How to Choose ParadeDB over MySQL in PostgreSQL

Why use ParadeDB over MySQL for AI search and analytics?

ParadeDB adds high-performance vector and full-text search to PostgreSQL, making it a stronger choice than MySQL for AI-driven applications.

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Description

Example H2

Example H3

Why is ParadeDB often better than MySQL for search-heavy apps?

ParadeDB extends PostgreSQL with dense-vector, sparse-vector, and full-text search in one engine. MySQL needs external services (e.g., Elastic, Redis) or limited built-in FULLTEXT. Fewer moving parts means lower latency, simpler ops, and ACID guarantees.

How does ParadeDB simplify AI features?

ParadeDB stores embeddings next to transactional data. You run a single SQL statement instead of syncing to a vector store.JOINs, window functions, and permissions work out of the box—capabilities MySQL lacks without plugins.

What performance gains can I expect?

ParadeDB uses HNSW and IVF indexes for sub-second ANN search over millions of vectors. MySQL’s FULLTEXT scales poorly and lacks vector distance operators, forcing costly work-arounds.

Does ParadeDB change my PostgreSQL workflow?

No. You create the extension once and keep writing standard SQL. Existing tooling—psql, Prisma, Django, Galaxy—continues to work.MySQL would require code rewrites or ORMs that support its distinct syntax.

When should I still use MySQL?

Choose MySQL if your workload is heavy on simple OLTP with minimal search or if your team’s skill set and infra are already MySQL-centric. Otherwise, ParadeDB covers search, analytics, and transactions in one stack.

Best practice: keep vectors in their own column

Store embeddings in a separate “embedding” column of type vector to avoid bloating row size.Index only the column you search.

Best practice: tune index parameters

Use CREATE INDEX ... USING hnsw for read-heavy, low-latency workloads. Adjust m and ef_search for your recall/latency budget.

Why How to Choose ParadeDB over MySQL in PostgreSQL is important

How to Choose ParadeDB over MySQL in PostgreSQL Example Usage


-- Find top 5 similar products to a query embedding
WITH q AS (
  SELECT '[0.12, 0.98, ...]'::vector AS query_vec
)
SELECT p.id, p.name, p.price
FROM   q, products p
ORDER  BY p.embedding <-> q.query_vec  -- L2 distance operator
LIMIT  5;

How to Choose ParadeDB over MySQL in PostgreSQL Syntax


-- Install ParadeDB once per database
CREATE EXTENSION paradedb;

-- Example table in ecommerce context
CREATE TABLE products (
    id            SERIAL PRIMARY KEY,
    name          TEXT,
    price         NUMERIC,
    stock         INT,
    embedding     VECTOR(768)  -- dense embedding of product description
);

-- Create ANN index
CREATE INDEX products_embedding_hnsw_idx ON products USING hnsw (embedding vector_l2_ops);

-- Optional: sparse full-text index
CREATE INDEX products_desc_fts_idx ON products USING gin (to_tsvector('simple', name));

Common Mistakes

Mistake: Skipping the ANN index. Without <code>CREATE INDEX ... USING hnsw</code> queries do a full table scan, causing 100× slower response times. Fix by adding the proper index before production.
Mistake: Storing embeddings as TEXT arrays. ParadeDB distance operators require the <code>vector</code> data type. Cast or migrate the column to <code>vector(N)</code> to enable index usage.

Frequently Asked Questions (FAQs)

Can ParadeDB and MySQL coexist in one stack?

Yes. Keep transactional data in MySQL and replicate to PostgreSQL+ParadeDB for search. However, this adds ETL overhead compared to staying fully on Postgres.

Does ParadeDB support horizontal scaling?

ParadeDB works with Postgres sharding solutions like Citus and pgCat. For billions of vectors, partition by customer or hash and create per-partition HNSW indexes.