Lesson 94/100

Tutorials PostgreSQL Tutorial

E-Commerce Backend — PostgresVerse Project

E-Commerce Backend — PostgresVerse Project: free step-by-step lesson with examples, common mistakes, and interview tips — part of PostgreSQL Tutorial on Toolliyo Academy.

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PostgreSQL Tutorial · Lesson 94 of 100

E-Commerce Backend

SQL ✓Advanced

Advanced · 2 — Production · ~10 min · PostgreSQL — Real-World Projects

What is this?

E-commerce backend schema covers products, inventory, carts, orders, payments — transactional checkout with row locks on stock and idempotent payment keys.

Why should you care?

PostgresVerse Flipkart-style shop must never oversell GPU stock during flash sale.

See it live — copy this example

Run in pgAdmin or psql.

CREATE TABLE products (
  product_id bigserial PRIMARY KEY,
  sku text UNIQUE,
  stock_qty int NOT NULL CHECK (stock_qty >= 0)
);
CREATE TABLE carts (
  cart_id uuid PRIMARY KEY,
  customer_id bigint,
  updated_at timestamptz DEFAULT now()
);
CREATE TABLE cart_items (
  cart_id uuid REFERENCES carts(cart_id),
  product_id bigint REFERENCES products(product_id),
  qty int CHECK (qty > 0),
  PRIMARY KEY (cart_id, product_id)
);

What happened?

  • Normalized cart model.
  • stock_qty CHECK prevents negative inventory at DB.
  • Checkout transaction locks product row FOR UPDATE before decrement.

Practice next

  1. Create products with low stock_qty test SKU.
  2. Add cart items in transaction.
  3. Simulate two checkouts; second waits or fails on stock.
  4. Add order_status enum and orders table from cart merge.
  5. Index products(sku) for barcode scan API.

Remember

Cart normalized; checkout is one transaction. FOR UPDATE on stock hot rows. SKU unique across catalog.

PostgresVerse flash sale

100 concurrent buy clicks on 10 GPUs; locking prevents oversell.

Outcome: Angry customers avoided; support load manageable.

Interview prep for this lesson

Practice these questions aloud after reading—each links to a full structured answer.

Junior Detailed
Describe a real-world scenario where Normalization mattered in a PostgreSQL project.
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define Normaliza…
Junior Detailed
Explain SQL queries in the context of PostgreSQL.
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define SQL queri…
Mid Detailed
What are common mistakes teams make with Schema design when using PostgreSQL?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define Schema de…
Senior Detailed
How would you debug a production issue related to Transactions in a PostgreSQL application?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define Transacti…
Mid Detailed
Compare two approaches to Indexing—when would you choose each?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define Indexing…
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PostgreSQL Tutorial
Course syllabus

PostgreSQL Tutorial

PostgreSQL — Foundations
PostgreSQL — SQL & Queries
PostgreSQL — Indexing & Performance
PostgreSQL — Transactions & MVCC
PostgreSQL — Functions & Automation
PostgreSQL — JSONB & Modern Features
PostgreSQL — Replication & High Availability
PostgreSQL — Security & Cloud
PostgreSQL — Monitoring & Troubleshooting
PostgreSQL — Real-World Projects
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