Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsDis lesson dey explore distributed consistency models, includin strong, causal, an eventual consistency, plus read-your-writes an monotonic reads, explainin de guarantees, anomalies, an how applications choose models wey 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)Dis lesson dey introduce Paxos an Raft consensus algorithms an dem roles in leader election, log replication, an configuration changes, den connect theory to practice thru systems like etcd an Consul wey dey use for metadata, locks, an 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-basedDis lesson dey detail sharding an partitioning strategies, includin range, hash, an directory-based schemes, focusin on data distribution, hotspot avoidance, rebalancing, an routing, an how to choose an evolve a strategy as workloads an 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 patternsDis lesson dey cover leader-follower, multi-leader, an leaderless replication, explainin write an read paths, failure handlin, lag, an conflict resolution, an how each model affect latency, throughput, durability, an 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 toleranceDis lesson dey explore de CAP theorem an its implications for distributed databases, clarifyin how consistency, availability, an partition tolerance interact, an how real systems navigate trade-offs usin practical design patterns an 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 linksDis lesson dey analyze how network partitions, latency, an failures manifest across WAN links, coverin timeouts, partial failures, an split-brain, an how to design detection, retries, an degradation strategies wey 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 semanticsDis lesson dey explain idempotency an its role in safe retries, distinguishin at-least-once, at-most-once, an exactly-once semantics, an showin patterns for deduplication, request trackin, an message processin 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 techniquesDis lesson dey examine concurrency control in distributed databases, contrastin optimistic an pessimistic approaches, explainin MVCC internals, an presentin conflict detection an resolution techniques wey preserve correctness while enablin 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 hybridDis lesson dey describe physical deployment topologies for distributed databases, includin single region, active-passive, active-active, an hybrid patterns, an analyze dem impact on latency, failover behavior, data consistency, an operational complexity.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
