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.

How do Operational Transformation (OT) and CRDTs power Real-Time Collaborative Editors? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

Real-time collaborative editors allow multiple users to edit the exact same document concurrently without conflicts or lost edits.

1. Operational Transformation (OT: Google Docs)

Relies on a central server to transform and sequence edit operations.
If User A inserts "X" at index 2 while User B deletes at index 1, the server transforms User A's operation index to 1 before broadcasting.
Pros: Minimal memory footprint per operation.
Cons: Extremely complex server transformation algorithms.

2. CRDTs (Conflict-Free Replicated Data Types: Figma / Notion)

Mathematical data structures that converge automatically to the exact same state across peer nodes without requiring a central server.
Pros: Supports offline editing and peer-to-peer synchronization natively.
Cons: Higher memory overhead due to unique operation identifier tombstones.

NOTE — Industry Trend

Modern collaborative tools (Notion, Figma, Automerge, Yjs) increasingly choose CRDTs over OT due to offline-first capabilities.

Key Architectural Comparison

Collaboration Algorithm	Central Sequencer Required?	Conflict Resolution Mechanism	Memory Overhead	Industry Adoption
Operational Transformation (OT)	Yes (Single server linearizes all operations).	Positional transformation matrices.	Low (Stores only current document state).	Google Docs, Etherpad.
CRDT (Conflict-Free Replicated)	No (Peer-to-peer / decentralized friendly).	Mathematically commutative immutable IDs.	High (Requires tombstones for deletions).	Figma, Apple Notes, Yjs, Automerge.

How do Operational Transformation (OT) and CRDTs power Real-Time Collaborative Editors?

1. Operational Transformation (OT: Google Docs)

2. CRDTs (Conflict-Free Replicated Data Types: Figma / Notion)

Key Architectural Comparison

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