Build foundational knowledge about what system design is and why it matters.
Here’s a detailed breakdown of the chapters for the first section, Fundamentals of System Design. Each chapter is designed to provide foundational knowledge and hands-on exercises for practical understanding.
- Purpose: Understand what system design is and its importance in solving real-world engineering problems.
- Chapters:
- Introduction to System Design
- What is system design?
- Difference between high-level and low-level design.
- Examples of real-world systems (e.g., e-commerce, social media platforms).
- Why System Design Matters
- Impact on scalability, reliability, and maintainability.
- Role of system design in interviews and real-world projects.
- System Design Process
- Gathering requirements.
- Defining key use cases and constraints.
- Designing high-level architecture and low-level components.
- Types of System Design
- Designing web applications vs distributed systems.
- Real-time systems vs batch systems.
- Offline-first systems vs always-online systems.
- Introduction to System Design
- Purpose: Learn core principles that guide the design of robust systems.
- Chapters:
- Scalability
- Vertical scaling vs horizontal scaling.
- Stateless vs stateful architectures.
- Examples of scalable designs (e.g., distributed databases).
- Availability
- Definitions: Availability vs uptime.
- Designing for high availability (HA).
- Redundancy and failover mechanisms.
- Reliability
- Reliability vs availability.
- Fault tolerance and graceful degradation.
- Techniques for improving reliability (e.g., retries, idempotency).
- Trade-offs Between Scalability, Availability, and Reliability
- How to balance trade-offs based on requirements.
- Real-world examples of trade-offs.
- Scalability
- Purpose: Understand key performance metrics for evaluating system efficiency.
- Chapters:
- Latency
- Network latency vs application latency.
- Sources of latency in distributed systems.
- Techniques for reducing latency (e.g., caching, CDN).
- Throughput
- Definition and measurement.
- Maximizing throughput with parallel processing and batching.
- Response Time
- Average response time vs percentile response times (e.g., P99).
- How to optimize response times.
- Connections Between Metrics
- How latency and throughput interact.
- Choosing the right metrics based on system requirements.
- Hands-On Exercises
- Simulate latency and throughput scenarios (e.g., HTTP requests under load).
- Use tools like Apache JMeter or k6 for load testing.
- Latency
- Purpose: Learn how distributed systems balance trade-offs in consistency, availability, and partition tolerance.
- Chapters:
- CAP Theorem Basics
- Definition and history of CAP Theorem.
- Explaining consistency, availability, and partition tolerance.
- Why you can only pick two of the three.
- Real-World Implications of CAP
- Examples of systems focusing on consistency (e.g., relational databases).
- Examples of systems focusing on availability (e.g., NoSQL databases).
- How network partitions affect system behavior.
- PACELC Theorem
- Introduction to PACELC (Partitioning, Availability, Consistency, Else Latency, Consistency).
- Real-world examples of latency vs consistency trade-offs.
- Comparing CAP and PACELC with diagrams.
- Hands-On Exercises
- Create a partitioned system simulation and test availability vs consistency trade-offs.
- Discuss PACELC in context of popular systems like DynamoDB or Cassandra.
- CAP Theorem Basics
- Purpose: Explore different consistency models used in distributed systems.
- Chapters:
- Introduction to Consistency
- What is consistency in distributed systems?
- Why consistency is challenging in distributed environments.
- Strong Consistency
- Definition and examples (e.g., RDBMS with ACID properties).
- Trade-offs and use cases.
- Eventual Consistency
- Definition and examples (e.g., DynamoDB, Cassandra).
- How eventual consistency works (e.g., anti-entropy, read-repair).
- Causal Consistency
- Definition and examples (e.g., Git versioning).
- Use cases where causal consistency is essential.
- Hands-On Exercises
- Simulate strong, eventual, and causal consistency in a distributed environment.
- Implement a simple key-value store with eventual consistency.
- Introduction to Consistency
- Drawing and Planning:
- Create diagrams to explain CAP and PACELC trade-offs.
- Map out a flow of metrics (latency, throughput) for a sample architecture.
- Gathering Requirements:
- Define functional and non-functional requirements for a simple system like a URL shortener.
- Identify constraints such as data storage, scalability, and high availability.
- Design High-Level Architecture:
- Use tools like draw.io or Lucidchart to create a high-level architecture diagram for the system.
- Include components such as frontend, backend, database, and caching layer.
- Explore Trade-offs:
- Discuss trade-offs in choosing a relational database vs a NoSQL database for the system.
- Create a document explaining decisions made based on scalability and consistency requirements.
- Scalability:
- Implement a load balancer using tools like Nginx or HAProxy to distribute traffic.
- Create a script to simulate increasing traffic and observe how horizontal scaling affects performance.
- Availability:
- Design and implement a failover mechanism for a database using read replicas.
- Perform manual failover testing to ensure availability during primary database downtime.
- Reliability:
- Implement a retry mechanism in an HTTP client to handle transient failures.
- Add idempotency logic to an API endpoint to ensure consistent behavior during retries.
- Latency Measurement:
- Write a script to send HTTP requests to a mock server and measure response times.
- Visualize latency distribution (e.g., using histograms or percentiles like P99).
- Throughput Analysis:
- Simulate a workload with multiple concurrent requests using a tool like Apache JMeter or k6.
- Measure the maximum requests per second (RPS) the system can handle before latency degrades.
- Response Time Optimization:
- Introduce caching at the application layer (e.g., with Redis) to reduce response times.
- Compare response times with and without caching enabled.
- CAP Trade-offs:
- Set up a distributed key-value store (e.g., Consul or Etcd).
- Simulate network partitions and observe behavior when prioritizing consistency vs availability.
- PACELC Exploration:
- Use a NoSQL database like DynamoDB or MongoDB to demonstrate latency vs consistency trade-offs.
- Write a report comparing latency in strongly consistent and eventually consistent reads.
- Visualization:
- Create diagrams illustrating scenarios where CAP and PACELC apply.
- Include real-world examples of systems (e.g., DynamoDB for AP, Spanner for CP).
- Strong Consistency Implementation:
- Create a relational database setup with ACID properties (e.g., PostgreSQL).
- Write a script to test transactional consistency by simulating concurrent writes.
- Eventual Consistency Simulation:
- Build a simple distributed key-value store where nodes asynchronously replicate data.
- Test consistency by performing writes and observing when all nodes eventually converge.
- Causal Consistency Experiment:
- Implement a versioning system (e.g., using vector clocks) to simulate causal consistency.
- Create scenarios demonstrating causal relationships, such as a collaborative editing tool.
- System Design Case Study:
- Design a simple, distributed chat application with the following requirements:
- Low latency for message delivery.
- High availability during network partitions.
- Eventual consistency for message order.
- Deliverables:
- High-level architecture diagram.
- CAP and PACELC trade-off decisions.
- Explanation of chosen consistency model.
- Implementation of core features focusing on latency, scalability, and reliability.
- Design a simple, distributed chat application with the following requirements: