Built to Last: Reliability & Scaling
On-call for large-scale production systems, postmortems, capacity planning, and reliability programs — with two realistic incident case studies.
Start reading →01Chapter 1: Why Reliability and Scale Matter02Chapter 2: Reliability Vocabulary: SLAs, SLOs, SLIs, and Error Budgets03Chapter 3: Redundancy and Fault Tolerance: Eliminating Single Points of Failure04Chapter 4: Horizontal vs. Vertical Scaling, and Load Balancing/Auto-Scaling05Chapter 5: Caching for Scale: Cache Stampede and Invalidation at Scale06Chapter 6: Database Scaling: Replication, Partitioning, Read Replicas, Connection Pooling07Chapter 7: Resilience Patterns: Timeouts, Retries, Circuit Breakers, Bulkheads, Load Shedding08Chapter 8: Observability: Metrics, Logging, Distributed Tracing, and Actionable Alerts09Chapter 9: Incident Response: Severity, Incident Command, Communication, Blameless Postmortems10Chapter 10: Chaos Engineering: Proactively Testing for Failure11Chapter 11: Capacity Planning and Load Testing12Chapter 12: Core Trade-offs: Consistency vs. Availability, Cost vs. Redundancy, Simplicity vs. Resilience13Chapter 13: Incident Case Study: The Cascading Failure14Chapter 14: Incident Case Study: The Silent Data Corruption15Chapter 15: Common Interview Questions About Reliability and Scaling
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Python DSA — CoursePython DSA — Interview PrepPython DSA — BeginnerPython Syntax & DSA ReferenceGit MasteryCognitive Skills & Mental ToolsCode Optimization RulesObjects First: OOP FundamentalsPatterns of Good DesignUnder the Hood: Operating SystemsHow the Bytes Travel: NetworkingData at Rest, Data in Motion: DatabasesSharing Time: Concurrency & MultithreadingClean by Default: Coding StandardsThe Review: Code ReviewFrom Idea to Production: the SDLCDesigning at Scale: System DesignWhiteboard to Blueprint: HLD & LLDTell Me About a Time: Behavioral Interviews