🧰 Engineering Essentials · Code Quality

Profile before optimizing (Scala)

Measure hot paths with real data shapes, optimize measured bottlenecks, verify wins.

intermediate~35 minAll Software Engineers

Steps

1Define target metric: p95 latency? memory ceiling? throughput?
2Build benchmark using PRODUCTION-shaped data volumes
3Profile CPU + memory + allocations; identify top three offenders
4Optimize one offender at a time; re-measure after each change
5Check second-order effects: cache misses, GC pressure, lock contention
6Record before/after numbers in the PR for future archaeologists
7Profile with async-profiler; watch GC pressure from allocations
8Enforce scalafmt/scalafix gates; mind closure capture costs

Common Pitfalls

▲Optimizing intuition while profile shows I/O waits
▲Micro-benchmarks on toy data lying about production

Commands

Install with skills CLI

$ npx skills add aniruddhaadak80/skills --skill code-quality-performance-profiling-scala

Install globally

$ npx skills add aniruddhaadak80/skills --skill code-quality-performance-profiling-scala -g

Tags

#performance#profiling#engineering-essentials#code-quality

Install this skill

$ npx skills add aniruddhaadak80/skills --skill code-quality-performance-profiling-scala -a opencode

SKILL.md

# Profile before optimizing (Scala)

> Measure hot paths with real data shapes, optimize measured bottlenecks, verify wins.

## When to Use This Skill

Measure hot paths with real data shapes, optimize measured bottlenecks, verify wins.

## Steps

1. Define target metric: p95 latency? memory ceiling? throughput?
2. Build benchmark using PRODUCTION-shaped data volumes
3. Profile CPU + memory + allocations; identify top three offenders
4. Optimize one offender at a time; re-measure after each change
5. Check second-order effects: cache misses, GC pressure, lock contention
6. Record before/after numbers in the PR for future archaeologists
7. Profile with async-profiler; watch GC pressure from allocations
8. Enforce scalafmt/scalafix gates; mind closure capture costs

## Common Pitfalls

- Optimizing intuition while profile shows I/O waits
- Micro-benchmarks on toy data lying about production

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