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 you achieve consistency across multiple microservices (Sagas vs. 2PC)? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

When a business transaction spans service boundaries (e.g., Order Service → Inventory Service → Payment Service), local DB transactions cannot guarantee atomicity.

1. Two-Phase Commit (2PC)

Prepare Phase: Coordinator asks participant DBs if they can commit. Participants lock records and reply Yes/No.
Commit Phase: If all reply Yes, coordinator issues Commit; otherwise Abort.
Drawback: 2PC is a blocking protocol. If the coordinator fails mid-process, resources remain locked indefinitely.

2. Saga Pattern (Eventual Consistency)

Executes a sequence of independent local transactions. Each service commits locally and publishes an event.

Compensating Transactions: If a step fails (e.g., Payment declined), the Saga controller executes reverse compensating actions (e.g., release reserved inventory).
Choreography: Decentralized services listen to event queues. Simple, but hard to trace.
Orchestration: Central Saga coordinator manages flow and failure recovery explicitly.

TIP — Production Recommendation

Default to Saga Orchestration for distributed microservices. Avoid 2PC in high-throughput cloud environments due to network latency, lock contention, and SPOF risks.

Key Architectural Comparison

Distributed Transaction Pattern	Coordination Style	Locking Duration	Network Partition Tolerance	Failure Recovery
Two-Phase Commit (2PC)	Centralized Coordinator.	Heavy (locks database rows across all phases).	Poor (coordinator failure locks entire cluster).	Rollback transaction.
Saga (Choreography)	Decentralized (Kafka events).	None (each service commits local ACID transaction).	High (services process events asynchronously).	Compensating transactions.
Saga (Orchestration)	Centralized Workflow Engine.	None (local transactions orchestrated sequentially).	High (orchestrator handles timeouts & retries).	Compensating transactions (Temporal/Cadence).

How do you achieve consistency across multiple microservices (Sagas vs. 2PC)?

1. Two-Phase Commit (2PC)

2. Saga Pattern (Eventual Consistency)

Key Architectural Comparison

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