Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsDives into distributed consistency types like strong, causal, and eventual, plus read-your-writes and monotonic reads, breaking down what they promise, the odd behaviours they allow, and how apps pick ones that fit 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 agreement methods and their jobs in picking leaders, copying logs, and changing setups, then links the ideas to real tools like etcd and Consul for handling metadata, locks, and teamwork.
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 into detail on sharding and splitting strategies like range, hash, and directory methods, zeroing in on spreading data, dodging busy spots, rebalancing, and directing traffic, plus picking and growing a plan as work and data expand.
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 patternsLooks at leader-follower, multi-leader, and no-leader copying setups, explaining write and read routes, handling breakdowns, delays, and sorting clashes, and how each setup hits speed, output, toughness, and running costs 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 closely at the CAP theorem and what it means for spread-out databases, making clear how sameness, uptime, and split-handling mix, and how actual setups handle the swaps using real designs and service targets.
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 linksBreaks down how network splits, delays, and breakdowns show up over wide area links, covering timeouts, half-failures, and split-brain issues, plus designing spotting, retries, and step-down plans to keep things 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 semanticsExplains repeat-safety and its part in safe retries, sorting at-least-once, at-most-once, and exactly-once meanings, and showing ways for removing doubles, tracking requests, and handling messages in shaky spread-out setups.
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 techniquesChecks out handling multiple jobs at once in spread-out databases, weighing hopeful against careful methods, explaining MVCC workings, and showing clash spotting and fixing ways that keep things right while allowing lots of parallel work.
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 hybridOutlines setup layouts for spread-out databases like single area, active-passive, active-active, and mixed types, and weighs their effects on speed, switchover, data sameness, and running hassle.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
