Why is our PostgreSQL query performance tanking on Supabase and what indexing strategies should we implement immediately?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Why is our PostgreSQL query performance tanking on Supabase and what indexing strategies should we implement immediately?
2 real searches · 1 real-search pass
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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
Supabase PostgreSQL performance indexing query optimization pg_stat_statements index advisor official docs Google ↗
PostgreSQL official documentation multicolumn indexes partial indexes expression indexes EXPLAIN ANALYZE Google ↗
- PostgreSQL: Documentation: 18: 11.3. Multicolumn Indexes · postgresql.org
- Query Optimization · supabase.com
- Steps to improve query performance with indexes · supabase.com
- supabase/apps/docs/content/guides/database/postgres/indexes.mdx at master · supabase/supabase · github.com
- Advisors · supabase.com
- Official documentation · supabase.com
- Supabase PostgreSQL connection management · supabase.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Supabase PostgreSQL performance indexing query optimization pg_stat_statements index advisor official docs OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
PostgreSQL official documentation multicolumn indexes partial indexes expression indexes EXPLAIN ANALYZE OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |