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 Database Deadlocks occur and how do engines detect and resolve them? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

A Deadlock occurs when two or more database transactions hold locks on records that the other transactions need, creating a circular wait state.

Deadlock Scenario

Tx A locks Row 1, requests lock on Row 2.
Tx B locks Row 2, requests lock on Row 1.
Neither transaction can proceed.

Resolution Strategies

Wait-For Graph Detection: DB engine maintains a directed graph of transactions waiting for locks. If a cycle is detected, the engine aborts the victim transaction with a lower execution cost.
Lock Timeout: Aborts any transaction that waits longer than lock_timeout (e.g. 5s).

TIP — Prevention Strategy

Always acquire locks on multiple tables or rows in the exact same deterministic order across application code routines.

Key Architectural Comparison

Deadlock Strategy	Detection Mechanism	Resolution Action	Performance Impact
Wait-For Graph Cycle Detection	Database engine traverses directed dependency graph.	Aborts the "victim" transaction with least work done.	Low background overhead.
Lock Timeout	Timer expires if lock cannot be acquired within threshold.	Aborts waiting query and rolls back transaction.	Simple, but can cause false aborts under heavy load.
Deterministic Ordering	Application code acquires locks in identical alphabetical order.	Prevents deadlocks from ever forming.	Zero database aborts; requires strict coding hygiene.

How do Database Deadlocks occur and how do engines detect and resolve them?

Deadlock Scenario

Resolution Strategies

Key Architectural Comparison

Continue Learning on Being SDE