tasks/offline-compute/MySQL/mysql_004_en/task.md

metadata

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