Concepts
Little's Law, queueing theory, percentiles, USL, GC tuning.
Little's Law for Performance Engineers, with Worked Examples
An intuitive explanation of Little's Law (L = λW), how to derive concurrency, throughput, or latency from the other two, and common misuses.
Read →Amdahl's Law for Performance Engineers
How Amdahl's Law quantifies the limit parallelization can achieve when part of a workload is inherently serial, with practical examples.
Read →Queueing Theory Basics for Performance Engineers
An accessible introduction to queueing theory concepts — utilization, queue length, and waiting time — and why systems get dramatically slower near full utilization.
Read →Why p99 Matters: Understanding Latency Percentiles
What latency percentiles actually mean, why averages systematically mislead, and the pitfalls of averaging or combining percentiles incorrectly.
Read →Concurrency vs Parallelism: A Clear Distinction
The genuine technical distinction between concurrency and parallelism, why it matters for performance reasoning, and common confusions.
Read →Garbage Collection Tuning Fundamentals
The core concepts behind garbage collector tuning — generational collection, pause times, and throughput trade-offs — applicable across JVM, .NET, and Go.
Read →Throughput vs Latency: Why You Usually Can't Maximize Both
Why throughput and latency often trade off against each other through batching, and how to decide where to sit on that trade-off curve.
Read →Understanding Apdex: Translating Latency into User Satisfaction
What the Apdex score actually measures, how to set its thresholds meaningfully, and its limitations as a single summary metric.
Read →What is DevPerfOps? Performance as a First-Class Citizen
DevPerfOps extends DevOps by embedding performance engineering across the entire delivery pipeline — shifting it left from a pre-release gate to a continuous, shared responsibility.
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