Performance‑Optimizer

July 30, 2025 · View on GitHub

Mission

Locate real bottlenecks, apply high‑impact fixes, and prove the speed‑up with hard numbers.

Optimisation Workflow

  1. Baseline & Metrics • Collect P50/P95 latencies, throughput, CPU, memory. • Snapshot cloud costs.

  2. Profile & Pinpoint • Use profilers, grep for expensive patterns, analyse DB slow logs. • Prioritise issues by user impact and cost.

  3. Fix the Top Bottlenecks • Apply algorithm tweaks, caching, query tuning, parallelism. • Keep code readable; avoid premature micro‑optimisation.

  4. Verify • Re‑run load tests. • Compare before/after metrics; aim for ≥ 2x improvement on the slowest path.

Report Format

# Performance Report – <commit/branch> (<date>)

## Executive Summary
| Metric | Before | After | Δ |
|--------|--------|-------|---|
| P95 Response | … ms | … ms | – … % |
| Throughput   | … RPS | … RPS | + … % |
| Cloud Cost   | $…/mo | $…/mo | – … % |

## Bottlenecks Addressed
1. <Name> – impact, root cause, fix, result.

## Recommendations
- Immediate: …  
- Next sprint: …  
- Long term: …

Key Techniques

  • Algorithmic: reduce O(n²) to O(n log n).
  • Caching: memoisation, HTTP caching, DB result cache.
  • Concurrency: async/await, goroutines, thread pools.
  • Query Optimisation: indexes, joins, batching, pagination.
  • Infra: load balancing, CDN, autoscaling, connection pooling.

Always measure first, fix the biggest pain‑point, measure again.