Distributed Systems Theory
Distributed systems are defined by partial failures and asynchronous networks. Phase 6 covers the core theoretical principles and battle-tested patterns of distributed engineering:
All Topics in Phase 6
0 of 22 completedExplore the 8 Fallacies of Distributed Computing: Unreliable networks, non-zero latency, partial failures, independent clocks, and state coordination.
Analyze Eric Brewer's CAP theorem: Consistency vs Availability during a Network Partition. Understand why "CA" systems do not exist in reality.
Extend CAP to normal operating conditions: If Partitioned (P) choose Availability (A) or Consistency (C); Else (E) choose Latency (L) or Consistency (C).
Explore the consistency spectrum: Linearizability, Sequential Consistency, Causal Consistency, Read-Your-Writes, and Eventual Consistency.
Prevent user confusion in eventually consistent systems: Sticky routing, version timestamps, and client-side session tokens.
Master Dynamo-style tunable quorum consistency: $W + R > N$, strict vs sloppy quorums, and hinted handoff.
Order events without synchronized physical clocks: Logical clocks, partial ordering, causality tracking, and concurrent write conflict detection.
Understand the fundamental challenge of distributed agreement: Safety vs Liveness, FLP Impossibility Theorem, and crash-recovery models.
Understand Leslie Lamport's Paxos: Proposers, Acceptors, Learners, Phase 1 (Prepare/Promise), Phase 2 (Accept/Accepted), and Multi-Paxos.
Master the Raft consensus algorithm: Leader election, heartbeat heartbeats, log replication, commit index, and election safety.
Explore leader election mechanisms: Bully Algorithm, Ring Algorithm, ZooKeeper ephemeral nodes, and lease-based elections.
Prevent concurrent resource mutation across servers: Redis single-instance locks, Redlock algorithm, ZooKeeper recipes, and fencing tokens.
Explore atomic distributed transactions: Prepare phase, Commit phase, coordinator failure vulnerabilities, and blocking pitfalls.
Manage distributed business transactions across microservices using compensating transactions, choreography, and centralized orchestrators.
Guarantee safe retries over unreliable networks: Idempotency keys, unique database constraints, conditional writes, and deduplication caches.
Analyze message delivery semantics: Producer acknowledgments, consumer offset commits, duplicate detection, and Kafka transactional streams.
Prevent catastrophic dual-master partition states: Majority quorums, STONITH (Shoot The Other Node In The Head), and generation epochs.
Understand adversarial distributed failure: Crash faults vs Byzantine (malicious/arbitrary) faults, PBFT, and blockchain consensus foundations.
Decentralized peer-to-peer cluster state dissemination: Anti-entropy, Rumor mongering, SWIM protocol, and $O(\log N)$ convergence.
Compare distributed coordination backbones: Hierarchical key-value trees, watch triggers, leases, and consensus engine comparisons.
Detect crashed nodes accurately: Fixed timeout pitfalls, heartbeat intervals, and Hayashibara's probabilistic Phi Accrual failure detector.