tasks/offline-compute/HiveSQL/hivesql_001_en/task.md

metadata

id: offline-compute_HiveSQL_hivesql_001
name: Message Queue Topic Dimension Table internal_platform_db.dim_mq_topic_d_su
category: offline-compute/HiveSQL
timeout_seconds: 600
modality: pure-text
engine: hivesql

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} : previous day in YYYYMMDD format
• You may also use Spark SQL built-in functions like current_date() and date_sub().

Input: internal_platform_db.dim_mq_topic_d_query_engine_001 (a partitioned table, partitioned by dt).

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.

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.

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.

Please write the final HiveSQL to result.sql and execute it.