| --- |
| 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. |
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| **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`). |
|
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| **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`. |
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|
| Please write the final HiveSQL to `result.sql` and execute it. |
|
|
