Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsLooks into how data stays the same in distributed setups, covering strong, causal, and eventual ways, plus read-your-writes and monotonic reads, showing what they promise, what can go wrong, and how apps pick the right one for 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)Teaches Paxos and Raft ways to agree in groups and their jobs in picking leaders, copying logs, and changing setups, then links ideas to real tools like etcd and Consul used for keeping track, locks, and working together.
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-basedGoes deep into sharding and splitting data ways, like range, hash, and list-based, looking at spreading data, avoiding busy spots, moving things around, and finding paths, and picking and growing a plan as work 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, many-leader, and no-leader copying, explaining write and read ways, handling breaks, delays, and fixing clashes, and how each way hits speed, amount handled, lasting safety, and running trouble in worldwide 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 rule and what it means for spread-out databases, making clear how up-to-date data, always ready, and handling splits work together, and how real setups handle choices using real designs and service aims.
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 splits, delays, and breaks show up over wide area links, covering time limits, part breaks, and split-mind issues, and how to plan spotting, trying again, and slowing down plans that keep systems steady under pressure.
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 semanticsShows what safe repeats mean and their part in safe tries again, telling apart at-least-once, at-most-once, and exactly-once meanings, and patterns for removing doubles, tracking asks, and handling messages in shaky spread-out 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 handling many at once in spread-out databases, comparing hopeful and careful ways, explaining MVCC inside workings, and showing clash spotting and fixing ways that keep things right while letting lots happen together.
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 hybridTells about setup shapes for spread-out databases, like one area, active-passive, active-active, and mixed, and checks their hit on speed, switchover ways, data sameness, and running trouble.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
