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 scale WebSockets to support millions of concurrent, stateful connections? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

Unlike stateless HTTP requests, WebSockets are persistent, stateful TCP connections. A single client maintains an open connection with a specific server instance.

1. OS-Level Socket Limits (File Descriptors)

In Linux, every TCP connection is represented as a File Descriptor (FD). By default, the OS limits FDs per process.

Increase limits by modifying /etc/security/limits.conf (nofile limit to 1,000,000).
Optimize port allocations: A single IP can only connect to ~65,000 ports on a target IP. Attach multiple virtual IPs to the load balancer or WebSocket nodes to scale.

2. Load Balancing & Layer 4 Routing

Use Layer 4 (TCP) HAProxy or Envoy load balancers to minimize CPU overhead compared to Layer 7 HTTP parsing.
Enable WebSocket HTTP Upgrade headers during initial handshakes.

3. Inter-Server Communication (Pub/Sub Backplane)

When Client A (connected to Server 1) sends a message to Client B (connected to Server 2):

Redis Pub/Sub or Kafka Backplane: Server 1 publishes the message to a central Redis channel.
All WebSocket servers subscribe to this channel. Server 2 reads the message, identifies that Client B is local to it, and pushes the payload down Client B's TCP socket.

NOTE — Architecture Pattern

Client → Global CDN / DNS → Layer 4 Load Balancer (HAProxy) → WebSocket Nodes (Go / Node / Java) ↔ Redis Cluster Pub/Sub.

Key Architectural Comparison

Scaling Layer	Component	Architecture Responsibility	Production Technology
Edge Routing	Layer 7 Load Balancer	TLS termination, WebSocket handshake upgrade, sticky sessions.	Cloudflare, AWS ALB, Nginx.
Connection Tier	Gateway Socket Pods	Maintains open TCP sockets (epoll/kqueue), 100k+ connections per node.	Node.js (ws), Go (Gorilla), Netty.
Message Backplane	Distributed Pub/Sub	Inter-server message fanout across disparate gateway nodes.	Redis Pub/Sub, Redis Streams, Kafka.
Presence Store	Distributed In-Memory Store	Tracks mapping of `user_id` -> `gateway_node_id`.	Redis Cluster, Apache Cassandra.

How do you scale WebSockets to support millions of concurrent, stateful connections?

1. OS-Level Socket Limits (File Descriptors)

2. Load Balancing & Layer 4 Routing

3. Inter-Server Communication (Pub/Sub Backplane)

Key Architectural Comparison

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