---
id: offline-compute_PrestoSQL_prestosql_007
name: APK Threat Scan Instance GPU Card-Hour 5-Minute Window Statistics
category: offline-compute/PrestoSQL
timeout_seconds: 900
modality: pure-text
engine: prestosql
---
## Prompt
**Task Objective**: Compute the GPU card-hour consumption of APK scan instances, aggregated by 5-minute time windows, joining with Pod mapping and task instance GPU configuration information, and write the results to the output table.

**Input Tables**:
- `internal_platform_db.t_gpu_monitor_parsed_prestosql_007` (GPU monitoring data table)
  - `container` STRING — container name
  - `pod_name` STRING — Pod name
  - `pkg_time` STRING — reporting time (epoch second string)
  - `gpu_name` STRING — GPU model
  - `metric` STRING — metric name
  - `value` STRING — metric value
  - `dt` STRING — partition field (format `'2026060800'`)

- `internal_platform_db.dwd_scan_instance_podname_prestosql_007` (Pod-to-scan-instance mapping table)
  - `dt` STRING — partition field
  - `instance_uuid` STRING — instance unique identifier
  - `pod_name` STRING — Pod name
  - `pod_phase` STRING — Pod phase
  - `namespace` STRING — namespace

- `internal_platform_db.dwd_scan_task_instance_prestosql_007` (task instance GPU configuration table 1)
  - `databus_imp_date` STRING — partition field
  - `instance_uuid` STRING — instance unique identifier
  - `host_gpu_num` DOUBLE — host GPU card count
  - `host_num` DOUBLE — host count
  - `last_modify` DOUBLE — last modification timestamp
  - `gpu_name` STRING — GPU model

- `internal_platform_db.scan_task_instance_prestosql_007` (scan task instance GPU configuration table 2)
  - `databus_imp_date` STRING — partition field
  - `instance_uuid` STRING — instance unique identifier
  - `host_gpu_num` DOUBLE — host GPU card count
  - `host_num` DOUBLE — host count
  - `last_modify` DOUBLE — last modification timestamp
  - `gpu_name` STRING — GPU model
  - `scan_type` STRING — scan type

**Computation Logic**:
1. Filter specified metrics from the GPU monitoring data, and compute runtime per Pod in 5-minute time windows
2. Join with the Pod-to-instance mapping to aggregate the GPU runtime and Pod count per instance per 5-minute window
3. Retrieve the latest GPU configuration information for each task instance
4. Compute the GPU card-hours (GPU-hour) per instance per 5-minute window
5. The specific time window bucketing method, join conditions, aggregation logic, and GPU-hour computation formula must be determined based on the table structures and business semantics

**Output Requirements**:
- Target table: `internal_platform_db.t_scan_instance_gpu_time_stats_cand_prestosql_007`
- Output fields and order: `instance_uuid` STRING, `time_5min` BIGINT, `host_gpu_num` DOUBLE, `sum_run_time_m` DOUBLE, `gpu_hour` DOUBLE, `gpu_name` STRING, `pod_count` BIGINT, `host_num` DOUBLE
- If the target table does not exist, first create the table, then write the data
- Use Presto/Trino SQL syntax; do not use Hive/Spark SQL dialects

**Environment and Execution Notes**:
- Presto is running, connected via the Hive catalog
- Execute SQL: `presto-cli --catalog hive --schema internal_platform_db -f /tmp_workspace/result.sql`
- After writing `result.sql`, you must execute it yourself to verify that it runs successfully and produces correct data