ColdFusion 2025 Performance Metrics Database
Real-world performance benchmarks across different workload types, hardware configurations, and JVM tuning scenarios. All tests conducted with identical application code to demonstrate optimization impact.
Testing Methodology
Load Testing Tool
Apache JMeter 5.6 with realistic user behavior patterns and think times
Monitoring
PMT (Performance Monitoring Toolset) + FusionReactor for comprehensive metrics
Test Duration
30-minute sustained load tests with 10-minute warmup period
Environment
Isolated dedicated servers, Ubuntu 22.04 LTS, PostgreSQL 15 database
Key Performance Insights
60-70%
Response Time Reduction
G1GC + Heap Tuning vs Default
2-3x
Throughput Improvement
Full Optimization Stack
85%
GC Pause Reduction
G1GC vs CMS
40-50%
CPU Utilization Drop
Caching + JVM Tuning
Detailed Performance Metrics
| Configuration | Workload | Hardware | Users | P50 (ms) | P95 (ms) | P99 (ms) | Req/sec | Error % | CPU % | Mem % | GC Pause (ms) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Baseline (Out-of-box CF 2025) 4 CPU, 8GB RAM Default: -Xms2g -Xmx2g, CMS GC | api | 4 CPU, 8GB RAM | 250 | 45 | 189 | 412 | 1,456 | 2.00% | 62% | 78% | 125 |
| Optimized (G1GC + Tuned Heap) 4 CPU, 8GB RAM Tuned: -Xms4g -Xmx4g, G1GC, -XX:MaxGCPauseMillis=200 | api | 4 CPU, 8GB RAM | 250 | 28 | 94 | 178 | 2,341 | 0.00% | 48% | 65% | 42 |
| Optimized + Connection Pooling 8 CPU, 16GB RAM Tuned: -Xms8g -Xmx8g, G1GC, optimized thread pools | api | 8 CPU, 16GB RAM | 500 | 22 | 82 | 145 | 4,687 | 0.00% | 52% | 62% | 38 |
| Baseline (No Caching) 4 CPU, 8GB RAM Default: -Xms2g -Xmx2g, CMS GC | page | 4 CPU, 8GB RAM | 200 | 385 | 1247 | 2156 | 487 | 8.00% | 71% | 82% | 245 |
| Template Cache + Query Cache 4 CPU, 8GB RAM Default: -Xms2g -Xmx2g, CMS GC | page | 4 CPU, 8GB RAM | 200 | 156 | 412 | 789 | 1,124 | 1.00% | 58% | 74% | 198 |
| Full Optimization (Cache + G1GC + Redis) 4 CPU, 8GB RAM Tuned: -Xms4g -Xmx4g, G1GC + Redis object cache | page | 4 CPU, 8GB RAM | 200 | 67 | 187 | 334 | 2,547 | 0.00% | 42% | 58% | 52 |
| Enterprise Stack (CDN + Load Balancer + Redis) 8 CPU, 16GB RAM Tuned: -Xms8g -Xmx8g, G1GC, optimized | page | 8 CPU, 16GB RAM | 500 | 28 | 156 | 312 | 2,847 | 0.00% | 34% | 62% | 45 |
| Baseline (Default Settings) 8 CPU, 16GB RAM Default: -Xms4g -Xmx4g, CMS GC | batch | 8 CPU, 16GB RAM | 50 | 2456 | 8934 | 15234 | 184 | 12.00% | 89% | 91% | 1247 |
| Optimized (Async Processing + G1GC) 8 CPU, 16GB RAM Tuned: -Xms8g -Xmx8g, G1GC, async threads | batch | 8 CPU, 16GB RAM | 50 | 987 | 3214 | 5678 | 456 | 1.00% | 72% | 68% | 156 |
| Container Deployment (Kubernetes) 16 CPU, 32GB RAM Container-optimized: -Xms16g -Xmx16g, G1GC | api | 16 CPU, 32GB RAM | 1000 | 18 | 67 | 123 | 8,934 | 0.00% | 58% | 61% | 34 |
| Baseline (Default) 4 CPU, 8GB RAM Default: -Xms2g -Xmx2g, CMS GC | mixed | 4 CPU, 8GB RAM | 300 | 278 | 1045 | 1876 | 734 | 6.00% | 76% | 84% | 287 |
| Convective Performance Framework Applied 4 CPU, 8GB RAM Optimized: -Xms4g -Xmx4g, G1GC, all optimizations | mixed | 4 CPU, 8GB RAM | 300 | 89 | 312 | 567 | 1,823 | 0.00% | 54% | 67% | 67 |
Configuration Comparisons
API Workload: Baseline vs Optimized (4 CPU, 8GB)
P95 Response Time
⚡ 50% faster
Throughput
⚡ 61% increase
GC Pause Time
⚡ 66% reduction
Optimizations Applied: G1GC, Heap tuning (-Xms4g -Xmx4g), Connection pool optimization, MaxGCPauseMillis=200
Page Rendering: No Cache vs Full Optimization (4 CPU, 8GB)
P50 Response Time
⚡ 83% faster
Throughput
⚡ 5.2x increase
Optimizations Applied: Template caching, Query caching, G1GC, Redis object cache, -Xms4g -Xmx4g
Hardware Scaling Analysis
API Workload Performance by Hardware Size
Medium (4 CPU, 8GB)
2,341 req/s @ 250 users
Large (8 CPU, 16GB)
4,687 req/s @ 500 users
X-Large (16 CPU, 32GB)
8,934 req/s @ 1,000 users
Efficiency: Near-linear scaling with proper JVM tuning. Doubling resources yields ~2x throughput when optimized.
Optimization Recommendations by Workload
🚀 API / REST Services
- Critical: G1GC with MaxGCPauseMillis=200
- Critical: Heap = 50-70% of RAM (-Xms = -Xmx)
- High: Connection pool sizing (2x CPU cores)
- High: Query result caching for frequent queries
- Medium: Tomcat connector thread tuning
- Expected: 60-70% response time improvement
📄 Page Rendering
- Critical: Template caching enabled
- Critical: Query caching with appropriate timespan
- High: Redis/Memcached for object caching
- High: G1GC for consistent response times
- Medium: CDN for static assets
- Expected: 70-85% response time improvement
⚙️ Batch Processing
- Critical: Async processing with thread pools
- Critical: Large heap (16GB+) with G1GC
- High: Batch size optimization (1000-5000 records)
- High: Database connection pooling
- Medium: Parallel processing where possible
- Expected: 50-65% processing time reduction
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