How to Choose Between Star and Snowflake Schema in ParadeDB

Galaxy Glossary

What is the difference between star and snowflake schema in ParadeDB?

Star and snowflake schemas are dimensional models—star favors query speed, snowflake favors normalization and maintenance.

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Description

What problem do star and snowflake schemas solve?

Both models structure analytical data for fast, flexible reporting in ParadeDB. They arrange facts (numeric measures) and dimensions (descriptive attributes) so BI queries scan fewer rows and avoid complex joins compared with fully normalized OLTP schemas.

When should I adopt a star schema?

Pick star when dashboard latency matters most. Denormalized dimensions keep everything in one table per dimension, so ParadeDB can join the central fact table to each dimension with a single hop. Less joins mean quicker GROUP BY scans and simpler SQL.

When does a snowflake schema shine?

Snowflake fits teams prioritizing storage efficiency and data governance. Normalizing large, slowly-changing dimensions—like Products into Products, Categories, and Brands—reduces duplication and enables isolated updates without touching the fact table.

How do I model an ecommerce star schema in ParadeDB?

Create one fact table (OrderItems) and denormalized dimension tables (Customers, Products, Dates). Each dimension’s descriptive columns sit in the same table, minimizing joins.

How do I model the same data as a snowflake?

Split large dimensions into sub-dimensions. For example, Products references Categories and Brands. Queries now join the fact table through multiple hops but gain normalized storage.

Which schema performs faster in ParadeDB?

Star usually wins for aggregate queries because fewer joins reduce execution time. Snowflake can approach star speed by adding foreign-key indexes and materialized views on common joins.

How to query total revenue by category?

In a star schema, join OrderItems directly to Products. In a snowflake schema, include Categories too. Example queries appear below.

Best practices for ParadeDB dimensional models

1. Index all foreign keys. 2. Partition large fact tables by date. 3. Use surrogate integer keys for dimensions. 4. Keep column naming consistent (customer_id, product_id). 5. Store numeric measures as NUMERIC(12,2) for currency.

What are common mistakes?

See the end of this guide for two pitfalls and fixes.

Why How to Choose Between Star and Snowflake Schema in ParadeDB is important

How to Choose Between Star and Snowflake Schema in ParadeDB Example Usage


-- Star schema revenue by category
SELECT p.category, SUM(oi.total_amount) AS revenue
FROM OrderItems oi
JOIN Products p       ON oi.product_id = p.id
GROUP BY p.category
ORDER BY revenue DESC;

-- Snowflake schema revenue by category
SELECT c.category, SUM(oi.total_amount) AS revenue
FROM OrderItems oi
JOIN Products       p ON oi.product_id = p.id
JOIN Categories     c ON p.category_id = c.id
GROUP BY c.category
ORDER BY revenue DESC;

How to Choose Between Star and Snowflake Schema in ParadeDB Syntax


-- Star schema (denormalized dimensions)
CREATE TABLE Customers (
    id SERIAL PRIMARY KEY,
    name TEXT,
    email TEXT,
    created_at DATE,
    city TEXT,
    state TEXT,
    country TEXT
);

CREATE TABLE Products (
    id SERIAL PRIMARY KEY,
    name TEXT,
    category TEXT,
    brand TEXT,
    price NUMERIC(12,2),
    stock INT
);

CREATE TABLE OrderItems (
    id SERIAL PRIMARY KEY,
    order_id INT,
    product_id INT REFERENCES Products(id),
    customer_id INT REFERENCES Customers(id),
    quantity INT,
    total_amount NUMERIC(12,2),
    order_date DATE
);

-- Snowflake schema (normalized product dimension)
CREATE TABLE Brands (
    id SERIAL PRIMARY KEY,
    brand TEXT UNIQUE
);

CREATE TABLE Categories (
    id SERIAL PRIMARY KEY,
    category TEXT UNIQUE
);

CREATE TABLE Products (
    id SERIAL PRIMARY KEY,
    name TEXT,
    brand_id INT REFERENCES Brands(id),
    category_id INT REFERENCES Categories(id),
    price NUMERIC(12,2),
    stock INT
);

Common Mistakes

Joining through snowflake dimensions without indexing foreign keys slows queries. Fix by adding B-tree indexes on every FK column (e.g., Products.category_id).
Over-denormalizing small lookup tables into the star slows loads and wastes space. Keep only high-cardinality or frequently joined attributes in dimensions; else leave facts as is.

Frequently Asked Questions (FAQs)

Is star schema always faster in ParadeDB?

Star queries typically run faster, but well-indexed snowflake schemas can close the gap. Benchmark your workload before deciding.

Can I mix star and snowflake approaches?

Yes. Many teams keep large dimensions snowflaked and small ones denormalized, balancing performance and maintainability.

How do materialized views help?

Create a view that pre-joins snowflake dimensions into one table. ParadeDB refreshes it on a schedule, giving star-like speed without sacrificing normalization.

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