---
id: offline-compute_MySQL_mysql_004
name: Ceph Path Coldness Score Cluster-Level Statistics
category: offline-compute/MySQL
timeout_seconds: 1800
modality: pure-text
engine: mysql
---
## Prompt
Please generate MySQL code based on the following requirements.

1. Task Objective
Perform cluster-level statistics on Ceph path coldness score data using three aggregation methods, and write the results to the target table.

2. Input
- Input table: `internal_platform_db.t_ceph_path_coldness_score_v2_mysql_004`
- Filter condition: `dt='20260507'`

3. Processing Rules
3.1 Three path selection method definitions:
- `level2_only`: Select only records where `path_level=2`
- `max_available`: For each `cluster_name`, group by the first-level directory of `path_level2` (group key = the first segment obtained by removing the leading `/` from `path_level2` and splitting by `/`; for example, `/data/logs` and `/data/backup` both have the group key `data`). For each group, select the record with the maximum `path_level`.
- `leaf_only`: Select leaf nodes (i.e., records for which no other record with the same `cluster_name` exists whose `path_level2` equals the parent path obtained by removing the last `/` and everything after it from the current record's `path_level2`; for example, if the current path is `/data/logs/app1`, the parent path is `/data/logs`, and if a record with this parent path exists, the current record is not a leaf).

3.2 After merging the data from all three methods, aggregate by `cluster_name` and `agg_method`:
- `total_path_count`: Total path count (count)
- `total_size_tb`: Total capacity in TB (sum)
- `total_access_count`: Total access count (sum)
- `avg_coldness_score`: Average coldness score (mean)
- `cluster_heat_category`: Categorize based on `avg_coldness_score`: >=60 is `'高热集群'` (high-heat cluster), >=40 is `'较热集群'` (warm cluster), >=20 is `'中等集群'` (moderate cluster), otherwise `'较冷集群'` (cool cluster)
- `p0_count`/`p0_size_tb`: Path count/capacity where `governance_level='P0'`
- `p1_count`/`p1_size_tb`: Path count/capacity where `governance_level='P1'`
- `p2_count`/`p2_size_tb`: Path count/capacity where `governance_level='P2'`
- `p3_count`/`p3_size_tb`: Path count/capacity where `governance_level='P3'`
- `cold_path_pct`: (P0 path count + P1 path count) / total path count * 100
- `cold_size_pct`: (P0 capacity + P1 capacity) / total capacity * 100

4. Output Requirements
Output field order: `cluster_name` (VARCHAR(256)), `agg_method` (VARCHAR(256)), `total_path_count` (BIGINT), `total_size_tb` (DOUBLE), `total_access_count` (BIGINT), `avg_coldness_score` (DOUBLE), `cluster_heat_category` (VARCHAR(256)), `p0_count` (BIGINT), `p0_size_tb` (DOUBLE), `p1_count` (BIGINT), `p1_size_tb` (DOUBLE), `p2_count` (BIGINT), `p2_size_tb` (DOUBLE), `p3_count` (BIGINT), `p3_size_tb` (DOUBLE), `cold_path_pct` (DOUBLE), `cold_size_pct` (DOUBLE), `dt` (VARCHAR(256))

5. Write Requirements
- Output table: `internal_platform_db.t_ceph_coldness_cluster_stats_v2_cand_mysql_004`
- Filter condition: `dt='20260507'`
- No joins, single-table processing
- Use standard MySQL syntax; do not use Hive/Spark SQL dialects

**Environment and Execution Notes**:
- Your final output must be written to the file `/tmp_workspace/result.py`, not `result.sql`
- The local MySQL is running at localhost:3306, username `root`, password `root123`
- Use Python `pymysql` in `result.py` to execute the SQL (do not use the `mysql` command-line tool)
- The script must include complete table creation (if the target table does not exist) and data writing logic
- After writing `result.py`, you must execute `python3 /tmp_workspace/result.py` yourself to verify that it runs successfully and produces correct data