High-Availability System Design

Lesson 1 • Reliability vs. Availability Trade-offs

Distinguishes reliability from availability and explores cost-benefit trade-offs. Sets expectations for design decisions made in later chapters.

Lesson 2 • Failure Modes and Fault Taxonomy

Classifies hardware, software, network, and human failures. Provides a shared taxonomy used throughout the course for root-cause analysis.

Lesson 3 • Availability Concepts and Metrics

Defines uptime, downtime, and the nines-of-availability scale. Anchors all subsequent design decisions in measurable reliability targets.

Lesson 1 • Active-Active Redundancy

Explains load distribution across multiple live nodes and conflict resolution. Contrasts with active-passive to guide pattern selection.

Lesson 2 • N+1 and N+M Capacity Models

Defines spare-capacity formulas for compute, storage, and network tiers. Connects redundancy math to availability budget from Chapter 1.

Lesson 3 • Quorum and Consensus Mechanisms

Introduces voting-based quorum to prevent split-brain in distributed clusters. Prepares students for distributed coordination covered in Chapter 5.

Lesson 4 • Active-Passive Redundancy

Covers standby node promotion, heartbeat detection, and failover timing. Builds on fault taxonomy to explain when passive standby is sufficient.

Lesson 1 • Load Balancing Algorithms

Compares round-robin, least-connections, IP-hash, and weighted algorithms. Connects algorithm choice to workload characteristics and session requirements.

Lesson 2 • Load Balancer Architectures

Surveys hardware, software, and DNS-based load balancers at L4 and L7. Establishes the traffic-management layer that protects redundant backends.

Lesson 3 • Health Checks and Failover

Defines passive and active health probes and their role in automatic failover. Ties back to MTTR goals established in Chapter 1.

Lesson 4 • Session Persistence and State

Addresses sticky sessions, shared session stores, and stateless design. Bridges load balancing with application-layer HA patterns.

Lesson 1 • Read Replicas and Read Scaling

Explains replica lag, read-your-writes consistency, and replica promotion. Connects replication lag to RPO calculations introduced in Chapter 2.

Lesson 2 • Database Clustering Fundamentals

Covers shared-disk and shared-nothing cluster architectures and their trade-offs. Builds on replication patterns from Chapter 2 for the database tier.

Lesson 3 • Sharding and Horizontal Partitioning

Introduces range, hash, and directory-based sharding for write scalability. Addresses cross-shard queries and resharding complexity.

Lesson 4 • Distributed SQL and NoSQL HA Patterns

Compares HA features of distributed SQL and NoSQL engines. Guides pattern selection based on consistency and availability requirements.

Lesson 1 • CAP Theorem and Consistency Models

Formalizes CAP trade-offs and maps consistency models to real system behaviors. Extends the database CAP discussion into general distributed services.

Lesson 2 • Timeout, Retry, and Backoff Strategies

Defines timeout budgets, retry policies, and exponential backoff with jitter. Prevents cascading failures introduced in Chapter 1.

Lesson 3 • Distributed Coordination Services

Covers leader election, distributed locks, and service registries. Builds on quorum concepts from Chapter 2 for practical coordination.

Lesson 4 • Distributed Tracing and Observability

Introduces trace context propagation, span correlation, and latency profiling. Enables diagnosis of distributed failures across multi-service topologies.

Lesson 1 • Chaos Engineering Fundamentals

Introduces controlled failure injection to validate HA assumptions in production. Provides the experimental foundation for advanced chaos practices in Chapter 8.

Lesson 2 • Deployment Strategies for Zero Downtime

Covers blue-green, canary, and rolling deployments to eliminate release-induced outages. Integrates session drain from Chapter 3 into deployment workflows.

Lesson 3 • Self-Healing and Automated Remediation

Designs watchdog processes, health-loop controllers, and runbook automation. Reduces MTTR by automating recovery actions identified in Chapter 1.

Lesson 4 • Infrastructure as Code for HA

Applies declarative IaC to enforce redundancy and immutable infrastructure. Ensures consistent, repeatable HA configurations across environments.

Lesson 1 • Disaster Recovery Fundamentals

Defines RTO, RPO, and the four DR tiers from backup-restore to hot standby. Formalizes recovery objectives first introduced in Chapter 4.

Lesson 2 • Failover Orchestration and Runbooks

Designs automated and manual failover workflows with clear decision trees. Integrates IaC automation from Chapter 6 into DR execution.

Lesson 3 • DR Testing and Validation

Establishes tabletop, simulation, and full-cutover DR test methodologies. Validates that RTO and RPO targets are achievable before a real disaster.

Lesson 4 • Data Synchronization Across Regions

Addresses cross-region replication lag, conflict resolution, and consistency guarantees. Applies distributed consistency models from Chapter 5 to geo-distributed data.

Lesson 1 • Advanced Chaos Engineering

Extends chaos fundamentals from Chapter 6 to production-scale, automated experiments. Validates multi-region and distributed-system resilience assumptions.

Lesson 2 • Capacity Planning and Growth Modeling

Applies traffic forecasting and headroom analysis to maintain HA under growth. Extends N+M models from Chapter 2 to long-term capacity strategy.

Lesson 3 • Evolving HA Strategy Over Time

Guides teams through iterative HA maturity progression from reactive to proactive. Provides a strategic roadmap for sustained reliability improvement.

Lesson 4 • Site Reliability Engineering Practices

Formalizes SLOs, error budgets, and toil reduction as operational HA levers. Connects all prior availability metrics into a unified SRE operating model.