PostgreSQL: Hands-On Projects

E-commerce Database Design#

Welcome to the wild world of e-commerce databases—where transactions happen fast, users are impatient, and downtime is unacceptable.

Designing a Scalable E-Commerce Database Schema#

A properly designed schema prevents your database from turning into a spaghetti mess. Think about:

• Normalization vs. Denormalization: Balancing efficiency and query performance.
• Sharding & Partitioning: Because one giant table for everything is a terrible idea.
• Indexes: Speeding up searches without bloating storage.

Implementing Indexing Strategies for Optimized Search Performance#

• B-tree indexes for quick lookups.
• GIN indexes for full-text search (because nobody types product names correctly).
• Partial indexes to reduce bloat on common queries.

Managing Transactions for Order Processing and Payment Handling#

BEGIN;
UPDATE orders SET status = 'Paid' WHERE id = 123;
UPDATE inventory SET stock = stock - 1 WHERE product_id = 42;
COMMIT;

One failure and we roll everything back—because half-paid orders are bad for business.

Ensuring Data Integrity with Constraints and Foreign Keys#

• Primary keys so every row has a unique identity.
• Foreign keys to avoid orphaned data.
• Check constraints because negative product prices are nonsense.

Writing Stored Procedures for Automated Stock Updates#

CREATE FUNCTION update_stock(product_id INT, quantity INT) RETURNS VOID AS $$
BEGIN
    UPDATE inventory SET stock = stock - quantity WHERE id = product_id;
END;
$$ LANGUAGE plpgsql;

Data Analysis with PostgreSQL#

Because knowing what happened last quarter is better than guessing.

Working with Large Datasets Using PostgreSQL#

PostgreSQL is built for big data. Use:

• Indexes to avoid full table scans.
• Partitioning to keep queries fast.
• CTEs to break down complex queries.

Using GROUP BY, HAVING, and Aggregate Functions for Summary Reports#

SELECT category, SUM(sales) FROM orders GROUP BY category HAVING SUM(sales) > 10000;

Implementing Window Functions for Advanced Analytics#

SELECT customer_id, order_date, SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS running_total FROM orders;

Writing and Optimizing Common Table Expressions (CTEs)#

WITH top_customers AS (
    SELECT customer_id, SUM(amount) AS total_spent FROM orders GROUP BY customer_id
)
SELECT * FROM top_customers WHERE total_spent > 5000;

Generating Business Insights from Complex Queries#

• Customer retention analysis
• Product performance tracking
• Fraud detection

Performance Optimization#

If your queries take longer than a cup of coffee, you need this section.

Identifying Slow Queries Using EXPLAIN ANALYZE#

EXPLAIN ANALYZE SELECT * FROM orders WHERE status = 'Pending';

Creating and Fine-Tuning Indexes for Better Query Performance#

CREATE INDEX idx_orders_status ON orders (status);

Implementing Materialized Views for Precomputed Query Results#

CREATE MATERIALIZED VIEW top_sellers AS SELECT product_id, COUNT(*) FROM orders GROUP BY product_id;

Optimizing Joins and Subqueries for Large-Scale Data Processing#

• Use JOINs instead of subqueries when possible.
• Avoid **SELECT ***—it’s lazy and slow.
• Consider denormalization for read-heavy queries.

Analyzing Query Execution Plans to Reduce Bottlenecks#

If your EXPLAIN ANALYZE results look like a horror story, it’s time to optimize.

Implementing Role-Based Access Control#

Because not everyone should have DROP DATABASE privileges.

Creating and Managing User Roles and Privileges#

CREATE ROLE read_only_user;
GRANT CONNECT ON DATABASE mydb TO read_only_user;

Using GRANT and REVOKE to Define Access Control Policies#

GRANT SELECT ON orders TO read_only_user;
REVOKE DELETE ON orders FROM read_only_user;

Implementing Row-Level Security (RLS) for Data Isolation#

ALTER TABLE orders ENABLE ROW LEVEL SECURITY;
CREATE POLICY user_orders_policy ON orders USING (user_id = current_user);

Securing Sensitive Data with Encryption and Access Controls#

UPDATE users SET email = pgp_sym_encrypt(email, 'my_secret_key');

Auditing and Monitoring Database Access Logs#

SELECT * FROM pg_stat_activity;

Replication and High Availability Setup#

For when “database downtime” isn’t an acceptable excuse.

Configuring Streaming Replication for Data Redundancy#

wal_level = replica
max_wal_senders = 5

Setting Up Logical Replication for Selective Table Synchronization#

CREATE PUBLICATION my_pub FOR TABLE orders;
CREATE SUBSCRIPTION my_sub CONNECTION 'host=primary dbname=mydb' PUBLICATION my_pub;

Implementing Failover Mechanisms with Patroni or repmgr#

Automate failover so your database doesn’t go down when you’re on vacation.

Monitoring Replication Status and Troubleshooting Issues#

SELECT * FROM pg_stat_replication;

Ensuring High Availability with Automated Failover Solutions#

Tools like Patroni and repmgr keep your PostgreSQL instances up and running—even when things go south.

Master these projects, and you’ll be the database expert everyone runs to when things break. Just don’t forget to actually document what you’ve done—future-you will appreciate it.