Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsLooks at distributed consistency models, including strong, causal, and eventual consistency, plus read-your-writes and monotonic reads, explaining promises, odd behaviours, and how apps pick models that match what users expect.
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 copying, and setup changes, then connects theory to practice through systems like etcd and Consul used for metadata, locks, and sorting out.
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 sharing, hotspot avoidance, rebalancing, and routing, and how to choose and grow a strategy as workloads and data increase.
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 copying, explaining write and read paths, failure handling, lag, and conflict sorting, and how each model affects delay, throughput, durability, and running complexity in global setups.
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 toleranceLooks at the CAP theorem and its meanings for distributed databases, making clear how sameness, availability, and partition tolerance work together, and how real systems handle 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 linksChecks how network partitions, delay, and failures show up across WAN links, covering timeouts, partial failures, and split-brain, and how to design detection, retries, and drop-back 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, separating at-least-once, at-most-once, and exactly-once meanings, and showing patterns for deduplication, request tracking, and message processing in unreliable distributed places.
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 techniquesLooks at concurrency control in distributed databases, contrasting optimistic and pessimistic ways, explaining MVCC inside workings, and showing conflict detection and sorting techniques that keep correctness while allowing 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 setup topologies for distributed databases, including single region, active-passive, active-active, and hybrid patterns, and checks their impact on delay, failover behaviour, data sameness, and running complexity.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
