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
Analyzing the query summary
To get execution steps and statistics in more detail than in the query plan that EXPLAIN produces, use the SVL_QUERY_SUMMARY and SVL_QUERY_REPORT 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.
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