E-commerce order reports joining 5+ tables (customers, orders, products, payments, shipping) - optimize from 45 seconds to under 2 seconds. Analytics dashboards joining fact and dimension tables - reduce query time by 50-100x.

Shopify - Order Dashboard Join Optimization

Shopify merchant dashboard shows orders with customer, product, and shipping details. Query joined 5 tables (orders, customers, products, order_items, shipping). With millions of orders per merchant, dashboard took 45 seconds to load.

Indexed all foreign keys (customer_id, product_id, order_id). Created composite index on orders(merchant_id, created_at, status) for filtering. Added covering indexes for frequently selected columns. Used CTEs to filter orders first (last 30 days), then join to other tables. Implemented query result caching for 5 minutes.

-- Before: 45 seconds

SELECT o.*, c.name, p.title, s.tracking

FROM orders o

JOIN customers c ON o.customer_id = c.id

JOIN order_items oi ON o.id = oi.order_id

JOIN products p ON oi.product_id = p.id

JOIN shipping s ON o.id = s.order_id

WHERE o.merchant_id = ?;

-- After: 0.8 seconds (56x faster)

CREATE INDEX idx_orders_merchant_date ON orders(merchant_id, created_at) INCLUDE (id, status);

CREATE INDEX idx_customers_id ON customers(id) INCLUDE (name);

CREATE INDEX idx_products_id ON products(id) INCLUDE (title);

WITH recent_orders AS (

SELECT id, customer_id, status, created_at

FROM orders

WHERE merchant_id = ? AND created_at > NOW() - INTERVAL '30 days'

)

SELECT ro.*, c.name, p.title, s.tracking

FROM recent_orders ro

JOIN customers c ON ro.customer_id = c.id

JOIN order_items oi ON ro.id = oi.order_id

JOIN products p ON oi.product_id = p.id

JOIN shipping s ON ro.id = s.order_id;

Query time: 45s to 0.8s (56x faster)

Dashboard load time: 50s to 2s

Merchant satisfaction: 40% increase

Support tickets: 60% reduction

Database CPU: 70% reduction

Indexes: 5 strategic indexes (400MB per merchant)

Write overhead: 35% (acceptable for read-heavy dashboard)

PostgreSQL

Netflix - Content Recommendation Join Performance

Netflix recommendation engine joins user viewing history (billions of rows) with content metadata (titles, genres, actors) and user preferences. Query joined 6 tables to generate personalized recommendations. Initial implementation took 12 seconds per user, too slow for real-time recommendations.

Partitioned viewing_history by date (monthly partitions). Created covering indexes on user_id with INCLUDE for frequently accessed columns. Used partial indexes for active users only (last 90 days). Implemented join order optimization: filter viewing history first (user + date range), then join to content metadata. Added materialized views for popular content joins.

-- Before: 12 seconds per user

SELECT c.title, c.genre, a.name, ur.rating