System Design

Question 1

ACID

Answer 1

ACID:
- Atomicity
- Consistency
- Isolation
- Durability

(traditional RDBMS)

Question 2

ACID vs. BASE

Answer 2

• ACID ensures strong consistency.
• BASE favors availability and partition tolerance.

Question 3

BASE

Answer 3

BASE:
- Basically Available
- Soft state
- Eventual consistency

(NoSQL)

Question 4

ACID applies to ___ DBMS

Answer 4

Traditional Relational

Question 5

BASE applies to ___ DB

Answer 5

NoSQL

Question 6

Microservice

Answer 6

Microservices: small, independently deployable services.

Question 7

Monolith

Answer 7

Monolith: single deployable artifact with all functionality.

Question 8

Microservices vs. Monolith

Answer 8

Microservices scale independently but add complexity in communication and deployment

Question 9

REST

Answer 9

REST: resource-based, typically over HTTP with JSON.

Question 10

RPC

Answer 10

RPC: direct function calls (e.g., gRPC) often with protocol buffers.

Question 11

REST vs. RPC

Answer 11

• REST is simpler, more flexible.
• RPC can be more efficient and strongly typed.

Question 12

CAP Theorem

Answer 12

Definition:
In a distributed system, you can guarantee ONLY TWO of:

• Consistency
• Availability
• Partition Tolerance

If a network is partitioned, systems choose to be CP (consistent, not fully available) or AP (available, not fully consistent).

Question 13

AP systems are ___ but not ___ (Partitioned network)

Answer 13

AP systems are AVAILABLE but not fully CONSISTENT

Question 14

CP systems are ___ but not ___ (Partitioned network)

Answer 14

CP systems are CONSISTENT but not fully AVAILABLE

Question 15

Scaling - Horizontal

Answer 15

Horizontal: adding more machines

Question 16

Scaling - Vertical

Answer 16

Vertical: adding more resources (CPU/RAM) to existing machines

Question 17

Horizontal vs. Vertical Scaling

Answer 17

Horizontal scaling is generally better for elasticity and resilience.

Definitions:
- Horizontal: adding more machines.
- Vertical: adding more resources (CPU/RAM) to existing machines.

Question 18

Load Balancer

Answer 18

Definition:
Distributes network or application traffic across multiple servers.

• Improves scalability, fault tolerance, and availability.
• Can operate at different layers (L4 vs. L7).

Question 19

Rate Limiter

Answer 19

Definition:
A mechanism or service that limits the number of requests a client can make in a defined time window.

• Prevents abuse or DoS (Denial of Service) attacks by throttling excessive requests.
• Common algorithms include Token Bucket or Leaky Bucket.

Question 20

Redis

Answer 20

Definition:
An in-memory key-value data store used for caching, messaging, and fast data operations.

• Offers high throughput and low latency.
• Supports data structures (strings, lists, sets, hashes) and persistence options.
• Often used for session management, leaderboards, rate limiting, etc.

Question 21

APIs

Answer 21

Definition:
Application Programming Interfaces provide a contract for how different software components communicate.

• Often exposed as REST, SOAP, GraphQL, or gRPC endpoints.
• Require versioning, authentication, and rate limiting considerations in production.

Question 22

Transactions

Answer 22

Definition:
A sequence of operations performed as a single logical unit of work, which must either fully complete or fully fail (rollback).

• In relational databases, transactions follow ACID properties.
• Distributed transactions can be more complex (e.g., two-phase commit).

Question 23

Primary Key

Answer 23

Definition:
A column (or set of columns) in a relational table that uniquely identifies each row.

• Ensures uniqueness of records.
• Cannot contain NULL (in most SQL implementations).
• Often indexed for fast lookups.