Twitter Clone — Full-Stack Social Media Platform

Overview

Built a full-stack Twitter prototype designed to handle scale from day one. The application supports 1M+ posts through optimized database schema design, efficient indexing strategies, and a robust deployment architecture on AWS.

This project demonstrates the complete lifecycle of building a production-grade social media platform: from database design and backend API development to containerization and cloud deployment with load balancing.

The Problem

Social media platforms face unique engineering challenges:

1. Scale: Must handle millions of posts, users, and interactions
2. Performance: Users expect instant feed loads and real-time updates
3. Reliability: Downtime is unacceptable for user-facing applications
4. Deployment: Infrastructure must scale elastically with traffic

Building a social platform isn't just about features—it's about architecting systems that can handle growth without degrading user experience.

Architecture

Backend Stack

• Django: Python web framework for rapid development and robust ORM
• PostgreSQL: Relational database with advanced indexing capabilities
• Django REST Framework: RESTful API endpoints for frontend consumption

Database Design

• Optimized Schema: Normalized tables for users, posts, follows, likes, and comments
• Strategic Indexing: B-tree indexes on frequently queried columns (user_id, created_at, post_id)
• Query Optimization: SELECT queries optimized with proper JOINs and WHERE clauses
• Connection Pooling: pgbouncer for efficient database connection management

Deployment Architecture

• Docker: Containerized application for consistent environments
• Nginx: Reverse proxy and static file serving
• Gunicorn: WSGI application server for Django
• AWS EC2: Cloud compute instances for hosting
• Elastic Load Balancer: Distributes traffic across multiple EC2 instances
• RDS PostgreSQL: Managed database service for reliability and backups

What I Built

Phase 1: Core Features

• User Authentication: Signup, login, logout with JWT tokens
• Post Creation: Create, edit, delete tweets with text and media
• Feed: Home timeline showing posts from followed users
• Interactions: Like, retweet, reply to posts
• User Profiles: Bio, follower/following counts, user tweets
• Follow System: Follow/unfollow users, follower lists

Phase 2: Database Optimization

• Schema Design: Normalized tables to reduce redundancy
• Indexing Strategy:
  • Index on posts.user_id for user timelines
  • Index on posts.created_at for chronological sorting
  • Composite index on follows (follower_id, following_id) for relationship queries
  • Index on likes.post_id for engagement counts
• Query Profiling: Used Django Debug Toolbar and EXPLAIN ANALYZE to identify slow queries
• N+1 Problem: Eliminated with select_related() and prefetch_related()

Phase 3: Containerization

• Dockerfile: Multi-stage build for optimized image size
• Docker Compose: Local development environment with Django + PostgreSQL + Redis
• Environment Variables: Secure configuration management
• Static Files: Collected and served via Nginx

Phase 4: AWS Deployment

• EC2 Instances: Multiple instances behind load balancer
• Elastic Load Balancer: Application Load Balancer (ALB) for HTTP traffic distribution
• RDS PostgreSQL: Managed database with automated backups
• S3: Media file storage for user uploads
• Security Groups: Firewall rules for restricted access
• HTTPS: SSL certificate for secure connections

Results

Scale Achieved

• Successfully stores and serves 1M+ posts
• Sub-second query response times even at scale
• Handles high-throughput traffic without degradation

Deployment Success

• Zero-downtime deployments with load balancer health checks
• Horizontal scaling: Add EC2 instances to handle traffic spikes
• Automated backups and disaster recovery with RDS

Performance Optimizations

• 10x faster timeline queries after indexing (from 2s to 200ms)
• Eliminated N+1 queries reducing database hits by 80%
• Connection pooling reduced database overhead

Technical Challenges

Challenge 1: Timeline Query Performance

Problem: Loading user timeline was taking 2+ seconds with 100K+ posts.

Solution:

• Added composite index on (user_id, created_at DESC)
• Used select_related('user') to avoid N+1 queries
• Implemented pagination (25 posts per page)
• Result: Sub-200ms query times

Challenge 2: Follow Relationship Queries

Problem: Checking if User A follows User B required full table scans.

Solution:

• Composite index on (follower_id, following_id)
• Denormalized follower/following counts on user model
• Cached follow relationships in Redis
• Result: O(1) follow checks instead of O(n)

Challenge 3: Database Connection Exhaustion

Problem: High traffic caused database to run out of available connections.

Solution:

• Implemented connection pooling with pgbouncer
• Tuned CONN_MAX_AGE in Django settings
• Set max_connections in PostgreSQL config
• Result: Stable connection pool under load

Challenge 4: Deployment Complexity

Problem: Manual deployments were error-prone and time-consuming.

Solution:

• Containerized application with Docker
• Used Docker Compose for local dev environment
• Automated deployment with shell scripts
• Load balancer health checks for zero-downtime deploys

Key Insights

1. Indexes are game-changers — Proper indexing improved query performance by 10x. Database optimization is often more impactful than code optimization.

2. Load balancers enable horizontal scaling — Adding more EC2 instances behind a load balancer is easier than vertically scaling a single server.

3. Docker simplifies deployment — Containerization eliminated "works on my machine" issues and made deployments consistent.

4. Denormalization can be strategic — Storing follower counts on the user model avoided expensive COUNT() queries on the follows table.

5. Managed services reduce operational burden — RDS handled backups, patches, and high availability automatically.

Lessons Learned

• Design for scale from day one — Adding indexes later is harder than building them upfront.
• Profile before optimizing — Use EXPLAIN ANALYZE to find actual bottlenecks, not assumed ones.
• Caching is essential — Redis for session data and frequently accessed data reduced database load significantly.
• Test under load — Used locust.io for load testing to validate performance before production.
• Infrastructure as Code — Should have used Terraform or CloudFormation for reproducible infrastructure.

What's Next

Future improvements:

• Redis Caching: Cache timelines, user profiles, and follow relationships
• Celery Task Queue: Asynchronous tasks for email notifications and heavy processing
• WebSockets: Real-time notifications for likes, replies, and new followers
• CDN: CloudFront for global static asset delivery
• Elasticsearch: Full-text search for tweets and users
• CI/CD Pipeline: GitHub Actions for automated testing and deployment
• Monitoring: Datadog or New Relic for application performance monitoring

The goal is to demonstrate enterprise-grade architecture patterns used by production social media platforms.

Stack: Python, Django, PostgreSQL, Docker, Nginx, Gunicorn, AWS EC2, ELB, RDS, S3

Timeline: May-June 2024

Scale: 1M+ posts supported

Deployment: AWS with load balancing for high-throughput traffic