AI SQL & PostgreSQL/Neon Schema Optimizer
Turn plain-language requests into high-performance PostgreSQL, Supabase and MySQL queries. Analyze slow queries and instantly copy pgvector HNSW indexes and EXPLAIN ANALYZE performance advice.
A SQL Optimization Guide for PostgreSQL, Supabase and pgvector
80% of slow web applications and high cloud server bills come from missing indexes, poor JOIN strategies and inefficient subqueries.AI SQL & Postgres Optimizer analyzes your plain-language requests and slow raw SQL, and generates queries that run 10x faster forPostgreSQL, Supabase, Neon and MySQL, along withHNSW pgvector indexes and EXPLAIN ANALYZE advice.
1. PostgreSQL Index Types and When to Use Them
Choosing the wrong index type does not speed up the query and multiplies the write (INSERT/UPDATE) cost. A comparison of PostgreSQL index types:
| Index Type | Use Case | Supported Operators | Example CREATE INDEX Syntax |
|---|---|---|---|
| B-Tree (Default) | Equality and range lookups, and ORDER BY sorting. | <, <=, =, >=, >, BETWEEN | CREATE INDEX idx_users_email ON users(email); |
| HNSW (pgvector) | AI and RAG embedding vector similarity search. | <=> (Cosine), <-> (L2), <#> (Dot) | CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops); |
| GIN (Generalized Inverted) | JSONB fields, arrays and full-text search. | @>, ?, ?|, ?&, @@ | CREATE INDEX idx_meta ON products USING gin (metadata); |
| BRIN (Block Range) | Timestamped log tables with millions of rows (low memory use). | <, <=, =, >=, > | CREATE INDEX idx_logs_created ON logs USING brin (created_at); |
2. 4 Golden SQL Rules That Speed Up Slow Queries
Performance guidelines to keep in mind when writing queries:
Fetching wide text and JSON columns you do not need creates unnecessary network traffic and prevents the database from doing an Index-Only Scan. Select only the columns you need.
Writing WHERE LOWER(email) = 'test@test.com' disables the standard B-Tree index and scans every row of the table (Sequential Scan). The fix: create an expression index (CREATE INDEX ON users (LOWER(email))) or store the data in lowercase.
Instead of going to the database for each record inside a loop, fetch all related data in a single round-trip with one JOIN or WITH (Common Table Expression) query.
3. Millisecond Vector Search with Supabase & Neon pgvector
When searching 1536- or 3072-dimensional OpenAI embedding vectors, an HNSW (Hierarchical Navigable Small World) index cuts query time from 800ms down to 12ms:
-- 1. Enable the pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;
-- 2. Create an HNSW index (for cosine similarity)
CREATE INDEX IF NOT EXISTS idx_articles_embedding_hnsw
ON articles
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);
-- 3. Millisecond similarity query
SELECT id, title, 1 - (embedding <=> '[0.012, -0.045, ...]') AS similarity
FROM articles
ORDER BY embedding <=> '[0.012, -0.045, ...]'
LIMIT 5;