Continuous Optimization Playbook (Senior / Staff)

A pragmatic lifecycle for sustaining performance excellence.

1. Hypothesis Intake

Source Example Action
SLO Alert p95 latency ↑ 20% Trace sample + hotspot tag filter
Bench Diff time/op +6% Reproduce locally with -count=15
Profile Drift New hotspot >4% flat Code archeology + changelog review
Cost Spike CPU hours +12% Check allocator churn & GC cycles

2. Triage Matrix

Impact vs Effort grid:

  • High Impact / Low Effort → immediate PR
  • High Impact / High Effort → design review
  • Low Impact / Low Effort → batch fixes
  • Low Impact / High Effort → defer/backlog

3. Investigation Toolkit

Signal Primary Tool Escalate To
Latency Tempo traces Trace + profile correlation
CPU pprof top/diff Block/Mutex profile
Memory alloc_space diff Escape analysis (-gcflags -m)
Alloc churn alloc_objects Object pooling experiment
Goroutine leak goroutine profile blocking ops instrumentation

4. Root Cause Patterns

Pattern Signature Strategy
Algorithmic O(n^2) Hotspot grows super-linearly with input Redesign data structure
Excess allocations High alloc_space without flat CPU Reuse buffers / sync.Pool
Lock contention Mutex profile spikes Shard lock / reduce critical section
I/O Bound Low CPU, high wall latency Parallelize / pipeline
GC Pressure Frequent short GC cycles Reduce transient allocations

5. Remediation Workflow

  1. Profile baseline (commit A)
  2. Implement change (branch)
  3. Run ci_profiles locally + AI analyzer
  4. Confirm improvements > regressions
  5. Add micro-bench if gap previously unmeasured
  6. Merge behind feature flag if risky

6. Definition of Done (Performance PR)

  • Baseline vs optimized profiles archived
  • Benchstat delta ≤ +2% for unrelated benchmarks
  • No new hotspots >5% without justification
  • Gates pass (or waiver documented)
  • Follow-up monitoring dashboard updated

7. Weekly Rituals

Ritual Outcome
Hotspot Review Rotate top 5 persistent CPU offenders
Allocation Audit Focus on top alloc_space regressions
Benchmark Trend Scan Detect slow drifts early
Gate Failure Postmortems Improve thresholds / detection logic

8. Backlog Taxonomy

Category Examples KPI
Preventive Pre-warm caches, pooling Reduced p95 latency
Corrective Remove quadratic join Flat% decrease
Hygiene Update benchmarks Coverage % of critical paths
Strategic Async pipeline redesign Throughput gain

9. Metrics & KPIs

KPI Target
Net Performance Win Rate >70% PRs with positive rating
Mean Time to Detect Regression <1 day
Hotspot Churn Rate <15% weekly
Benchmark Coverage (critical funcs) >80%

10. Escalation Criteria

Escalate to arch review if:

  • Any single regression >15% persists 3 PRs
  • Overall rating <4 twice in a sprint
  • Hotspot churn >25% (instability signal)

11. Tooling Enhancements Queue

Rank Idea Leverage
1 Span-linked profile URLs Pyroscope + Tempo
2 Selective dynamic profiling Agent API
3 Historical baseline server Object store + diff API
4 SARIF export GitHub code scanning UI
5 Performance budget dashboard Grafana JSON datasource

Treat performance as a product: observable state, feedback loops, user centric outcomes, and continuous iteration. This playbook institutionalizes that mindset.

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