How to Follow ParadeDB Best Practices in PostgreSQL

How do I apply ParadeDB best practices to speed up vector search in PostgreSQL?

ParadeDB best practices ensure fast, accurate vector search in PostgreSQL through correct extension setup, indexing strategy, and query design.

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Description

Example H2

Example H3

What is ParadeDB and why should you care?

ParadeDB layers lightning-fast vector and hybrid search on top of PostgreSQL. It reuses familiar SQL while adding approximate-nearest-neighbor (ANN) indexes such as IVFFLAT and HNSW. Following best practices avoids slow queries, bloated indexes, and downtime.

How do you install ParadeDB correctly?

Run CREATE EXTENSION IF NOT EXISTS paradedb; in a superuser session. Verify with \dx. Store the extension in postgresql.conf if you want it pre-loaded at startup (shared_preload_libraries = 'paradedb').

Do you need pgvector too?

Yes. ParadeDB builds on pgvector. Install it first, then add ParadeDB. Keep both extensions at identical minor versions to avoid operator signature mismatches.

Which index type should you choose?

Use IVFFLAT for < 1 M rows and frequent writes; choose HNSW for > 1 M rows or read-heavy workloads. Always create indexes with a filter to cut the search space (WHERE deleted_at IS NULL) to improve recall.

How do you store embeddings efficiently?

Store vectors in a vector(n) column sized to your embedding model (e.g., 1536 for OpenAI text-embedding-3-small). Compress rows with ALTER TABLE ... SET (toast.compress = 'lz4') to shrink cold storage without harming performance.

How to write fast ParadeDB queries?

Always combine similarity search with relational filters. Set SET ivfflat.probes = k (1–20) per session to balance accuracy and latency. Fetch only the columns you need—especially avoid SELECT * on large vector tables.

Filtering + similarity search example

SELECT id, name, price FROM products WHERE stock > 0 ORDER BY embedding <-> $1 LIMIT 5;

How do you maintain ParadeDB indexes?

Recluster IVFFLAT indexes after large inserts: ALTER INDEX products_embedding_ivf ATTACH PARTITION ... then REINDEX. For HNSW, set maintenance_work_mem generously (≥4 GB) during build to avoid disk spills.

When should you refresh IVFFLAT lists?

Every time the table grows by ~20 %. Drop and recreate the index in a transaction or use a concurrently-built shadow index to stay online.

What about security?

Grant USAGE on the extension schema only to roles that need vector search. Revoke EXECUTE on experimental operators in production.

Why How to Follow ParadeDB Best Practices in PostgreSQL is important

How to Follow ParadeDB Best Practices in PostgreSQL Example Usage


-- Find the five most similar in-stock products to a customer’s query
SET ivfflat.probes = 10;

SELECT id, name, price
FROM Products
WHERE stock > 0
ORDER BY embedding <-> :query_embedding
LIMIT 5;

How to Follow ParadeDB Best Practices in PostgreSQL Syntax


-- Enable required extensions
CREATE EXTENSION IF NOT EXISTS pgvector;
CREATE EXTENSION IF NOT EXISTS paradedb;

-- Add vector column to Products table
ALTER TABLE Products
ADD COLUMN embedding vector(1536);

-- Populate embeddings (example uses OpenAI, run in application code)
UPDATE Products
SET embedding = :openai_embed(name || ' ' || description)
WHERE embedding IS NULL;

-- Choose an index strategy
-- IVFFLAT for smaller, write-heavy tables
CREATE INDEX products_embedding_ivf
ON Products USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 200);

-- HNSW for large, read-heavy tables
CREATE INDEX products_embedding_hnsw
ON Products USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 200);

-- Query syntax
SELECT id, name, price
FROM Products
ORDER BY embedding <-> :query_embedding
LIMIT 10;

Common Mistakes

Creating IVFFLAT indexes with too few lists leads to poor recall. Fix by sizing lists to √N rows (e.g., 10 K lists for 100 M rows) and tuning <code>ivfflat.probes</code>.
Storing embeddings as <code>bytea</code> instead of <code>vector</code> blocks operator usage and index scans. Always use <code>vector(n)</code> columns.