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 S3 and HDFS store petabytes of unstructured blob data reliably? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

Traditional file systems crash when storing billions of large media assets or unstructured log files.

1. Separation of Metadata and Data

Metadata Tier: Stores file names, permissions, and block mapping locations in a fast in-memory key-value database.
Block Storage Tier: Splits files into fixed chunks (e.g., 128 MB blocks) stored on commodity storage nodes.

2. Erasure Coding vs 3x Replication

3x Replication: Copies every data block across 3 physical racks. Fast, but incurs 200% storage overhead.
Erasure Coding (Reed-Solomon 8+4): Splits data into 8 data blocks + 4 parity blocks. Can recover from 4 node failures with only 50% storage overhead.

TIP — Storage Layer Choice

Use AWS S3 / Google Cloud Storage for object data storage instead of building custom HDFS clusters unless running bare-metal Hadoop.

Key Architectural Comparison

Storage Architecture	Metadata Handling	Block Size	Scalability Limit	Ideal Workload
Amazon S3 (Object Storage)	Distributed flat namespace (Key-Value).	Arbitrary (Up to 5 TB per object).	Unlimited exabytes.	Media files, backups, data lake storage.
Hadoop HDFS	Centralized NameNode in RAM.	Large contiguous blocks (128 MB).	Petabytes (bounded by NameNode memory).	Batch MapReduce & big data analytical jobs.

How do S3 and HDFS store petabytes of unstructured blob data reliably?

1. Separation of Metadata and Data

2. Erasure Coding vs 3x Replication

Key Architectural Comparison

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