How to Apply Data Modeling Best Practices in ParadeDB

What are the best practices for data modeling in ParadeDB?

ParadeDB data modeling optimizes PostgreSQL schemas to store relational and vector data efficiently while keeping queries fast.

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Description

Example H2

Example H3

What makes ParadeDB modeling different?

ParadeDB extends PostgreSQL with fast vector search, so your schema must support both traditional relations and high-dimensional embedding data. Proper types, constraints, and indexes keep similarity queries and joins snappy.

How should I create core relational tables?

Start with clear PRIMARY KEYs, FOREIGN KEYs, and not-null columns.Use numeric surrogate keys for joins; avoid composite keys that bloat indexes.

Example

CREATE TABLE Customers (id BIGSERIAL PRIMARY KEY, name TEXT NOT NULL, email TEXT UNIQUE NOT NULL, created_at TIMESTAMPTZ DEFAULT NOW());

How do I add vector columns safely?

Store Embeddings in VECTOR(dim) columns provided by ParadeDB. Keep them in separate tables when sparsity or multiple embeddings per row are possible.

Example

ALTER TABLE Products ADD COLUMN embedding VECTOR(384);

When should I normalize vs. denormalize?

Normalize transactional data (Customers, Orders) to avoid anomalies.Denormalize read-heavy product catalogs or pre-computed recommendation tables to speed vector similarity joins.

How do I index for similarity search?

Use CREATE INDEX ... USING pgvectors for approximate search or ivfflat for ANN.Always set lists and probes according to dataset size.

Example

CREATE INDEX products_vec_idx ON Products USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);

What are transaction-safe patterns?

Wrap writes to relational and vector tables in a single transaction to keep embeddings consistent with the source row.

How do I evolve ParadeDB schemas?

Use ADD COLUMN for new embeddings, backfill in batches, then build indexes. For large tables, build indexes concurrently to prevent downtime.

Why use partitioning?

Partition Orders by month or Products by category to prune similarity scans.Keep partitions under a few million rows for faster ANN indexing.

Best practices checklist

Prefer BIGINT keys
Keep vectors in fixed dimensions
Apply CHECK (vector_dim(embedding)=384)
Use ANALYZE after bulk loads
Monitor pg_stat_user_indexes

Common mistakes

Storing vectors in JSONB: slows distance calculations. Use VECTOR type instead.

Building ivfflat on tiny tables: linear scan is faster under 1k rows; skip ANN indexes until data grows.

Why How to Apply Data Modeling Best Practices in ParadeDB is important

How to Apply Data Modeling Best Practices in ParadeDB Example Usage


-- Find top 5 products similar to a given embedding
SELECT id, name, price
FROM Products
ORDER BY embedding <-> '[0.12,0.98,...]'::VECTOR
LIMIT 5;

How to Apply Data Modeling Best Practices in ParadeDB Syntax


-- Core table with vector column
CREATE TABLE Products (
    id          BIGSERIAL PRIMARY KEY,
    name        TEXT NOT NULL,
    price       NUMERIC(10,2) NOT NULL,
    stock       INTEGER DEFAULT 0,
    embedding   VECTOR(384)             -- ParadeDB vector type
);

-- Relational link table
CREATE TABLE OrderItems (
    id          BIGSERIAL PRIMARY KEY,
    order_id    BIGINT REFERENCES Orders(id),
    product_id  BIGINT REFERENCES Products(id),
    quantity    INTEGER NOT NULL CHECK (quantity > 0)
);

-- ANN index options
CREATE INDEX products_vec_idx
    ON Products USING ivfflat (embedding vector_l2_ops)
    WITH (lists = 100);

Common Mistakes

Using JSONB to store embeddings: JSONB lacks operator classes for fast vector distance. Fix by switching to VECTOR type and creating ivfflat or HNSW indexes.
Placing vector columns in wide transactional tables: Increases row size and vacuum cost. Fix by moving embeddings to a separate, keyed table or partition.