Reviewing query alerts by table - Amazon Redshift
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Amazon Redshift will no longer support the creation of new Python UDFs starting November 1, 2025. If you would like to use Python UDFs, create the UDFs prior to that date. Existing Python UDFs will continue to function as normal. For more information, see the blog post .

Reviewing query alerts by table

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 or VACUUM 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;