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See ColdFusion Observability in Action

This is a live ColdFusion app running on Kubernetes with full observability. Trigger real scenarios below and watch real dashboards respond in real time. Every metric, log, and trace is real.

5
Live Dashboards
7
Test Scenarios
2
CF Pods Running
15s
Dashboard Refresh

How to Use This Demo

Three steps. No setup. Takes about two minutes.

1

Open Grafana & Log In

Click the button below and log in with these credentials. Click the values to copy them.

Username:
Password:
Open Grafana →
2

Run a Test Scenario

Scroll down and click any test button. Each one triggers a real event on the live CF application — CPU spikes, errors, traffic bursts, and more.

3

Watch the Dashboards

Switch to Grafana and watch the metrics, logs, and traces update within 15–30 seconds. This is exactly what your CF app monitoring would look like.

Dashboards

Platform Overview Health & performance
JVM Deep Dive Heap, GC & threads
Live Logs Real-time streaming
Request Tracing End-to-end latency

Auto Demo Mode

Generates realistic traffic patterns every 10 seconds. Automatically stops after 20 actions.

Test Scenarios

Click any test to run it against the live app. Results appear in the dashboards within seconds.

Performance & Stress

CPU Burn

Maxes out CPU for ~15 seconds to simulate heavy computational load.

What you'll see CPU spike to 100%, response time increase, performance alerts
View in JVM Deep Dive →
Metric: jvm_cpu_recent_utilization_ratio

Memory Allocation

Allocates 2M+ array elements to trigger memory pressure and garbage collection.

What you'll see Heap usage spike, GC activity, memory pressure warnings
View in JVM Deep Dive →
Metric: jvm_memory_used_bytes

Thread Stress

Spawns 200 sleeping threads plus 1 CPU-intensive thread.

What you'll see Thread count spike (200+), pool saturation, CPU + thread pressure
View in JVM Deep Dive →
Metric: jvm_thread_count

Traffic Burst

Generates 100 concurrent requests to simulate a traffic spike.

What you'll see Request rate spike, throughput increase, load distribution across pods
View in Platform Overview →
Metric: http_server_request_duration_seconds_count
Errors, Latency & Logging

Slow Request

Executes a request that takes 5 seconds to complete.

What you'll see P95/P99 latency spike, slow request trace in distributed tracing
View in Request Tracing →
Metric: http_server_request_duration_seconds

Generate Error

Throws an exception to test error tracking and log aggregation.

What you'll see 500 error in metrics, error log with stack trace, error rate increase
View in Live Logs →
Metric: http_response_status_code=500

Log Generation

Generates INFO, WARN, and ERROR logs to demonstrate log aggregation.

What you'll see Logs appear in Loki within seconds, filterable by severity
View in Live Logs →
Logs: INFO, WARN, ERROR