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. 

# Analyzing the query summary
<a name="c-analyzing-the-query-summary"></a>

To get execution steps and statistics in more detail than in the query plan that [EXPLAIN](r_EXPLAIN.md) produces, use the [SVL\_QUERY\_SUMMARY](r_SVL_QUERY_SUMMARY.md) and [SVL\_QUERY\_REPORT](r_SVL_QUERY_REPORT.md) system views.

SVL\_QUERY\_SUMMARY provides query statistics by stream. You can use the information it provides to identify issues with expensive steps, long-running steps, and steps that write to disk. 

The SVL\_QUERY\_REPORT system view enables you to see information similar to that for SVL\_QUERY\_SUMMARY, only by compute node slice rather than by stream. You can use the slice-level information for detecting uneven data distribution across the cluster (also known as data distribution skew), which forces some nodes to do more work than others and impairs query performance.

**Topics**
+ [Using the SVL\_QUERY\_SUMMARY view](using-SVL-Query-Summary.md)
+ [Using the SVL\_QUERY\_REPORT view](using-SVL-Query-Report.md)
+ [Mapping the query plan to the query summary](query-plan-summary-map.md)