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References: Performance Engineering and Scaling

Curated sources for deeper study of latency, throughput, Amdahl's law, profiling, load testing, capacity planning, vertical and horizontal scaling, database sharding, CDN architecture, and observability.

Books

  • Gregg, Brendan. (2020). Systems Performance: Enterprise and the Cloud (2nd ed.). Addison-Wesley. The definitive systems performance engineering reference, covering the USE method (Utilization, Saturation, Errors), profiling techniques, and capacity analysis — all central to this chapter's evidence-based performance engineering approach.

  • Kleppmann, Martin. (2017). Designing Data-Intensive Applications. O'Reilly Media. Covers database sharding, connection pooling, the latency-throughput relationship, and distributed system performance bottlenecks with the depth required for ATAM performance scenario analysis in this chapter.

  • Beyer, Betsy, Chris Jones, Jennifer Petoff, and Niall Murphy, eds. (2016). Site Reliability Engineering: How Google Runs Production Systems. O'Reilly Media. Google's SRE book covers the RED method, capacity planning, load testing methodology, and the performance-cost tradeoffs that map directly to this chapter's ATAM performance evaluation framework.

Articles and Papers

  • Amdahl, Gene. (1967). "Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities." Proceedings of the AFIPS Spring Joint Computer Conference. The original paper establishing Amdahl's Law — the mathematical foundation for this chapter's analysis of horizontal scaling limits and the non-parallelizable fraction sensitivity point.

  • Little, John. (1961). "A Proof for the Queuing Formula L = λW." Operations Research, 9(3). The foundational paper proving Little's Law, which this chapter uses as the primary analytical tool for relating throughput, response time, and concurrency in ATAM performance scenarios.

Online Resources

  • "Performance Under Load." Adrian Cockcroft. https://medium.com/@adriancockcroft Cockcroft's influential writing on performance engineering, capacity planning, and cloud-native performance patterns directly relevant to this chapter's treatment of scaling tactics and observability.

  • "USE Method." Brendan Gregg. https://brendangregg.com/usemethod.html Gregg's official USE Method resource, defining the Utilization-Saturation-Errors framework for systematic resource-level performance analysis described in this chapter.

  • "HikariCP Connection Pool Sizing." Brett Wooldridge. GitHub. https://github.com/brettwooldridge/HikariCP/wiki/About-Pool-Sizing The definitive guide to database connection pool sizing formula referenced in this chapter, explaining why over-sized pools degrade database performance through context-switching overhead.

  • "Load Testing with k6." Grafana Labs. https://grafana.com/docs/k6/latest/ Documentation for k6, one of the load testing tools referenced in this chapter, covering workload modeling, scenario scripting, and the distributed load testing required for valid ATAM performance scenario validation.

Videos

  • "Mastering Chaos: A Netflix Guide to Microservices." Josh Evans. QCon. YouTube. Netflix's engineering experience with performance bottlenecks, capacity planning, and resilience in a large-scale distributed system — the real-world counterpart to this chapter's theoretical performance engineering framework.