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.

Back to all questions

Question #43 of 60•Scaling•Medium• ~3 min read

How do Leaky Bucket, Token Bucket, and Fixed Window Rate Limiting algorithms compare?

Executive Takeaway

Analyze memory footprints, burst handling capabilities, and implementation complexity across rate limiting algorithms.

Architectural Breakdown & AnalysisFAANG Aligned

Rate limiters protect infrastructure using different traffic shaping algorithms.

Algorithm	Handles Traffic Bursts?	Memory Footprint	Implementation Complexity
Token Bucket	Yes (up to bucket capacity)	Low (Tokens + LastRefillTime)	Simple
Leaky Bucket	No (Smooths output rate)	Low (FIFO Queue)	Medium
Fixed Window	Yes (Resets per window)	Extremely Low (Counter integer)	Simple
Sliding Window Log	Yes	High (Stores timestamps)	Complex

Algorithm Choice Guidelines

Use Token Bucket for general API rate limiting where brief user bursts are acceptable.
Use Leaky Bucket when downstream services require smooth, non-bursty ingress rates.

NOTE — Industry Standard

Token Bucket powers rate limiters in Nginx, AWS API Gateway, and Guava RateLimiter.

Previous QuestionWhat are the trade-offs of Event Sourcing vs CQRS (Command Query Responsibility Segregation)?Next Question How do Database Deadlocks occur and how do engines detect and resolve them?

Continue Learning on Being SDE

Explore deep-dive system architecture modules, low-level object-oriented designs, and interview cheat sheets.

70+ HLD TopicsDistributed caching, Kafka, and sharding architectures.25+ LLD BlueprintsClean OOP code in Java, Python, and C++ with UML diagrams.SDE Cheat SheetLatency numbers, capacity formulas, and trade-off tables.