How do you choose between SQL and NoSQL databases?

Choose SQL (PostgreSQL, MySQL) for structured data requiring ACID transactions, complex JOINs, and strict schema enforcement. Choose NoSQL (MongoDB, DynamoDB, Cassandra) for flexible schemas, massive horizontal scale, high write throughput, and eventual consistency workloads.

When should you choose Vertical Scaling vs Horizontal Scaling?

Start with vertical scaling (adding CPU/RAM to one server) for simplicity. Transition to horizontal scaling (adding more servers) when hardware limits are reached, cost curves become prohibitive, or high availability is required. Horizontal scaling introduces distributed system complexity.

What is the difference between Sharding, Partitioning, and Indexing?

Indexing speeds up read queries within a single table using B-Tree or Hash structures. Partitioning splits a single table into logical physical chunks within one database instance. Sharding distributes rows across multiple distinct database servers using a shard key, scaling write throughput and storage.

How do you scale WebSockets to millions of concurrent connections?

Increase Linux file descriptor limits, use Layer 4 load balancing with sticky sessions, and implement a Redis Pub/Sub backplane so any WebSocket server can relay messages to clients connected to other servers. Pattern: Client → HAProxy → WS Servers ↔ Redis Pub/Sub.

What is the difference between LSI and GSI in DynamoDB?

A Local Secondary Index (LSI) shares the same partition key as the base table but uses a different sort key. It is limited to 10GB per item collection and supports strong consistency. A Global Secondary Index (GSI) uses a completely different partition key, spans all partitions, supports only eventual consistency, and has its own dedicated throughput capacity.

Which database should I use for transactions, user profiles, logs, and analytics?

Use PostgreSQL for financial transactions (ACID compliance). Use MongoDB for user profiles (flexible JSON). Use Cassandra for logs and high-throughput writes (LSM tree). Use Apache Druid for real-time analytics (columnar storage). Use Elasticsearch for search and catalog (inverted index). Use Redis for sessions and caching (in-memory).

How does Consistent Hashing work?

Consistent hashing maps both servers and keys onto a circular ring. A key routes clockwise to the nearest server. When a node is added or removed, only 1/N keys need to migrate (vs nearly 100% in modulo hashing). Virtual nodes (100-200 per server) ensure even distribution and prevent hotspots.

How do you design a distributed rate limiter?

Use a Token Bucket algorithm for burst-friendly limiting. For distributed enforcement across multiple API gateway instances, use Redis with Lua scripts to atomically evaluate and increment counters, preventing race conditions (dirty reads) where concurrent requests could bypass the limit.

When should you choose Vertical Scaling (Scale Up) vs. Horizontal Scaling (Scale Out)? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

Scaling refers to increasing system capacity to handle higher request volumes, storage sizes, and concurrent connections.

Vertical Scaling (Scale Up)

Adding hardware resources (CPU, RAM, NVMe storage) to a single existing server instance.

Pros: Simplicity. Zero network latency between nodes, simple programming model, transactions work natively.
Cons: Hard hardware limits, exponential cost curve for high-end bare metal, single point of failure (SPOF).
Best For: Early-stage applications, relational databases with low write traffic, and monolithic background workers.

Horizontal Scaling (Scale Out)

Adding more commodity servers to the resource pool and distributing load across them.

Pros: Infinite scaling limits, elastic provisioning, high availability with no single point of failure.
Cons: Distributed system complexity, data consistency challenges, load balancer dependencies, network latency overhead.
Best For: High-traffic web servers, caching layers, distributed databases, and high-volume media processing.

TIP — Interview Golden Rule

Always mention that you would leverage Vertical Scaling until hardware limits or cost curves make it non-viable. Jumping straight to complex horizontal sharding architectures for low-scale designs is a common anti-pattern.

Key Architectural Comparison

Scaling Approach	Scaling Dimension	Hardware Upper Bounds	Downtime Risk	Engineering Complexity
Vertical Scaling (Scale Up)	Add CPU, RAM, NVMe disk to existing server.	Hard physical limit (e.g. 128 cores, 4 TB RAM).	Requires downtime for hardware resize.	Very Low (Single codebase, zero distributed logic).
Horizontal Scaling (Scale Out)	Add more commodity worker nodes behind a Load Balancer.	Virtually unbounded linear scalability.	Zero Downtime (Rolling deployments & failover).	High (Network partitions, state synchronization, consistency).

When should you choose Vertical Scaling (Scale Up) vs. Horizontal Scaling (Scale Out)?

Vertical Scaling (Scale Up)

Horizontal Scaling (Scale Out)

Key Architectural Comparison

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