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.

What are the trade-offs of Event Sourcing vs CQRS (Command Query Responsibility Segregation)? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

Event Sourcing and CQRS decouple write domain business logic from complex query reporting models.

1. Event Sourcing

Instead of storing current state in a DB table row, store an immutable sequence of state-changing events (OrderCreated, PaymentReceived, OrderShipped).
Current state is reconstructed by replaying events sequentially.
Pros: Complete audit log, time-travel debugging.
Cons: Event schema evolution complexity and slow state rebuilds without periodic snapshots.

2. CQRS

Separates write operations (Commands) from read operations (Queries).
Writes hit a normalized OLTP DB; changes stream via Kafka to populate an optimized read-model DB (e.g., Elasticsearch).

TIP — Production Advice

You can adopt CQRS without full Event Sourcing to scale read/write operations independently.

Key Architectural Comparison

Architecture Paradigm	State Representation	Read Model	Auditability	Refactoring Complexity
Traditional CRUD	Mutable rows in database tables.	Queries hit same tables or replicas.	Lost historical updates without separate audit logs.	Low.
Event Sourcing	Immutable append-only log of domain events.	Replayed state from beginning of time or snapshot.	100% complete forensic audit trail.	High (Event schema migration challenges).
CQRS	Separate Command (Write) and Query (Read) models.	Optimized read database (e.g. Elasticsearch).	High when combined with event streams.	Moderate (Eventual consistency between models).

What are the trade-offs of Event Sourcing vs CQRS (Command Query Responsibility Segregation)?

1. Event Sourcing

2. CQRS

Key Architectural Comparison

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