How to Denormalize Data with ParadeDB in PostgreSQL

How do I denormalize related tables with ParadeDB in PostgreSQL?

ParadeDB’s denormalize command flattens related tables into a materialized view for faster analytics and full-text search.

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Description

Example H2

Example H3

What does ParadeDB’s denormalize command do?

The command reads multiple related tables, joins them, and stores the result as a materialized view managed by ParadeDB. Queries hit the view, not the base tables, so reads become faster.

When should I denormalize ecommerce data?

Use it for heavy reporting dashboards, text search, or vector search where JOINs across Orders, Customers, and OrderItems slow you down.

How do I pick source tables and columns?

Select high-value columns often queried together—e.g., customer_name, email, order_date, total_amount, product_name, quantity.

What is the basic workflow?

1) Create denormalized view with ParadeDB’s denormalize command.
2) Run ANALYZE to update stats.
3) Schedule REFRESH when base data changes.

Can I filter rows during denormalization?

Yes. Use a WHERE clause inside the SELECT passed to denormalize to keep only relevant rows, such as completed orders.

Example: denormalizing recent orders

Denormalize the last 90 days of orders, including customer and product info, into a view called recent_order_view.

How do I refresh the view?

Call REFRESH MATERIALIZED VIEW concurrently or use ParadeDB’s schedule_refresh option for automated updates.

Best practices for ParadeDB denormalization

• Index foreign keys before running.
• Keep the view narrow—only required columns.
• Use incremental refresh if the table has a monotonically increasing id or timestamp.

What are performance considerations?

Denormalization speeds reads but increases storage. Write workloads remain unaffected because the view updates asynchronously.

Why How to Denormalize Data with ParadeDB in PostgreSQL is important

How to Denormalize Data with ParadeDB in PostgreSQL Example Usage


-- Query the denormalized view for a dashboard
SELECT customer_name,
       product_name,
       SUM(quantity) AS units,
       SUM(total_amount) AS revenue
FROM   customer_order_items_view
WHERE  order_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP  BY customer_name, product_name
ORDER  BY revenue DESC
LIMIT  10;

How to Denormalize Data with ParadeDB in PostgreSQL Syntax


SELECT parade_denormalize(
    view_name        => 'customer_order_items_view',
    select_query     => $$
        SELECT o.id            AS order_id,
               o.order_date,
               o.total_amount,
               c.id            AS customer_id,
               c.name          AS customer_name,
               c.email         AS customer_email,
               p.id            AS product_id,
               p.name          AS product_name,
               p.price,
               oi.quantity
        FROM   Orders      o
        JOIN   Customers   c  ON c.id = o.customer_id
        JOIN   OrderItems  oi ON oi.order_id = o.id
        JOIN   Products    p  ON p.id = oi.product_id
    $$,
    refresh_mode     => 'incremental',      -- full | incremental
    schedule_refresh => '5 minutes',        -- cron text or interval
    index_columns    => ARRAY['order_id','customer_id','product_id']
);

Common Mistakes

Forgetting to index join keys before denormalization increases initial build time. Always add indexes on foreign keys like Orders.customer_id and OrderItems.order_id.
Using full refresh on large tables causes long locks. Switch to incremental refresh with a unique, ever-increasing column such as created_at or id.