ColdFusion 2025 Performance Benchmarks
Real-world performance metrics and JDK comparisons
Test Environment
Hardware
- CPU: AMD EPYC 7763 (16 cores, 32 threads)
- RAM: 64GB DDR4-3200
- Storage: NVMe SSD (7000MB/s read)
- Network: 10Gbps
Software
- OS: Red Hat Enterprise Linux 9.5
- ColdFusion: 2025 (Build 2025.0.0.330518)
- JDK: Eclipse Temurin 17.0.13 & 11.0.25
- Web Server: Apache 2.4.62
- Database: PostgreSQL 16.6
Configuration
- Heap: 24GB (-Xms24g -Xmx24g)
- GC: G1GC (default for JDK 17)
- Threads: 250 max request threads
- Connections: 100 per datasource
JDK 17 vs JDK 11 Performance
ColdFusion 2025 with JDK 17 shows significant performance improvements over JDK 11 across all metrics.
| Metric | JDK 11 | JDK 17 | Improvement |
|---|---|---|---|
| Average Response Time | 142ms | 98ms | 31% faster |
| P95 Response Time | 385ms | 243ms | 37% faster |
| P99 Response Time | 892ms | 521ms | 42% faster |
| Throughput (req/sec) | 3,420 | 4,680 | 37% increase |
| GC Pause Time (avg) | 124ms | 67ms | 46% reduction |
| Memory Footprint | 18.2GB | 16.8GB | 8% reduction |
Database Operation Benchmarks
Performance metrics for common database operations using PostgreSQL datasource with connection pooling.
| Operation Type | Operations/sec | Avg Latency | P99 Latency |
|---|---|---|---|
| Simple SELECT (indexed) | 28,400 | 3.5ms | 12ms |
| Complex JOIN (3 tables) | 8,200 | 12.2ms | 38ms |
| INSERT with transaction | 12,600 | 7.9ms | 24ms |
| UPDATE with WHERE | 11,800 | 8.5ms | 26ms |
| Stored Procedure Call | 15,300 | 6.5ms | 21ms |
| Paginated Query (50 rows) | 9,400 | 10.6ms | 32ms |
Application-Level Performance
Real-world application scenarios with typical ColdFusion operations.
| Scenario | Throughput | Response Time | Notes |
|---|---|---|---|
| Simple CRUD Page | 5,800 req/sec | 45ms avg | Database + template rendering |
| Complex Dashboard | 1,200 req/sec | 185ms avg | 10 queries, charts, aggregation |
| REST API Endpoint | 8,400 req/sec | 28ms avg | JSON serialization |
| File Upload (10MB) | 320 req/sec | 312ms avg | Multipart form processing |
| PDF Generation | 85 req/sec | 1.18s avg | 5-page report with images |
| Email Sending | 420 req/sec | 238ms avg | SMTP with 2 attachments |
Caching Performance Impact
Performance gains from implementing various caching strategies.
| Cache Type | Without Cache | With Cache | Improvement |
|---|---|---|---|
| Template Cache | 142ms avg | 95ms avg | 33% faster |
| Query Cache (Redis) | 45ms avg | 8ms avg | 82% faster |
| Object Cache (Redis) | 120ms avg | 12ms avg | 90% faster |
| Page Cache (Full Page) | 185ms avg | 5ms avg | 97% faster |
Concurrency & Scalability
Performance under increasing concurrent load.
| Concurrent Users | Throughput | Response Time | Error Rate |
|---|---|---|---|
| 100 | 4,680 req/sec | 21ms avg | 0% |
| 500 | 5,200 req/sec | 96ms avg | 0% |
| 1,000 | 5,450 req/sec | 183ms avg | 0.01% |
| 2,000 | 5,580 req/sec | 358ms avg | 0.08% |
| 5,000 | 5,420 req/sec | 922ms avg | 1.2% |
Key Findings
JDK 17 Performance
JDK 17 delivers 31-42% performance improvements over JDK 11 across all metrics. GC pause times reduced by 46%, significantly improving user experience.
Database Optimization
Properly indexed queries achieve 28,400 operations/sec with 3.5ms average latency. Connection pooling is critical for maintaining performance under load.
Caching Impact
Redis-backed query and object caching provides 82-90% response time improvements. Full-page caching delivers 97% faster responses for cacheable content.
Scalability
ColdFusion 2025 maintains consistent throughput up to 2,000 concurrent users with minimal error rates. Proper JVM sizing and thread pool tuning are essential.
Benchmark Methodology
- Load Testing Tool: Apache JMeter 5.6 with distributed testing
- Test Duration: 30 minutes per scenario after 5-minute warmup
- Monitoring: FusionReactor for JVM metrics, PostgreSQL pg_stat_statements for database
- Multiple Runs: Each test executed 5 times, median values reported
- Isolation: Dedicated test environment with no other workloads
- Network: Local network with <1ms latency to eliminate network variables
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