Amazon Redshift will no longer support the use of Python UDFs after June 30, 2026. We will start enforcing it in phases. For more information on the details of Python end of life and migration options, see the [ blog post ](https://aws.amazon.com/blogs/big-data/amazon-redshift-python-user-defined-functions-will-reach-end-of-support-after-june-30-2026/) that was published on June 30, 2025. 

# Reviewing query alerts by table
<a name="review-query-alerts-by-table"></a>

The following query identifies tables that have had alert events logged for them, and also identifies what type of alerts are most frequently raised.

If the `minutes` value for a row with an identified table is high, check that table to see if it needs routine maintenance, such as having [ANALYZE](r_ANALYZE.md) or [VACUUM](r_VACUUM_command.md) run against it.

If the `count` value is high for a row but the `table` value is null, run a query against STL\_ALERT\_EVENT\_LOG for the associated `event` value to investigate why that alert is getting raised so often.

```
select trim(s.perm_table_name) as table, 
(sum(abs(datediff(seconds, s.starttime, s.endtime)))/60)::numeric(24,0) as minutes, trim(split_part(l.event,':',1)) as event,  trim(l.solution) as solution, 
max(l.query) as sample_query, count(*) 
from stl_alert_event_log as l 
left join stl_scan as s on s.query = l.query and s.slice = l.slice 
and s.segment = l.segment and s.step = l.step
where l.event_time >=  dateadd(day, -7, current_Date) 
group by 1,3,4 
order by 2 desc,6 desc;
```