| --- |
| id: offline-compute_MySQL_mysql_018 |
| name: URL Safety Detection Multi-Dimensional Access Statistics Report |
| category: offline-compute/MySQL |
| timeout_seconds: 1800 |
| modality: pure-text |
| engine: mysql |
| --- |
| ## Prompt |
| I need you to generate a MySQL script that computes multi-dimensional access statistics from the URL safety detection detail table, producing a URL access statistics report. |
|
|
| **Business Background and Objective**: The URL safety detection system collects URL access records for each `taid` daily. It needs to compute UV and its 1d/7d/30d/1m periodic metrics across multiple dimensions (taid, taid+url, taid+domain/site, taid+cgi), and output a single-row summary result to the report table. |
|
|
| **Input Tables (full name + brief description)**: |
| - `internal_platform_db.t_dws_urlsafe_rela_mobile_browser_taid_url_di_mysql_018` (URL safety detection detail table) |
| - Historical partition data: Historical records in the output table itself where `ds = 20260531, channel = 'mobile_browser'` (left join source, used to obtain 1m metrics) |
|
|
| (Please connect to the database and query to confirm the table structures and field semantics.) |
|
|
| **Filter Condition**: |
| - Input table: `ds > 20260509 AND ds <= 20260608` |
|
|
| **Processing Rules**: |
| ### Subquery a (taid-level UV statistics) |
| - Group by `taid`, compute: |
| - `uv_1d = COUNT(IF(ds_max = 20260608, 1, NULL))` |
| - `uv_avg_7d = (COUNT(state_6d=1) + ... + COUNT(state_daily=1)) / 7`, where `state_*d = MAX(IF(ds = corresponding_date, 1, 0))` |
| - `uv_7d = COUNT(IF(ds_max > 20260601, 1, NULL))` |
| - `uv_30d = COUNT(1)` |
|
|
| ### Subquery c (taid+url-level statistics) |
| - Group by `taid`, `url` and aggregate, computing `userid_url_1d`/`avg_7d`/`7d`/`30d` using the same logic as above |
|
|
| ### Subquery d (taid+domain/site-level statistics) |
| - First group by `taid`, `site` and aggregate `domain`, `ds_max`, `state_*d` |
| - Domain value rule: Under the same taid+site, if multiple domains exist, take `MAX(domain)` (lexicographically largest value) as the domain attribution for that site |
| - Then group by `taid`, `domain` and aggregate, computing: |
| - `userid_domain_1d`/`avg_7d`/`7d`/`30d` |
| - `userid_site_1d = SUM(site_cnt_daily)` |
| - `userid_site_avg_7d = (SUM(site_cnt_6d) + ... + SUM(site_cnt_daily)) / 7` |
| - `userid_site_7d = SUM(site_cnt_7d)` |
| - `userid_site_30d = SUM(site_cnt)` |
|
|
| ### Subquery e (taid+cgi-level statistics) |
| - Group by `taid`, `cgi` and aggregate, computing `userid_cgi_1d`/`avg_7d`/`7d`/`30d` using the same logic as above |
|
|
| ### Join Logic |
| - `a LEFT JOIN b` (historical partition, joined on `data_par`): `IFNULL(b.uv_1m, 0)` and 4 other fields |
| - `a JOIN c, d, e` (all inner joins, joined on `data_par`) |
|
|
| ### Final Output Fields (in order) |
| - `access_type = '网址检测API'` |
| - `access_channel = '手机IM平台Q浏览器'` |
| - `uv_1d`, `uv_avg_7d` (`ROUND(x, 0)`), `uv_7d`, `uv_30d`, `uv_1m` |
| - `userid_url_1d`, `userid_url_avg_7d` (ROUND), `userid_url_7d`, `userid_url_30d`, `userid_url_1m` |
| - `userid_domain_1d`, `userid_domain_avg_7d` (ROUND), `userid_domain_7d`, `userid_domain_30d`, `userid_domain_1m` |
| - `userid_site_1d`, `userid_site_avg_7d` (ROUND), `userid_site_7d`, `userid_site_30d`, `userid_site_1m` |
| - `userid_cgi_1d`, `userid_cgi_avg_7d` (ROUND), `userid_cgi_7d`, `userid_cgi_30d`, `userid_cgi_1m` |
|
|
| **Output Requirements**: |
| - Target table: `internal_platform_db.t_app_urlsafe_report_url_access_stat_di_cand_mysql_018` |
| - Single-row result, 27 fields |
| - All avg fields retain 0 decimal places (using ROUND) |
| - 1m fields use IFNULL to handle NULL as 0 |
| - If the target table does not exist, first create it using standard MySQL InnoDB format, then write the data |
| - 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 |
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