Design Patterns

Design Patterns for System Design

1. Microservices vs. Monolithic Architecture

Definition:

1. Monolithic Architecture:

   • A single, unified application where all components (e.g., UI, business logic, database) are tightly coupled.
   • Pros:
     • Simpler to develop, test, and deploy.
     • Easier to debug and monitor.
   • Cons:
     • Hard to scale individual components.
     • Difficult to adopt new technologies.
     • Single point of failure.
   • Use Cases: Small applications, startups, or projects with limited scope.

2. Microservices Architecture:

   • A collection of loosely coupled, independently deployable services, each responsible for a specific business function.
   • Pros:
     • Scalable and flexible.
     • Easier to adopt new technologies.
     • Fault isolation (failure in one service doesn’t affect others).
   • Cons:
     • Complex to develop, test, and deploy.
     • Requires robust monitoring and logging.
     • Increased latency due to inter-service communication.
   • Use Cases: Large-scale, complex applications (e.g., Netflix, Amazon).

How to Present in Interviews:

• Compare pros and cons: Highlight the trade-offs between simplicity (monolithic) and scalability (microservices).
• Use examples: Mention companies like Netflix (microservices) vs. small startups (monolithic).
• Relate to the problem: If designing a scalable system, emphasize microservices. For simpler systems, consider monolithic.

2. Event-Driven Architecture

Definition:

A design pattern where the flow of the application is determined by events (e.g., user actions, system events). Components react to events asynchronously.

Key Concepts:

1. Event Producers: Generate events (e.g., user clicks, API calls).
2. Event Consumers: React to events (e.g., process data, send notifications).
3. Event Bus/Message Broker: Facilitates communication between producers and consumers (e.g., Kafka, RabbitMQ).

Use Cases:

• Real-time systems (e.g., chat applications, stock trading platforms).
• Decoupling components in microservices architectures.

How to Present in Interviews:

• Explain the flow: Describe how events are produced, transmitted, and consumed.
• Mention tools: Use examples like Kafka for event streaming or RabbitMQ for message queuing.
• Relate to the problem: If designing a real-time system, emphasize event-driven architecture.

3. Publisher-Subscriber Model

Definition:

A messaging pattern where publishers send messages to a topic, and subscribers receive messages from topics they are interested in.

Key Concepts:

1. Publisher: Produces messages and sends them to a topic.
2. Subscriber: Registers interest in a topic and receives messages.
3. Message Broker: Manages topics and ensures message delivery (e.g., Kafka, RabbitMQ).

Use Cases:

• Decoupling components in distributed systems.
• Broadcasting messages to multiple consumers (e.g., notifications, updates).

How to Present in Interviews:

• Explain the pattern: Describe how publishers and subscribers interact via topics.
• Mention tools: Use examples like Kafka or RabbitMQ.
• Relate to the problem: If designing a system with multiple consumers, emphasize the pub-sub model.

4. Sharding and Partitioning

Definition:

Techniques for splitting large datasets into smaller, manageable pieces to improve performance and scalability.

Key Concepts:

1. Sharding:

   • Splits data across multiple databases or servers.
   • Types:
     • Horizontal Sharding: Divides rows across tables (e.g., user data by region).
     • Vertical Sharding: Divides columns into separate tables (e.g., user profile vs. user activity).
   • Use Cases: Scaling databases for large datasets.

2. Partitioning:

   • Splits data within a single database into smaller chunks.
   • Types:
     • Range Partitioning: Divides data based on a range (e.g., dates).
     • Hash Partitioning: Distributes data using a hash function.
   • Use Cases: Improving query performance for large tables.

How to Present in Interviews:

• Explain the difference: Sharding splits data across servers, while partitioning splits data within a server.
• Mention use cases: Use examples like sharding for global applications or partitioning for large tables.
• Relate to the problem: If designing a scalable database, emphasize sharding and partitioning.

How to Present Design Patterns in Interviews

1. Start with Definitions:

   • Clearly define the design pattern and its purpose.
   • Use analogies or examples to make it relatable.

2. Explain Trade-offs:

   • Discuss the pros and cons of each pattern.
   • Relate trade-offs to real-world systems.

3. Use Tools and Examples:

   • Mention specific tools (e.g., Kafka, RabbitMQ) and their use cases.
   • Provide examples of systems that use these patterns.

4. Relate to the Problem:

   • Tailor your explanation to the system being designed.
   • Explain why a particular pattern is relevant to the problem.

5. Be Clear and Concise:

   • Avoid jargon unless necessary.
   • Use diagrams or whiteboard sketches to illustrate your points.

Example Interview Response

Interviewer: “How would you design a real-time chat application?”

You:
“For a real-time chat application, I’d use an event-driven architecture to handle messages asynchronously.
Messages would be published to a message broker like Kafka, and subscribers (e.g., chat clients) would receive messages in real-time.
To scale the system, I’d use sharding to split user data across multiple databases based on regions.
For high availability, I’d implement a publisher-subscriber model to ensure messages are delivered even if some components fail.
Finally, I’d use microservices to decouple components like authentication, messaging, and notifications for better scalability and fault tolerance.”