Getting Started with System Design

1. Fundamentals of System Design

Purpose:

Build foundational knowledge about what system design is and why it matters.

Chapter list

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.

1.1 Overview of System Design

1.2 Principles of Scalability, Availability, and Reliability

1.3 Key Metrics: Latency, Throughput, Response Time, etc.

1.4 CAP Theorem and PACELC Theorem

1.5 Consistency Models (Strong, Eventual, Causal)

Implementation Tasks for Fundamentals

1. Overview of System Design

  1. 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.
  2. 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.
  3. 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.

2. Principles of Scalability, Availability, and Reliability

  1. 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.
  2. Availability:
    • Design and implement a failover mechanism for a database using read replicas.
    • Perform manual failover testing to ensure availability during primary database downtime.
  3. 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.

3. Key Metrics: Latency, Throughput, Response Time

  1. 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).
  2. 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.
  3. 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.

4. CAP Theorem and PACELC Theorem

  1. 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.
  2. 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.
  3. Visualization:
    • Create diagrams illustrating scenarios where CAP and PACELC apply.
    • Include real-world examples of systems (e.g., DynamoDB for AP, Spanner for CP).

5. Consistency Models (Strong, Eventual, Causal)

  1. 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.
  2. 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.
  3. 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.

Project suggestion for practicing the “Fundamentals”