Real-world system design explained like a human, not a whiteboard diagram.

Every massive online platform — Flipkart, Netflix, Swiggy — runs on systems that handle millions of requests per second without collapsing. But how? What’s happening behind those smooth UI clicks?

This series breaks down 26 essential system-design concepts in simple, relatable terms. Part 1 focuses on how large systems scale, balance, cache, and monitor themselves. Part 2 dives into resilience, recovery, and reliability.

⚙️ 1. Scalability 101

Systems must scale vertically (bigger servers) or horizontally (more servers). Flipkart scales horizontally using distributed microservices during festive sales.

🌍 2. Load Balancing

Distribute incoming traffic smartly to avoid overloading one server. Examples: Nginx, HAProxy, AWS ELB.

💾 3. Caching

Caching reduces load and latency by serving repeated data fast.

In-memory: Redis, Memcached

CDN: Cloudflare, Akamai

Analogy: Like remembering answers from yesterday’s test.

🧩 4. Database Design

Choose between:

SQL for transactions

NoSQL for scalability

Hybrid for flexibility

Example: Flipkart mixes MySQL + Elasticsearch.

🚀 5. Indexing & Query Optimization

Use indexes to find rows faster, just like an index in a book.

🧱 6. Partitioning & Sharding

Split big databases into smaller chunks for performance. Zomato shards data by city or restaurant ID.

🔁 7. Replication

Keep multiple live copies of data. If one fails, another takes over.

🧠 8. Caching Strategies

Types:

Write-through

Write-around

Write-back

Use wisely depending on data criticality.

🧮 9. Consistency Models

CAP Theorem trade-off:

Choose any 2 — Consistency, Availability, Partition Tolerance.

Example: Banking → Consistency; Social apps → Availability.

🛠️ 10. CDN (Content Delivery Networks)

Bring content closer to users for faster response. Netflix caches video at edge nodes near Indian metros.

🧵 11. API Design & Gateway

Every request flows through an API gateway for routing, throttling, and auth.

Tools: Kong, AWS API Gateway, Nginx.

📡 12. Async Processing & Queues

Don’t block users! Use queues like Kafka or RabbitMQ for background jobs.

🔍 13. Observability

Metrics, logs, and traces form the “nervous system” of your app. Stack: Prometheus + Grafana + ELK.

🔚 Wrapping Up Part 1

We covered how systems scale and observe themselves. But scaling isn’t everything — systems must also survive when failures strike.

👉 Continue to Part 2

→ Resilience, Fault Tolerance & Real-World Recovery Patterns