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 does Amazon DynamoDB implement Partition Keys, LSIs, and GSIs? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

DynamoDB is a fully managed wide-column NoSQL database. Scaling seamlessly to petabytes requires understanding its partitioning and index architecture:

1. Primary Key: Partition Key (PK) & Sort Key (SK)

Partition Key (Hash Key): Hashed by DynamoDB to route items to physical storage partitions. Even distribution prevents hot partitions.
Sort Key (Range Key): Determines physical storage order within a partition, enabling range queries (begins_with, between).

2. Local Secondary Index (LSI)

Shares the same Partition Key as the base table, but uses a different Sort Key.
Shares read/write capacity units (RCU/WCU) with the base table.
Supports Strongly Consistent and Eventual Consistent reads.
Must be defined at table creation time.

3. Global Secondary Index (GSI)

Defines a completely new Partition Key and Sort Key.
Spans across all partitions with no size limits.
Requires dedicated provisioned RCU/WCU. If GSI writes throttle, parent table writes throttle too.
Supports Eventual Consistency only due to asynchronous replication.

WARNING — GSI Cost Optimization

Only project necessary attributes into GSIs. Projecting ALL attributes duplicates storage costs and multiplies write throughput consumption.

Key Architectural Comparison

DynamoDB Key / Index	Partitioning Scope	Sort Capabilities	Consistency Options	Throughput Billing
Partition Key (PK)	Determines physical storage partition.	Hash match (`=`) only.	Strong or Eventual.	Base Table RCU / WCU.
Sort Key (SK)	Physical row order within partition.	Range queries (`begins_with`, `between`).	Strong or Eventual.	Base Table RCU / WCU.
Local Secondary Index (LSI)	Same PK as base table; different SK.	Range queries on new attribute.	Strong or Eventual.	Shares Base Table capacity (10 GB max limit).
Global Secondary Index (GSI)	Completely new PK and SK.	Global range queries across all items.	Eventual Consistency Only.	Dedicated provisioned RCU / WCU (Unbounded).

How does Amazon DynamoDB implement Partition Keys, LSIs, and GSIs?

1. Primary Key: Partition Key (PK) & Sort Key (SK)

2. Local Secondary Index (LSI)

3. Global Secondary Index (GSI)

Key Architectural Comparison

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