Datasets:

dicemy
/

DataClawEval

task_id stringlengths 9 16	id stringlengths 31 39	name stringlengths 7 145	workload stringclasses 2 values	engine stringclasses 5 values	category stringclasses 5 values	language stringclasses 2 values	modality stringclasses 1 value	timeout_seconds int64 600 1.8k	prompt stringlengths 386 5.82k	ground_truth stringlengths 317 30k	expected_csv stringlengths 0 5.39k	grade_spec_csv stringlengths 0 1.01k	path stringlengths 37 48
hivesql_001_en	offline-compute_HiveSQL_hivesql_001	Message Queue Topic Dimension Table internal_platform_db.dim_mq_topic_d_su	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt 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. Time Variables: The platform provides these variables for dynamic date computation: - `${yyyymmdd}` : current day in YYYYMMDD format - `${yyyymmdd-1}` : previo...	INSERT overwrite TABLE internal_platform_db.dim_mq_topic_d_copilot_query_engine_001 PARTITION (dt = '20260507') SELECT business_id ,business_name ,cluster_set ,tenant ,namespaces ,topic ,mq_type ,dw_appgroup ,in_charge ,description ,create_time ,modify_time ,cluster_id ,cluster_type ,clust...	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 bid001,BizName1,cluster_set_a,tenant_...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,25列(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,25,非key字段逐列值匹配率 D,D_field_completeness,field_completeness,15,关键字段非空/非空串比例 F,F_insert_overwrite,insert_overwrite,...	tasks/offline-compute/HiveSQL/hivesql_001_en
hivesql_002	offline-compute_HiveSQL_hivesql_002	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	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入： - `internal_platform_db.notebook_span_info_query_engine_005` - `internal_platform_db.dwd_gputj_service_instance_map_query_engine_005` - `internal_platform_db.dwd_ml_platform_instance_podname_query_engine_005` - `internal_platform_db.gp...	INSERT overwrite TABLE internal_platform_db.dwd_notebook_instance_pod_cross_day_detail_d_query_engine_005 PARTITION (dt = '20260507') WITH base_data_raw AS ( SELECT DISTINCT trace_id, span_name, start_time, end_time, datawd_project_id, datawd_task_id, datawd_t...	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 trace_001,proj_...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,22列 + 列名匹配 C,C_row_alignment,row_consistency,10,行数比例 + key覆盖率 D,D_time_calculation,time_calculation,30,"instance_run_time, code_run_time 等时间指标" D,D_cross_day_split,cross_day_split,15,trace_001 应出现在两天中 D,D_dimension_join,dimens...	tasks/offline-compute/HiveSQL/hivesql_002
hivesql_003	offline-compute_HiveSQL_hivesql_003	从 `internal_platform_db.notebook_span_info_query_engine	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 1) 任务目标识别并统计 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。 2) 输入 - `internal_platform_db.notebook_span_info_query_engine_007` - `internal_platform_db.notebook_engine_info_query_engine_007` 3) 处理规则 - 目标实例筛选：从 span_info 筛选 `compute_type='ray'` 且 `service_name='notebook-runner'` 的实...	INSERT overwrite TABLE internal_platform_db.dwd_notebook_killed_instance_detail_d_copilot_query_engine_007 PARTITION (dt = '20260507') WITH base_trace AS ( SELECT trace_id FROM internal_platform_db.notebook_span_info_query_engine_007 WHERE databus_imp_date >= '2026050700' AND databus_imp_date <= '2026...	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 2025-05-07,trace_001,proj_01,task_01,inst_01,ray,2,15,13...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,18列 + 列名匹配 C,C_row_alignment,row_consistency,10,行数比例 + key覆盖率 D,D_time_calculation,time_calculation,30,"instance_run_time, code_run_time 等时间指标" D,D_cross_day_split,cross_day_split,15,跨天 trace 按天拆分正确性 D,D_engine_join,engine_joi...	tasks/offline-compute/HiveSQL/hivesql_003
hivesql_004	offline-compute_HiveSQL_hivesql_004	将源表 internal_platform_db.app_group_product_info_sup	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入：internal_platform_db.app_group_product_info_query_engine_011 - 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...	insert overwrite TABLE internal_platform_db.app_group_product_info_history_query_engine_011 PARTITION (dt = '20260507') SELECT 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_nam...	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 use...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,21列(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,25,非key字段逐列值匹配率 D,D_field_completeness,field_completeness,15,关键字段非空/非空串比例 F,F_insert_overwrite,insert_overwrite,...	tasks/offline-compute/HiveSQL/hivesql_004
hivesql_005	offline-compute_HiveSQL_hivesql_005	从 internal_platform_db.dws_mq_production_featur	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入： - 表：internal_platform_db.dws_mq_production_feature_d_increase_query_engine_013 - 分区字段：dt（STRING，格式 YYYYMMDD） - 关键字段：business_id、business_name、topic、cluster_set、tenant、namespaces、system_belong、dw_appgroup、in_charge、description、create_time、mo...	INSERT OVERWRITE TABLE internal_platform_db.ads_mq_topic_governance_item_d_query_engine_013 PARTITION (dt = '20260507') SELECT business_id, business_name, topic, cluster_set, IF ( tenant IS NOT NULL AND namespaces IS NOT NULL, CONCAT( 'persistent://', tenant, '/', namespaces, '/', topic ), topic ) AS mq_full_...	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,p...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,30列(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_mapping,field_mapping,25,字段别名映射正确性（app_group/bid_*等） D,D_derived_mq_full_topic,derived_mq_full_topic,15,mq_full_topic 派生列拼接逻辑正确性 F,F_insert_ov...	tasks/offline-compute/HiveSQL/hivesql_005
hivesql_006_en	offline-compute_HiveSQL_hivesql_006	Input Table internal_platform_db.ods_t_databus_access_topic	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task 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. Input: `internal_platform_db.ods_t_databus_access_topic_cost_date_d_query_engine_015`, partitioned by field `dt` (STRING, YYYYMMDD). Key fields: `s...	INSERT overwrite TABLE internal_platform_db.dwd_databus_topic_cost_detail_d_query_engine_015 PARTITION (dt = '20260507') SELECT MAX(systemname) AS system_belong, MAX(dwproductname) AS category_name, MAX(dwappgroup) AS dw_appgroup, MAX(cityid) AS city_id, MAX(iset) AS cluster_set, topic, MAX(data_size) AS ...	system_belong,category_name,dw_appgroup,city_id,cluster_set,topic,total_data_size_d,total_cost,in_charge,dt SystemAlpha,数据仓库DWProdA,AppGroupX,101,ClusterA,topic_billing,2560000,162.0,user_zhang,20260507 SystemBeta,数据仓库DWProdB,AppGroupY,202,ClusterB,topic_log,1200000,81.0,user_li,20260507 SystemGamma,数据仓库DWProdC,AppGrou...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,25,非key字段逐列值匹配率 D,D_field_completeness,field_completeness,15,关键字段非空/非空串比例 F,F_insert_overwrite,insert_overwrite,5...	tasks/offline-compute/HiveSQL/hivesql_006_en
hivesql_007_en	offline-compute_HiveSQL_hivesql_007	Aggregate Notebook instance runtime statistics by trace_id and p_date for the day: Input table w	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Aggregate Notebook instance runtime statistics grouped by `trace_id` and `p_date`. Input: `internal_platform_db.dws_notebook_instance_execute_minute_stat_d_query_engine_019` - Partition field: `dt` (STRING, format YYYYMMDD) - Key fields: `trace_id`, `p_date`, `datawd_project_id`, `datawd_task...	INSERT overwrite TABLE internal_platform_db.dws_notebook_instance_execute_stat_d_query_engine_019 PARTITION (dt = '20260507') SELECT t1.trace_id, t1.p_date, t1.datawd_project_id, t1.datawd_task_id, t1.datawd_task_instance_id, t1.compute_type, t1.status_code, t1.instance_run_time, t1.code_run_time, t...	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 trace_001...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,25,非key字段逐列值匹配率 D,D_field_completeness,field_completeness,15,关键字段非空/非空串比例 F,F_insert_overwrite,insert_overwrite,5...	tasks/offline-compute/HiveSQL/hivesql_007_en
hivesql_008_en	offline-compute_HiveSQL_hivesql_008	Count tasks matching the shuffle_split tuning rule. Input table: internal_platform_db	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Count tasks that match the `shuffle_split` tuning rule, producing task-level tuning rule hit results. Inputs: - `internal_platform_db.nextgen_platform_dsl_spark_props_fht0_query_engine_021` - `internal_platform_db.ods_spark_props_extra_query_engine_021` - `internal_platform_db.deep_tuning_rul...	insert overwrite table internal_platform_db.shuffle_split_tuning_rule_hit_task_info_query_engine_021 partition (databus_imp_date = '20260507') with spark_props as ( select t1.app_id ,max(spark_partition_min) spark_partition_min ,max(spark_partition_max) spark_partition_max ,max(round(ifnull(spark_ta...	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_b...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,20列 + 列名匹配 C,C_row_alignment,row_consistency,10,行数比例 + key覆盖率 D,D_shuffle_logic,shuffle_logic,20,shuffle_split 条件逻辑（CASE WHEN 版本判断） D,D_deduplication,deduplication,15,ROW_NUMBER 按 usp_task_id 去重 D,D_numeric_values,numeric_valu...	tasks/offline-compute/HiveSQL/hivesql_008_en
hivesql_009_en	offline-compute_HiveSQL_hivesql_009	Compute GPU card-hours for task instances within 5-minute windows, joining Pod mapping and task instance configuration, outputting hourly instanc	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Compute GPU card-hours for task instances within 5-minute windows, outputting hourly instance-level GPU usage statistics. Inputs: - `internal_platform_db.gputj_gpu_info_parsed_query_engine_022` - `internal_platform_db.dwd_ml_platform_instance_podname_query_engine_022` - `internal_platform_db....	INSERT OVERWRITE TABLE internal_platform_db.task_instance_gpu_time_stats_query_engine_022 PARTITION (dt='2026050700') WITH -- Step 1: 从GPU监控数据中提取pod运行记录，按分钟去重 pod_run_minutes AS ( SELECT pod_name, gpu_name, FLOOR(CAST(pkg_time AS BIGINT) / 60) * 60 AS minute_timestamp, FLOOR(CAST(pkg...	instance_uuid,time_5min,host_gpu_num,sum_run_time_m,gpu_hour,gpu_name,pod_count,host_num inst-uuid-aaa,1767225600,8.0,7.0,0.9333333333333333,A100,2,2.0 inst-uuid-ccc,1767225600,2.0,1.0,0.03333333333333333,A100,1,1.0 inst-uuid-bbb,1767225900,4.0,2.0,0.13333333333333333,V100,1,1.0 inst-uuid-ddd,1767225900,8.0,1.0,0.13333...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,8列 + 列名匹配 C,C_row_alignment,row_consistency,10,行数比例 + key覆盖率 D,D_gpu_hour_calculation,gpu_hour_calculation,30,gpu_hour 核心指标计算正确性 D,D_window_aggregation,window_aggregation,15,"sum_run_time_m, pod_count 等窗口聚合指标" D,D_gpu_config_l...	tasks/offline-compute/HiveSQL/hivesql_009_en
hivesql_010	offline-compute_HiveSQL_hivesql_010	从输入表中筛选状态为 killed 的 Notebook runner trace 数据，按天拆分后	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 1) 任务目标产出 killed 状态的 Notebook 实例 Pod 运行明细宽表。 2) 输入 - internal_platform_db.gputj_gpu_info_parsed_agg_1min_query_engine_024 - internal_platform_db.notebook_span_info_query_engine_024 - internal_platform_db.dwd_gputj_service_instance_map_query_engine_024 - internal_platform_db.dwd_ml_platform_instance_podname_...	INSERT OVERWRITE TABLE internal_platform_db.dwd_notebook_killed_instance_pod_detail_d_query_engine_024 PARTITION (dt = '20260507') WITH base_trace AS ( SELECT trace_id FROM internal_platform_db.notebook_span_info_query_engine_024 WHERE databus_imp_date >= '2026050700' AND databus_imp_date <= '20260507...	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 trace001,proj1,...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,22列 + 列名匹配 C,C_row_alignment,row_consistency,10,行数比例 + key覆盖率 D,D_time_calculation,time_calculation,25,"instance_run_time, code_run_time 等时间指标" D,D_cross_day_split,cross_day_split,15,跨天 trace 按天拆分正确性 D,D_gpu_join,gpu_join,15,"...	tasks/offline-compute/HiveSQL/hivesql_010
hivesql_011	offline-compute_HiveSQL_hivesql_011	从 notebook_span_info_query_engine_026 和 notebook_engin	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入： - internal_platform_db.notebook_span_info_query_engine_026 - internal_platform_db.notebook_engine_info_query_engine_026 处理规则： - 两表关联查询，关联条件需基于表结构确定 - 具体过滤条件和字段选择需基于表结构确定输出要求： - 输出表：internal_platform_db.dwd_notebook_killed_instance_detail_d_cand_qu...	INSERT overwrite TABLE internal_platform_db.dwd_notebook_killed_instance_detail_d_query_engine_026 PARTITION (dt = '20260507') WITH base_trace AS ( SELECT trace_id FROM internal_platform_db.notebook_span_info_query_engine_026 WHERE databus_imp_date >= '2026050700' AND databus_imp_date <= '2026050700' ...	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 2025-05-07,T001,P100,TASK001,INST001,ray,2,105,55,50,202...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,18列 + 列名匹配 C,C_row_alignment,row_consistency,10,行数比例 + key覆盖率 D,D_time_calculation,time_calculation,30,"instance_run_time, code_run_time 等时间指标" D,D_cross_day_split,cross_day_split,15,跨天 trace 按天拆分正确性 D,D_engine_join,engine_joi...	tasks/offline-compute/HiveSQL/hivesql_011
hivesql_012_en	offline-compute_HiveSQL_hivesql_012	From input table internal_platform_db.t_ed_socialbook_qq_high_v2_ta	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective Filter out specified `tag_id` values from the tag incremental table and write the results to the target table. Input - `internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_query_engine_100` Processing Rules 1. Filter condition: `ds = 20260608` AND `tag_id NOT IN (10212021124, 106060...	insert overwrite table internal_platform_db.t_ed_socialbook_qq_high_v2_tag_guid_incr_filter_query_engine_100 partition(ds=20260608) select guid, tag_id, tag_value, redis_sub_key, val_type, splitter1, splitter2, limit_size, tag_status fro...	guid,tag_id,tag_value,redis_sub_key,val_type,splitter1,splitter2,limit_size,tag_status,ds guid001,10212021100,val1,sub1,type1,:,",",10,1,20260608 guid005,10212021199,val5,sub5,type1,:,",",8,3,20260608	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,25,非key字段逐列值匹配率 D,D_field_completeness,field_completeness,15,关键字段非空/非空串比例 F,F_insert_overwrite,insert_overwrite,5...	tasks/offline-compute/HiveSQL/hivesql_012_en
hivesql_013	offline-compute_HiveSQL_hivesql_013	从 internal_platform_db.dws_ug_app_ad_material_actio	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入： - internal_platform_db.dws_ug_app_ad_material_action_di_query_engine_101 处理规则： - 无 Join，单表处理 - 过滤条件：imp_date = 20260608 - 维度字段转换（使用 CASE WHEN）： - material_type: 1→'图片', 2→'视频' - source: 2→'人工素材', 3→'人机素材', 6→'AI素材' - video_model_type: 1→'镜头拆分模型(老模型)', 2→'剧情理解模型...	INSERT OVERWRITE TABLE internal_platform_db.ads_ug_app_ad_material_action_di_query_engine_101 PARTITION (imp_date=20260608) SELECT 20260608 AS update_date , CASE WHEN material_type = 1 THEN '图片' WHEN material_type = 2 THEN '视频' ...	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 20260608,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 20260608,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...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数17 + 列名匹配 C,C_row_alignment,row_consistency,15,行数比例 + key覆盖率 D,D_case_when_labels,case_when_labels,20,7个维度标签列逐行匹配 D,D_agg_metrics,agg_metrics,20,8个聚合列按权重匹配 F,F_union_all_sentinel,union_all_sentinel,10,是否存在NULL哨兵行 F,F_insert_...	tasks/offline-compute/HiveSQL/hivesql_013
hivesql_014_en	offline-compute_HiveSQL_hivesql_014	Filter add-friend behavior from internal_platform_db.log_17047_query_engine_102	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Perform cluster aggregation analysis on add-friend behavior logs to identify malicious clusters, and output statistical metrics for each cluster. Input: - `internal_platform_db.log_17047_query_engine_102` (add-friend behavior log table) Processing Rules: 1. No joins, single-table processing,...	INSERT INTO TABLE internal_platform_db.t_acct_addfri_action_cluster_minutely_query_engine_102 PARTITION(ds=202606090010) select appname_, clientversion_, scene_, ticketscene_, headmd5_, uinipcountryid_, uinipprovinceid_, count(*) as addfri_pv, count(distinct user_id_) as user_id_cnt, sum(if(uinhighquality_=0, 1, 0)) as...	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 app_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,...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数16 + 列名匹配 C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,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)" F,F_insert_overwrite,insert_overwrite...	tasks/offline-compute/HiveSQL/hivesql_014_en
hivesql_015_en	offline-compute_HiveSQL_hivesql_015	From internal_platform_db.t_mg_dws_user_tag_preset_241	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Incrementally insert new user records (`user_id`, `loss_day`) into the `ds=20260608` partition of the target table that have not appeared before. Inputs: - `internal_platform_db.t_mg_dws_user_tag_preset_24128_query_engine_103` (source table, containing fields such as `ds`, `user_id`, `loss_da...	insert into internal_platform_db.t_mg_dws_user_tag_user_id_once_24128_query_engine_103 (user_id, loss_day) select user_id,loss_day from internal_platform_db.t_mg_dws_user_tag_preset_24128_query_engine_103 where ds = 20260608 and user_id not in (select user_id from internal_platform_db.t_mg_dws_user_tag_user_id_once_24...	user_id,loss_day user_a,5 user_b,3 user_e,1	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数匹配 + 列名匹配 C,C_row_alignment,row_consistency,15,行数比例 + key覆盖率 D,D_anti_join,anti_join,25,NOT IN 排除已存在 user_id 的正确性 D,D_value_correctness,value_correctness,20,loss_day 等数值列逐行匹配 F,F_insert_overwrite,insert_mode,5,INSERT INTO（非 ...	tasks/offline-compute/HiveSQL/hivesql_015_en
hivesql_016	offline-compute_HiveSQL_hivesql_016	从用户活跃日志表统计每日各渠道的活跃用户数	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 任务目标：统计各渠道的日活跃用户数。输入： - internal_platform_db.dwd_user_activity_log_hi_query_engine_104（用户活跃日志表）处理规则： 1. 过滤条件：imp_date = 20260608 2. 分组维度：channel 3. 聚合指标：COUNT(DISTINCT user_id) AS dau 4. 派生列：imp_date 固定值 20260608 输出要求： - 字段顺序：imp_date（BIGINT，固定值20260608）、channel（STRING）、dau（BIGINT） - 按 channel 升序排列写入要求...	insert overwrite table internal_platform_db.dws_user_daily_active_by_channel_query_engine_104 partition(ds='20260608') select 20260608 as imp_date , channel , count(distinct user_id) as dau from internal_platform_db.dwd_user_activity_log_hi_query_engine_104 where imp_date = 20260608 group by channel order by channel	imp_date,channel,dau,ds 20260608,appstore,3,20260608 20260608,huawei,2,20260608 20260608,oppo,1,20260608 20260608,xiaomi,1,20260608	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数(4=3数据+1分区) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,25,非key字段逐列值匹配率 D,D_field_completeness,field_completeness,15,关键字段非空比例 F,F_insert_overwrite,insert_overwri...	tasks/offline-compute/HiveSQL/hivesql_016
hivesql_017	offline-compute_HiveSQL_hivesql_017	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	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 任务目标：按 SDK 类型和小时统计活跃用户数。输入： - internal_platform_db.dws_event_tracking_ul2l6h5c_events_di_query_engine_114（事件明细表） - internal_platform_db.dws_event_tracking_ul2l6h5c_all_user_df_query_engine_114（用户维度表）处理规则： - Join 条件：e.user_id = u.user_id - 过滤条件：e.imp_date = 20260608，u.imp_date = 20260608，u.last_login_date ...	INSERT INTO internal_platform_db.ads_event_tracking_ul2l6h5c_user_active_hour_di_query_engine_114 SELECT 20260608 as `imp_date` , ROW_NUMBER() OVER(ORDER BY e.`sdk_id`, e.`event_time_hour`) as `id` , e.`sdk_id` as `sdk_id` , e.`event_time_hour` as `hour` , COUNT(DISTINCT e.user_id) as `active_users` FROM internal_platf...	imp_date,id,sdk_id,hour,active_users 20260608,1,sdk_android,10,2 20260608,2,sdk_ios,11,2	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数(5) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,45,非key字段逐列值匹配率 F,F_insert_overwrite,insert_mode,5,INSERT INTO 写入模式 F,F_source_filter,source_coverage,5,2张源表均被引用 ...	tasks/offline-compute/HiveSQL/hivesql_017
hivesql_018_en	offline-compute_HiveSQL_hivesql_018	Compute scores for first-level comments from view records and comment logs. Group by recall_uin + channel_id + fe	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Compute the composite score for first-level comments newly added by recalled read users after their first post view, for content heat analysis. Inputs: - `internal_platform_db.dws_social_group_content_forum_hot_feed_recall_feed_view_hi_query_engine_122` (view records, filter `imp_hour` within...	insert overwrite table internal_platform_db.dws_social_group_content_forum_hot_feed_recall_comment_score_hi_query_engine_122 partition (imp_hour = 2026060910) with social_group_feed_view as ( select uin as recall_uin , channel_id , feed_id , min(first_view_time) as first_view_time , max(last_view_time) as last_view_tim...	uin,channel_id,feed_id,comment_id,comment_time,comment_score,normal_comment_like_score,key_author_comment_like_score,key_owner_comment_like_score,normal_comment_reply_score,key_author_comment_reply_score,key_owner_comment_reply_score,avg_normal_comment_reply_score,normal_comment_like_uv,normal_comment_like_cnt,key_auth...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数26 + 列名匹配 C,C_row_alignment,row_consistency,10,"行数比例 + key(uin,channel_id,feed_id,comment_id)覆盖率" D,D_comment_score_correctness,comment_score_correctness,25,comment_score + 7项分档评分列逐行匹配 D,D_uv_cnt_correctness,uv_cnt_correctne...	tasks/offline-compute/HiveSQL/hivesql_018_en
hivesql_019_en	offline-compute_HiveSQL_hivesql_019	From input table internal_platform_db.t_app_urlsafe_cont_topweb	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task Objective: Extract qualifying potential risk domains from the top websites collection table and write them to the target table. Input: - `internal_platform_db.t_app_urlsafe_cont_topwebsites_collect_df_query_engine_125` (fields: `domain`, `site`, `ds`, `src`) Processing Rules: 1. Filter condition: `src=...	INSERT OVERWRITE TABLE internal_platform_db.t_dws_urlsafe_cont_potential_risk_domain_di_query_engine_125 with topsites as ( select domain as original_domain from internal_platform_db.t_app_urlsafe_cont_topwebsites_collect_df_query_engine_125 where src='transco' and ds=20260608 and length(split(domain, '\\.')[0])>3 limi...	ds,type,fuzzer,result_domain,original_domain 20260608,transco,-1,-1,long-domain-name.org 20260608,transco,-1,-1,test.site.net 20260608,transco,-1,-1,abcd.domain.cn	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,col_count(5) + col_names(5) C,C_row_alignment,row_consistency,15,row_ratio(7) + key_coverage(8) D,D_domain_filter_correct,domain_filter_correct,25,original_domain 列值匹配率（核心过滤逻辑） D,D_fixed_value_correct,fixed_value_correct,20,ds...	tasks/offline-compute/HiveSQL/hivesql_019_en
hivesql_020	offline-compute_HiveSQL_hivesql_020	从 internal_platform_db.t_boss_v1_dict_item_hour_query_engine_130 筛选	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入： - internal_platform_db.t_boss_v1_dict_item_hour_query_engine_130 处理规则： 1. 无 Join，单表处理 2. 过滤条件：imp_hour=2026060910 3. 分组聚合：按 dim_name='AEGIS_ALL' 和 dim_value='AEGIS_ALL' 分组 4. 聚合计算：count(1) 得到 compute_item_51323 5. 派生列： - imp_time = 2026060910 - data_type = ...	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') SELECT imp_time, data_type, data_id, check_rule_id, check_item_id, dim_name, dim_value, compute_value, compare_value, check_value FROM ( WITH temp_table_1 AS...	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 2026060910,monitor,1::dept_om::t_boss_v1_dict_item_hour,51322,51323,AEGIS_ALL,AEGIS_ALL,3,,3,p_2026060910,p_monitor	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数(12=10数据+2分区) + 列名匹配(5) C,C_row_alignment,row_consistency,15,行数比例(7) + key覆盖率(8) D,D_field_value_match,field_value_match,45,非key字段逐列值匹配率 F,F_insert_overwrite,insert_overwrite,5,INSERT OVERWRITE + PARTITION 写入模式 F,F_partition...	tasks/offline-compute/HiveSQL/hivesql_020
hivesql_021_en	offline-compute_HiveSQL_hivesql_021	Filter Chinese add-friend content from log table and write to hourly cluster table	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt ### Task Objective Filter Chinese content from the add-friend log table for the specified hour, deduplicate, and write to the hourly cluster table. ### Input - `internal_platform_db.log_17047_query_engine_131` ### Processing Rules 1. No joins, single-table processing 2. Filter conditions: - `day_ = '2026...	INSERT INTO TABLE internal_platform_db.data_team_member14_addcontent_cluster_hour_query_engine_131 PARTITION(ds='2026060914') select distinct content_ from internal_platform_db.log_17047_query_engine_131 where day_='2026-06-09 00:00:00' and hour_ = '2026-06-09 14:00:00' and commfrinum_=0 and appname_ in ('app_hello_txt...	content_ 加好友一起聊天交流分享你好我想加你好友认识这是一个测试内容信息	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数2(含分区列ds) + 列名匹配 C,C_row_alignment,row_consistency,15,行数比例(7) + key(content_)覆盖率(8) D,D_values,values,40,content_列值集合匹配率 F,F_insert_overwrite,insert_overwrite,5,写入模式应为 INSERT INTO F,F_partition_value,partition_value,5,ds='20...	tasks/offline-compute/HiveSQL/hivesql_021_en
hivesql_022	offline-compute_HiveSQL_hivesql_022	从 internal_platform_db.log_16159_query_engine_134 表中提取审核数据，过滤 ds=202	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 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。输入： - 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_）处理规则： 1. 过滤条件：ds=2026...	insert overwrite table internal_platform_db.dwmid_daily_ecommerce_shop_level_func_2141_audit_result_query_engine_134 partition(ds ='2026060916') with audit_result as ( select 2026060916 ds,regexp_extract(othercol1_,'bizuin:(.?);')bizuin, regexp_extract(othercol1_,'nickname:(.?);')nickname, split(regexp_extract(result...	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 biz002,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_user2,1...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,10,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数20 + 列名匹配 C,C_row_alignment,row_consistency,10,"行数比例 + key(uniqueauditid_,strategyid_,orderid_,providerid_)覆盖率" D,D_regex_extraction,regex_extraction,20,"正则提取字段逐行匹配(bizuin,nickname,account_tag1/2/3,remark)" D,D_account_tag_m...	tasks/offline-compute/HiveSQL/hivesql_022
hivesql_023_en	offline-compute_HiveSQL_hivesql_023	Compute multi-dimensional CUBE aggregation of user open-start-path data by referer_type, referer, and path	offline-compute	HiveSQL	offline-compute/HiveSQL	en	pure-text	600	## Prompt Task 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`). Inputs: - `internal_platform_db.t_ed_...	insert overwrite table internal_platform_db.t_md_mapservice_user_open_start_path_dwa_di_query_engine_135 partition(ds=20260608) with t as ( select event_time,platform,app_version,channel,t1.uin,event_code,event_value,city,brand,device_id_type,bg,event_timestamp,referer,path,is_first ,case when referer in ('com.food...	referer_type,referer,path,uv,first_uv,ds FoodChainA,com.foodchain_a.android.activity,qqmap://map/routeplan?type=walk,1,1,20260608 CourierCoB,couriercob,qqmap://map/navi,1,0,20260608 CourierCoB,total,total,2,0,20260608 CourierCoB,total,qqmap://map/search,1,0,20260608 CourierCoB,total,qqmap://map/navi,1,0,20260608 Courie...	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数6 + 列名匹配 C,C_row_alignment,row_consistency,15,"行数比例 + key(referer_type,referer,path)覆盖率" D,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)" D,D_cube_aggregation,cube_aggre...	tasks/offline-compute/HiveSQL/hivesql_023_en
hivesql_024	offline-compute_HiveSQL_hivesql_024	从 internal_platform_db.dwd_relationship_strength_fe	offline-compute	HiveSQL	offline-compute/HiveSQL	zh	pure-text	600	## Prompt 任务目标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。输入 - internal_platform_db.dwd_relationship_strength_features_v4_di_query_engine_138（无 Join，单表处理）处理规则 1. 过滤条件：imp_date = 20260608，且 uin >= 10000 且 touin >= 10000 2. 特征处理： - 对所有分数字段使用 COALESCE(field, 0) 填充空值 - c2c_cnt_score、common_frd_num、frd_tag_nu...	INSERT OVERWRITE TABLE internal_platform_db.ads_qq_sq_frd_recommendation_result_list_df_query_engine_138 PARTITION (imp_date = 20260608) WITH feature_modified_v2 AS ( SELECT uin , touin , COALESCE(c2c_score, 0) AS c2c_score , COALESCE(LOG(1 + c2c_cnt_score), 0) AS log_c2c_cnt_score , COALESCE(socialzone_visit_score, 0...	uin,touin,score,raw_score 10009,10010,570.0,0.0569 10001,10002,307.0,0.0306 10003,10004,238.0,0.0237 10007,10008,31.0,0.003	维度,维度全名,子维度,满分,说明 A,A_executability,executability,15,result.sql 能跑通且产出非空 B,B_schema,schema,10,列数5 + 列名匹配 C,C_row_alignment,row_consistency,15,"行数比例 + key(uin,touin)覆盖率" D,D_score_correctness,score_correctness,25,最终得分列逐行匹配 D,D_raw_score_correctness,raw_score_correctness,20,原始分列逐行匹配 F,F_null_handling,null_handling,5,COAL...	tasks/offline-compute/HiveSQL/hivesql_024

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DataClawEval

An executable benchmark for end-to-end data-engineering agents in industrial environments.

DataClawEval measures an autonomous agent's ability to inspect data, implement and debug pipelines, and materialize correct artifacts in realistic data-engineering workflows. It contains 100 production-grounded tasks across five execution engines: PySpark, MySQL, HiveSQL, PrestoSQL/Trino, and FlinkSQL. Each task runs in an isolated Docker sandbox and is evaluated by a case-specific, deterministic, rule-based grader.

What's Inside

Production-grounded tasks — 100 end-to-end tasks reconstructed from production-grade implementations written by professional enterprise data engineers and validated through execution checks, expert perturbations, and expert review.
Batch and streaming coverage — batch transformations in PySpark, MySQL, HiveSQL, and PrestoSQL/Trino, plus streaming-oriented FlinkSQL tasks involving event time, watermarks, windows, joins, and aggregations.
End-to-end agent workflows — schema inspection, data exploration, implementation, execution, debugging, output validation, and artifact materialization.
Artifact- and process-oriented grading — task-specific graders assess executable outputs and engineering behaviors such as exploration, execution efficiency, and self-verification.
Reproducible environments — every run starts in a fresh container with task-specific data, services, execution engines, and deterministic initialization.
Flexible agent integration — run evaluations with the codebuddy, claude-code, or codex backend. The paper evaluates 16 model configurations through a unified CodeBuddy scaffold.
Batch experiments — evaluate one or more engines in parallel and generate combined reports.

Task Suite

Execution engine	Workload	Tasks
PySpark	Batch (`offline-compute`)	20
MySQL	Batch (`offline-compute`)	20
HiveSQL	Batch (`offline-compute`)	28
PrestoSQL/Trino	Batch (`offline-compute`)	12
FlinkSQL	Streaming (`online-compute`)	20
Total		100

The suite is also balanced between 50 English and 50 Chinese task prompts and covers five business domains: Ops & Resource Governance (30), Data Analytics & User Growth (30), Security & Risk Control (16), Content, Community & Dev-Efficiency (14), and Advertising & Marketing (10).

Each task lives in tasks/<workload>/<engine>/<task_id>/ and contains:

task.md — the natural-language request and metadata such as timeout and engine
init/ — initialization or verification assets; batch tasks initialize tables here, while FlinkSQL tasks generally define generated streaming inputs in the submitted SQL
gt/ — the reference implementation and task-specific grade.py used during evaluation

How It Works

For every model–task run, the harness:

Starts a fresh Docker container from the configured DOCKER_IMAGE.
Starts the services required by the selected engine and initializes task-specific inputs.
Prepares the agent workspace with the task prompt and engineering tools.
Runs the selected agent backend and records its execution trajectory.
Loads gt/ after agent execution and runs the task-specific grader in the same container.
Collects the submitted solution files, workspace snapshot, scores, usage data, and execution transcript into output/, then removes the container.

Requirements

Component	Requirement
Docker	Docker CLI and a running daemon
Python	Python 3.11 on the host; the scripts invoke `python3.11`
Storage	Enough free space for the multi-engine image and task outputs
Model access	Credentials and network access for the selected agent backend

The evaluation image provides a ready-to-use multi-engine environment with Spark 3.5.0, Flink 1.18.1, Trino 435, MySQL, a Hive Metastore, Python 3.11.13, and the supported agent runtimes.

Quick Start

0. Verify prerequisites

Make sure Docker is installed and running, and that Python 3.11 is available.

docker version          # verifies the Docker CLI and daemon connection
python3.11 --version    # Python 3.11 is required

1. Install Python dependencies

python3.11 -m pip install -r requirements.txt

2. Get the evaluation image

You have two options.

Option A — Pull the prebuilt image (recommended)

Skip the local build and pull the ready-made image from the registry:

docker pull dicemy/dataclaweval:v1.0
docker tag dicemy/dataclaweval:v1.0 dataclaw-eval:v1.0   # match DOCKER_IMAGE in .env

The default DOCKER_IMAGE in .env is dataclaw-eval:v1.0. Either retag the pulled image as shown above, or set DOCKER_IMAGE=dicemy/dataclaweval:v1.0 in your .env.

Option B — Build locally

prepare.sh vendors the installed CodeBuddy Agent SDK and builds the dataclaw-eval:v1.0 evaluation image.

bash script/prepare.sh

3. Configure credentials

Copy the example env file and fill in the values for the agent backend you plan to use.

cp .env.example .env

Then edit .env:

DOCKER_IMAGE=dataclaw-eval:v1.0

# CodeBuddy auth (required for the `codebuddy` harness)
CODEBUDDY_AUTH_TOKEN=
CODEBUDDY_API_KEY=
CODEBUDDY_INTERNET_ENVIRONMENT=ioa   # public | internal | ioa

# OpenRouter (required for `claude-code` and `codex` harnesses)
OPENROUTER_API_KEY=
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1

Get a CodeBuddy API key: overseas → https://www.codebuddy.ai/profile/keys · China → https://copilot.tencent.com/profile/ · iOA (Tencent staff) → https://tencent.sso.copilot.tencent.com/profile/keys. Set CODEBUDDY_INTERNET_ENVIRONMENT to match your account type.

4. Run the evaluation

# Run a single execution engine
bash script/run_batch.sh -P     # PySpark
bash script/run_batch.sh -M     # MySQL
bash script/run_batch.sh -H     # HiveSQL
bash script/run_batch.sh -F     # FlinkSQL
bash script/run_batch.sh -PR    # PrestoSQL/Trino

# Run workload groups
bash script/run_batch.sh -offline   # PySpark + MySQL + HiveSQL + PrestoSQL/Trino
bash script/run_batch.sh -online    # FlinkSQL
bash script/run_batch.sh -all       # all 100 tasks

Use script/run_batch.sh -all to evaluate the complete 100-task suite. Results and a combined report are written to output/.

Usage Reference

run_batch.sh options:

Flag / Option	Description
`-P` `-M` `-H` `-F` `-PR`	Select PySpark / MySQL / HiveSQL / FlinkSQL / PrestoSQL/Trino
`-offline` `-online` `-all`	Run predefined workload groups
`--parallel N`	Number of tasks to run concurrently (default: 10)
`--harness NAME`	Agent backend: `codebuddy` (default), `claude-code`, `codex`
`--model MODEL`	Model name (repeatable, `codebuddy` only) for multi-model runs

Examples:

# PySpark with 5 workers
bash script/run_batch.sh -P --parallel 5

# Use the Claude Code harness
bash script/run_batch.sh -P --harness claude-code

# Compare two CodeBuddy models on PySpark
bash script/run_batch.sh -P --model model_a --model model_b

To run a single task directly (bypassing batch mode):

python3.11 eval/run_batch.py \
  --agent-backend codebuddy \
  --task tasks/offline-compute/PySpark/pyspark_001/task.md \
  --model deepseek-v4-flash-ioa

Output & Scoring

Each run is saved under output/<harness>/<category>/<task_id>/<run_suffix>/ with the submitted solution, workspace snapshot, transcript, usage statistics, and score.json.

DataClawEval evaluates both the final data product and the engineering workflow:

Artifact quality — executability, schema correctness, row-level alignment, numerical accuracy, and categorical or business correctness.
Process quality — exploration adequacy, execution efficiency, and self-verification.

The overall score combines both dimensions:

[ S = \alpha S_{artifact} + (1-\alpha)S_{process} ]

The artifact weight (\alpha) is configured per task, with 0.7 as the most common value. Repository outputs normalize overall_score to 0–1, while the paper presents scores on a 0–100 scale.

Batch runs aggregate model and engine summaries into output/experiment_report_<timestamp>.json and report the average score and the number of tasks with overall_score >= 0.5:

  Category/Model                            Tasks  Avg Score  Pass(>=0.5)
  offline-compute/PySpark/model_a              20     0.7350        15/20

Paper Results

The paper evaluates 16 model configurations from eight families on all 100 tasks: 1,600 primary runs in total. Every model uses the same fixed Tencent CodeBuddy scaffold, and only the underlying LLM changes. The primary table uses one run per model–task pair; task prompts are evenly split between English and Chinese (50 each).

Model	Overall score (0–100)	Avg. tokens/task
GPT 5.5	74.9	299.8k
Claude Opus 4.8	74.3	318.3k
Claude Sonnet 5	73.8	457.9k
Gemini 3.1 Pro	73.7	292.4k
Gemini 3.5 Flash	73.3	973.8k
DeepSeek V4 Flash	73.0	419.6k
MiniMax M3	71.8	714.1k
GLM 5.1	71.6	355.4k
DeepSeek V4 Pro	70.6	359.3k
Kimi K2.6	69.0	428.8k
GLM 5.2	68.8	403.9k
Kimi K2.7	68.1	407.5k
GPT 5.3 Codex	66.4	271.4k
Hy3	66.0	468.7k
MiniMax M2.7	63.7	501.0k
GLM 5V Turbo	60.3	287.8k

Research highlights:

GPT 5.5 achieves the highest overall score at 74.9.
Engine-level results reveal complementary strengths: Claude Opus 4.8 leads PySpark; GPT 5.5 leads HiveSQL and MySQL; DeepSeek V4 Pro and Gemini 3.5 Flash tie on PrestoSQL/Trino; and DeepSeek V4 Flash leads FlinkSQL.
Token and tool-call analyses identify models that combine strong task performance with efficient agent execution.
Repeated-run experiments add execution consistency as a first-class evaluation dimension.
Execution-grounded, rule-based graders provide stable, case-specific assessment of both generated artifacts and engineering processes.

See the paper for complete engine-level results, efficiency and stability analyses, bilingual performance, and all task listings.

Project Structure

DataClawEval/
├── Dockerfile              # Self-contained sandbox (Spark, Flink, Trino, MySQL, Python 3.11)
├── requirements.txt        # Host-side Python dependencies
├── .env.example            # Credentials template
├── eval/
│   └── run_batch.py        # Evaluation entry point
├── script/
│   ├── prepare.sh          # Vendor SDK + build the Docker image
│   ├── run_batch.sh        # Batch runner + report generator
│   ├── run.sh              # Thin wrapper around run_batch.py
│   └── ...                 # Metastore / Trino / entrypoint helpers
├── src/
│   ├── agents/             # Agent backends: codebuddy, claudecode, codex, common
│   └── utils/              # Docker orchestration, DB init, grading, task parsing
└── tasks/
    ├── offline-compute/    # PySpark, MySQL, HiveSQL, PrestoSQL
    └── online-compute/     # FlinkSQL

Citation

If you use DataClawEval in research, cite the project using CITATION.cff. The archived software release is available at https://doi.org/10.5281/zenodo.21621566.

License

Released under the MIT License.

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Paper for dicemy/DataClawEval

DataClawEval: A Benchmark for Data Engineering Agents in Real Industrial Harness

Paper • 2607.28033 • Published 5 days ago