Distributed Systems
Architectural patterns, consensus algorithms, and coordination-free paradigms for distributed computing challenges.
Distributed systems involve components that operate independently but collaborate through message passing. Challenges include eventual consistency, network partitions, and CAP theorem trade-offs while maintaining high availability.
Consensus Algorithms
Raft
Leader-based consensus for replicated log. Simplifies PBFT with single leader election mechanism and log replication strategy.
PBFT
Practical Byzantine Fault Tolerance for partially synchronous systems with threshold quorums and multi-phase commit. Requires 3f+1 replicas for f faults.
Architectural Patterns
Centralized Architecture
Single decision authority with all clients connecting through central server.
P2P Network
Decentralized nodes with direct communication. No single point of failure.
Eventual Consistency
Eventually consistent data across distributed nodes. Conflict resolution via vector clocks or CRDTs.
CAP Theorem
Consistency
Every read receives latest written data
Availability
Guarantee read/write availability
Partition Tolerance
Guarantee operation during network failures
Network partitions force trade-offs in distributed system design