Amazon Redshift will no longer support the creation of new Python UDFs starting Patch 198. Existing Python UDFs will continue to function until June 30, 2026. For more information, see the [ blog post ](https://amazonaws-china.com/blogs/big-data/amazon-redshift-python-user-defined-functions-will-reach-end-of-support-after-june-30-2026/).
# STL\$1QUERY\$1METRICS
Contains metrics information, such as the number of rows processed, CPU usage, input/output, and disk use, for queries that have completed running in user-defined query queues (service classes). To view metrics for active queries that are currently running, see the [STV\$1QUERY\$1METRICS](r_STV_QUERY_METRICS.md) system view.
Query metrics are sampled at one second intervals. As a result, different runs of the same query might return slightly different times. Also, query segments that run in less than one second might not be recorded.
STL\$1QUERY\$1METRICS tracks and aggregates metrics at the query, segment, and step level. For information about query segments and steps, see [Query planning and execution workflow](c-query-planning.md). Many metrics (such as `max_rows`, `cpu_time`, and so on) are summed across node slices. For more information about node slices, see [Data warehouse system architecture](c_high_level_system_architecture.md).
To determine the level at which the row reports metrics, examine the `segment` and `step_type` columns.
+ If both `segment` and `step_type` are `-1`, then the row reports metrics at the query level.
+ If `segment` is not `-1` and `step_type` is `-1`, then the row reports metrics at the segment level.
+ If both `segment` and `step_type` are not `-1`, then the row reports metrics at the step level.
The [SVL\$1QUERY\$1METRICS](r_SVL_QUERY_METRICS.md) view and the [SVL\$1QUERY\$1METRICS\$1SUMMARY](r_SVL_QUERY_METRICS_SUMMARY.md) view aggregate the data in this view and present the information in a more accessible form.
STL\$1QUERY\$1METRICS is visible to all users. Superusers can see all rows; regular users can see only their own data. For more information, see [Visibility of data in system tables and views](cm_chap_system-tables.md#c_visibility-of-data).
Some or all of the data in this table can also be found in the SYS monitoring view [SYS\$1QUERY\$1DETAIL](SYS_QUERY_DETAIL.md). The data in the SYS monitoring view is formatted to be easier to use and understand. We recommend that you use the SYS monitoring view for your queries.
## Table columns
[\[See the AWS documentation website for more details\]](http://docs.amazonaws.cn/en_us/redshift/latest/dg/r_STL_QUERY_METRICS.html)
## Sample query
To find queries with high CPU time (more the 1,000 seconds), run the following query.
```
Select query, cpu_time / 1000000 as cpu_seconds
from stl_query_metrics where segment = -1 and cpu_time > 1000000000
order by cpu_time;
query | cpu_seconds
------+------------
25775 | 9540
```
To find active queries with a nested loop join that returned more than one million rows, run the following query.
```
select query, rows
from stl_query_metrics
where step_type = 15 and rows > 1000000
order by rows;
query | rows
------+-----------
25775 | 2621562702
```
To find active queries that have run for more than 60 seconds and have used less than 10 seconds of CPU time, run the following query.
```
select query, run_time/1000000 as run_time_seconds
from stl_query_metrics
where segment = -1 and run_time > 60000000 and cpu_time < 10000000;
query | run_time_seconds
------+-----------------
25775 | 114
```