3K-Y3O56-IPXWH-UPBUQ','WBBBZ-GEPW6-I7DSA-MMCFV-LSCX7-VLB77','WB6BZ-7AOWK-PREJZ-AWMIR-JEXNO-5IBB4','WAWBZ-7HI3X-KUG4N-Z2Q5A-URJ6J-RZBT7','WA6BZ-3TN6F-PTYJH-N4U2N-WLJ35-2YFBT','W7FBZ-EVU65-ELVIZ-QZ3ES-PCKO6-CBFG7','W6JBZ-SZGK4-4KKUS-XL2WH-NC425-Z5BLY','W64BZ-QLMRW-ABPRE-OJP6G-5MZJJ-SCB54','VVZBZ-QGVKT-324XO-LRJPN-C7XDT-KDBGF','VN2BZ-O6YCJ-46SF4-KV7ND-CBM56-G6BE6','VKEBZ-S2SCK-5AMJU-ANYMZ-5F55H-ZVBUA','VJSBZ-JJVKR-S7YWV-WGCJ5-LMWPE-3FFJU','VEOBZ-KMYC6-4K7SJ-E42RI-VXGWQ-K2BYM','VB5BZ-URGWI-P46GJ-5TU4Z-6HAD6-RABYZ','VAYBZ-QVN6X-2GG4C-ZSRCY-P4A3J-PSBTJ','V4GBZ-NIZCG-SYYQH-IEHM2-6U2DV-HYBTJ','UWIBZ-DPEKD-KZ44R-HRQXO-TBYIZ-UOF3Z','URNBZ-XJ7CV-KXUPB-5BBOH-YSGG7-TRFER','UNIBZ-RG53P-EXPD3-V3K3J-MWD47-ZDBMO','UIWBZ-GMHE3-RMA3D-3QWOS-OOPW5-NLFNI','UI2BZ-JGQKS-SPJOW-6V7MA-7DACS-LMBJH','UDEBZ-AC6LV-5YEPU-UQPBQ-ZUQUQ-JBF4N','UALBZ-W数据仓库DWU-J47VQ-4KX4R-3UPW3-BEFYZ','U5WBZ-FCKWF-BLOJL-JCSMG-AKUY3-4IB55','U4ZBZ-PLQLU-LZ5VM-2A3YV-ROE2K-VSBNV','TWQBZ-QNWWM-W3Y6Y-6VQIY-GIDBF-7QFNJ','TVXBZ-WGC33-BFE35-3YMNI-MDJJ7-KUB4J','TUKBZ-5PRWF-MDNJ2-JLFXE-I6C72-JLBEX','TSVBZ-2TOWD-MLB4H-H6QND-BCDFF-UNBA6','TSSBZ-OYCWU-C3RVQ-4PTW6-MJ5XF-3PFB7','TQYBZ-BY464-CPWUZ-DN7UF-CXN4F-BJFU6','TMABZ-73FCT-QHDXC-L2FXB-WDW2F-MDB36','TM6BZ-MREWX-M674T-7Y577-HUHT5-73BRI','TLGBZ-ZQ2L5-5EYIW-QXWRQ-GZY7O-GRBJ7','TL7BZ-4E7KM-Q6Y6Y-6REVK-Z54RQ-OIBZ4','TJZBZ-UAZLJ-KDOFO-XZST5-LETF3-3PF6Q','TJ3BZ-2VN3D-6LW4P-P2V2R-J3SLK-OOFSS','THCBZ-HSY3J-YFEFB-F65JZ-GUAYH-7OFV5','TDMBZ-L7BWJ-XNOF3-F3J7H-7YVYE-BLBJ7','T6TBZ-34Z6Q-DCM55-2DWW7-PZDR3-QFF7W','T35BZ-FLSW5-6HTIH-IUMMI-D2MUH-ZOBXE','SZ6BZ-5PQWQ-BWO5M-BN2MP-KTUPJ-TNF57','SYSBZ-6FEC3-OYQ3L-OU7G6-74RZ2-IZFQA','SXTBZ-BOSWV-7R4PP-UJYEB-ZSF3F-3ABGZ','SUTBZ-2TOWC-HJR2P-ALIS3-MJC37-KNFHK','SU3BZ-ZYMYJ-IZ7FU-D3SCZ-P3W6J-4JFCZ','SS6BZ-BJQLS-7UEO7-63YPZ-CKIYV-LRBNP','SN4BZ-HJBWH-L3MD3-W5XNH-O2HR7-ZJFXW','SKJBZ-2NPCW-SYPR4-RDNEB-UPHP3-VNFF4','SJNBZ-DHWKD-DZ44H-HZTPA-5TGIE-3GFMK','SHNBZ-UQCKP-M5IDN-VZH5O-VEUEE-4SBGS','SGGBZ-L7BRL-NXRPA-EZOGT-TEYJ5-6EBNL','SFABZ-HQAKR-RRUWO-WPRRX-5RDYE-RTFPH','S5JBZ-I56K3-K2O3U-O3LKZ-VZBJ3-Y7BLH','S4YBZ-JXWE2-6RSUP-CJWMQ-JOBX2-6NF3D','S3DBZ-XUOCX-RDC4K-77XXV-RP262-NNBEB','RTDBZ-QWNWB-FRUUP-J4YBH-LVS5T-T7FC4','RICBZ-VHI65-IA4IB-QSTW5-SWUJK-H4B2G','RCNBZ-2A56D-CUZ4W-PRRI4-TMCFH-N5F7H','RAXBZ-CEZL3-VDN34-3QGSC-PXJVS-UCBFQ','RA4BZ-2UN6S-JVYO5-654A2-VVX42-CZBPI','R5XBZ-VPX3U-FV4VJ-2CCEG-M4PNF-R3FZR','R36BZ-5DXK6-2M4SI-MPQYI-CF4PO-IOB6Q','QZOBZ-XSAKU-FLFVN-BKHFO-KIKC3-IBBEV','QZIBZ-GGRLR-L2EWA-WJTQN-2YPBK-X7B3O','QUWBZ-A6JCJ-WY7FR-KMHQI-35GHO-RZBDJ','QRPBZ-5YLLU-GHMVC-BPOKS-N5KAE-FDF5R','QMCBZ-PLTC3-N443X-YS6EC-IS4YZ-NYBBT','QCFBZ-XFPHU-RLBVL-B7FZG-ALZOK-GGFXC','Q73BZ-DOGWQ-V3V5G-4ADG2-DTAZ3-QOFGC','PWTBZ-3QPCQ-ZMW53-GZA6H-YO7QF-5FFML','PVBBZ-ZYVCF-CI7J3-JZBV7-VXARZ-4MFU7','PRTBZ-2HZE4-TLHUA-D3KVI-T5GJQ-WFFAX','PQDBZ-O2B3H-4ZLDA-WTTJK-Q4CMF-RJB66','PJHBZ-EHDCN-R7FFD-SYVY4-IWH3H-VMBS5','PIXBZ-ZRREQ-O6G5D-2Y4K3-IMMAH-BXFB3','OW4BZ-MAZL7-LOTXV-HVTA3-XUWZK-2KBBO','OS3BZ-GC2CN-CL7FB-STVNJ-YW3S2-7MFIH','OQVBZ-WAD6X-RMY4Z-7XZJG-Q7AMH-FPFP6','OOUBZ-KKX3W-7JSR4-R2AQG-U6R6T-RXBMI','OMZBZ-EMQK4-DCIUU-DHOQA-XLPPK-3GFTM','OLVBZ-7L2HG-YPGQE-I3NON-J3X5Z-3CFQL','OL3BZ-ZBIKX-DHL4B-7EEWX-N33F6-N5FZQ','OH4BZ-RDVKP-RNSDY-VQKUA-SPQGE-7QFCY','OCKBZ-ZAGKK-RXLJR-A3PAM-YO7QH-V6FMJ','NZFBZ-EVYLZ-WQMXM-TYJRV-ZNYYF-W5FCZ','NYSBZ-EJLLC-HDT2X-A3ZPN-ISW3V-4JBBF','NU3BZ-JAPWV-VVNP7-5CEVQ-7NXXZ-R6BKN','NSKBZ-5VYKL-PFTPA-ESBZB-LGDF6-YEFKN','NRXBZ-PNV3R-LCPWU-WL562-YA427-NXBND','NQ4BZ-U7Y6D-7OM4W-HODRT-LK5Z6-DLBT4','NPFBZ-YZNKW-OIMR2-OIK23-OZPQ7-BVFWI','NMQBZ-WKRCQ-FFX5J-BFCGT-ORQHO-MFBXD','NJXBZ-ALDWU-HXSVU-27OKA-U4RE7-RQBGU','NIDBZ-A5TWU-G22V6-B25Y3-HQ6RV-MSFBQ','NENBZ-TUCW4-LL4U5-XGLVN-6YTPJ-CFBQ7','NDPBZ-SPFWW-2J6RH-RFUV3-CYPVK-XRFQ3','NC5BZ-4S2CP-UKODO-L4557-ZGPRO-DXBIE','NBWBZ-6URYV-Z6ZPL-5ISTO-TJW23-6TFNV','NAZBZ-5BYWL-JENPH-EV6GL-75ZMQ-L6B2Q','N7HBZ-ORS6J-AYQF3-KBXS3-VFRIV-QOBYY','N6VBZ-FLTWD-MAM4Q-HZR6Q-CVNV7-OOFE4','N6HBZ-6GSCU-R4YVE-BBFYK-KVP3T-F5BZJ','MVPBZ-KU7CP-YIPDZ-VFN4W-FDFMT-NTFMR','MVLBZ-FCW6Q-RIL5G-G5WTY-EV4PT-GQB4U','MPMBZ-ML5CU-V4OVT-BDVGM-K3NMQ-JIBRP','MFHBZ-S77KZ-N5YXF-TCKSB-HCOY6-PNF7K','MDIBZ-OZ6KU-REJV2-B5G46-HUJY7-IHFXS','MBCBZ-DE3LN-55AFX-SG2IE-2RYHT-ZJFQO','M6PBZ-QPUAF-KSJJD-JFNMG-NS5F2-UDB7F','LW7BZ-TK23O-SFQWS-SP6ZB-OD2J3-MNB6T','LVQBZ-N3PRG-VOTQC-I6MIR-EAAKK-TJBLZ','LUSBZ-HVG33-CXH3W-RCONB-6UMIK-ELBFL','LKMBZ-LVVK6-KG2SJ-E34GA-CHH7H-P5BIX','LHMBZ-R6CK2-V4FUE-CCFN7-AVGDF-SCFKF','LHKBZ-2IQ6U-HYRVB-45WJ7-KYKY3-67FHU','LBKBZ-ZP53Q-IBY5T-4TL3J-SDAM2-FKBYB','L6LBZ-Y3DLC-6UO2U-AJSTD-3TNR3-NLBOU','L4QBZ-4TOKG-7OKQE-QLF7O-IYCE3-S3F44','KZ2BZ-4S4RV-2TOP6-URXZA-MQD4T-6GBOK','KUEBZ-2OWCU-DP7VP-4QHTL-PMY66-RSFJC','KOHBZ-T3Q34-IBLU5-XWIEP-POSSO-SQBZV','K6IBZ-T4DCU-X5OVA-4GHZ6-WSYWO-K6BYJ','K2MBZ-AH2CR-6GWWL-WRT2W-QWLTT-O4FWV','JXJBZ-IORWR-CVDWX-WCZHK-M762V-RTFUU','JVSBZ-2KBW3-DK33H-33SVV-LYWVT-GDFAQ','JVKBZ-TN33Z-D2KXG-TJQDB-TCJNO-UKB6B','JUVBZ-FZYLJ-EMQFS-XZNK5-AT4UT-ASBDL','JQMBZ-UQ5ES-H6TON-6QLUQ-AM5P7-LXF7Z','JQHBZ-6T4RI-AHBGM-52MTT-UEKF7-WVFMR','JOEBZ-MTVHP-JR6D3-L6DVQ-RMXL7-RLFTI','JN2BZ-WX5C4-TFLUP-XCG3I-7NF2F-VLFEY','JKDBZ-QGTKO-GWFWF-SQ2ZZ-5S2GE-S4FVN','JJVBZ-JG4LV-4AAPM-UPFVK-MEW57-WXF2D','JG7BZ-VJBKO-HG7W7-SGOCJ-2YYYH-TUBFJ','JCVBZ-PHRW3-7VI3J-YXP72-HBHX2-VGB2Z','J4LBZ-W7SKW-HCGRQ-O3WEE-RI4K6-4QBLI','IXBBZ-IFKHF-CIM平台QJF-JBHXY-VP7S3-JVFOL','IWUBZ-SRQLF-KJSJ5-JIO4T-X3AVS-LSFB6','IV4BZ-TY2CV-NDRPU-U4MCE-RVSI5-2YFAA','IUMBZ-Z63W3-ZGS3J-OTXZX-5IAJ5-EZBRB','ITRBZ-PJEC4-JUIUL-XBAWG-OZKTK-3TF54','IOVBZ-R3S6Z-TJVXH-ZJ6QF-7UIUS-TRB6D','INNBZ-UVFWR-HXAW4-WAR6D-2KMDO-ZKBR3','INKBZ-PMMET-OPLX7-LE6XQ-IUNEZ-HSF6A','I6ZBZ-6NP36-W6OSU-MNXRW-RTAPQ-DWFUX','I5HBZ-2CQC4-WERUN-K5PCX-PTGUJ-AHBJY','I5ABZ-GVKHQ-HPF5Q-BLFQP-O6FPH-3XFHN','I2XBZ-H5PW5-HDKIC-IMWLW-4JT6O-QSBCK','HXPBZ-PHZWI-U5OGG-5JDIW-3OIKJ-3FBPR','HU2BZ-XXPC6-L3NSP-M3RDC-MMFP5-QRFPW','HSUBZ-2WS3K-RCLJG-AJRLN-666OK-2CFE6','HSKBZ-5A4C3-LN43L-OAIGR-JZCJJ-LZFKP','HO7BZ-WVQH3-YPC3A-OJE4P-DEQDT-CRF3Y','HJOBZ-EBJW3-Y6R3A-3TID4-CIMD3-CFB5F','HFEBZ-FBNCJ-FVEFY-FCVG2-TOQ5Z-L6FKL','HELBZ-6V5KJ-HEDFF-KZBRN-4XDF5-JFBCK','H7FBZ-DYMC6-4ERSD-MKEGK-PS5PH-6LBHI','H42WY-75WY3-Y4VSE-VXEO5-JWA7A','GWVBZ-7PCK2-XHBUZ-CTNHR-3TY45-7DBNP','GRSBZ-6YGCW-AHYRE-RSUQP-KKIVS-JPFN3','GQEBZ-GNUKS-7APOK-67QIV-Q2K2V-VCBA3','GPLBZ-N23WW-TSBRF-ROZNE-A5PQ3-Q2BUJ','GOOBZ-SDKWU-CNIV6-4CXMK-77YI7-I7BYN','GKEBZ-YYKC4-QLPU6-XNBQ5-CUY4E-22BXW','GANBZ-OWZLU-YJKVS-G5WBV-2FEGQ-P2B67','G2HBZ-YSLCD-BQS4K-PTUBH-AWJWJ-NDFHU','FYBBZ-OHOKN-HMBFJ-S7PG6-D5BZZ-NSBKK','FYABZ-ZLM3W-UM5RX-OZT4N-K5XSV-HTBKW','FRCBZ-CXMC3-YBG3U-YEJGU-YUPWO-ZRFOY','FQHBZ-RUQEJ-NUBFI-K6QF3-5MQ2J-6DFPD','FNJBZ-PM4K5-KAKIK-QJ5O3-WLU3S-PJBGV','FKXBZ-WZ36J-NIHF5-K2PVL-DMIWQ-YZBQ4','FJ5BZ-URIWP-TA5DK-V5AY7-S2DQS-ROF6C','FIUBZ-DSU6X-KUP4H-ZXFGL-W6SN2-UWFH7','FGRBZ-2FJCU-EAMVP-2G7QH-ZZN45-V7BL7','FEBBZ-3FUH2-AGDU2-CACFJ-OILEH-SYF6N','FCTBZ-2HMHJ-BDMFO-FT3FC-IPE3O-G7FLK','FBPBZ-A7Z3X-3MU4V-Z5VGB-53KG3-WEBC5','F6ZBZ-CJK3X-MT64P-TOOKY-GAPGH-TGB5D','F3ZBZ-TQAWX-42L4W-7LKDL-7PF6E-ZGB57','EZRBZ-27CRQ-VRQ5G-43QTK-ZOFK7-MBBCQ','EYHBZ-ENXW3-DHK3N-3AETB-TDFJ2-TWFLX','EXQBZ-NRYLD-KZB46-H4KUD-X63JJ-3BFNX','EVOBZ-EGIWB-PKRUO-NO4ST-RZAV6-GBB66','EQEBZ-N2FLD-MVV4U-HTQBV-43J57-WRBWZ','EM7BZ-OT3RZ-LP3X6-77G5Z-G7ONH-W3FJI','ECZBZ-6UBCF-RTVJT-NELGC-VS7D6-53F66','ECOBZ-VPKW4-AMDUB-XHLW4-5UYCO-BQBC5','EBVBZ-V553U-SGCV4-BONLC-37RRT-SOF56','E57BZ-II4EQ-ZRJ5V-252LK-QNUZO-AGF55','DZNBZ-6ZEWZ-NZCX4-7BIV5-TWU7J-RVBAN','DWGBZ-W3QWZ-E4WXE-7OQG5-GILJJ-SOBIX','DSHBZ-2IR65-SFHIN-QDW4K-R53TO-UMFKO','DOYBZ-2463Q-GMF5Y-BNKN2-3WNJT-6DFGU','DOEBZ-AZ6KS-7NZOQ-6R5WD-GJP5S-4ZFMP','DIDBZ-YFP2U-VKXVR-2FHE6-Z7ZJZ-EJFXZ','DH5BZ-V5N6K-DE4JB-AZNG6-C7SXV-Q2BNO','D4JBZ-XRXEX-XZO4W-TWL2P-66PWS-2YBVK','D24BZ-J7GLQ-T3G5T-GEUQ4-FPVMQ-R5F62','CZBBZ-KGDRT-HQUXZ-V7MLQ-CXNOK-37BGU','CZ7BZ-MXT6F-RC6J5-NY3LQ-WFP3F-OCFDD','CWUBZ-ONSH4-YBXUD-DIUOX-EHV42-RKFLA','CURBZ-YFVH3-66B35-YHMLK-Q53IH-SBFUO','CNDBZ-H4KKD-BNF45-P7NVU-6DRL3-NLFDY','CMUBZ-Z6OWF-57PJS-JCXUX-ZQCO6-44F2Q','CL7BZ-HUD6A-HMGKH-CWO2X-V2NO3-K7BN4','CJXBZ-5ZE3U-WYGVC-4IUHB-ROX3O-QVFWO','CGJBZ-FVWCT-TF7XX-VUDAR-XQYRE-Z3FHJ','CEYBZ-JABWZ-3FCXW-76VMJ-I3GGV-DZBFR','CA3BZ-CE4C7-RTKXM-HNHNO-N6QM5-3ZB3E','C6GBZ-UC7RK-LH2JM-AOZSC-RO2CQ-YSBGS','C6ABZ-VA63U-C7XVU-BNC67-ZQ3HO-F3FWB','C3WBZ-AZHCQ-2NO5C-BFVET-LRTSV-Y3BTM','BQRBZ-CBKKQ-A745F-4KR5E-3AVFT-ZAFFV','BPABZ-7E4LJ-5GCFE-FC645-RIXFJ-6PFRG','BNZBZ-MBPR3-KDB33-YNKYF-HK6AQ-GGBLD','BIBBZ-DUWE6-MGVSF-EZ6JP-OSVFZ-4LBUX','BH5BZ-VPT6Z-MCKXT-ZF2ME-4ZCUH-NEBGY','BCGBZ-IJQKG-H6PQV-INE5X-5QRPZ-2TBOV','BAZBZ-AKVCU-CKGV6-2GUBD-IZV75-MYFS2','AVLBZ-SC6KO-ACNWT-SGVGV-7H4F3-S5B4M','AVIBZ-YEXWI-A4KGK-5HBSA-KOUDJ-3CFRX','AVABZ-EIPWP-7RTDK-L6UQ3-SHYET-V5B6V','ATABZ-JVC6G-DQ7QT-I4INI-6JKLT-YZF24','AQUBZ-XIBWU-EGIV2-GBVYZ-TA3CS-2ABDS','ALMBZ-2OTKC-TXA25-ARGEJ-6OAQZ-4IFCT','AJPBZ-GSAKU-VPUV4-4ZE5K-IF7U2-S4F6Y','AETBZ-C5E64-RHOUO-FI6E2-J6VJZ-QOBIE','AE2BZ-M2FCQ-P6T5D-4NZHY-2Q7PH-GKBAB','AAUBZ-QIMWX-E2W4R-7BY7B-RAAJF-QKBA4','A7LBZ-LFGRZ-3CSX2-72TLZ-ZJZH5-UHB6K','A3TBZ-3GMKS-VXGON-6QKJ4-44752-ZTFJ5','A25BZ-QWECZ-IMRXP-7A55H-TQEPQ-YJFR5','7X4BZ-ZG4RF-RQMJS-NBS7N-UVJ75-G5BMX','7RJBZ-O6I64-XANUL-KKPHS-UOCAJ-EPFPU','7OKBZ-QB6KZ-LJ2XP-ZPBJA-455DQ-GTFRT','7MNBZ-XENKD-LUX4P-HOX5Q-IZB35-YHBVT','7L5BZ-MNBWK-3EGJ3-AMR2R-ETLV7-OTBEQ','7JOBZ-5OF3U-QUNVL-GYLKC-WCM73-U7BSJ','7IUBZ-DTMWI-3TMGU-5Y6UJ-7G2FJ-MHB7K','7FUBZ-HSMWV-7WPPQ-U6JSM-4HNPV-DRBGV','77KBZ-2XCC3-LKI3S-3C6VV-DTUEO-ESB6B','74EBZ-T35WQ-OZP57-GJQWS-LPEPQ-GWBJP','6VZBZ-J6Q6G-DN7QV-QVA4M-TGJMS-VMB6E','6TRBZ-7KN64-Y54U3-DYZ3U-5XCE3-H6BG7','6RQBZ-54BLD-XT74B-HNJC5-UXN4S-SNB2A','6Q5BZ-VGL3J-LRCF5-FCMF3-TJK3Z-XLBU2','6O2BZ-WI26J-QJ7FT-F7EBH-YJXGS-OQBFF','6N4BZ-EHK3X-WXI44-ZAKLI-DZE2K-B6FU3','6LWBZ-ZR7WL-JNUPI-EV4L2-NY3BJ-B3FZW','6FYBZ-3ULN3-4RX3X-3XJWY-2JGQT-NMFK5','6FIBZ-AWZL6-CILSN-EAND2-WZNTO-ZPF6J','6FDBZ-LW262-LMUUO-C6BDS-LWNM5-G5FMT','6BJBZ-SXG6C-BIA23-ASIXC-VPJEE-ESBG4','67ABZ-HLPRQ-XZM53-GNCSR-GNSXF-DSBDK','64HBZ-6M53I-DQ7GJ-5L2I6-6UJQS-QSBEC','63SBZ-2F7WZ-6X4XO-ZCCHG-NBTCE-T3BPH','62VBZ-ZPHCR-E6AWD-WL3XU-KLBRH-V3BG3','5YZBZ-MJH6W-7J5RS-3VYK7-MFSC7-RBBCZ','5V4BZ-KQBC6-V44ST-MBFLW-IJXQO-JRFBP','5POBZ-JFI3J-IY6FQ-KVKDL-BW7B7-56FDX','5PBBZ-A5PC4-77TUH-KHGMD-IQULE-BZFTG','5LZBZ-5JSWX-3CQ4A-ZINRT-FUDG7-7OFSU','5KUBZ-E3YKM-VTX6L-67DTE-ZOA2O-VFBW3','5KQBZ-YGR6P-EDSDJ-VAUU5-GIV23-6UFHO','5HKBZ-SRKCD-5CQ4C-PF56K-NUHCS-QKFKN','5G2BZ-K42CP-NI4DH-V4BHN-BL6BF-BWBL3','5FNBZ-EP2WJ-AK6FK-FUR5W-RKA6Z-RLBSQ','5EZBZ-BBHRW-WEBRF-O2F5N-J7MUS-I6FH7','5EYBZ-HMMCR-DETWU-W2QZL-V3PXT-UDB2J','57SBZ-BCG6P-6KRDU-VC4RA-376DO-YTFKB','55SBZ-ZK63U-AZ5VB-BJD33-XUUZF-FKBZI','55KBZ-V4RCW-4OERQ-O2EYS-D6ZSZ-S5BMP','552BZ-RXYW4-PIIU4-XFZ3V-WQWLJ-7WBLE','4YOBZ-BW7KQ-6WW5C-BLJGI-BZDN7-QABU4','4XCBZ-4GVCK-3EWJ3-A6CCD-CF5D7-XEB3Z','4WJBZ-CUILX-DK642-7BSIM平台Q-KODTZ-BEBCH','4WIBZ-BZRCU-MQHVK-4N62Y-ART3K-2JFTF','4QUBZ-J4FWT-RE3XR-VQUOB-NXO32-GGBOK','4QUBZ-3TQCQ-UIR5Q-GVYXK-UBX6K-PVBMS','4QEBZ-C2HWP-VLBDE-V6LZK-DIM72-VWB34','4OFBZ-IVZKR-UXWWX-WTHNH-Q7KS5-MCBDD','4LDBZ-Z2QLC-WOD23-ASUUK-56TZ7-N2BTX','4KIBZ-2VEWJ-OIMFP-D3NBK-Q3XLE-W5F7Z','4H5BZ-RIEKQ-FOZ5K-BU4CI-BJNJE-3HBRM','4FKBZ-QBWKQ-3HT5N-2SHOO-QGYOQ-AOBYA','4DYBZ-2IJ6X-IEO45-Z4M7A-3IPS7-7IFUV','4ARBZ-RE4KJ-XSUF3-DCMEA-YYAHE-GLFVI','42DBZ-MDEK3-IKS3Q-3MI6O-YN2EE-NJFSL','3WOBZ-WUVKU-B2AV4-4BE6P-SVMDE-7UBOM','3QNBZ-MDCKC-5YS23-AH35L-QDYJS-R6B6T','3PFBZ-XNV67-ZZFXP-HROTO-DXXI3-LFFK4','3JQBZ-XLML4-YZWUG-X5JJY-RXYY2-IFFX4','3HHBZ-QJ6C6-PJSSA-ELG33-X3FVF-HMBNX','3H4BZ-5UXLO-3P2WE-SYGSW-KOUX5-SWFEN','3F7BZ-FTT3G-LFGQE-I7CAD-LIW47-PBBET','3EYBZ-MBJW4-ZMFUH-XHDPO-CUOOH-OOBQM','3DIBZ-KYXY3-GZW3C-OPJ5O-RRF62-7NBPU','37RBZ-6DN66-N2KSK-EH6MX-VAV3F-QLBYJ','36FBZ-PPF3S-UCCO6-6JYDP-EJUGJ-FLBV7','2Z4BZ-R7S6W-COZRH-OH35E-EM52T-FLBGX','2YZBZ-C7L3W-GPTRB-YOGKI-3I2XE-WCBBJ','2YUBZ-WXWLJ-55FFS-KWX4Q-BWXQS-WMFLS','2UBBZ-MKIWP-MNGDA-VGW72-VKZBH-2DFZR','2OOBZ-SPFE4-UJ7UB-KCSVO-POIU6-4VFME','2ODBZ-FEIK4-RHYUZ-XFUVG-X3DCF-7SB5K','2J5BZ-NFMR6-QO3SZ-M5S34-KXMRQ-MZFC7','2EVBZ-MGC6J-2THFU-XGYYL-CHH5T-5QBCB'\n)\n)t1 \njoin \n(\nSELECT uin\nfrom internal_platform_db.t_ed_mapservice_user_q36_active_dwd_di_query_engine_135\nwhere ds= 20260608\ngroup by uin\n)t2 on t1.uin = t2.uin \n)\nselect\n referer_type\n ,if(grouping(referer) = 1,'total',referer) as referer \n ,if(grouping(path) = 1,'total',path) as path\n\t,count(distinct uin) \n\t,count(distinct case when is_first = 1 then uin else null end)\nfrom t\ngroup by referer_type,cube(referer,path)", "expected_csv": "referer_type,referer,path,uv,first_uv,ds\nFoodChainA,com.foodchain_a.android.activity,qqmap://map/routeplan?type=walk,1,1,20260608\nCourierCoB,couriercob,qqmap://map/navi,1,0,20260608\nCourierCoB,total,total,2,0,20260608\nCourierCoB,total,qqmap://map/search,1,0,20260608\nCourierCoB,total,qqmap://map/navi,1,0,20260608\nCourierCoB,couriercob,total,1,0,20260608\nEcommerceC,com.ecommerce_c.mall,未知,1,0,20260608\nEcommerceC,com.ecommerce_c.mall,total,1,0,20260608\nEcommerceC,com.delivery_d.mobile.android,total,1,1,20260608\nFoodChainA,FoodChainA,total,1,1,20260608\nFoodChainA,com.foodchain_a.android.activity,total,1,1,20260608\nEcommerceC,total,qqmap://map/routeplan?type=bus,1,0,20260608\nEcommerceC,com.delivery_d.mobile.android,qqmap://map/poi,1,1,20260608\nEcommerceC,total,qqmap://map/poi,1,1,20260608\nEcommerceC,com.dada.knight.hm,total,1,0,20260608\nCourierCoB,com.couriercob.huoyun,total,1,0,20260608\nEcommerceC,total,未知,1,0,20260608\nEcommerceC,com.dada.knight.hm,qqmap://map/routeplan?type=bus,1,0,20260608\nEcommerceC,total,total,2,1,20260608\nFoodChainA,FoodChainA,qqmap://map/routeplan?type=drive,1,1,20260608\nFoodChainA,total,qqmap://map/routeplan?type=drive,1,1,20260608\nFoodChainA,total,qqmap://map/routeplan?type=walk,1,1,20260608\nCourierCoB,com.couriercob.huoyun,qqmap://map/search,1,0,20260608\nFoodChainA,total,total,1,1,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数6 + 列名匹配\nC,C_row_alignment,row_consistency,15,\"行数比例 + key(referer_type,referer,path)覆盖率\"\nD,D_path_clean_referer_map,path_clean_referer_map,25,\"路径清洗+referer映射维度值逐行匹配(referer_type,referer,path)\"\nD,D_cube_aggregation,cube_aggregation,20,\"CUBE聚合指标逐行匹配(uv,first_uv)\"\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE 写入模式\nF,F_partition,partition,5,ds=20260608 分区正确\nF,F_grouping_total,grouping_total,5,CUBE汇总行grouping维度置为'total'\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,\"referer_type, referer, path\"\n# 预期列数,6\n# 输出表,internal_platform_db.t_md_mapservice_user_open_start_path_dwa_di_cand_query_engine_135", "path": "tasks/offline-compute/HiveSQL/hivesql_023_en"} |
| {"task_id": "hivesql_024", "id": "offline-compute_HiveSQL_hivesql_024", "name": "从 internal_platform_db.dwd_relationship_strength_fe", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标\n基于好友关系特征数据计算关系强度推荐分数，输出每个用户对（uin, touin）的加权评分结果。\n\n输入\n- internal_platform_db.dwd_relationship_strength_features_v4_di_query_engine_138（无 Join，单表处理）\n\n处理规则\n1. 过滤条件：imp_date = 20260608，且 uin >= 10000 且 touin >= 10000\n2. 特征处理：\n - 对所有分数字段使用 COALESCE(field, 0) 填充空值\n - c2c_cnt_score、common_frd_num、frd_tag_num 使用 LOG(1 + field) 变换\n - age_diff 使用 LOG(100 - age_diff) 变换\n3. 加权求和公式：score = (0.0327 * log_frd_tag_num + 0.0386 * touser_id_active_layer + 0.0853 * (1 - is_same_city) + 0.0564 * c2c_score + 0.1895 * log_c2c_cnt_score + 0.2455 * socialzone_visit_score + 0.1055 * socialzone_like_score + 0.1514 * socialzone_comment_score + 0.1556 * profile_view_score + 0.1531 * profile_like_score) / 28\n4. 结果字段：raw_score = ROUND(score, 4)，最终 score = 1 + ROUND(score, 4) * 10000\n\n输出要求\n- 字段顺序：imp_date（固定值 20260608）、uin、touin、score、raw_score\n- imp_date 作为分区字段\n- 不要求去重或聚合\n\n写入要求\n- 输出表：internal_platform_db.ads_qq_sq_frd_recommendation_result_list_df_cand_query_engine_138\n- 分区：imp_date = 20260608\n- 写入方式：INSERT OVERWRITE 覆盖写入该分区\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.ads_qq_sq_frd_recommendation_result_list_df_query_engine_138 PARTITION (imp_date = 20260608)\nWITH feature_modified_v2 AS (\nSELECT\n uin\n, touin\n, COALESCE(c2c_score, 0) AS c2c_score\n, COALESCE(LOG(1 + c2c_cnt_score), 0) AS log_c2c_cnt_score\n, COALESCE(socialzone_visit_score, 0) AS socialzone_visit_score\n, COALESCE(socialzone_like_score, 0) AS socialzone_like_score\n, COALESCE(socialzone_comment_score, 0) AS socialzone_comment_score\n, COALESCE(profile_view_score, 0) AS profile_view_score\n, COALESCE(profile_like_score, 0) AS profile_like_score\n, COALESCE(touser_id_active_layer, 0) AS touser_id_active_layer\n, COALESCE(LOG(100 - age_diff), 0) AS log_age_diff_complement\n, COALESCE(LOG(1 + common_frd_num), 0) AS log_common_frd\n, COALESCE(active_layer_score, 0) AS active_layer_score\n, COALESCE(LOG(1 + frd_tag_num), 0) AS log_frd_tag_num\n, COALESCE(is_same_city, 0) AS is_same_city\n, COALESCE(is_focus_frd, 0) AS is_focus_frd\n, COALESCE(comsg_score, 0) AS comsg_score\nFROM\ninternal_platform_db.dwd_relationship_strength_features_v4_di_query_engine_138\nWHERE\nimp_date = 20260608\n),\nweighted_score_v2 AS (\nSELECT\nfm.uin\n, fm.touin\n, (0.0327 * fm.log_frd_tag_num +\n0.0386 * fm.touser_id_active_layer +\n0.0853 * (1 - fm.is_same_city) +\n0.0564 * fm.c2c_score +\n0.1895 * fm.log_c2c_cnt_score +\n0.2455 * fm.socialzone_visit_score +\n0.1055 * fm.socialzone_like_score +\n0.1514 * fm.socialzone_comment_score +\n0.1556 * fm.profile_view_score +\n0.1531 * fm.profile_like_score) / 28 AS score\nFROM\nfeature_modified_v2 fm\n)\nSELECT\nws.uin\n, ws.touin\n, 1 + (ROUND(ws.score, 4) * 10000) AS score\n, ROUND(ws.score, 4) AS raw_score\nFROM\nweighted_score_v2 ws\nWHERE\nuin >= 10000\nAND touin >= 10000", "expected_csv": "uin,touin,score,raw_score\n10009,10010,570.0,0.0569\n10001,10002,307.0,0.0306\n10003,10004,238.0,0.0237\n10007,10008,31.0,0.003", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数5 + 列名匹配\nC,C_row_alignment,row_consistency,15,\"行数比例 + key(uin,touin)覆盖率\"\nD,D_score_correctness,score_correctness,25,最终得分列逐行匹配\nD,D_raw_score_correctness,raw_score_correctness,20,原始分列逐行匹配\nF,F_null_handling,null_handling,5,COALESCE 验证（无不当 NULL）\nF,F_boundary_filter,boundary_filter,5,uin>=10000 && touin>=10000\nF,F_insert_overwrite,insert_overwrite,3,写入模式+分区\nF,F_partition_value,partition_value,2,imp_date=20260608\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,\"uin, touin\"\n# 预期列数,\n# 输出表,internal_platform_db.ads_qq_sq_frd_recommendation_result_list_df_cand_query_engine_138", "path": "tasks/offline-compute/HiveSQL/hivesql_024"} |
| {"task_id": "hivesql_025", "id": "offline-compute_HiveSQL_hivesql_025", "name": "任务：将两个商品数据源（精细采集和泛化采集）合并，对每个字段生成质量评分。输入表：ads_sec_ec_su", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n任务目标：将两个商品数据源（精细采集表 hi 和泛化采集表 extensive）按日期分区过滤后 UNION ALL 合并，对合并后的每条记录的每个字段生成质量评分（0/1），输出到评分结果表。\n\n输入：\n- internal_platform_db.ads_sec_ec_sup_ec_tele_item_hi_query_engine_140（精细采集表，小时分区 p_hour）\n- internal_platform_db.ads_sec_ec_sup_ec_tele_security_platform_lk_extensive_item_query_engine_140（泛化采集表，日分区 p_date）\n- 第三个输入表 internal_platform_db.ads_sec_ec_sup_ec_tele_item_score_query_engine_140 在本任务中不使用\n\n处理规则：\n1. 过滤条件：\n - hi 表：p_hour >= '2026060800' AND p_hour < '2026060900'\n - extensive 表：p_date = '20260608'\n\n2. UNION ALL 前的字段映射（两表字段不完全一致）：\n - hi 表：使用 sale_price，新增 source_type='精细采集'\n - extensive 表：使用 price（映射为 sale_price），新增 source_type='泛化采集'\n - 其他字段直接对齐\n\n3. is_exp 派生规则（是否交付）：\n - 精细采集来源：required_quality_score=1 AND status=1 → 1，否则 0\n - 泛化采集来源：required_quality_score=1 → 1，否则 0\n\n4. is_off_shelf 派生规则（是否下架）：status<>1 → 1，否则 0\n\n5. 质量评分字段生成规则（所有字段名加 _score 后缀，除 uniqu_id 外）：\n - STRING 类型字段（id/name/url/shop_uniqu_id/shop_name/shop_url/platform_name/task_id/batch_id）：coalesce(field,'') <> '' → 1，否则 0\n - STRING 类型字段（property/sku_list/desc_info/main_image_url/sub_image_url/category_level1_name/category_level2_name/category_level3_name/shipping_address/price_text/detail_image_url/service_guarantee/brand_name/category_level1_original/category_level2_original/category_level3_original/brand_name_original/product_name/approval_number/ccc_certificate_number/efficacy/production_place/manufacturer/production_license_number/factory_name/factory_address/brand_authorization_image_url/main_image_ocr/sub_image_ocr/detail_image_ocr/industry_id/industry_name）：coalesce(field,'') = '' → 0，否则 1\n - 非空判断字段（status/sale_price/sale_qty/stock_qty/category_level1_id/category_level2_id/category_level3_id/shop_id/platform_id/brand_id/comments_cnt/high_remark_cnt/med_remark_cnt/bad_remark_cnt/sku_cnt/license_collection_status/screenshot_url）：field IS NOT NULL → 1，否则 0\n - 特殊字段：\n - logo_brand_score、similarity_img_url_score、data_method_keyword_score、task_name_score：固定 NULL\n - required_quality_score、core_quality_score、other_quality_score、platform_name、source_type、is_exp、is_off_shelf：直接传递，不生成评分\n\n输出要求：\n- 输出字段顺序（共 89 字段）：p_date、uniqu_id、id_score、name_score、sub_title_score、property_score、sku_list_score、desc_info_score、url_score、status_score、price_score、sale_qty_score、stock_qty_score、main_image_url_score、sub_image_url_score、category_level1_id_score、category_level1_name_score、category_level2_id_score、category_level2_name_score、category_level3_id_score、category_level3_name_score、shop_uniqu_id_score、shop_id_score、shop_name_score、shop_url_score、platform_id_score、platform_name_score、shipping_address_score、price_text_score、flag_score、need_cutpic_score、is_ocr_score、soe_flag_score、data_channel_score、data_method_score、data_source_score、detail_image_url_score、service_guarantee_score、brand_id_score、brand_name_score、is_ad_score、is_selfoperated_score、create_time_score、task_id_score、batch_id_score、category_level1_original_score、category_level2_original_score、category_level3_original_score、brand_name_original_score、product_name_score、approval_number_score、ccc_certificate_number_score、efficacy_score、production_place_score、manufacturer_score、production_license_number_score、factory_name_score、factory_address_score、brand_authorization_image_url_score、is_virtual_score、is_invoice_score、is_guarantee_score、is_free_ship_score、comments_cnt_score、industry_id_score、industry_name_score、high_remark_cnt_score、med_remark_cnt_score、bad_remark_cnt_score、main_image_ocr_score、sub_image_ocr_score、detail_image_ocr_score、ml_category_tag_score、logo_brand_score、similarity_img_url_score、sku_cnt_score、license_collection_status_score、data_method_keyword_score、task_name_score、screenshot_url_score、required_quality_score、core_quality_score、other_quality_score、platform_name、source_type、is_exp、is_off_shelf\n\n写入要求：\n- 输出表：internal_platform_db.ads_sec_ec_sup_ec_tele_item_score_cand_query_engine_140\n- 写入方式：INSERT OVERWRITE，覆盖分区 p_date='20260608'\n- 分区字段：p_date='20260608'（硬编码，非从源表继承）\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "insert overwrite table internal_platform_db.ads_sec_ec_sup_ec_tele_item_score_query_engine_140 partition(p_date='20260608')\nwith view_union as (\nselect uniqu_id,\nid,\nname,\nsub_title,\nproperty,\nsku_list,\ndesc_info,\nurl,\nstatus,\nsale_price,\nsale_qty,\nstock_qty,\nmain_image_url,\nsub_image_url,\ncategory_level1_id,\ncategory_level1_name,\ncategory_level2_id,\ncategory_level2_name,\ncategory_level3_id,\ncategory_level3_name,\nshop_uniqu_id,\nshop_id,\nshop_name,\nshop_url,\nplatform_id,\nplatform_name,\nshipping_address,\nprice_text,\nflag,\nneed_cutpic,\nis_ocr,\nsoe_flag,\ndata_channel,\ndata_method,\ndata_source,\ndetail_image_url,\nservice_guarantee,\nbrand_id,\nbrand_name,\nis_ad,\nis_selfoperated,\ncreate_time,\ntask_id,\nbatch_id,\ncategory_level1_original,\ncategory_level2_original,\ncategory_level3_original,\nbrand_name_original,\nproduct_name,\napproval_number,\nccc_certificate_number,\nefficacy,\nproduction_place,\nmanufacturer,\nproduction_license_number,\nfactory_name,\nfactory_address,\nbrand_authorization_image_url,\nis_virtual,\nis_invoice,\nis_guarantee,\nis_free_ship,\ncomments_cnt,\nindustry_id,\nindustry_name,\nhigh_remark_cnt,\nmed_remark_cnt,\nbad_remark_cnt,\nmain_image_ocr,\nsub_image_ocr,\ndetail_image_ocr,\nml_category_tag,\nsku_cnt,\nlicense_collection_status,\nscreenshot_url,\nrequired_quality_score,\ncore_quality_score,\nother_quality_score,\n'精细采集' as source_type,\ncase when required_quality_score = 1 and status = 1 then 1 else 0 end is_exp\nfrom internal_platform_db.ads_sec_ec_sup_ec_tele_item_hi_query_engine_140\nWHERE p_hour>='2026060800' and p_hour<'2026060900'\nunion all\nselect uniqu_id,\nid,\nname,\nsub_title,\nproperty,\nsku_list,\ndesc_info,\nurl,\nstatus,\nprice as sale_price,\nsale_qty,\nstock_qty,\nmain_image_url,\nsub_image_url,\ncategory_level1_id,\ncategory_level1_name,\ncategory_level2_id,\ncategory_level2_name,\ncategory_level3_id,\ncategory_level3_name,\nshop_uniqu_id,\nshop_id,\nshop_name,\nshop_url,\nplatform_id,\nplatform_name,\nshipping_address,\nprice_text,\nflag,\nneed_cutpic,\nis_ocr,\nsoe_flag,\ndata_channel,\ndata_method,\ndata_source,\ndetail_image_url,\nservice_guarantee,\nbrand_id,\nbrand_name,\nis_ad,\nis_selfoperated,\ncreate_time,\ntask_id,\nbatch_id,\ncategory_level1_original,\ncategory_level2_original,\ncategory_level3_original,\nbrand_name_original,\nproduct_name,\napproval_number,\nccc_certificate_number,\nefficacy,\nproduction_place,\nmanufacturer,\nproduction_license_number,\nfactory_name,\nfactory_address,\nbrand_authorization_image_url,\nis_virtual,\nis_invoice,\nis_guarantee,\nis_free_ship,\ncomments_cnt,\nindustry_id,\nindustry_name,\nhigh_remark_cnt,\nmed_remark_cnt,\nbad_remark_cnt,\nmain_image_ocr,\nsub_image_ocr,\ndetail_image_ocr,\nml_category_tag,\nsku_cnt,\nlicense_collection_status,\nscreenshot_url,\nrequired_quality_score,\ncore_quality_score,\nother_quality_score,\n'泛化采集' as source_type,\ncase when required_quality_score=1 then 1 else 0 end as is_exp\nfrom internal_platform_db.ads_sec_ec_sup_ec_tele_security_platform_lk_extensive_item_query_engine_140\nwhere(p_date='20260608')\n)\nselect\nuniqu_id as uniqu_id,\ncase when coalesce(id,'') <> '' then 1 else 0 end as id_socre,\ncase when coalesce(name,'') <> '' then 1 else 0 end as name_socre,\ncase when coalesce(sub_title,'') <> '' then 1 else 0 end as sub_title_socre,\ncase when coalesce(property, '') = '' then 0 else 1 end as property_socre,\ncase when coalesce(sku_list, '') = '' then 0 else 1 end as sku_list_socre,\ncase when coalesce(desc_info, '') = '' then 0 else 1 end as desc_info_socre,\ncase when coalesce(url,'') <> '' then 1 else 0 end as url_socre,\ncase when status is not null then 1 else 0 end as status_socre,\ncase when sale_price is not null then 1 else 0 end as price_socre,\ncase when sale_qty is not null then 1 else 0 end as sale_qty_socre,\ncase when stock_qty is not null then 1 else 0 end as stock_qty_socre,\ncase when coalesce(main_image_url, '') = '' then 0 else 1 end as main_image_url_socre,\ncase when coalesce(sub_image_url, '') = '' then 0 else 1 end as sub_image_url_socre,\ncase when category_level1_id is not null then 1 else 0 end as category_level1_id_socre,\ncase when coalesce(category_level1_name, '') = '' then 0 else 1 end as category_level1_name_socre,\ncase when category_level2_id is not null then 1 else 0 end as category_level2_id_socre,\ncase when coalesce(category_level2_name, '') = '' then 0 else 1 end as category_level2_name_socre,\ncase when category_level3_id is not null then 1 else 0 end as category_level3_id_socre,\ncase when coalesce(category_level3_name, '') = '' then 0 else 1 end as category_level3_name_socre,\ncase when coalesce(shop_uniqu_id,'') <> '' then 1 else 0 end as shop_uniqu_id_socre,\ncase when coalesce(shop_id, '') = '' then 0 else 1 end as shop_id_socre,\ncase when coalesce(shop_name,'') <> '' then 1 else 0 end as shop_name_socre,\ncase when coalesce(shop_url,'') <> '' then 1 else 0 end as shop_url_socre,\ncase when platform_id is not null then 1 else 0 end as platform_id_socre,\ncase when coalesce(platform_name,'') <> '' then 1 else 0 end as platform_name_socre,\ncase when coalesce(shipping_address, '') = '' then 0 else 1 end as shipping_address_socre,\ncase when coalesce(price_text, '') = '' then 0 else 1 end as price_text_socre,\ncase when flag is not null then 1 else 0 end as flag_socre,\ncase when need_cutpic is not null then 1 else 0 end as need_cutpic_socre,\ncase when is_ocr is not null then 1 else 0 end as is_ocr_socre,\ncase when soe_flag is not null then 1 else 0 end as soe_flag_socre,\ncase when data_channel is not null then 1 else 0 end as data_channel_socre,\ncase when data_method is not null then 1 else 0 end as data_method_socre,\ncase when data_source is not null then 1 else 0 end as data_source_socre,\ncase when coalesce(detail_image_url, '') = '' then 0 else 1 end as detail_image_url_socre,\ncase when coalesce(service_guarantee, '') = '' then 0 else 1 end as service_guarantee_socre,\ncase when brand_id is not null then 1 else 0 end as brand_id_socre,\ncase when coalesce(brand_name, '') = '' then 0 else 1 end as brand_name_socre,\ncase when is_ad is not null then 1 else 0 end as is_ad_socre,\ncase when is_selfoperated is not null then 1 else 0 end as is_selfoperated_socre,\ncase when create_time is not null then 1 else 0 end as create_time_socre,\ncase when coalesce(task_id,'') <> '' then 1 else 0 end as task_id_socre,\ncase when coalesce(batch_id,'') <> '' then 1 else 0 end as batch_id_socre,\ncase when coalesce(category_level1_original, '') = '' then 0 else 1 end as category_level1_original_socre,\ncase when coalesce(category_level2_original, '') = '' then 0 else 1 end as category_level2_original_socre,\ncase when coalesce(category_level3_original, '') = '' then 0 else 1 end as category_level3_original_socre,\ncase when coalesce(brand_name_original, '') = '' then 0 else 1 end as brand_name_original_socre,\ncase when coalesce(product_name, '') = '' then 0 else 1 end as product_name_socre,\ncase when coalesce(approval_number, '') = '' then 0 else 1 end as approval_number_socre,\ncase when coalesce(ccc_certificate_number, '') = '' then 0 else 1 end as ccc_certificate_number_socre,\ncase when coalesce(efficacy, '') = '' then 0 else 1 end as efficacy_socre,\ncase when coalesce(production_place, '') = '' then 0 else 1 end as production_place_socre,\ncase when coalesce(manufacturer, '') = '' then 0 else 1 end as manufacturer_socre,\ncase when coalesce(production_license_number, '') = '' then 0 else 1 end as production_license_number_socre,\ncase when coalesce(factory_name, '') = '' then 0 else 1 end as factory_name_socre,\ncase when coalesce(factory_address, '') = '' then 0 else 1 end as factory_address_socre,\ncase when coalesce(brand_authorization_image_url, '') = '' then 0 else 1 end as brand_authorization_image_url_socre,\ncase when is_virtual is not null then 1 else 0 end as is_virtual_socre,\ncase when is_invoice is not null then 1 else 0 end as is_invoice_socre,\ncase when is_guarantee is not null then 1 else 0 end as is_guarantee_socre,\ncase when is_free_ship is not null then 1 else 0 end as is_free_ship_socre,\ncase when comments_cnt is not null then 1 else 0 end as comments_cnt_socre,\ncase when coalesce(industry_id, '') = '' then 0 else 1 end as industry_id_socre,\ncase when coalesce(industry_name, '') = '' then 0 else 1 end as industry_name_socre,\ncase when high_remark_cnt is not null then 1 else 0 end as high_remark_cnt_socre,\ncase when med_remark_cnt is not null then 1 else 0 end as med_remark_cnt_socre,\ncase when bad_remark_cnt is not null then 1 else 0 end as bad_remark_cnt_socre,\ncase when coalesce(main_image_ocr, '') = '' then 0 else 1 end as main_image_ocr_socre,\ncase when coalesce(sub_image_ocr, '') = '' then 0 else 1 end as sub_image_ocr_socre,\ncase when coalesce(detail_image_ocr, '') = '' then 0 else 1 end as detail_image_ocr_socre,\ncase when ml_category_tag is not null then 1 else 0 end as ml_category_tag_socre,\nnull as logo_brand_socre,\nnull as similarity_img_url_socre,\ncase when sku_cnt is not null then 1 else 0 end as sku_cnt_socre,\ncase when license_collection_status is not null then 1 else 0 end as license_collection_status_socre,\nnull as data_method_keyword_socre,\nnull as task_name_socre,\ncase when screenshot_url is not null then 1 else 0 end as screenshot_url_socre,\nrequired_quality_score,\ncore_quality_score,\nother_quality_score,\nplatform_name,\nsource_type,\nis_exp,\ncase when status <>1 then 1 else 0 end as is_off_shelf\nfrom view_union", "expected_csv": "uniqu_id,id_socre,name_socre,sub_title_socre,property_socre,sku_list_socre,desc_info_socre,url_socre,status_socre,price_socre,sale_qty_socre,stock_qty_socre,main_image_url_socre,sub_image_url_socre,category_level1_id_socre,category_level1_name_socre,category_level2_id_socre,category_level2_name_socre,category_level3_id_socre,category_level3_name_socre,shop_uniqu_id_socre,shop_id_socre,shop_name_socre,shop_url_socre,platform_id_socre,platform_name_socre,shipping_address_socre,price_text_socre,flag_socre,need_cutpic_socre,is_ocr_socre,soe_flag_socre,data_channel_socre,data_method_socre,data_source_socre,detail_image_url_socre,service_guarantee_socre,brand_id_socre,brand_name_socre,is_ad_socre,is_selfoperated_socre,create_time_socre,task_id_socre,batch_id_socre,category_level1_original_socre,category_level2_original_socre,category_level3_original_socre,brand_name_original_socre,product_name_socre,approval_number_socre,ccc_certificate_number_socre,efficacy_socre,production_place_socre,manufacturer_socre,production_license_number_socre,factory_name_socre,factory_address_socre,brand_authorization_image_url_socre,is_virtual_socre,is_invoice_socre,is_guarantee_socre,is_free_ship_socre,comments_cnt_socre,industry_id_socre,industry_name_socre,high_remark_cnt_socre,med_remark_cnt_socre,bad_remark_cnt_socre,main_image_ocr_socre,sub_image_ocr_socre,detail_image_ocr_socre,ml_category_tag_socre,logo_brand_socre,similarity_img_url_socre,sku_cnt_socre,license_collection_status_socre,data_method_keyword_socre,task_name_socre,screenshot_url_socre,required_quality_score,core_quality_score,other_quality_score,platform_name,source_type,is_exp,is_off_shelf,p_date\nuid_ext_001,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,,1,1,,,1,18,12,8,PlatformB,泛化采集,0,0,20260608\nuid_hi_001,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,,1,1,,,1,20,15,10,PlatformA,精细采集,0,0,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数87 + 列名匹配\nC,C_row_alignment,row_consistency,10,行数比例 + key(uniqu_id)覆盖率\nD,D_score_fields,score_fields,25,~80个0/1评分字段逐行正确率（均分）\nD,D_derived_flags,derived_flags,20,is_exp和is_off_shelf派生逻辑逐行正确率\nD,D_union_source,union_source,10,source_type两值(精细采集/泛化采集)覆盖率\nF,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION\nF,F_partition_value,partition_value,5,p_date='20260608'\nF,F_union_completeness,union_completeness,5,UNION ALL两分支完整\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,HARD\n# 权重,\"product=0.7, process=0.3\"\n# 范式,new\n# Key列,uniqu_id\n# 预期列数,87\n# 输出表,internal_platform_db.ads_sec_ec_sup_ec_tele_item_score_cand_query_engine_140", "path": "tasks/offline-compute/HiveSQL/hivesql_025"} |
| {"task_id": "hivesql_026_en", "id": "offline-compute_HiveSQL_hivesql_026", "name": "Merge 6 business detail tables from different scenarios (group, pd, socialzone, social_scene, profile_page, group", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\n### Task Objective\nMerge 6 business detail tables from different scenarios using UNION ALL after date filtering, writing the results into a unified detail partitioned table.\n\n### Inputs\n- `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_group_di_query_engine_142`\n- `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_pd_di_query_engine_142`\n- `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_social_di_query_engine_142`\n- `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_social_scene_di_query_engine_142`\n- `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_profile_page_di_query_engine_142`\n- `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_group_profile_di_query_engine_142`\n\n### Processing Rules\n1. No joins, single-table processing\n2. Filter condition: `imp_date = 20260608` for all tables\n3. Field alignment rules:\n - 5 tables (group, socialzone, social_scene, profile_page, group_profile) use `SELECT *` to output all 13 fields\n - The `pd` table has 15 fields; explicitly list the first 13 fields: `imp_date`, `appid`, `message_id`, `puin`, `code`, `group_no`, `scene`, `sub_scene`, `hit_msg`, `is_punish_beat`, `new_reg_tag`, `payload_req`, `reg_date` (excluding the trailing `strategy_id` and `strategy_status`)\n4. Merge all results using UNION ALL, without deduplication\n\n### Output Requirements\nOutput field order: `imp_date`, `appid`, `message_id`, `puin`, `code`, `group_no`, `scene`, `sub_scene`, `hit_msg`, `is_punish_beat`, `new_reg_tag`, `payload_req`, `reg_date`\n\n### Write Requirements\n- Target table: `internal_platform_db.dwd_im_ads_businesses_core_scene_detail_di_cand_query_engine_142`\n- Write method: `INSERT OVERWRITE`\n- Partition field: `imp_date = 20260608`\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.dwd_im_ads_businesses_core_scene_detail_di_query_engine_142 PARTITION (imp_date = 20260608)\nselect appid, message_id, puin, code, group_no, scene, sub_scene, hit_msg, is_punish_beat, new_reg_tag, payload_req, reg_date\nfrom internal_platform_db.dwd_im_ads_businesses_core_scene_detail_group_di_query_engine_142\nwhere imp_date = 20260608\nunion all\nselect appid, message_id, puin, code, group_no, scene, sub_scene, hit_msg, is_punish_beat, new_reg_tag, payload_req, reg_date\nfrom internal_platform_db.dwd_im_ads_businesses_core_scene_detail_pd_di_query_engine_142\nwhere imp_date = 20260608\nunion all\nselect appid, message_id, puin, code, group_no, scene, sub_scene, hit_msg, is_punish_beat, new_reg_tag, payload_req, reg_date\nfrom internal_platform_db.dwd_im_ads_businesses_core_scene_detail_social_di_query_engine_142\nwhere imp_date = 20260608\nunion all\nselect appid, message_id, puin, code, group_no, scene, sub_scene, hit_msg, is_punish_beat, new_reg_tag, payload_req, reg_date\nfrom internal_platform_db.dwd_im_ads_businesses_core_scene_detail_social_scene_di_query_engine_142\nwhere imp_date = 20260608\nunion all\nselect appid, message_id, puin, code, group_no, scene, sub_scene, hit_msg, is_punish_beat, new_reg_tag, payload_req, reg_date\nfrom internal_platform_db.dwd_im_ads_businesses_core_scene_detail_profile_page_di_query_engine_142\nwhere imp_date = 20260608\nunion all\nselect appid, message_id, puin, code, group_no, scene, sub_scene, hit_msg, is_punish_beat, new_reg_tag, payload_req, reg_date\nfrom internal_platform_db.dwd_im_ads_businesses_core_scene_detail_group_profile_di_query_engine_142\nwhere imp_date = 20260608", "expected_csv": "appid,message_id,puin,code,group_no,scene,sub_scene,hit_msg,is_punish_beat,new_reg_tag,payload_req,reg_date,imp_date\napp001,msg_g1,puin001,1,grp001,scene_group,sub_group1,hit1,1,3,\"{\"\"key\"\":\"\"val1\"\"}\",20250101,20260608\napp006,msg_jb1,puin006,0,grp006,scene_social_scene,sub_jb1,,0,15,\"{\"\"key\"\":\"\"val6\"\"}\",20220601,20260608\napp002,msg_g2,puin002,0,grp002,scene_group,sub_group2,,0,7,\"{\"\"key\"\":\"\"val2\"\"}\",20240601,20260608\napp005,msg_qz1,puin005,1,grp005,scene_socialzone,sub_qz1,hit5,1,2,\"{\"\"key\"\":\"\"val5\"\"}\",20250301,20260608\napp007,msg_zl1,puin007,1,grp007,scene_profile_page,sub_zl1,hit7,1,1,\"{\"\"key\"\":\"\"val7\"\"}\",20250601,20260608\napp004,msg_pd2,puin004,0,grp004,scene_pd,sub_pd2,,0,10,\"{\"\"key\"\":\"\"val4\"\"}\",20231201,20260608\napp003,msg_pd1,puin003,1,grp003,scene_pd,sub_pd1,hit3,1,5,\"{\"\"key\"\":\"\"val3\"\"}\",20230901,20260608\napp008,msg_gp1,puin008,0,grp008,scene_gprofile,sub_gp1,,0,20,\"{\"\"key\"\":\"\"val8\"\"}\",20210301,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数13 + 列名匹配\nC,C_row_alignment,row_consistency,15,\"行数比例(7) + key(appid,message_id,imp_date)覆盖率(8)\"\nD,D_row_values,row_values,40,\"10个非key数据列逐行匹配(puin,code,group_no,scene,sub_scene,hit_msg,is_punish_beat,new_reg_tag,payload_req,reg_date)\"\nF,F_insert_overwrite,insert_overwrite,5,写入模式应为 INSERT OVERWRITE\nF,F_partition_value,partition_value,5,imp_date=20260608\nF,F_union_all_tables,union_all_tables,10,6个源表均在SQL中被引用\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(40) + F(20)\n# 难度,EASY\n# 权重,\"product=0.5, process=0.5\"\n# 范式,new\n# Key列,\"appid, message_id, imp_date\"\n# 预期列数,\n# 输出表,internal_platform_db.dwd_im_ads_businesses_core_scene_detail_di_cand_query_engine_142", "path": "tasks/offline-compute/HiveSQL/hivesql_026_en"} |
| {"task_id": "hivesql_027", "id": "offline-compute_HiveSQL_hivesql_027", "name": "从 dim_ds_org_info_d_ql_query_engine_143（机构信息）、dim_ds_project_info_d", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "zh", "modality": "pure-text", "timeout_seconds": 900, "prompt": "## Prompt\n1) 任务目标\n构建数字化工具平台数据日报，按日期+平台维度汇总机构、项目、用户、转账四类指标的累计值和新增值，用于平台运营分析。\n\n2) 输入\n- internal_platform_db.dim_ds_org_info_d_ql_query_engine_143：机构信息（分区 imp_date=20260608，is_test_org=0）\n- internal_platform_db.dim_ds_project_info_d_ql_query_engine_143：项目信息（分区 imp_date=20260608，is_test_project=0）\n- internal_platform_db.dim_ds_grantee_data_d_ql_query_engine_143：受助人数据（分区 imp_date=20260608，ds_grantee_state in (0,1)）\n- internal_platform_db.dwd_project_ds_transfer_log_d_ql_query_engine_143：转账流水（分区 imp_date=20260608，transfer_state=2）\n\n3) 处理规则\n**平台维度**：fundraising_platform=2 为'慈善平台X'，其他为'其他平台'，同时需产出'所有平台'汇总行\n**机构-项目数据 org_pid_data**：\n- 机构表 LEFT JOIN 项目表 ON ds_org_id\n- 计算机构创建日期（substring(create_time,1,10)）、项目创建日期\n- 按机构、项目维度生成各类 row_number，用于首次出现判断\n**用户数据 user_data**：\n- 受助人表 JOIN 项目表 ON ds_project_id\n- 平台W认证日期：取 platformw_auth_time 和 agent_platformw_auth_time 非空者最早日期\n- 是否平台W认证：open_id 或 agent_open_id 非空且对应 auth_time 非空\n- 是否银行认证：bank_account 非空\n- 按机构、项目、用户、代理人、认证维度生成各类 row_number\n**转账数据 transfer_data**：\n- 转账表 JOIN 项目表 ON ds_project_id\n- 转账日期：transfer_mode=1 用 trans_succ_time，否则用 create_time（substring 到日期）\n- 按机构、项目、用户、转账方式维度生成各类 row_number\n**累计指标计算**：通过窗口函数按平台维度计算累计总数（首次出现即 row_number=1 的实体计数）\n**新增指标计算**：按 stat_date 分组统计当日首次出现的实体数量或金额汇总\n**聚合方式**：先按各业务日期分组聚合，再 UNION ALL 多个维度（机构创建、项目创建、用户创建、平台W认证、转账）的结果，最后按 fundraising_platform+stat_date 再次聚合取 MAX\n\n4) 输出要求\n- 字段顺序：imp_date, fundraising_platform, stat_date, 然后是 52 个指标字段（total_* 26 个，new_* 26 个）\n- 52 个指标：机构数/项目机构数/项目数，用户机构数/项目数/用户数/代理人数/平台W认证用户数/银行认证用户数，转账金额/笔数/机构数/项目数/用户数（均分总计/平台W/银行三类）\n- imp_date 固定值 20260608\n- stat_date 为各业务日期（yyyy-MM-dd），部分行为 NULL（所有平台汇总行）\n- fundraising_platform 为'慈善平台X'/'其他平台'/'所有平台'\n\n5) 写入要求\n- 目标表：internal_platform_db.ads_gy_digital_tool_used_plat_data_d_ql_cand_query_engine_143\n- 写入方式：INSERT INTO\n- 输出需包含所有 fundraising_platform 与 stat_date 的组合（含 stat_date 为 NULL 的汇总行）\n\n请将最终 HiveSQL 写入 result.sql 并执行。", "ground_truth": "INSERT INTO TABLE internal_platform_db.ads_gy_digital_tool_used_plat_data_d_ql_query_engine_143\n(\nimp_date\n,`fundraising_platform`\n,`stat_date`\n,`total_org_cnt`\n,`total_pid_org_cnt`\n,`total_pid_cnt`\n,`total_user_org_cnt`\n,`total_user_pid_cnt`\n,`total_user_cnt`\n,`total_agent_cnt`\n,`total_platformw_auth_user_cnt`\n,`total_bank_auth_user_cnt`\n,`total_transfer_amount`\n,`total_platformw_transfer_amount`\n,`total_bank_transfer_amount`\n,`total_transfer_cnt`\n,`total_platformw_transfer_cnt`\n,`total_bank_transfer_cnt`\n,`total_transfer_org_cnt`\n,`total_platformw_transfer_org_cnt`\n,`total_bank_transfer_org_cnt`\n,`total_transfer_pid_cnt`\n,`total_platformw_transfer_pid_cnt`\n,`total_bank_transfer_pid_cnt`\n,`total_transfer_user_cnt`\n,`total_platformw_transfer_user_cnt`\n,`total_bank_transfer_user_cnt`\n,`new_org_cnt`\n,`new_pid_org_cnt`\n,`new_pid_cnt`\n,`new_user_org_cnt`\n,`new_user_pid_cnt`\n,`new_user_cnt`\n,`new_agent_cnt`\n,`new_platformw_auth_user_cnt`\n,`new_bank_auth_user_cnt`\n,`new_transfer_amount`\n,`new_platformw_transfer_amount`\n,`new_bank_transfer_amount`\n,`new_transfer_cnt`\n,`new_platformw_transfer_cnt`\n,`new_bank_transfer_cnt`\n,`new_transfer_org_cnt`\n,`new_platformw_transfer_org_cnt`\n,`new_bank_transfer_org_cnt`\n,`new_transfer_pid_cnt`\n,`new_platformw_transfer_pid_cnt`\n,`new_bank_transfer_pid_cnt`\n,`new_transfer_user_cnt`\n,`new_platformw_transfer_user_cnt`\n,`new_bank_transfer_user_cnt`\n)with org_pid_data as\n(\nselect\n*\n, row_number() over (partition by ds_org_id order by org_create_date) as org_rk\n, row_number() over (partition by fundraising_platform,ds_org_id order by org_create_date) as org_plat_rk\n, row_number() over (partition by ds_org_id,is_org_pid order by org_create_date) as org_pid_rk\n, row_number() over (partition by fundraising_platform,ds_org_id,is_org_pid order by org_create_date) as org_pid_plat_rk\n, row_number() over (partition by ds_project_id order by pid_create_date) as pid_rk\n, row_number() over (partition by fundraising_platform,ds_project_id order by pid_create_date) as pid_plat_rk\nfrom\n(\nselect\ncase when b.fundraising_platform = 2 then '慈善平台X' else '其他平台' end as fundraising_platform\n, b.ds_project_id\n, a.ds_org_id\n, substring(a.create_time,1,10) as org_create_date\n, substring(b.create_time,1,10) as pid_create_date\n, case when b.ds_project_id is not null then 1 else 0 end as is_org_pid\nfrom\n(\nselect\nds_org_id\n, create_time\nfrom\ninternal_platform_db.dim_ds_org_info_d_ql_query_engine_143\nwhere\nimp_date = 20260608\nand is_test_org = 0\n) a\nleft join\n(\nselect\nds_org_id\n, ds_project_id\n, create_time\n, fundraising_platform\nfrom\ninternal_platform_db.dim_ds_project_info_d_ql_query_engine_143\nwhere\nimp_date = 20260608\nand is_test_project = 0\n) b on a.ds_org_id = b.ds_org_id\n)\n),\nuser_data as\n(\nselect\n*\n, row_number() over (partition by ds_org_id order by user_create_date) as org_rk\n, row_number() over (partition by fundraising_platform,ds_org_id order by user_create_date) as org_plat_rk\n, row_number() over (partition by ds_project_id order by user_create_date) as pid_rk\n, row_number() over (partition by fundraising_platform,ds_project_id order by user_create_date) as pid_plat_rk\n, row_number() over (partition by ds_grantee_id order by user_create_date) as user_rk\n, row_number() over (partition by fundraising_platform,ds_grantee_id order by user_create_date) as user_plat_rk\n, row_number() over (partition by ds_grantee_id,is_platformw_auth order by platformw_auth_date) as user_auth_rk\n, row_number() over (partition by fundraising_platform,ds_grantee_id,is_platformw_auth order by platformw_auth_date) as user_plat_auth_rk\n, row_number() over (partition by ds_grantee_id,is_bank_auth order by user_create_date) as user_bank_auth_rk\n, row_number() over (partition by fundraising_platform,ds_grantee_id,is_bank_auth order by user_create_date) as user_plat_bank_auth_rk\n, row_number() over (partition by ds_agent_id order by user_create_date) as agent_rk\n, row_number() over (partition by fundraising_platform,ds_agent_id order by user_create_date) as agent_plat_rk\nfrom\n(\nselect\ncase when b.fundraising_platform = 2 then '慈善平台X' else '其他平台' end as fundraising_platform\n, b.ds_project_id\n, b.ds_org_id\n, a.ds_grantee_id\n, a.ds_agent_id\n, substring(a.create_time,1,10) as user_create_date\n, case when a.platformw_auth_time <> '' and a.agent_platformw_auth_time <> '' then least(substring(a.platformw_auth_time,1,10),substring(a.agent_platformw_auth_time,1,10))\nwhen a.platformw_auth_time <> '' and a.agent_platformw_auth_time = '' then substring(a.platformw_auth_time,1,10)\nelse substring(a.agent_platformw_auth_time,1,10) end as platformw_auth_date\n, case when coalesce(a.bank_account,'') != '' then 1 else 0 end as is_bank_auth\n, case when (coalesce(a.platformw_auth_time,'') != '' and coalesce(a.open_id,'') != '') or (coalesce(a.agent_platformw_auth_time,'') != '' and coalesce(a.agent_open_id,'') != '') then 1 else 0 end as is_platformw_auth\nfrom\ninternal_platform_db.dim_ds_grantee_data_d_ql_query_engine_143 a\njoin internal_platform_db.dim_ds_project_info_d_ql_query_engine_143 b on a.ds_project_id = b.ds_project_id\nwhere\na.imp_date = 20260608\nand b.imp_date = 20260608\nand a.ds_grantee_state in (0,1)\nand b.is_test_project = 0\n)\n),\ntransfer_data as\n(\nselect\n*\n, row_number() over (partition by ds_org_id order by transfer_date) as org_rk\n, row_number() over (partition by ds_org_id,transfer_mode order by transfer_date) as org_mode_rk\n, row_number() over (partition by fundraising_platform,ds_org_id order by transfer_date) as org_plat_rk\n, row_number() over (partition by fundraising_platform,ds_org_id,transfer_mode order by transfer_date) as org_plat_mode_rk\n, row_number() over (partition by ds_project_id order by transfer_date) as pid_rk\n, row_number() over (partition by ds_project_id,transfer_mode order by transfer_date) as pid_mode_rk\n, row_number() over (partition by fundraising_platform,ds_project_id order by transfer_date) as pid_plat_rk\n, row_number() over (partition by fundraising_platform,ds_project_id,transfer_mode order by transfer_date) as pid_plat_mode_rk\n, row_number() over (partition by ds_grantee_id order by transfer_date) as user_rk\n, row_number() over (partition by fundraising_platform,ds_grantee_id order by transfer_date) as user_plat_rk\n, row_number() over (partition by ds_grantee_id,transfer_mode order by transfer_date) as user_mode_rk\n, row_number() over (partition by fundraising_platform,ds_grantee_id,transfer_mode order by transfer_date) as user_plat_mode_rk\nfrom\n(\nselect\ncase when b.fundraising_platform = 2 then '慈善平台X' else '其他平台' end as fundraising_platform\n, case when a.transfer_mode = 1 then substring(a.trans_succ_time,1,10) else substring(a.create_time,1,10) end as transfer_date\n, b.ds_project_id\n, b.ds_org_id\n, a.ds_grantee_id\n, a.transfer_amount\n, a.transfer_mode\n, a.id\nfrom\ninternal_platform_db.dwd_project_ds_transfer_log_d_ql_query_engine_143 a\njoin internal_platform_db.dim_ds_project_info_d_ql_query_engine_143 b on a.ds_project_id = b.ds_project_id\nwhere\na.imp_date = 20260608\nand b.imp_date = 20260608\nand a.transfer_state = 2\nand b.is_test_project = 0\n)\n)\nselect\n20260608 as imp_date\n, fundraising_platform\n, stat_date\n, max(coalesce(total_org_cnt,0)) as total_org_cnt\n, max(coalesce(total_pid_org_cnt,0)) as total_pid_org_cnt\n, max(coalesce(total_pid_cnt,0)) as total_pid_cnt\n, max(coalesce(total_user_org_cnt,0)) as total_user_org_cnt\n, max(coalesce(total_user_pid_cnt,0)) as total_user_pid_cnt\n, max(coalesce(total_user_cnt,0)) as total_user_cnt\n, max(coalesce(total_agent_cnt,0)) as total_agent_cnt\n, max(coalesce(total_platformw_auth_user_cnt,0)) as total_platformw_auth_user_cnt\n, max(coalesce(total_bank_auth_user_cnt,0)) as total_bank_auth_user_cnt\n, max(coalesce(total_transfer_amount,0)) as total_transfer_amount\n, max(coalesce(total_platformw_transfer_amount,0)) as total_platformw_transfer_amount\n, max(coalesce(total_bank_transfer_amount,0)) as total_bank_transfer_amount\n, max(coalesce(total_transfer_cnt,0)) as total_transfer_cnt\n, max(coalesce(total_platformw_transfer_cnt,0)) as total_platformw_transfer_cnt\n, max(coalesce(total_bank_transfer_cnt,0)) as total_bank_transfer_cnt\n, max(coalesce(total_transfer_org_cnt,0)) as total_transfer_org_cnt\n, max(coalesce(total_platformw_transfer_org_cnt,0)) as total_platformw_transfer_org_cnt\n, max(coalesce(total_bank_transfer_org_cnt,0)) as total_bank_transfer_org_cnt\n, max(coalesce(total_transfer_pid_cnt,0)) as total_transfer_pid_cnt\n, max(coalesce(total_platformw_transfer_pid_cnt,0)) as total_platformw_transfer_pid_cnt\n, max(coalesce(total_bank_transfer_pid_cnt,0)) as total_bank_transfer_pid_cnt\n, max(coalesce(total_transfer_user_cnt,0)) as total_transfer_user_cnt\n, max(coalesce(total_platformw_transfer_user_cnt,0)) as total_platformw_transfer_user_cnt\n, max(coalesce(total_bank_transfer_user_cnt,0)) as total_bank_transfer_user_cnt\n, max(coalesce(new_org_cnt,0)) as new_org_cnt\n, max(coalesce(new_pid_org_cnt,0)) as new_pid_org_cnt\n, max(coalesce(new_pid_cnt,0)) as new_pid_cnt\n, max(coalesce(new_user_org_cnt,0)) as new_user_org_cnt\n, max(coalesce(new_user_pid_cnt,0)) as new_user_pid_cnt\n, max(coalesce(new_user_cnt,0)) as new_user_cnt\n, max(coalesce(new_agent_cnt,0)) as new_agent_cnt\n, max(coalesce(new_platformw_auth_user_cnt,0)) as new_platformw_auth_user_cnt\n, max(coalesce(new_bank_auth_user_cnt,0)) as new_bank_auth_user_cnt\n, max(coalesce(new_transfer_amount,0)) as new_transfer_amount\n, max(coalesce(new_platformw_transfer_amount,0)) as new_platformw_transfer_amount\n, max(coalesce(new_bank_transfer_amount,0)) as new_bank_transfer_amount\n, max(coalesce(new_transfer_cnt,0)) as new_transfer_cnt\n, max(coalesce(new_platformw_transfer_cnt,0)) as new_platformw_transfer_cnt\n, max(coalesce(new_bank_transfer_cnt,0)) as new_bank_transfer_cnt\n, max(coalesce(new_transfer_org_cnt,0)) as new_transfer_org_cnt\n, max(coalesce(new_platformw_transfer_org_cnt,0)) as new_platformw_transfer_org_cnt\n, max(coalesce(new_bank_transfer_org_cnt,0)) as new_bank_transfer_org_cnt\n, max(coalesce(new_transfer_pid_cnt,0)) as new_transfer_pid_cnt\n, max(coalesce(new_platformw_transfer_pid_cnt,0)) as new_platformw_transfer_pid_cnt\n, max(coalesce(new_bank_transfer_pid_cnt,0)) as new_bank_transfer_pid_cnt\n, max(coalesce(new_transfer_user_cnt,0)) as new_transfer_user_cnt\n, max(coalesce(new_platformw_transfer_user_cnt,0)) as new_platformw_transfer_user_cnt\n, max(coalesce(new_bank_transfer_user_cnt,0)) as new_bank_transfer_user_cnt\nfrom\n(\nselect\n'所有平台' as fundraising_platform\n, org_create_date as stat_date\n, max(total_org_cnt) as total_org_cnt\n, max(total_pid_org_cnt) as total_pid_org_cnt\n, max(total_pid_cnt) as total_pid_cnt\n, 0 as total_user_org_cnt\n, 0 as total_user_pid_cnt\n, 0 as total_user_cnt\n, 0 as total_agent_cnt\n, 0 as total_platformw_auth_user_cnt\n, 0 as total_bank_auth_user_cnt\n, 0 as total_transfer_amount\n, 0 as total_platformw_transfer_amount\n, 0 as total_bank_transfer_amount\n, 0 as total_transfer_cnt\n, 0 as total_platformw_transfer_cnt\n, 0 as total_bank_transfer_cnt\n, 0 as total_transfer_org_cnt\n, 0 as total_platformw_transfer_org_cnt\n, 0 as total_bank_transfer_org_cnt\n, 0 as total_transfer_pid_cnt\n, 0 as total_platformw_transfer_pid_cnt\n, 0 as total_bank_transfer_pid_cnt\n, 0 as total_transfer_user_cnt\n, 0 as total_platformw_transfer_user_cnt\n, 0 as total_bank_transfer_user_cnt\n, count(case when org_rk = 1 then ds_org_id end) as new_org_cnt\n, 0 as new_pid_org_cnt\n, 0 as new_pid_cnt\n, 0 as new_user_org_cnt\n, 0 as new_user_pid_cnt\n, 0 as new_user_cnt\n, 0 as new_agent_cnt\n, 0 as new_platformw_auth_user_cnt\n, 0 as new_bank_auth_user_cnt\n, 0 as new_transfer_amount\n, 0 as new_platformw_transfer_amount\n, 0 as new_bank_transfer_amount\n, 0 as new_transfer_cnt\n, 0 as new_platformw_transfer_cnt\n, 0 as new_bank_transfer_cnt\n, 0 as new_transfer_org_cnt\n, 0 as new_platformw_transfer_org_cnt\n, 0 as new_bank_transfer_org_cnt\n, 0 as new_transfer_pid_cnt\n, 0 as new_platformw_transfer_pid_cnt\n, 0 as new_bank_transfer_pid_cnt\n, 0 as new_transfer_user_cnt\n, 0 as new_platformw_transfer_user_cnt\n, 0 as new_bank_transfer_user_cnt\nfrom\n(\nselect\n*\n, count(case when org_rk = 1 then ds_org_id end) over (partition by 1) as total_org_cnt\n, count(case when is_org_pid = 1 and org_pid_rk = 1 then ds_org_id end) over (partition by 1) as total_pid_org_cnt\n, count(case when pid_rk = 1 then ds_project_id end) over (partition by 1) as total_pid_cnt\nfrom org_pid_data\n) a\ngroup by\norg_create_date\nunion all\nselect\nfundraising_platform\n, org_create_date as org_date\n, 0 as total_org_cnt\n, max(total_pid_org_cnt) as total_pid_org_cnt\n, max(total_pid_cnt) as total_pid_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, count(case when org_plat_rk = 1 then ds_org_id end) as new_org_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom\n(\nselect\n*\n, count(case when org_plat_rk = 1 then ds_org_id end) over (partition by fundraising_platform) as total_org_cnt\n, count(case when is_org_pid = 1 and org_pid_plat_rk = 1 then ds_org_id end) over (partition by fundraising_platform) as total_pid_org_cnt\n, count(case when pid_plat_rk = 1 then ds_project_id end) over (partition by fundraising_platform) as total_pid_cnt\nfrom org_pid_data\n) a\ngroup by\nfundraising_platform\n, org_create_date\nunion all\nselect\n'所有平台' as fundraising_platform\n, pid_create_date\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, 0\n, count(case when is_org_pid = 1 and org_pid_rk = 1 then ds_org_id end) as new_pid_org_cnt\n, count(case when pid_rk = 1 then ds_project_id end) as new_pid_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom\norg_pid_data\ngroup by\npid_create_date\nunion all\nselect\nfundraising_platform\n, pid_create_date\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, 0\n, count(case when is_org_pid = 1 and org_pid_plat_rk = 1 then ds_org_id end) as new_pid_org_cnt\n, count(case when pid_plat_rk = 1 then ds_project_id end) as new_pid_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom\norg_pid_data\ngroup by\nfundraising_platform\n, pid_create_date\nunion all\nselect\n'所有平台' as fundraising_platform\n, user_create_date as user_date\n, 0,0,0\n, max(total_user_org_cnt) as total_user_org_cnt\n, max(total_user_pid_cnt) as total_user_pid_cnt\n, max(total_user_cnt) as total_user_cnt\n, max(total_agent_cnt) as total_agent_cnt\n, max(total_platformw_auth_user_cnt) as total_platformw_auth_user_cnt\n, max(total_bank_auth_user_cnt) as total_bank_auth_user_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, 0,0,0\n, count(case when org_rk = 1 then ds_org_id end) as new_user_org_cnt\n, count(case when pid_rk = 1 then ds_project_id end) as new_user_pid_cnt\n, count(case when user_rk = 1 then ds_grantee_id end) as new_user_cnt\n, count(case when agent_rk = 1 then ds_agent_id end) as new_agent_cnt\n, 0 as new_platformw_auth_user_cnt\n, count(case when is_bank_auth = 1 and user_bank_auth_rk = 1 then ds_grantee_id end) as new_bank_auth_user_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom\n(\nselect\n*\n, count(case when org_rk = 1 then ds_org_id end) over (partition by 1) as total_user_org_cnt\n, count(case when pid_rk = 1 then ds_project_id end) over (partition by 1) as total_user_pid_cnt\n, count(case when user_rk = 1 then ds_grantee_id end) over (partition by 1) as total_user_cnt\n, count(case when agent_rk = 1 then ds_agent_id end) over (partition by 1) as total_agent_cnt\n, count(case when is_platformw_auth = 1 and user_auth_rk = 1 then ds_grantee_id end) over (partition by 1) as total_platformw_auth_user_cnt\n, count(case when is_bank_auth = 1 and user_bank_auth_rk = 1 then ds_grantee_id end) over (partition by 1) as total_bank_auth_user_cnt\nfrom user_data\n) a\ngroup by\nuser_create_date\nunion all\nselect\nfundraising_platform\n, user_create_date\n, 0,0,0\n, max(total_user_org_cnt) as total_user_org_cnt\n, max(total_user_pid_cnt) as total_user_pid_cnt\n, max(total_user_cnt) as total_user_cnt\n, max(total_agent_cnt) as total_agent_cnt\n, max(total_platformw_auth_user_cnt) as total_platformw_auth_user_cnt\n, max(total_bank_auth_user_cnt) as total_bank_auth_user_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, 0,0,0\n, count(case when org_plat_rk = 1 then ds_org_id end) as new_user_org_cnt\n, count(case when pid_plat_rk = 1 then ds_project_id end) as new_user_pid_cnt\n, count(case when user_plat_rk = 1 then ds_grantee_id end) as new_user_cnt\n, count(case when agent_plat_rk = 1 then ds_agent_id end) as new_agent_cnt\n, 0 as new_platformw_auth_user_cnt\n, count(case when is_bank_auth = 1 and user_plat_bank_auth_rk = 1 then ds_grantee_id end) as new_bank_auth_user_cnt\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom\n(\nselect\n*\n, count(case when org_plat_rk = 1 then ds_org_id end) over (partition by fundraising_platform) as total_user_org_cnt\n, count(case when pid_plat_rk = 1 then ds_project_id end) over (partition by fundraising_platform) as total_user_pid_cnt\n, count(case when user_plat_rk = 1 then ds_grantee_id end) over (partition by fundraising_platform) as total_user_cnt\n, count(case when agent_plat_rk = 1 then ds_agent_id end) over (partition by fundraising_platform) as total_agent_cnt\n, count(case when is_platformw_auth = 1 and user_plat_auth_rk = 1 then ds_grantee_id end) over (partition by fundraising_platform) as total_platformw_auth_user_cnt\n, count(case when is_bank_auth = 1 and user_plat_bank_auth_rk = 1 then ds_grantee_id end) over (partition by fundraising_platform) as total_bank_auth_user_cnt\nfrom user_data\n) a\ngroup by\nfundraising_platform\n, user_create_date\nunion all\nselect\n'所有平台' as fundraising_platform\n, platformw_auth_date\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, 0,0,0\n, 0,0,0,0\n, count(case when is_platformw_auth = 1 and user_plat_auth_rk = 1 then ds_grantee_id end) as new_platformw_auth_user_cnt\n, 0\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom user_data\ngroup by\nplatformw_auth_date\nunion all\nselect\nfundraising_platform\n, platformw_auth_date\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n, 0,0,0\n, 0,0,0,0\n, count(case when is_platformw_auth = 1 and user_plat_auth_rk = 1 then ds_grantee_id end) as new_platformw_auth_user_cnt\n, 0\n, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\nfrom user_data\ngroup by\nfundraising_platform\n, platformw_auth_date\nunion all\nselect\n'所有平台' as fundraising_platform\n, transfer_date\n, 0,0,0,0,0,0,0,0,0\n, max(total_transfer_amount) as total_transfer_amount\n, max(total_platformw_transfer_amount) as total_platformw_transfer_amount\n, max(total_bank_transfer_amount) as total_bank_transfer_amount\n, max(total_transfer_cnt) as total_transfer_cnt\n, max(total_platformw_transfer_cnt) as total_platformw_transfer_cnt\n, max(total_bank_transfer_cnt) as total_bank_transfer_cnt\n, max(total_transfer_org_cnt) as total_transfer_org_cnt\n, max(total_platformw_transfer_org_cnt) as total_platformw_transfer_org_cnt\n, max(total_bank_transfer_org_cnt) as total_bank_transfer_org_cnt\n, max(total_transfer_pid_cnt) as total_transfer_pid_cnt\n, max(total_platformw_transfer_pid_cnt) as total_platformw_transfer_pid_cnt\n, max(total_bank_transfer_pid_cnt) as total_bank_transfer_pid_cnt\n, max(total_transfer_user_cnt) as total_transfer_user_cnt\n, max(total_platformw_transfer_user_cnt) as total_platformw_transfer_user_cnt\n, max(total_bank_transfer_user_cnt) as total_bank_transfer_user_cnt\n, 0,0,0,0,0,0,0,0,0\n, sum(transfer_amount) as transfer_amount\n, sum(case when transfer_mode = 1 then transfer_amount end) as platformw_transfer_amount\n, sum(case when transfer_mode = 2 then transfer_amount end) as bank_transfer_amount\n, count(id) as transfer_cnt\n, count(case when transfer_mode = 1 then id end) as platformw_transfer_cnt\n, count(case when transfer_mode = 2 then id end) as bank_transfer_cnt\n, count(case when org_rk = 1 then ds_org_id end) as new_transfer_org_cnt\n, count(case when transfer_mode = 1 and org_mode_rk = 1 then ds_org_id end) as new_platformw_transfer_org_cnt\n, count(case when transfer_mode = 2 and org_mode_rk = 1 then ds_org_id end) as new_bank_transfer_org_cnt\n, count(case when pid_rk = 1 then ds_project_id end) as new_transfer_pid_cnt\n, count(case when transfer_mode = 1 and pid_mode_rk = 1 then ds_project_id end) as new_platformw_transfer_pid_cnt\n, count(case when transfer_mode = 2 and pid_mode_rk = 1 then ds_project_id end) as new_bank_transfer_pid_cnt\n, count(case when user_rk = 1 then ds_grantee_id end) as new_transfer_user_cnt\n, count(case when transfer_mode = 1 and user_mode_rk = 1 then ds_grantee_id end) as new_platformw_transfer_user_cnt\n, count(case when transfer_mode = 2 and user_mode_rk = 1 then ds_grantee_id end) as new_bank_transfer_user_cnt\nfrom\n(\nselect\n*\n, sum(transfer_amount) over (partition by 1) as total_transfer_amount\n, sum(case when transfer_mode = 1 then transfer_amount end) over (partition by 1) as total_platformw_transfer_amount\n, sum(case when transfer_mode = 2 then transfer_amount end) over (partition by 1) as total_bank_transfer_amount\n, count(id) over (partition by 1) as total_transfer_cnt\n, count(case when transfer_mode = 1 then id end) over (partition by 1) as total_platformw_transfer_cnt\n, count(case when transfer_mode = 2 then id end) over (partition by 1) as total_bank_transfer_cnt\n, count(case when org_rk = 1 then ds_org_id end) over (partition by 1) as total_transfer_org_cnt\n, count(case when transfer_mode = 1 and org_mode_rk = 1 then ds_org_id end) over (partition by 1) as total_platformw_transfer_org_cnt\n, count(case when transfer_mode = 2 and org_mode_rk = 1 then ds_org_id end) over (partition by 1) as total_bank_transfer_org_cnt\n, count(case when pid_rk = 1 then ds_project_id end) over (partition by 1) as total_transfer_pid_cnt\n, count(case when transfer_mode = 1 and pid_mode_rk = 1 then ds_project_id end) over (partition by 1) as total_platformw_transfer_pid_cnt\n, count(case when transfer_mode = 2 and pid_mode_rk = 1 then ds_project_id end) over (partition by 1) as total_bank_transfer_pid_cnt\n, count(case when user_rk = 1 then ds_grantee_id end) over (partition by 1) as total_transfer_user_cnt\n, count(case when transfer_mode = 1 and user_mode_rk = 1 then ds_grantee_id end) over (partition by 1) as total_platformw_transfer_user_cnt\n, count(case when transfer_mode = 2 and user_mode_rk = 1 then ds_grantee_id end) over (partition by 1) as total_bank_transfer_user_cnt\nfrom transfer_data\n) a\ngroup by\ntransfer_date\nunion all\nselect\nfundraising_platform\n, transfer_date\n, 0,0,0,0,0,0,0,0,0\n, max(total_transfer_amount) as total_transfer_amount\n, max(total_platformw_transfer_amount) as total_platformw_transfer_amount\n, max(total_bank_transfer_amount) as total_bank_transfer_amount\n, max(total_transfer_cnt) as total_transfer_cnt\n, max(total_platformw_transfer_cnt) as total_platformw_transfer_cnt\n, max(total_bank_transfer_cnt) as total_bank_transfer_cnt\n, max(total_transfer_org_cnt) as total_transfer_org_cnt\n, max(total_platformw_transfer_org_cnt) as total_platformw_transfer_org_cnt\n, max(total_bank_transfer_org_cnt) as total_bank_transfer_org_cnt\n, max(total_transfer_pid_cnt) as total_transfer_pid_cnt\n, max(total_platformw_transfer_pid_cnt) as total_platformw_transfer_pid_cnt\n, max(total_bank_transfer_pid_cnt) as total_bank_transfer_pid_cnt\n, max(total_transfer_user_cnt) as total_transfer_user_cnt\n, max(total_platformw_transfer_user_cnt) as total_platformw_transfer_user_cnt\n, max(total_bank_transfer_user_cnt) as total_bank_transfer_user_cnt\n, 0,0,0,0,0,0,0,0,0\n, sum(transfer_amount) as transfer_amount\n, sum(case when transfer_mode = 1 then transfer_amount end) as platformw_transfer_amount\n, sum(case when transfer_mode = 2 then transfer_amount end) as bank_transfer_amount\n, count(id) as transfer_cnt\n, count(case when transfer_mode = 1 then id end) as platformw_transfer_cnt\n, count(case when transfer_mode = 2 then id end) as bank_transfer_cnt\n, count(case when org_plat_rk = 1 then ds_org_id end) as new_transfer_org_cnt\n, count(case when transfer_mode = 1 and org_plat_mode_rk = 1 then ds_org_id end) as new_platformw_transfer_org_cnt\n, count(case when transfer_mode = 2 and org_plat_mode_rk = 1 then ds_org_id end) as new_bank_transfer_org_cnt\n, count(case when pid_plat_rk = 1 then ds_project_id end) as new_transfer_pid_cnt\n, count(case when transfer_mode = 1 and pid_plat_mode_rk = 1 then ds_project_id end) as new_platformw_transfer_pid_cnt\n, count(case when transfer_mode = 2 and pid_plat_mode_rk = 1 then ds_project_id end) as new_bank_transfer_pid_cnt\n, count(case when user_plat_rk = 1 then ds_grantee_id end) as new_transfer_user_cnt\n, count(case when transfer_mode = 1 and user_plat_mode_rk = 1 then ds_grantee_id end) as new_platformw_transfer_user_cnt\n, count(case when transfer_mode = 2 and user_plat_mode_rk = 1 then ds_grantee_id end) as new_bank_transfer_user_cnt\nfrom\n(\nselect\n*\n, sum(transfer_amount) over (partition by fundraising_platform) as total_transfer_amount\n, sum(case when transfer_mode = 1 then transfer_amount end) over (partition by fundraising_platform) as total_platformw_transfer_amount\n, sum(case when transfer_mode = 2 then transfer_amount end) over (partition by fundraising_platform) as total_bank_transfer_amount\n, count(id) over (partition by fundraising_platform) as total_transfer_cnt\n, count(case when transfer_mode = 1 then id end) over (partition by fundraising_platform) as total_platformw_transfer_cnt\n, count(case when transfer_mode = 2 then id end) over (partition by fundraising_platform) as total_bank_transfer_cnt\n, count(case when org_plat_rk = 1 then ds_org_id end) over (partition by fundraising_platform) as total_transfer_org_cnt\n, count(case when transfer_mode = 1 and org_plat_mode_rk = 1 then ds_org_id end) over (partition by fundraising_platform) as total_platformw_transfer_org_cnt\n, count(case when transfer_mode = 2 and org_plat_mode_rk = 1 then ds_org_id end) over (partition by fundraising_platform) as total_bank_transfer_org_cnt\n, count(case when pid_plat_rk = 1 then ds_project_id end) over (partition by fundraising_platform) as total_transfer_pid_cnt\n, count(case when transfer_mode = 1 and pid_plat_mode_rk = 1 then ds_project_id end) over (partition by fundraising_platform) as total_platformw_transfer_pid_cnt\n, count(case when transfer_mode = 2 and pid_plat_mode_rk = 1 then ds_project_id end) over (partition by fundraising_platform) as total_bank_transfer_pid_cnt\n, count(case when user_plat_rk = 1 then ds_grantee_id end) over (partition by fundraising_platform) as total_transfer_user_cnt\n, count(case when transfer_mode = 1 and user_plat_mode_rk = 1 then ds_grantee_id end) over (partition by fundraising_platform) as total_platformw_transfer_user_cnt\n, count(case when transfer_mode = 2 and user_plat_mode_rk = 1 then ds_grantee_id end) over (partition by fundraising_platform) as total_bank_transfer_user_cnt\nfrom transfer_data\n) a\ngroup by\nfundraising_platform\n, transfer_date\n) t\ngroup by\nfundraising_platform\n, stat_date", "expected_csv": "imp_date,fundraising_platform,stat_date,total_org_cnt,total_pid_org_cnt,total_pid_cnt,total_user_org_cnt,total_user_pid_cnt,total_user_cnt,total_agent_cnt,total_platformw_auth_user_cnt,total_bank_auth_user_cnt,total_transfer_amount,total_platformw_transfer_amount,total_bank_transfer_amount,total_transfer_cnt,total_platformw_transfer_cnt,total_bank_transfer_cnt,total_transfer_org_cnt,total_platformw_transfer_org_cnt,total_bank_transfer_org_cnt,total_transfer_pid_cnt,total_platformw_transfer_pid_cnt,total_bank_transfer_pid_cnt,total_transfer_user_cnt,total_platformw_transfer_user_cnt,total_bank_transfer_user_cnt,new_org_cnt,new_pid_org_cnt,new_pid_cnt,new_user_org_cnt,new_user_pid_cnt,new_user_cnt,new_agent_cnt,new_platformw_auth_user_cnt,new_bank_auth_user_cnt,new_transfer_amount,new_platformw_transfer_amount,new_bank_transfer_amount,new_transfer_cnt,new_platformw_transfer_cnt,new_bank_transfer_cnt,new_transfer_org_cnt,new_platformw_transfer_org_cnt,new_bank_transfer_org_cnt,new_transfer_pid_cnt,new_platformw_transfer_pid_cnt,new_bank_transfer_pid_cnt,new_transfer_user_cnt,new_platformw_transfer_user_cnt,new_bank_transfer_user_cnt\n20260608,所有平台,2025-01-10,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-03-15,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-06-01,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-03-15,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-01-10,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,其他平台,2025-06-01,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-02-01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-03-20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-06-05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-02-01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,其他平台,2025-06-05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-03-20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-04-01,0,0,0,3,3,4,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-02-10,0,0,0,3,3,4,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-06-08,0,0,0,3,3,4,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-03-22,0,0,0,3,3,4,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-02-10,0,0,0,2,2,3,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-03-22,0,0,0,2,2,3,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,其他平台,2025-06-08,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-04-01,0,0,0,2,2,3,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-06-10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-02-15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,慈善平台X,2025-02-15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,其他平台,2025-06-10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n20260608,所有平台,2025-05-10,0,0,0,0,0,0,0,0,0,26000,8000,18000,4,2,2,3,1,2,3,1,2,4,2,2,0,0,0,0,0,0,0,0,0,3000,3000,0,1,1,0,0,0,0,0,0,0,1,1,0\n20260608,所有平台,2025-06-15,0,0,0,0,0,0,0,0,0,26000,8000,18000,4,2,2,3,1,2,3,1,2,4,2,2,0,0,0,0,0,0,0,0,0,10000,0,10000,1,0,1,1,0,1,1,0,1,1,0,1\n20260608,所有平台,2025-02-20,0,0,0,0,0,0,0,0,0,26000,8000,18000,4,2,2,3,1,2,3,1,2,4,2,2,0,0,0,0,0,0,0,0,0,5000,5000,0,1,1,0,1,1,0,1,1,0,1,1,0\n20260608,所有平台,2025-03-30,0,0,0,0,0,0,0,0,0,26000,8000,18000,4,2,2,3,1,2,3,1,2,4,2,2,0,0,0,0,0,0,0,0,0,8000,0,8000,1,0,1,1,0,1,1,0,1,1,0,1\n20260608,慈善平台X,2025-02-20,0,0,0,0,0,0,0,0,0,16000,8000,8000,3,2,1,2,1,1,2,1,1,3,2,1,0,0,0,0,0,0,0,0,0,5000,5000,0,1,1,0,1,1,0,1,1,0,1,1,0\n20260608,慈善平台X,2025-05-10,0,0,0,0,0,0,0,0,0,16000,8000,8000,3,2,1,2,1,1,2,1,1,3,2,1,0,0,0,0,0,0,0,0,0,3000,3000,0,1,1,0,0,0,0,0,0,0,1,1,0\n20260608,其他平台,2025-06-15,0,0,0,0,0,0,0,0,0,10000,0,10000,1,0,1,1,0,1,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,10000,0,10000,1,0,1,1,0,1,1,0,1,1,0,1\n20260608,慈善平台X,2025-03-30,0,0,0,0,0,0,0,0,0,16000,8000,8000,3,2,1,2,1,1,2,1,1,3,2,1,0,0,0,0,0,0,0,0,0,8000,0,8000,1,0,1,1,0,1,1,0,1,1,0,1", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,10,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数51 + 列名匹配\nC,C_row_alignment,row_consistency,10,\"行数比例 + key(fundraising_platform,stat_date)覆盖率\"\nD,D_cumulative_metrics,cumulative_metrics,30,26个total_*列逐行匹配\nD,D_incremental_metrics,incremental_metrics,25,26个new_*列逐行匹配\nF,F_platform_coverage,platform_coverage,5,'慈善平台X'+'其他平台'+'所有平台'\nF,F_join_completeness,join_completeness,3,4张源表都被引用\nF,F_insert_overwrite,insert_mode,4,INSERT INTO（非 OVERWRITE）\nF,F_partition_value,partition_value,3,imp_date=20260608\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(10) + B(10) + C(10) + D(55) + F(15)\n# 难度,EXPERT\n# 权重,\"product=0.8, process=0.2\"\n# 范式,new\n# Key列,\"fundraising_platform, stat_date\"\n# 预期列数,\n# 输出表,internal_platform_db.ads_gy_digital_tool_used_plat_data_d_ql_cand_query_engine_143", "path": "tasks/offline-compute/HiveSQL/hivesql_027"} |
| {"task_id": "hivesql_028_en", "id": "offline-compute_HiveSQL_hivesql_028", "name": "From input table internal_platform_db.t_eis_feedaggregator2_feedforredd", "workload": "offline-compute", "engine": "HiveSQL", "category": "offline-compute/HiveSQL", "language": "en", "modality": "pure-text", "timeout_seconds": 600, "prompt": "## Prompt\nTask Objective: Extract qualifying records from the input table, parse the JSON array, and explode it into multiple rows, then output to the target table.\n\nInput:\n- `internal_platform_db.t_eis_feedaggregator2_feedforreddot_check_result_summary_classification_df_query_engine_152`\n\nProcessing Rules:\n1. Filter condition: `ds='20260608'`, `regexp_replace(second_update_date, '-', '') = ds`, `second_status in (1, 2)`\n2. Core transformation:\n - Extract `$.info.titles` from the `json_buffer_` field as a JSON array string\n - Remove the outermost square brackets of the array, replace `},{` with `}|{`, and split by `|` to obtain multiple JSON objects\n - Use `LATERAL VIEW explode` to expand into multiple rows, with one `title_obj` per row\n - Extract from each `title_obj`: `$.title`, `$.title_type`, `$.quality`\n3. No joins, single-table processing\n\nOutput Requirements:\n- Output field order: `ds`, `feedid`, `feedid_v1`, `type_source`, `classification`, `title`, `title_type`, `quality`, `second_status`\n- The `ds` field value is fixed as `20260608`\n- The `feedid_v1` field value equals `feedid`\n- No deduplication or aggregation required\n\nWrite Requirements:\n- Output table: `internal_platform_db.dws_rd_feedaggregator_feed_title_quality_di_cand_query_engine_152`\n- Partition field: `ds=20260608`\n- Write method: `INSERT OVERWRITE`\n\nPlease write the final HiveSQL to `result.sql` and execute it.", "ground_truth": "INSERT OVERWRITE TABLE internal_platform_db.dws_rd_feedaggregator_feed_title_quality_di_query_engine_152 PARTITION (ds = '20260608')\nWITH tbl AS (\nSELECT\nfeedid,\ntype_source,\nclassification,\nsecond_status,\nget_json_object(json_buffer_, '$.info.titles') AS titles_info\nFROM internal_platform_db.t_eis_feedaggregator2_feedforreddot_check_result_summary_classification_df_query_engine_152\nWHERE ds='20260608'\nAND regexp_replace(second_update_date, '-', '') = ds\nAND second_status in (1, 2)\n)\nSELECT\nfeedid,\nfeedid as feedid_v1,\ntype_source,\nclassification,\nget_json_object(title_obj, '$.title') AS title,\nget_json_object(title_obj, '$.title_type') AS title_type,\nget_json_object(title_obj, '$.quality') AS quality,\nsecond_status\nFROM tbl\nLATERAL VIEW explode(\nsplit(\nregexp_replace(\nregexp_replace(\ntitles_info,\n'^\\\\[|\\\\]$',\n''\n),\n'\\\\}\\\\,\\\\{',\n'}\\\\|\\\\{'\n),\n'\\\\|'\n)\n) exploded AS title_obj", "expected_csv": "feedid,feedid_v1,type_source,classification,title,title_type,quality,second_status,ds\nfeed001,feed001,1,classA,Hello World,ocr,high,1,20260608\nfeed001,feed001,1,classA,Second Title,asr,medium,1,20260608\nfeed002,feed002,2,classB,Good Morning,manual,low,2,20260608", "grade_spec_csv": "维度,维度全名,子维度,满分,说明\nA,A_executability,executability,15,result.sql 能跑通且产出非空\nB,B_schema,schema,10,列数9 + 列名匹配\nC,C_row_alignment,row_consistency,15,\"行数比例 + key(feedid,title)覆盖率\"\nD,D_explode_correctness,explode_correctness,25,LATERAL VIEW展开行正确性（feedid+title组合匹配）\nD,D_json_field_values,json_field_values,20,title_type/quality等JSON提取字段逐行匹配\nF,F_insert_overwrite,insert_overwrite,5,写入模式+分区\nF,F_partition_value,partition_value,5,ds=20260608\nF,F_partition_value,partition_filter,5,源表分区过滤ds='20260608'\n\n总计,,,100,\n\n# 评分模板,评分范式: 产物100分 = A(15) + B(10) + C(15) + D(45) + F(15)\n# 难度,MEDIUM\n# 权重,\"product=0.6, process=0.4\"\n# 范式,new\n# Key列,\"feedid, title\"\n# 预期列数,\n# 输出表,internal_platform_db.dws_rd_feedaggregator_feed_title_quality_di_cand_query_engine_152", "path": "tasks/offline-compute/HiveSQL/hivesql_028_en"} |
|
|
