System Design & Architecture

Question 1

Monolithic Architecture

Answer 1

Single executable;
simpler deployment but less flexible and harder to scale.

Question 2

Event-driven Architecture

Answer 2

Components communicate via events/messages;
fits market data handling and reactive trading engines.

Question 3

Service-Oriented Architecture
(SOA)

Answer 3

Services expose interfaces via well-defined protocols;
emphasizes interoperability.

Question 4

Message/Event Bus

Answer 4

Central channel for decoupled components to publish/subscribe messages

Question 5

Publish/Subscribe
(Pub/Sub)

Answer 5

Decouples producers and consumers

Question 6

Ring Buffer

Answer 6

Data structure for buffering messages/events

Question 7

Horizontal Scaling

Answer 7

Adding more machines or processes to handle load

Question 8

Vertical Scaling

Answer 8

Increasing resources (CPU, memory) in a single node

Question 9

Partitioning

Answer 9

Splitting data across multiple processing units

Question 10

Load Balancing

Answer 10

Distributing requests/messages evenly across workers to maximize throughput.

Question 11

Batching

Answer 11

Aggregating multiple operations to reduce overhead and improve throughput.

Question 12

Idempotency

Answer 12

Operations can be repeated safely without changing result

Question 13

Transmission Control Protocol
(TCP)

Answer 13

Reliable, ordered, connection-oriented protocol; ensures all packets arrive and in order.
Used for order routing or critical data transfer.

Question 14

User Datagram Protocol
(UDP)

Answer 14

Fast Unreliable, connectionless protocol; no guarantee of delivery or order.
Commonly used for market data feeds in low-latency systems.

Question 15

Inter-Process Communication
(IPC)

Answer 15

Mechanisms for processes to exchange data

Question 16

SQL
(Structured Query Language)

Answer 16

Relational database querying language.

Good for structured, transactional data (positions, trades).

Question 17

NoSQL

Answer 17

Non-relational databases;
optimized for large-scale, flexible data storage.

Examples: key-value stores for caching, document stores for logs.

Question 18

Data Lake

Answer 18

Centralized repository for raw, unstructured or semi-structured data;

used for analytics, historical market data storage.

Question 19

Data Warehouse

Answer 19

Structured storage optimized for reporting and analytics;

often built on cleaned, processed data for dashboards or regulatory reporting.

Question 20

Real-time

Answer 20

Systems or processes that respond with minimal latency, often deterministic