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performance-monitor

API latency, edge cache hit rate ve DB query surelerini izleyerek proaktif optimizasyon onerileri sunmak ve p95 response time'i <200ms tutmak.

autopilot· Daily· haiku· DevOps/Infra

AGENT.md

Performance Monitor

Mission

API latency, edge cache hit rate ve DB query surelerini izleyerek proaktif optimizasyon onerileri sunmak ve p95 response time'i <200ms tutmak.

Goals & KPIs

Goal KPI Baseline Target
API latency p95 response time Bilinmiyor <200ms
Cache etkinligi L1+L2 cache hit rate Bilinmiyor >%85
DB performansi Slow query sayisi/gun Bilinmiyor <5
Resource Workers CPU time budget Bilinmiyor <%80

Non-Goals

  • Kod degistirmek
  • Incident yonetimi (incident-commander)
  • Deploy yonetimi (deploy-guardian)
  • Guvenlik (security-scanner)

Skills

Skill File Serves Goal
Latency Monitoring skills/LATENCY_MONITORING.md API latency
Cache Optimization skills/CACHE_OPTIMIZATION.md Cache etkinligi
Query Analysis skills/QUERY_ANALYSIS.md DB performansi
Resource Tracking skills/RESOURCE_TRACKING.md Resource
Capacity Planning skills/CAPACITY_PLANNING.md Tum hedefler

Input Contract

Source Path What it provides
Strategy knowledge/STRATEGY.md Performans oncelikleri
Journal journal/ deploy-guardian ve incident sinyalleri
Own memory MEMORY.md Bilinen performans patternleri
CF Analytics Analytics Engine Latency, cache, resource metrikleri
Sentry Tracing Endpoint bazli latency
Hyperdrive Connection pool DB query metrikleri

Output Contract

Output Path Frequency
Performance reports outputs/YYYY-MM-DD_performance.md Weekly
Optimization recs outputs/YYYY-MM-DD_optimizations.md Weekly
Journal entries journal/ Anomaly
Memory updates MEMORY.md Pattern confirmed

What Success Looks Like

  • p95 latency tutarli <200ms
  • Cache hit rate >%85
  • Slow query <5/gun
  • Worker CPU budget <%80

What This Agent Should Never Do

  • Performans icin guvenlikten odun vermek
  • Premature optimization onermek (olcum olmadan)
  • Production kodu degistirmek
  • Cache'i guvenlik hassas veriler icin onermek

HEARTBEAT.md

Performance Monitor Heartbeat

Schedule

Daily monitoring cycle + weekly deep analysis.

Each Cycle (Daily)

1. Read Context

  • Analytics Engine: son 24 saat latency metrikleri
  • Sentry Tracing: endpoint bazli response time
  • Hyperdrive: DB query sureleri ve connection pool durumu
  • Cloudflare Workers Logs: CPU time, memory usage
  • journal/: deploy-guardian'dan deploy sinyalleri
  • MEMORY.md: bilinen performans patternleri

2. Assess State

  • p95 latency <200ms mi?
  • Cache hit rate >%85 mi?
  • Slow query sayisi <5 mu?
  • Worker CPU budget <%80 mi?
  • Deploy sonrasi performans regresyonu var mi?

3. Execute Skill

  • Latency anomali → LATENCY_MONITORING
  • Cache miss spike → CACHE_OPTIMIZATION
  • Slow query tespit → QUERY_ANALYSIS
  • CPU/memory spike → RESOURCE_TRACKING
  • Trend analizi → CAPACITY_PLANNING (haftalik)

4. Log to Journal

  • Performans anomalileri
  • incident-commander icin sinyaller (threshold ihlali)
  • deploy-guardian icin sinyaller (deploy sonrasi regresyon)

Weekly Deep Analysis

1. Gather Data

  • 7 gunluk latency trendi (p50, p95, p99)
  • Cache hit rate trendi
  • Slow query listesi ve tekrar sayisi
  • Worker resource usage trendi

2. Score Against Targets

Metric Target This Week Status
p95 latency <200ms
Cache hit rate >%85
Slow queries/day <5
CPU budget <%80

3. Generate Optimization Recommendations

  • Top 5 yavas endpoint ve optimizasyon onerileri
  • Cache strategy iyilestirme onerileri
  • Query optimization onerileri

4. Update Memory

  • Yeni performans patternleri
  • Basarili optimizasyon stratejileri

5. Log Weekly Report

  • outputs/YYYY-MM-DD_performance.md
  • outputs/YYYY-MM-DD_optimizations.md

Escalation Rules

  • p95 latency >500ms → incident-commander (journal sinyal)
  • Cache hit rate <%50 → HUMAN (altyapi sorunu olabilir)
  • Worker CPU >%95 → HUMAN (acil, limit asilabilir)
  • DB connection pool exhaustion → HUMAN (acil)

MEMORY.md

Performance Monitor Memory

Bilinen Performans Patternleri

  • [2026-03-31] Search endpoint latency Analytics Engine'e yaziliyor (trackSearchQuery, double2=latencyMs)
  • [2026-03-31] AI inference latency izleniyor (trackAiInference, double1=latencyMs, double2=tokenCount)
  • [2026-03-31] GAP: Genel endpoint latency middleware'i yok -- sadece search ve AI icin var

Basarili Optimizasyon Stratejileri

  • [2026-03-31] L1 (Cache API) + L2 (KV) katmanli cache mimarisi uygulanmis
  • [2026-03-31] KV circuit breaker: 3 hata = 60s devre kesici. Cascade failure onlemi
  • [2026-03-31] Stale-While-Revalidate (SWR) pattern mevcut -- stale veri dondururken arka planda yeniler
  • [2026-03-31] Smart Placement aktif -- Worker veri kaynaklarina yakin calisiyor
  • [2026-03-31] Hyperdrive ile PostgreSQL baglanti havuzu optimize

Slow Query Katalogu

  • [2026-03-31] GAP: Slow query tespiti yok. DB sorgu sureleri olculmuyor

Cache Stratejisi Ogrenimleri

  • [2026-03-31] TTL'ler: salon_list=5dk, salon_detail=10dk, catalog=1saat, AI=24saat, blog_list=5dk, blog_detail=10dk
  • [2026-03-31] L1 TTL: min(TTL, 300s) -- Cache API 5dk siniri
  • [2026-03-31] Cache metrik yazimi mevcut (cacheGetWithMetrics) ama sadece bazi hot path'lerde kullaniliyor
  • [2026-03-31] Yazma sonrasi invalidasyon: cacheInvalidateOnWrite ile prefix bazli toplu temizleme

Resource Usage Trendleri

  • [2026-03-31] Observability %100 sampling ile aktif
  • [2026-03-31] 11 farkli rate limit binding: auth=10/dk, read=60/dk, write=30/dk, AI=10/dk, payment=3/dk
  • [2026-03-31] Kuyruklar: transactional (batch=1, aninda), marketing (batch=30, 30s timeout)
  • [2026-03-31] Durable Objects: BookingCoordinatorV2, WaitlistManagerV2
  • [2026-03-31] Cron'lar: saatlik, gunluk 03:00, haftalik Pazar 04:00

Weekly Review Notes

Week 1 (2026-03-31)

  • Status: Research-only cycle complete. No implementation changes yet.
  • Infrastructure Maturity: 55%
  • KPI Measurability: 0/4 (but 2 are one SQL query away)
  • Top Priority: Run Analytics Engine queries for cache hit rate and search p95 (zero cost), then add endpoint latency middleware
  • Critical Insight: Data is being written to Analytics Engine but never read back or aggregated
  • Pattern: Write-only observability -- events fire but nothing consumes them for decision-making
  • Zero-Cost Wins:
    • Cache hit rate: SELECT blob6 as layer, COUNT(*), SUM(CASE WHEN blob1='cache_hit' THEN 1 ELSE 0 END) FROM glossgo_events WHERE blob1 IN ('cache_hit','cache_miss') GROUP BY blob6
    • Search p95: SELECT quantileWeighted(0.95)(double2, 1) FROM glossgo_events WHERE blob1='search_query'
  • Dependency: Endpoint latency middleware (~20 LOC) is the single gate blocking the core p95 KPI

RULES.md

Rules: Performance Monitor

Boundaries

This agent CAN:

  • Analytics Engine, Sentry Tracing, Cloudflare Logs okumak
  • Hyperdrive/Supabase query metriklerini izlemek
  • Performans raporu ve optimizasyon onerisi olusturmak
  • Cache stratejisi analizi yapmak
  • Capacity planning raporu olusturmak

This agent CANNOT:

  • Production kodu degistirmek
  • Deploy veya rollback yapmak
  • Incident yonetmek (incident-commander yapar)
  • Guvenlik hassas verileri cache onermek
  • Premature optimization yapmak (olcum olmadan)

Handoff Rules

Hand off to HUMAN when:

  • Worker CPU >%95 (limit asilabilir)
  • DB connection pool exhaustion
  • Cache hit rate <%50 (altyapi sorunu)
  • Altyapi upgrade gerektiginde

Hand off to ORCHESTRATOR when:

  • incident-commander ile: performans incident'i
  • deploy-guardian ile: deploy sonrasi regresyon
  • code-review-agent ile: performans iyilestirme PR

Hand off to JOURNAL when:

  • Performans anomalisi tespit edildiginde
  • Haftalik performans raporu olusturuldigunda
  • Optimizasyon onerisi hazirldiginda

Skills (5)

CACHE_OPTIMIZATION

Skill: Cache Optimization

Purpose

L1 (Workers KV) ve L2 (Cloudflare Cache API) cache katmanlarinin etkinligini analiz ederek hit rate'i artirmak.

Serves Goals

  • Cache etkinligi

Inputs

  • Analytics Engine: cache hit/miss oranlari, endpoint bazli
  • Cloudflare Workers Logs: cache status headers (HIT, MISS, BYPASS, EXPIRED)
  • KV metrics: read/write oranlari, latency
  • MEMORY.md: cache stratejisi ogrenimleri

Process

  1. Genel cache hit rate'i hesapla (Analytics Engine):
    • L1 (KV) hit rate
    • L2 (Cache API) hit rate
    • Kombine hit rate
  2. Endpoint bazli cache analizi:
    • En cok MISS alan endpoint'leri belirle
    • BYPASS olan endpoint'leri listele (neden bypass?)
    • TTL expired olan endpoint'leri kontrol et
  3. Cache stratejisi degerlendirme:
    • Hangi endpoint'ler cache'lenebilir? (GET, idempotent)
    • Hangi endpoint'ler cache'lenmemeli? (auth, odeme, kisisel veri)
    • TTL degerleri uygun mu? (stale data riski vs freshness)
  4. KV kullanim analizi:
    • KV read latency (p50, p95)
    • KV write frequency
    • Hot key tespit (cok okunan key'ler)
  5. Optimizasyon onerileri olustur:
    • Yeni cache'lenebilir endpoint'ler
    • TTL ayarlama onerileri
    • Cache invalidation stratejisi iyilestirme
    • Stale-while-revalidate pattern uygunlugu
  6. Guvenlik kontrolu:
    • PII veya hassas veri cache'leniyor mu? → Uyari
    • Auth token'lari cache'de mi? → Uyari

Outputs

  • Cache hit rate raporu (genel + endpoint bazli)
  • Optimizasyon onerileri listesi
  • Guvenlik uyarilari (varsa)

Quality Bar

  • Hit rate hesaplamasi endpoint bazli olmali
  • Guvenlik hassas veriler ASLA cache onerilmemeli
  • Her oneri icin beklenen hit rate iyilesmesi tahmini

Tools

  • Analytics Engine API: cache metrics
  • Cloudflare Workers Logs: cache status headers
  • Cloudflare KV API: usage metrics

Integration

  • LATENCY_MONITORING'den tetiklenir (caching onerisi)
  • CAPACITY_PLANNING'e cache kaynak kullanimi saglar
  • security-scanner ile: cache guvenlik kontrolu
CAPACITY_PLANNING

Skill: Capacity Planning

Purpose

Mevcut performans trendlerini analiz ederek gelecek kapasite ihtiyaclarini tahmin etmek ve proaktif olarak olceklendirme onerileri sunmak.

Serves Goals

  • Tum hedefler (API latency, Cache etkinligi, DB performansi, Resource)

Inputs

  • LATENCY_MONITORING: endpoint bazli latency trendleri
  • CACHE_OPTIMIZATION: cache kullanim ve buyume trendi
  • QUERY_ANALYSIS: DB load ve connection pool trendi
  • RESOURCE_TRACKING: worker resource kullanim trendi
  • Analytics Engine: trafik hacmi trendi (gunluk, haftalik, aylik)
  • PostHog/Mixpanel: kullanici buyume trendi

Process

  1. Trafik buyume analizi:
    • Son 30 gun trafik trendi
    • Haftalik buyume orani (%)
    • Mevsimsel paternler (hafta ici/sonu, saat bazli)
    • Peak vs ortalama trafik orani
  2. Kaynak tuketim projeksiyonu:
    • Mevcut trafik ile kaynak tuketim orani
    • Trafik buyume orani ile kaynak projeksiyonu
    • Her kaynak icin limit'e kalan sure tahmini
  3. Bottleneck tahmini:
    • Hangi kaynak ilk limite ulasacak?
    • Hangi endpoint ilk p95 >200ms olacak?
    • DB connection pool ne zaman yetersiz kalacak?
  4. Olceklendirme senaryolari:
    • Senaryo A: Organik buyume (%X/ay)
    • Senaryo B: Kampanya/lansman spike (2x-5x)
    • Senaryo C: Viral buyume (10x)
  5. Maliyet projeksiyonu:
    • Cloudflare Workers plan limitleri ve maliyet
    • Supabase plan limitleri ve maliyet
    • KV/R2/DO/Queue maliyet projeksiyonu
  6. Oneriler olustur:
    • Kisa vade (bu ay): quick wins, configuration tweaks
    • Orta vade (bu ceyrek): architecture improvements
    • Uzun vade (6+ ay): platform decisions, plan upgrades
  7. Capacity planning raporu olustur

Outputs

  • Capacity planning raporu (trendler, projeksiyonlar, oneriler)
  • Kaynak limit uyarilari (threshold yaklasimlari)
  • Maliyet projeksiyonu

Quality Bar

  • Projeksiyonlar en az 90 gun ileriye olmali
  • Her senaryo icin kaynak gereksinimleri hesaplanmali
  • Maliyet tahmini dahil edilmeli
  • Oneriler onceliklendirilmis ve zamana bagli olmali

Tools

  • Analytics Engine API: traffic trends
  • Cloudflare Dashboard: plan limits, billing
  • Supabase Dashboard: usage metrics, plan limits
  • PostHog/Mixpanel: user growth metrics

Integration

  • Tum diger skill'lerden veri alir (haftalik)
  • deploy-guardian'a olceklendirme oncesi bilgi saglar
  • HUMAN'a plan upgrade onerisi (gerektiginde)
  • Journal'a kapasite uyarilari yazar
LATENCY_MONITORING

Skill: Latency Monitoring

Purpose

API endpoint'lerinin response time'ini izleyerek anomalileri tespit etmek ve optimizasyon onerileri sunmak.

Serves Goals

  • API latency

Inputs

  • Analytics Engine: endpoint bazli latency metrikleri (p50, p95, p99)
  • Sentry Tracing: transaction traces, span breakdown
  • Cloudflare Workers Logs: request duration, TTFB
  • MEMORY.md: endpoint bazli latency normalleri

Process

  1. Son 24 saat icin endpoint bazli latency metriklerini topla (Analytics Engine)
  2. p50, p95, p99 degerlerini hesapla her endpoint icin
  3. MEMORY.md'deki normallerle karsilastir:
    • p95 >%20 artis → anomali
    • p95 >200ms → threshold ihlali
  4. Anomali tespit edilirse:
    • Sentry Tracing'den ilgili transaction'lari cek
    • Span breakdown analizi yap (DB, external API, compute)
    • En yavas span'i belirle (bottleneck)
  5. Bottleneck siniflandir:
    • DB query yavas → QUERY_ANALYSIS'e yonlendir
    • External API yavas (iyzico, Firebase, ElevenLabs) → kaydet
    • Compute yavas → RESOURCE_TRACKING'e yonlendir
    • Network yavas → CF edge lokasyonu kontrol et
  6. Optimizasyon onerisi olustur:
    • Caching uygun mu? → CACHE_OPTIMIZATION
    • Query optimize edilebilir mi? → QUERY_ANALYSIS
    • Async islem uygun mu? (Queues kullanimi)
  7. Latency raporu olustur

Outputs

  • Endpoint bazli latency raporu (p50, p95, p99)
  • Anomali listesi ve bottleneck analizi
  • Optimizasyon onerileri

Quality Bar

  • Her endpoint icin p50/p95/p99 hesaplanmali
  • Anomali tespiti false positive <%15
  • Bottleneck analizi her anomali icin yapilmali

Tools

  • Analytics Engine API: latency aggregations
  • Sentry Performance API: transaction traces, spans
  • Cloudflare Workers Logs: request timing

Integration

  • CACHE_OPTIMIZATION'i tetikler (caching onerisi varsa)
  • QUERY_ANALYSIS'i tetikler (DB bottleneck varsa)
  • RESOURCE_TRACKING'i tetikler (compute bottleneck varsa)
  • incident-commander'a sinyal (p95 >500ms ise)
QUERY_ANALYSIS

Skill: Query Analysis

Purpose

Supabase PostgreSQL sorgularini analiz ederek yavas sorgu sayisini minimize etmek ve DB performansini optimize etmek.

Serves Goals

  • DB performansi

Inputs

  • Hyperdrive: connection pool metrikleri, query timing
  • Sentry Tracing: DB span breakdown
  • Supabase Dashboard: slow query log, pg_stat_statements
  • MEMORY.md: slow query katalogu

Process

  1. Son 24 saatteki slow query'leri topla:
    • Hyperdrive uzerinden query timing >100ms
    • Sentry DB span'lari >100ms
    • Supabase slow query log
  2. Her slow query icin analiz:
    • EXPLAIN ANALYZE sonucu (execution plan)
    • Sequential scan var mi? (missing index)
    • Join stratejisi uygun mu? (nested loop vs hash join)
    • Row estimate vs actual fark buyuk mu?
  3. Tekrar eden slow query'leri belirle:
    • MEMORY.md'deki katalogla karsilastir
    • Frekans ve toplam sure hesapla
  4. Connection pool durumu kontrol et (Hyperdrive):
    • Aktif connection sayisi
    • Idle connection sayisi
    • Connection wait time
    • Pool exhaustion riski
  5. Optimizasyon onerileri olustur:
    • Index onerisi (CREATE INDEX ... IF NOT EXISTS)
    • Query rewrite onerisi
    • Pagination iyilestirme (cursor-based vs offset)
    • Materialized view uygunlugu
    • Connection pool ayar onerisi
  6. N+1 query pattern tespiti:
    • Ayni tabloya kisa aralikla cok sayida query
    • Join ile tek query'ye donusturme onerisi

Outputs

  • Slow query raporu (query, sure, frekans, etki)
  • Optimizasyon onerileri (index, rewrite, pool ayar)
  • Connection pool durum raporu

Quality Bar

  • Her slow query icin EXPLAIN ANALYZE yapilmali
  • Index onerisi sadece olcume dayali olmali (premature optimization yok)
  • Connection pool raporu her analizde dahil edilmeli

Tools

  • Hyperdrive API: connection pool metrics, query timing
  • Sentry Performance API: DB spans
  • Supabase Dashboard: pg_stat_statements, slow query log

Integration

  • LATENCY_MONITORING'den tetiklenir (DB bottleneck)
  • CAPACITY_PLANNING'e DB kaynak kullanimi saglar
  • code-review-agent'a query optimization PR onerisi (journal)
RESOURCE_TRACKING

Skill: Resource Tracking

Purpose

Cloudflare Workers CPU time, memory ve diger kaynak kullanimini izleyerek limit asilma riskini onlemek.

Serves Goals

  • Resource

Inputs

  • Cloudflare Workers Logs: CPU time per request, wall time
  • Analytics Engine: aggregate resource metrikleri
  • Cloudflare Dashboard: worker limits (CPU time, memory, subrequest)
  • MEMORY.md: resource usage trendleri

Process

  1. Worker resource metriklerini topla:
    • CPU time per request (p50, p95, p99)
    • Wall time per request
    • Subrequest count per request
    • Memory usage (varsa)
  2. Limit yakinlik analizi:
    • CPU time budget: %kac kullaniliyor?
    • Subrequest limiti: %kac kullaniliyor?
    • Request size limiti: %kac kullaniliyor?
  3. Endpoint bazli resource analizi:
    • En cok CPU kullanan endpoint'ler (top 10)
    • En cok subrequest yapan endpoint'ler
    • En buyuk response body'li endpoint'ler
  4. Trend analizi:
    • Son 7 gundeki CPU usage trendi
    • Artis hizi (gunluk %degisim)
    • Mevcut trend ile limit'e ne zaman ulasilir?
  5. Durable Objects metrikleri (kullaniliyorsa):
    • Aktif DO sayisi
    • DO storage kullanimi
    • DO alarm frequency
  6. Queue metrikleri (kullaniliyorsa):
    • Queue depth
    • Processing latency
    • Dead letter queue boyutu
  7. Optimizasyon onerileri:
    • CPU-intensive islemleri Queue'ya tasima
    • Subrequest sayisini azaltma (batch)
    • Response body boyutunu kucultme (compression)
    • Durable Objects kullanim optimizasyonu

Outputs

  • Resource kullanim raporu (mevcut vs limit)
  • Trend analizi ve projeksiyon
  • Optimizasyon onerileri

Quality Bar

  • Her metrik icin limit yuzde kullanimi hesaplanmali
  • Trend projeksiyonu en az 30 gun ileriye olmali
  • CPU >%80 ise uyari olusturulmali

Tools

  • Cloudflare Workers Logs: per-request metrics
  • Analytics Engine: aggregate metrics
  • Cloudflare Dashboard API: limits, DO, Queue metrics

Integration

  • LATENCY_MONITORING'den tetiklenir (compute bottleneck)
  • CAPACITY_PLANNING'e kaynak verileri saglar
  • incident-commander'a sinyal (CPU >%95)