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 search queries on a large product catalog (Elasticsearch)? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

SQL LIKE '%keyword%' queries cause full table scans that fail at scale. Elasticsearch uses an Inverted Index for instant fuzzy searches and faceted filtering.

1. Inverted Index Mechanics

A dictionary mapping every unique word to the specific document IDs containing it. Searching "Wireless Noise Canceling Headphones" intersects candidate lists in milliseconds.

2. Sharding & Replica Distribution

Primary Shards: Document collections are partitioned across primary shards (hash(_id) % primary_shards).
Replica Shards: Read-only copies of primary shards. Replicas scale concurrent search queries horizontally and guarantee failover.

WARNING — Primary Shard Immutability

Primary shard count cannot be changed after index creation without re-indexing. Plan primary shard sizing based on 3-year data growth projections.

Key Architectural Comparison

Catalog Search Pattern	Query Engine	Search Latency	Fuzzy / Typo Support	Multi-Facet Filters
SQL `LIKE '%term%'`	Relational DB	$> 1,000\text{ms}$ (Full scan).	None.	Slow (requires multi-index scans).
Elasticsearch (Lucene)	Inverted Index	$< 30\text{ms}$.	Levenshtein edit distance built-in.	Instantaneous facet aggregations.
Vector Search (ANN)	HNSW Graph Index	$< 50\text{ms}$.	Semantic conceptual matching.	Requires hybrid vector + scalar filter.

How do you scale search queries on a large product catalog (Elasticsearch)?

1. Inverted Index Mechanics

2. Sharding & Replica Distribution

Key Architectural Comparison

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