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/). 

# Analyzing the query summary
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To get execution steps and statistics in more detail than in the query plan that [EXPLAIN](r_EXPLAIN.md) produces, use the [SVL\$1QUERY\$1SUMMARY](r_SVL_QUERY_SUMMARY.md) and [SVL\$1QUERY\$1REPORT](r_SVL_QUERY_REPORT.md) system views.

SVL\$1QUERY\$1SUMMARY 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\$1QUERY\$1REPORT system view enables you to see information similar to that for SVL\$1QUERY\$1SUMMARY, 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\$1QUERY\$1SUMMARY view](using-SVL-Query-Summary.md)
+ [Using the SVL\$1QUERY\$1REPORT view](using-SVL-Query-Report.md)
+ [Mapping the query plan to the query summary](query-plan-summary-map.md)