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
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