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Zahir Fahmi
Zahir Fahmi

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PostgreSQL AI: The Everything Database (+ Hidden 10x Performance Tricks)

#postgres #ai #database #vectorsearch

PostgreSQL just ate everyone's lunch. Here's proof:

The Numbers Don't Lie

  • 49% developers switched from MySQL (Stack Overflow 2024)
  • 70% infrastructure cost reduction
  • 10M vectors with <100ms queries
  • 50K queries/sec at Uber production
  • 600M vectors at Instacart

The Secret Sauce Nobody Talks About

Found 4 optimization tricks that changed everything:

1. Parallel Partitioned Search (10x faster) 

-- Create partitioned table CREATE TABLE embeddings_partitioned ( id BIGSERIAL, embedding vector(1536) ) PARTITION BY HASH (id); -- Create 8 partitions = 8 CPU cores DO $$ BEGIN FOR i IN 0..7 LOOP EXECUTE format('CREATE TABLE embeddings_part_%s PARTITION OF embeddings_partitioned FOR VALUES WITH (modulus 8, remainder %s)', i, i); END LOOP; END $$; -- Enable parallel execution SET max_parallel_workers_per_gather = 8;
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2. Binary Quantization (30x faster!) 

-- Convert float vectors to binary ALTER TABLE docs ADD COLUMN embedding_binary bit(1536); UPDATE docs SET embedding_binary = ( SELECT string_agg( CASE WHEN unnest > 0 THEN '1' ELSE '0' END, '' )::bit(1536) FROM unnest(embedding::float[]) ); -- Two-stage search: binary first, then refine WITH candidates AS ( SELECT id, embedding FROM docs WHERE bit_count(embedding_binary # query_binary) < 400 LIMIT 1000 ) SELECT * FROM candidates ORDER BY embedding <=> query_vector LIMIT 10;
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3. Memory-Mapped Indexes 

-- Force index to stay in RAM CREATE EXTENSION pg_prewarm; SELECT pg_prewarm('idx_embeddings_ivfflat', 'buffer'); SET shared_buffers = '8GB';
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Migration Success Stories

  • Instagram: Billions of vectors, zero new infrastructure
  • Spotify: Recommendation engine serving 500M users
  • Discord: 150M users, p99 latency <50ms
  • DoorDash: 40% latency reduction after migration
  • Uber: Driver matching at 50K queries/sec

The Cost Breakdown That Matters

Solution 10M Vectors/mo Setup Time Vendor Lock-in
Pinecone $227 Days Yes
Weaviate $300+ Days Yes
Milvus Self-host complexity Weeks Yes
PostgreSQL + pgvector $0 Minutes No

When PostgreSQL AI Makes Sense

Perfect for:

  • You're already using PostgreSQL
  • 1-100M vectors (covers 95% of use cases)
  • Need ACID compliance + vectors
  • Want to avoid vendor lock-in
  • Team knows SQL

Consider alternatives when:

  • Billions of vectors across 100+ nodes
  • Need GPU acceleration
  • Purely vector-only workload

One Database to Rule Them All

PostgreSQL now handles:

  • Vector embeddings (pgvector)
  • JSON documents (JSONB)
  • Time-series data (TimescaleDB)
  • Full-text search (tsvector)
  • Graph data (Apache AGE)
  • Regular ACID SQL (obviously)

No more data pipeline nightmares. No more sync issues. Just SQL.

Full Deep Dive

Production code, benchmarks, and more tricks:
https://ncse.info/postgresql-ai-integration/

Question for the community:
Are you still using separate vector databases or have you consolidated to PostgreSQL? What's been your experience with pgvector at scale?

Drop your thoughts below!

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