Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsExamines distributed consistency models like strong, causal, eventual consistency, read-your-writes, and monotonic reads, detailing guarantees, potential issues, and selecting models that meet user needs effectively.
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)Covers Paxos and Raft consensus methods for leader election, log replication, and config changes, linking theory to real-world tools like etcd and Consul for metadata, locks, and coordination tasks.
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-basedExplains sharding and partitioning approaches such as range, hash, and directory-based, covering data distribution, avoiding hotspots, rebalancing, routing, and adapting strategies as data and workloads 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 patternsDiscusses leader-follower, multi-leader, and leaderless replication, including write/read paths, failure recovery, lag management, conflict resolution, and impacts on latency, throughput, durability 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 toleranceDelves into CAP theorem implications for distributed databases, explaining interactions between consistency, availability, partition tolerance, and how systems handle trade-offs with design patterns and service 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 linksAnalyses network partitions, latency, failures over WAN, including timeouts, partial failures, split-brain risks, and strategies for detection, retries, degradation to maintain system predictability 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 semanticsCovers idempotency for safe retries, comparing at-least-once, at-most-once, exactly-once semantics, with patterns for deduplication, tracking, processing in unreliable distributed networks.
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 techniquesReviews concurrency control methods in distributed databases, optimistic vs pessimistic, MVCC workings, conflict detection/resolution to ensure correctness and high concurrency levels.
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 deployment topologies like single region, active-passive, active-active, hybrid for distributed databases, assessing effects on latency, failover, consistency, and operations.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
