actionbook/rust-skills

m10-performance

CRITICAL: Use for performance optimization.

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npx skills add https://github.com/actionbook/rust-skills --skill m10-performance

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Original skill document

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Performance Optimization

Layer 2: Design Choices

Core Question

What's the bottleneck, and is optimization worth it?

Before optimizing:

  • Have you measured? (Don't guess)
  • What's the acceptable performance?
  • Will optimization add complexity?

Performance Decision → Implementation

GoalDesign ChoiceImplementation
Reduce allocationsPre-allocate, reusewith_capacity, object pools
Improve cacheContiguous dataVec, SmallVec
ParallelizeData parallelismrayon, threads
Avoid copiesZero-copyReferences, Cow<T>
Reduce indirectionInline datasmallvec, arrays

Thinking Prompt

Before optimizing:

  1. Have you measured?
  • Profile first → flamegraph, perf
  • Benchmark → criterion, cargo bench
  • Identify actual hotspots
  1. What's the priority?
  • Algorithm (10x-1000x improvement)
  • Data structure (2x-10x)
  • Allocation (2x-5x)
  • Cache (1.5x-3x)
  1. What's the trade-off?
  • Complexity vs speed
  • Memory vs CPU
  • Latency vs throughput

Trace Up ↑

To domain constraints (Layer 3):

"How fast does this need to be?"
    ↑ Ask: What's the performance SLA?
    ↑ Check: domain-* (latency requirements)
    ↑ Check: Business requirements (acceptable response time)
QuestionTrace ToAsk
Latency requirementsdomain-*What's acceptable response time?
Throughput needsdomain-*How many requests per second?
Memory constraintsdomain-*What's the memory budget?

Trace Down ↓

To implementation (Layer 1):

"Need to reduce allocations"
    ↓ m01-ownership: Use references, avoid clone
    ↓ m02-resource: Pre-allocate with_capacity

"Need to parallelize"
    ↓ m07-concurrency: Choose rayon or threads
    ↓ m07-concurrency: Consider async for I/O-bound

"Need cache efficiency"
    ↓ Data layout: Prefer Vec over HashMap when possible
    ↓ Access patterns: Sequential over random access

Quick Reference

ToolPurpose
cargo benchMicro-benchmarks
criterionStatistical benchmarks
perf / flamegraphCPU profiling
heaptrackAllocation tracking
valgrind / cachegrindCache analysis

Optimization Priority

1. Algorithm choice     (10x - 1000x)
2. Data structure       (2x - 10x)
3. Allocation reduction (2x - 5x)
4. Cache optimization   (1.5x - 3x)
5. SIMD/Parallelism     (2x - 8x)

Common Techniques

TechniqueWhenHow
Pre-allocationKnown sizeVec::with_capacity(n)
Avoid cloningHot pathsUse references or Cow<T>
Batch operationsMany small opsCollect then process
SmallVecUsually smallsmallvec::SmallVec<[T; N]>
Inline buffersFixed-size dataArrays over Vec

Common Mistakes

MistakeWhy WrongBetter
Optimize without profilingWrong targetProfile first
Benchmark in debug modeMeaninglessAlways --release
Use LinkedListCache unfriendlyVec or VecDeque
Hidden .clone()Unnecessary allocsUse references
Premature optimizationWasted effortMake it work first

Anti-Patterns

Anti-PatternWhy BadBetter
Clone to avoid lifetimesPerformance costProper ownership
Box everythingIndirection costStack when possible
HashMap for small setsOverheadVec with linear search
String concat in loopO(n^2)String::with_capacity or format!

Related Skills

WhenSee
Reducing clonesm01-ownership
Concurrency optionsm07-concurrency
Smart pointer choicem02-resource
Domain requirementsdomain-*

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