Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsExplores distributed consistency models, including strong, causal, and eventual consistency, plus read-your-writes and monotonic reads, explaining guarantees, anomalies, and how applications choose models that match user expectations.
Strong consistency guaranteesEventual consistency and convergenceCausal consistency and orderingRead-your-writes and monotonic readsChoosing models for applicationsLesson 2Distributed consensus algorithms: Paxos, Raft, and practical implementations (etcd, Consul)Introduces Paxos and Raft consensus algorithms and their roles in leader election, log replication, and configuration changes, then connects theory to practice through systems like etcd and Consul used for metadata, locks, and coordination.
Consensus problem and safety goalsPaxos algorithm core ideasRaft algorithm and log replicationCluster membership and reconfigurationUsing etcd and Consul in practiceLesson 3Sharding and partitioning strategies: range, hash, and directory-basedDetails sharding and partitioning strategies, including range, hash, and directory-based schemes, focusing on data distribution, hotspot avoidance, rebalancing, and routing, and how to choose and evolve a strategy as workloads and data grow.
Range-based partitioning designHash-based sharding and hashingDirectory and lookup-based routingRebalancing and resharding methodsAvoiding hotspots and skewed keysLesson 4Replication models: leader-follower, multi-leader, and leaderless patternsCovers leader-follower, multi-leader, and leaderless replication, explaining write and read paths, failure handling, lag, and conflict resolution, and how each model affects latency, throughput, durability, and operational complexity in global deployments.
Leader-follower replication flowsMulti-leader replication and conflictsLeaderless quorum-based replicationReplication lag and read consistencyOperational trade-offs of each modelLesson 5CAP theorem and trade-offs between consistency, availability, and partition toleranceExplores the CAP theorem and its implications for distributed databases, clarifying how consistency, availability, and partition tolerance interact, and how real systems navigate trade-offs using practical design patterns and service-level goals.
Formal statement of the CAP theoremConsistency vs availability in practicePartition tolerance in real networksDesigning around CAP with SLAsLesson 6Network partitions, latency, and failure modes across WAN linksAnalyzes how network partitions, latency, and failures manifest across WAN links, covering timeouts, partial failures, and split-brain, and how to design detection, retries, and degradation strategies that keep systems predictable under stress.
Characteristics of WAN linksDetecting partitions and timeoutsHandling partial and asymmetric failuresSplit-brain risks and mitigationGraceful degradation strategiesLesson 7Idempotency, retries, and at-least-once vs exactly-once semanticsExplains idempotency and its role in safe retries, distinguishing at-least-once, at-most-once, and exactly-once semantics, and showing patterns for deduplication, request tracking, and message processing in unreliable distributed environments.
Defining idempotent operationsDesigning safe retry mechanismsAt-least-once vs at-most-onceExactly-once semantics limitationsDeduplication and request trackingLesson 8Concurrency control: optimistic vs pessimistic, MVCC, conflict resolution techniquesExamines concurrency control in distributed databases, contrasting optimistic and pessimistic approaches, explaining MVCC internals, and presenting conflict detection and resolution techniques that preserve correctness while enabling high concurrency.
Pessimistic locking in distributed systemsOptimistic control and validationMVCC snapshots and version chainsConflict detection and resolutionDeadlocks, timeouts, and retriesLesson 9Physical topology patterns: single region, active-passive, active-active, and hybridDescribes physical deployment topologies for distributed databases, including single region, active-passive, active-active, and hybrid patterns, and analyzes their impact on latency, failover behavior, data consistency, and operational complexity.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
