Cache Hit Rate Health
About This MicroSim
Four small-multiple line charts of cache hit rate over 30 days. Each chart represents a real production pattern an oncall engineer must learn to recognize at a glance: healthy, sudden drop (cache invalidation event), slow erosion (slowly-drifting cache key), sawtooth (TTL too short for traffic gaps). Hover any chart to see the diagnostic caption, likely root cause, and what to investigate.
How to Use
- Hover the healthy chart. Note the small daily TTL dips — these are normal.
- Hover sudden drop. Read the most likely cause: a change to the cached prefix on day 14.
- Hover slow erosion. Read about cache-key drift — the most insidious failure mode.
- Hover sawtooth. Read about TTL/gap mismatch.
Bloom Level
Analyze (L4) — distinguish healthy and degraded cache hit-rate patterns and diagnose the likely root cause of each.
Iframe Embed Code
1 | |
Lesson Plan
Audience
Oncall engineers and platform-team members responsible for cost-related alerts.
Duration
10–15 minutes inside Chapter 14.
Prerequisites
Chapter 14 sections on Cache Hit Rate, Cache Invalidation, Cache Invariant, Stable Prefix, Cache TTL, Cache Stampede.
Activities
- Pattern recognition drill (5 min). Cover the captions; look at each chart and try to name the pattern from shape alone.
- Diagnostic walk (5 min). Hover each pattern and verify your diagnosis matches.
- Bring-your-own dashboard (5 min). Use the practice scenarios.
Practice Scenarios
| # | Observed pattern | Most likely cause | Action |
|---|---|---|---|
| 1 | 88% → 5% on Tuesday | ? | ? |
| 2 | Slow drift from 80% to 50% over the month | ? | ? |
| 3 | Daily oscillation 5% / 60% | ? | ? |
| 4 | Steady 88% with weekly small dips | ? | ? |
| 5 | 88% → 60% over 3 days, then plateau | ? | ? |
Assessment
Learner has met the objective when, given an unfamiliar 30-day cache-hit-rate plot, they can match the shape to one of the four canonical patterns and propose a remediation.
References
- Anthropic Documentation — Prompt caching: cache invariants and invalidation.
- SRE: How Google Runs Production Systems — chapter on alerting and pattern recognition.
- Chapter 14 of this textbook — Cache Hit Rate Metric, Cache Invariant.
Senior Instructional Designer Quality Review
Reviewer perspective: 15+ years designing engineering and SRE curricula for adult professional learners.
Overall verdict
Approve as-is for Chapter 14. Score: 88/100 (B+). Pattern-recognition by small multiples is canonical for L4 "distinguish." The four patterns are the right canonical set for cache hit rate.
What works
- Bloom alignment correct. L4 "distinguish" is exactly what hovering and reading captions does.
- Diagnostic + remediation pair. Most pattern-recognition diagrams stop at "this is X." This one says "this is X, here's what likely caused it, here's what to check."
- Sawtooth is included. The TTL-too-short pattern is the most-undertaught failure mode in cache literature.
- 30-day mean is shown for each chart. Calibrates the eye to the typical baseline.
Gaps
- No interactive overlay for "what changed when?" The dropdown spec mentioned hypothesis-generation prompts; current implementation skips them. Score impact: −3.
- Patterns are static. A "blend two patterns" toggle would teach that real production data often shows multiple problems simultaneously. Score impact: −2.
- No quantitative cost impact. A 50% drop in hit rate has a real dollar cost; surfacing "this would cost an extra $X/month" would translate diagnosis to budget urgency. Score impact: −2.
Accessibility
Color-blind safe (single-color line charts). Tooltip on hover provides text alternative.
Cognitive load
4 small charts in a 2×2 grid. Tractable. Single-action affordance (hover) keeps the surface clean.
Recommendation
Approve. Open follow-up for cost-impact annotation (gap 3) and the hypothesis-prompt dropdown (gap 1).