System Debugging & Recovery Simulator


Difficulty	Advanced
Team Size	3-5 people
Time	~30-40 hours
Demo-ready by	Step 5
Prerequisites	Node.js, Docker, Linux administration, debugging skills
Built by	SadServers, KodeKloud, Instruqt, KataCoda

Skills you'll earn: Scenario generation, Docker environment provisioning, scoring systems, log analysis, systematic debugging methodology

Start by presenting a broken system and asking the user to find the problem. End with a platform that generates debugging scenarios, scores diagnosis speed, and teaches systematic troubleshooting.

Step 1: Break a system on purpose (~2-3 hours)

You learn debugging by debugging. You need something broken.

Spin up a simple web app stack with Docker Compose: frontend, backend, database
Introduce a single fault: wrong database password in the backend config
Present the user with the running (broken) system and symptoms: "the app shows a 500 error"
The user must find and fix the fault

You now have: A debugging challenge.

Step 2: Multiple fault types (~3-4 hours)

One kind of bug is not enough training.

Build a library of faults:
- Config errors (wrong ports, bad credentials, missing env vars)
- Resource exhaustion (out of memory, disk full, connection pool exhausted)
- Network issues (firewall blocking a port, DNS misconfigured)
- Application bugs (infinite loop, unhandled exception, deadlock)
Randomly inject one or more faults into the stack

You now have: A fault library.

Step 3: Guided diagnosis tools (~3-4 hours)

Dumping someone into a broken system with no tools is frustrating.

Provide a web terminal (use xterm.js) connected to the environment
Pre-install common tools: curl, dig, netstat, top, docker logs, psql/mongo
Show a hint system: after N minutes, offer a hint pointing toward the right area
Track which commands the user runs

You now have: An interactive debugging environment.

Step 4: Scoring and feedback (~3-4 hours)

Fixing the problem is good. Fixing it fast and systematically is better.

Start a timer when the scenario begins
Score based on: time to resolution, number of commands used, whether hints were needed
After resolution, show an ideal debugging path: "here's how an expert would find this in 3 steps"
Track scores across scenarios to show improvement

You now have: Gamified debugging practice.

Step 5: Multi-service scenarios (~3-4 hours)

Real systems have more than three containers.

Build complex stacks: load balancer → multiple app servers → cache → database → queue → workers
Inject faults that cascade: a slow database causes queue backup, which causes worker OOM
The user must trace the root cause through the dependency chain
Provide a service map showing the architecture

You now have: Distributed system debugging.

Step 6: Scenario editor (~3-4 hours)

Let instructors create custom scenarios: define the stack, pick the faults, write hints
Scenario definition in YAML: services, fault injection points, expected resolution
Share scenarios via URL or export as a file

Step 7: Multiplayer and classroom mode (~3-4 hours)

Race mode: multiple users debug the same scenario, fastest wins
Instructor dashboard: see all students' progress, where they're stuck
Post-scenario discussion: compare approaches

Useful Resources

Where to go from here

Auto-generated scenarios (randomly compose stacks and faults)
Kubernetes scenarios (broken deployments, crashlooping pods, network policies)
Time-bomb faults (system works for 5 minutes, then breaks)
Integration with your observability stack (debug using Grafana dashboards and logs)
Certification mode (pass N scenarios to earn a badge)

System Debugging & Recovery Simulator ​

Step 1: Break a system on purpose (~2-3 hours) ​

Step 2: Multiple fault types (~3-4 hours) ​

Step 3: Guided diagnosis tools (~3-4 hours) ​

Step 4: Scoring and feedback (~3-4 hours) ​

Step 5: Multi-service scenarios (~3-4 hours) ​

Step 6: Scenario editor (~3-4 hours) ​

Step 7: Multiplayer and classroom mode (~3-4 hours) ​

Useful Resources ​

Where to go from here ​