Distributed Systems Reading List

Distributed Systems Reading List

Curated collection of essential resources for understanding distributed systems:

Why Study Distributed Systems:

  • Scale Requirements: Modern applications require distribution across multiple machines
  • Fault Tolerance: Systems must continue operating despite component failures
  • Performance: Distribution enables parallel processing and reduced latency
  • Availability: Geographic distribution improves service availability

Core Topics Covered:

Foundational Papers:

  • Time and Ordering: Lamport timestamps and vector clocks
  • Consensus: Paxos, Raft, and Byzantine fault tolerance
  • Consistency Models: CAP theorem, ACID vs BASE properties
  • Failure Detection: Detecting node failures in distributed environments

Distributed Data Management:

  • Replication: Primary-backup, multi-master, and quorum-based systems
  • Partitioning: Sharding strategies and partition tolerance
  • Distributed Transactions: Two-phase commit, saga pattern
  • Eventually Consistent Systems: CRDTs and conflict resolution

Essential Reading Categories:

Classic Papers:

  1. “Time, Clocks, and the Ordering of Events” - Leslie Lamport

    • Fundamental concepts of distributed system ordering
    • Logical timestamps and causality

  2. “The Byzantine Generals Problem” - Lamport, Shostak, Pease

    • Fault tolerance in the presence of malicious actors
    • Foundation for blockchain consensus algorithms

  3. “Impossibility of Distributed Consensus” - Fischer, Lynch, Paterson

    • Theoretical limits of distributed agreement
    • FLP impossibility result

Modern System Papers:

  1. “The Google File System” - Ghemawat, Gobioff, Leung

    • Large-scale distributed file system design
    • Principles applied in HDFS and other systems

  2. “MapReduce: Simplified Data Processing” - Dean & Ghemawat

    • Programming model for large-scale data processing
    • Foundation for modern big data systems

  3. “Dynamo: Amazon’s Highly Available Key-value Store”

    • Eventually consistent distributed storage
    • Influenced Cassandra, Riak, and other NoSQL systems

Practical System Design:

Consistency Patterns:

  • Strong Consistency: All nodes see the same data simultaneously
  • Eventual Consistency: Nodes will eventually converge to the same state
  • Weak Consistency: No guarantees about when consistency will be achieved
  • Causal Consistency: Related operations maintain their causal order

Availability Patterns:

  • Fail-over: Active-passive and active-active configurations
  • Replication: Master-slave and master-master setups
  • Load Balancing: Distributing traffic across multiple servers
  • Circuit Breakers: Preventing cascade failures

Modern Distributed Systems:

Microservices Architecture:

  • Service Discovery: How services find and communicate with each other
  • API Gateway: Single entry point for client requests
  • Event Sourcing: State changes as sequence of events
  • CQRS: Command Query Responsibility Segregation

Container Orchestration:

  • Kubernetes: Container orchestration and service mesh
  • Service Mesh: Infrastructure layer for service communication
  • Observability: Monitoring, logging, and tracing in distributed systems

Implementation Challenges:

Network Partitions:

  • Split-brain: When network partition creates multiple active leaders
  • Quorum Systems: Majority-based decision making
  • Partition Tolerance: System continues operating during network splits

Data Consistency:

  • Read Repair: Fixing inconsistencies during read operations
  • Anti-entropy: Background processes to synchronize replicas
  • Version Vectors: Tracking causality in distributed updates

Learning Path:

Beginner Level:

  1. Understand CAP theorem and its implications
  2. Learn about basic replication strategies
  3. Study simple consensus algorithms like Raft

Intermediate Level:

  1. Deep dive into Paxos and its variants
  2. Understand distributed transaction protocols
  3. Learn about conflict-free replicated data types (CRDTs)

Advanced Level:

  1. Study Byzantine fault tolerance algorithms
  2. Understand advanced consistency models
  3. Learn about cutting-edge research in distributed systems

Practical Applications:

  • Database Systems: Distributed databases like Cassandra, MongoDB
  • Message Queues: Apache Kafka, RabbitMQ clustering
  • Caching Systems: Redis clustering, Memcached
  • Storage Systems: Distributed file systems, object storage

This reading list provides a structured approach to understanding the theoretical foundations and practical implementations of distributed systems, essential knowledge for building modern scalable applications.