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latency-vs-throughput

Explain latency and throughput trade-offs using Little's Law and queueing theory.

Updated Jun 5, 2026
Search Tags:#system design#latency#throughput#capacity planning#queueing theory
One-click install
npx skills add https://github.com/hung-phan/system-skills --skill latency-vs-throughput
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: latency-vs-throughput
Source: https://github.com/hung-phan/system-skills/tree/main/skills/system-review/references/performance/latency-vs-throughput
Command: npx skills add https://github.com/hung-phan/system-skills --skill latency-vs-throughput

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit clarifies the intricate relationship between latency and throughput in high-load systems, empowering you to diagnose capacity issues, set effective SLOs, and optimize your applications for real-world demand.

Core Features & Use Cases

  • Latency vs Throughput Definitions: Explains Little's Law, the importance of percentiles over means, and user-perceived latency.
  • Queueing Theory Basics: Covers the M/M/1 queue model and utilization curves to understand how load impacts performance.
  • Capacity Planning Insights: Guides on designing effective capacity plans with practical application examples and formulas.
  • Batching & Scaling Strategies: Provides insights on how to apply batching for performance without incurring unexpected penalties.

Quick Start

Explore the relationship between latency and throughput for a system using Little's Law calculator provided within the 'mm1.py' script in the 'scripts/' directory.

Frequently Asked Questions about latency-vs-throughput

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does latency relate to throughput in system design?▼

Latency and throughput in system design are related through Little's Law and queueing theory, where higher utilization progressively degrades response times and increases user-perceived latency.

How do I use Little's Law for capacity planning in scalable systems?▼

Calculate capacity planning limits using the mm1.py Little's Law calculator to model M/M/1 queue utilization curves and set accurate service level objectives.

Why should I use latency percentiles instead of mean for SLOs?▼

Latency percentiles are critical for SLOs because mean averages hide tail latencies, whereas percentiles reveal actual user-perceived performance and queueing delays.

Does batching always improve throughput without latency penalties?▼

Batching improves throughput but can incur unexpected latency penalties if applied incorrectly, as waiting to fill batches increases queueing delays and user-perceived latency.

What is the M/M/1 queue model and when do I need it for performance diagnosis?▼

The M/M/1 queue model is a single-server queueing theory framework needed when diagnosing capacity issues to understand how arrival rates and service times impact system latency.

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