How to Choose PostgreSQL over ParadeDB

Why choose PostgreSQL over ParadeDB for search and analytics workloads?

PostgreSQL offers broader functionality, maturity, and ecosystem support compared with ParadeDB, making it the safer, more flexible choice for most production workloads.

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Description

Example H2

Example H3

Why pick PostgreSQL instead of ParadeDB?

PostgreSQL is a full-featured, ACID-compliant relational database with 25+ years of development. ParadeDB focuses on search and analytics but lacks many core OLTP features. Choosing Postgres means one engine for both transactional and analytical tasks, fewer moving parts, and battle-tested reliability.

Does PostgreSQL handle search and analytics too?

Yes. GIN/GiST indexes, tsvector, jsonb, and extensions like pgvector let Postgres power fast text, JSON, and vector search.You can match most ParadeDB use cases while keeping SQL standards and transactions.

How do I migrate ParadeDB queries to PostgreSQL?

Convert ParadeDB’s search() calls to Postgres full-text syntax: @@ to_tsquery(). Replace vector operations with pgvector’s <-> operator.Postgres supports CTEs and window functions ParadeDB already understands, so SQL usually ports with minor tweaks.

What is the exact syntax for text & vector search in Postgres?

See the Syntax section below for full commands, options, and parameter explanations.

Best practices when replacing ParadeDB

Enable the needed extensions (CREATE EXTENSION pg_trgm; and pgvector) before running migrations. Add covering indexes for every query path.Keep maintenance windows small by using CONCURRENTLY when creating indexes.

Common pitfalls to avoid

Do not assume default configurations are enough—tune work_mem, maintenance_work_mem, and shared_buffers. Also, remember to VACUUM regularly to keep GIN indexes fast.

What real-world benefits will I see?

Unifying on Postgres cuts infra cost, simplifies backups, and leverages a vast talent pool. It also unlocks powerful join, aggregation, and constraint features ParadeDB doesn’t offer, reducing application logic.

Why How to Choose PostgreSQL over ParadeDB is important

How to Choose PostgreSQL over ParadeDB Example Usage


-- Find customers who bought products costing > $500 using joins and full-text search on product names
SELECT c.id, c.name, COUNT(*) AS high_value_orders
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
JOIN OrderItems oi ON oi.order_id = o.id
JOIN Products p ON p.id = oi.product_id
WHERE p.tsv @@ plainto_tsquery('laptop')
  AND o.total_amount > 500
GROUP BY c.id, c.name
ORDER BY high_value_orders DESC;

How to Choose PostgreSQL over ParadeDB Syntax


-- Enable full-text and vector capabilities
CREATE EXTENSION IF NOT EXISTS pg_trgm;
CREATE EXTENSION IF NOT EXISTS pgvector;

-- Example table with search columns
CREATE TABLE Products (
  id SERIAL PRIMARY KEY,
  name TEXT,
  description TEXT,
  price NUMERIC(10,2),
  stock INT,
  tsv tsvector GENERATED ALWAYS AS (
    setweight(to_tsvector('english', coalesce(name,'') ), 'A') ||
    setweight(to_tsvector('english', coalesce(description,'') ), 'B')
  ) STORED,
  embedding vector(768)
);

-- Text search (replaces ParadeDB search())
SELECT id, name
FROM Products
WHERE tsv @@ plainto_tsquery('wireless mouse');

-- Vector similarity search (ParadeDB topK equivalent)
SELECT id, name
FROM Products
ORDER BY embedding <-> '[0.12,0.55,...]'::vector
LIMIT 10;

Common Mistakes

Relying on default GIN index settings. Default configurations may underperform on large text columns. Fix: increase <code>gin_pending_list_limit</code> and routinely VACUUM to keep index size and speed optimal.
Ignoring vector dimensionality. ParadeDB auto-infers vector size, but pgvector requires you to specify dimensions (e.g., <code>vector(768)</code>). Fix: match the model’s embedding size when declaring columns.

Frequently Asked Questions (FAQs)

Is PostgreSQL slower than ParadeDB for vector search?

With pgvector and ivfflat indexes, Postgres achieves millisecond-level ANN search comparable to ParadeDB, especially for ≤1 M vectors.

Can I keep analytical queries fast without a columnar engine?

Yes. Use CREATE MATERIALIZED VIEW, BRIN indexes, and partitioning. For heavy workloads, attach Citus or Timescale for columnar compression.

Do I lose anything by dropping ParadeDB?

You trade ParadeDB’s built-in columnar storage for Postgres’s richer API and ecosystem. Extensions like cstore_fdw or using external warehouses can bridge the gap.