Distributed Systems 101
Explore the architecture, design patterns, and challenges in decentralized networks through interactive examples and real-world applications.
Core Principles
- 🔷 CAP Theorem tradeoffs
- 🔷 Eventual consistency patterns
- 🔷 Quorum systems
- 🔷 Distributed consensus algorithms
Design Patterns
Chain Replication
A sequential consistency model ensuring ordered operations across geographically distributed systems.
Sagas
Long-running transactions broken into microsteps with compensating actions for fault tolerance.
Event Sourcing
Storing state as a series of events to rebuild state and debug complex system behaviors.
System Design
Sharding
Partition data across multiple nodes to improve scalability while maintaining fault tolerance.
Leader Election
Mechanisms like Raft or Paxos used to coordinate distributed consensus in partitioned systems.
State Machines
Using deterministic state transitions to ensure consistent behavior across distributed nodes.
Service Mesh
Managing network traffic, security, and observability across microservices in distributed architectures.
Common Challenges
Network Partitions
Dealing with split-brain scenarios when subnets lose communication during network failures.
Time Synchronization
Achieving logical ordering of events using vector clocks and hybrid logical clocks.
Consistency Models
Balancing performance and correctness through different consistency guarantees like eventual, causal, or strong.
Fault Tolerance
Designing systems to gracefully handle node failures without data loss or inconsistency.