What are the best practices outlined in a standard PostgreSQL performance tuning guide for identifying slow running queries and optimizing database indexes?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the best practices outlined in a standard PostgreSQL performance tuning guide for identifying slow running queries and optimizing database indexes?
1 real search · 1 real-search pass
PostgreSQL official documentation EXPLAIN ANALYZE pg_stat_statements indexes multicolumn partial indexes vacuum analyze performance tuningGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
PostgreSQL official documentation EXPLAIN ANALYZE pg_stat_statements indexes multicolumn partial indexes vacuum analyze performance tuning Google ↗
- PostgreSQL: Documentation: 18: 11.3. Multicolumn Indexes · postgresql.org
- PostgreSQL: Documentation: 18: 11.5. Combining Multiple Indexes · postgresql.org
- PostgreSQL: Documentation: 18: 11.7. Indexes on Expressions · postgresql.org
- PostgreSQL: Documentation: 18: Chapter 11. Indexes · postgresql.org
- PostgreSQL: Documentation: 17: CREATE INDEX · postgresql.org
- PostgreSQL: Documentation: 18: 11.1. Introduction · postgresql.org
- PostgreSQL: Documentation: 17: ANALYZE · postgresql.org
- PostgreSQL: Documentation: 18: VACUUM · postgresql.org
- PostgreSQL: Documentation: 18: 14.2. Statistics Used by the Planner · postgresql.org