Distributed Systems in System Design

Week 5: Distributed Systems

1. Distributed Computing Fundamentals:

Lecture 1: Introduction to Distributed Systems
Define distributed systems and explain their importance in modern computing.
Compare it to a team of people working together remotely.
Lecture 2: Key Characteristics of Distributed Systems
Discuss decentralization, concurrency, and fault tolerance.
Explore how these characteristics impact system design.
Lab 1: Analyzing Distributed Systems
Study the architecture of a distributed social media platform and identify components that make it distributed.
Discuss potential challenges and benefits.

2. Distributed Databases:

Lecture 3: What Are Distributed Databases?
Introduce the concept of distributed databases, where data is spread across multiple nodes.
Example: A global e-commerce platform with data centers in different regions.
Lecture 4: Types of Distributed Databases
Explore replicated databases, sharded databases, and distributed cache systems.
Discuss when to use each type.
Lab 2: Designing a Distributed Database
Given a scenario, design a distributed database system to meet scalability and availability requirements.
Consider data partitioning and replication.

3. Consistency Models (e.g., Eventual Consistency):

Lecture 5: Consistency in Distributed Systems
Explain why maintaining consistency in a distributed system is challenging.
Introduce the concept of eventual consistency.
Lecture 6: Eventual Consistency Explained
Dive into eventual consistency, where all replicas of data will converge to the same value over time.
Discuss trade-offs between strong and eventual consistency.
Lab 3: Experimenting with Eventual Consistency
Simulate eventual consistency in a simple distributed database.
Observe how updates propagate across replicas.

4. Fault Tolerance and Distributed Consensus (e.g., Raft):

Lecture 7: Fault Tolerance in Distributed Systems
Discuss the inevitability of failures in distributed systems.
Explain the need for fault tolerance to ensure system reliability.
Lecture 8: Distributed Consensus Algorithms
Introduce distributed consensus algorithms like Raft and Paxos.
Explain how they ensure consistency and fault tolerance.
Lab 4: Simulating Fault Tolerance with Raft
Implement a simplified Raft consensus algorithm in a distributed system simulator.
Explore how it handles node failures and maintains data consistency.

Homework Assignment:

Research and document a real-world example where the use of distributed systems significantly improved an application’s scalability and reliability.

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