Distributed Systems Theory

Explore 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.

Design resilient systems: Exponential backoff with jitter, circuit breakers, fallback degradation, deadlocks, and bulkhead isolation.