# Performance Monitoring Setup ## Purpose Guide proactive performance monitoring configuration (not incident-driven alerting). ## Key Performance Indicators (KPIs) ### Tier 1 - Critical Metrics | Metric | Target | Warning | Critical | Tool | |--------|--------|---------|----------|------| | Cache Hit Ratio | > 80% | < 70% | < 60% | `monitor_metrics` | | P95 Response Time | < 300ms | > 500ms | > 1000ms | `monitor_metrics` | | Error Rate (5xx) | < 0.1% | > 1% | > 5% | `monitor_metrics` | | Availability | > 99.9% | < 99.5% | < 99% | `monitor_metrics` | ### Tier 2 - Optimization Metrics | Metric | Target | Investigation Trigger | Tool | |--------|--------|----------------------|------| | Cache Miss Rate | < 20% | > 30% | `inspect_cache` | | Average Cache TTL | > 5 min | < 1 min | `inspect_cache` | | Filter Deny Rate | < 1% | > 5% | `trace_request` | | Rewrite Redirect Rate | < 5% | > 15% | `trace_request` | ### Tier 3 - Capacity Metrics | Metric | Target | Tool | |--------|--------|------| | Requests/Second (Throughput) | Baseline + 20% headroom | `monitor_metrics` | | Cache Size Growth Rate | < 10% per day | `inspect_cache` | | Log Volume Growth Rate | < 15% per day | `tail_logs` | ## Cloud Mode Monitoring (AEMaaCS) ### Container Metrics ```text # Docker-based host metrics collection (outside MCP) docker stats --no-stream # Dispatcher-specific MCP metrics monitor_metrics({"window_minutes":60,"breakdown_by":"status_code"}) tail_logs({"lines":200}) ``` ### Example External Metrics Pipeline ```yaml # Example scrape config for an external metrics system scrape_configs: - job_name: 'aem-dispatcher' static_configs: - targets: ['dispatcher:9113'] metrics_path: '/metrics' scrape_interval: 30s # Key metrics to track metric_relabel_configs: - source_labels: [__name__] regex: '(dispatcher_cache_hit_ratio|dispatcher_requests_total|dispatcher_request_duration_seconds)' action: keep ``` ### Alert Configuration (Cloud) ```yaml # Example alert rules groups: - name: dispatcher_performance interval: 1m rules: - alert: DispatcherCacheHitRatioLow expr: dispatcher_cache_hit_ratio < 0.70 for: 5m annotations: summary: "Cache hit ratio below 70% for 5 minutes" - alert: DispatcherP95LatencyHigh expr: histogram_quantile(0.95, dispatcher_request_duration_seconds) > 0.5 for: 5m annotations: summary: "P95 latency above 500ms for 5 minutes" ``` ## Dashboard Design ### Essential Dashboard Widgets 1. **Cache Efficiency Panel** - Cache hit ratio (line chart, 24h) - Cache miss rate by URL pattern (bar chart) - Cache size trend (line chart, 7d) 2. **Latency Panel** - P50, P95, P99 response time (multi-line chart, 24h) - Response time by content type (heatmap) - Slow request top 10 (table) 3. **Throughput Panel** - Requests/second (line chart, 24h) - Status code distribution (stacked area chart) - Error rate (line chart with threshold markers) 4. **Capacity Panel** - Resource utilization (CPU, memory, disk) - Worker/thread saturation (gauge) - Traffic forecast vs capacity (dual-axis line chart) ### Example Dashboard Definition ```json { "dashboard": { "title": "Dispatcher Performance", "panels": [ { "title": "Cache Hit Ratio", "targets": [ { "expr": "rate(dispatcher_cache_hits_total[5m]) / rate(dispatcher_requests_total[5m])" } ], "thresholds": [ { "value": 0.6, "color": "red" }, { "value": 0.7, "color": "yellow" }, { "value": 0.8, "color": "green" } ] }, { "title": "P95 Response Time", "targets": [ { "expr": "histogram_quantile(0.95, rate(dispatcher_request_duration_seconds_bucket[5m]))" } ] } ] } } ``` ## Trend Analysis & Reporting ### Daily Performance Report ```text Dispatcher Performance Summary - [Date] 1. Cache Efficiency - Hit Ratio: 78.5% (target: >80%, [WARN] below target) - Miss Rate: 21.5% - Top 10 Missed URLs: /api/*, /content/dynamic/* 2. Latency - P50: 125ms ([OK] within target) - P95: 480ms ([OK] within target) - P99: 1,200ms ([WARN] investigate) 3. Availability - Uptime: 99.95% ([OK] within target) - Error Rate: 0.08% ([OK] within target) 4. Capacity - Peak Throughput: 1,250 req/sec (85% of capacity) - Headroom: 15% ([WARN] plan for scaling) 5. Recommendations - Optimize caching for /api/* paths (potential +8% hit ratio) - Investigate P99 latency spikes during 14:00-16:00 UTC - Plan capacity increase within 30 days ``` ### Weekly Trend Analysis ```text Week of [Date Range] 1. Performance Trends (vs. previous week) - Cache Hit Ratio: 78.5% -> 82.1% ([OK] +3.6pp improvement) - P95 Latency: 480ms -> 420ms ([OK] -60ms improvement) - Throughput: +12% (traffic growth) 2. Optimization Impact - Static asset caching: +5% hit ratio - Cache TTL increase: -40ms latency 3. Capacity Forecast - Current growth rate: +2.5% weekly - Capacity exhaustion: ~12 weeks - Recommended action: Plan scaling for week of [Date] ``` ## Monitoring Setup Checklist ### Initial Setup - [ ] Identify a monitoring platform or metrics sink that fits the environment - [ ] Configure metric collection (MCP tools, log parsing, or custom exporters) - [ ] Define KPI targets and alert thresholds - [ ] Create performance dashboard with essential widgets - [ ] Set up alert routing (for example: email, chat, or incident-management tooling) - [ ] Document escalation procedures ### Cloud Mode Specific - [ ] Configure Docker stats collection - [ ] Set up container resource limits - [ ] Integrate with your cloud-native monitoring stack - [ ] Configure CDN performance metrics (if applicable) ### Ongoing Maintenance - [ ] Review dashboard weekly for trends - [ ] Update alert thresholds based on baseline shifts - [ ] Archive old metrics data per retention policy - [ ] Quarterly capacity planning review - [ ] Monthly performance optimization review ## MCP Tool Integration ### Automated Metrics Collection Pattern ```text # Poll every N seconds from your scheduler/automation runner: metrics = monitor_metrics({"window_minutes":1,"breakdown_by":"status_code"}) logs = tail_logs({"lines":100}) # Persist serialized results to your metrics sink/log store. # Compare current summary.cache_hit_ratio and latency percentiles # against your alert thresholds. ``` ### Daily Performance Check Pattern ```text daily = monitor_metrics({"window_minutes":1440,"breakdown_by":"status_code"}) # Evaluate thresholds: # - daily.metrics.summary.cache_hit_ratio # - daily.metrics.summary.error_rate # - daily.metrics.latency.p95_ms / p99_ms ``` ## References - Dispatcher caching docs: https://experienceleague.adobe.com/en/docs/experience-manager-dispatcher/using/configuring/dispatcher-configuration#caching-documents - Caching docs: https://experienceleague.adobe.com/en/docs/experience-manager-cloud-service/content/implementing/content-delivery/caching - Cloud Dispatcher overview: https://experienceleague.adobe.com/en/docs/experience-manager-cloud-service/content/implementing/content-delivery/disp-overview