Learn system design with Nodivex
Nodivex turns system design into a place to explore trade-offs. As you move from brief to diagram to simulation, you feel what each decision costs and what it unlocks. Build real judgment, not just theory.
System design simulation
Run a system design simulation on your diagram and get immediate, concrete signals.
- Put your system under pressure and find bottlenecks
- End-to-end latency and utilization for each actor path
- Real-time lint warnings across performance, reliability, security, and operability
- Pressure points explained with garnular metrics
Everything on this page maps to real Nodivex features.
This is a detailed list of what the Nodivex app and engine already do, plus the learning benefits each feature enables.
Start from real requirements, not a blank canvas.
The kata brief and attempt flow keep practice structured and grounded.
- Full kata payloads: Business context, goals, constraints, trade-offs, and learning outcomes help you begin with real requirements.
- Actors with acceptance criteria: Latency targets and role bindings tie your design to the user experience you are trying to protect.
- Starting topology + hints: When provided, you can launch from a realistic baseline instead of drawing everything from scratch.
- Attempt flow: Brief -> canvas -> review keeps each practice session focused and repeatable.
Build the architecture, not just the story.
Concrete diagramming tools make system design decisions visible and testable.
- Component catalogue: Typed components from the engine keep your diagrams consistent and grounded.
- System boundaries + traffic multipliers: Show flow, fan-out, and system ownership directly on the canvas.
- Role bindings panel: Bind kata roles to nodes so actor paths map to the correct services.
- Undo/redo + snap grid: Iterate fast while keeping your layout readable.
- Auto-save + graph YAML export: Drafts persist locally and you can export the starting topology when needed.
System design simulation that shows pressure.
Deterministic outputs make trade-offs visible without guesswork.
- Deterministic simulate endpoint: Identical inputs return identical results so you can compare iterations cleanly.
- Bottlenecks + capacity signals: Simulation returns bottleneck nodes, estimated path capacity, node flow health, and component metrics.
- Actor observations: End-to-end latency percentiles, throughput, utilization, and workload reveal what each actor experiences.
- Architecture lint: Severity-tagged findings across availability, performance, operability, security, and maintainability.
Replay story chapters and review readiness.
Stress your design over time and review decisions with concrete signals.
- Playback simulation: Story segments return frames and results so you can see demand shifts over time.
- Chapter acceptance criteria: Each story chapter reports pass/fail against defined criteria such as budgets.
- Cost breakdowns: Pricing configuration provides cost totals and per-component spend signals.
- Production readiness review: Verdicts, summaries, risks, blockers, strengths, recommendations, and trade-offs stay grounded in simulation outputs.
System design matters. The path is hard.
Engineers need to learn system design to make trade-offs, communicate decisions, and grow. The hard part is getting feedback that feels real.
System design shows up everywhere.
- Trade-offs are the job: Latency, reliability, cost, and operability collide in every system design decision.
- Decisions ripple downstream: Early architecture choices shape scaling paths, team ownership, and delivery speed.
- Communication matters: Engineers are expected to defend design choices with clear, structured reasoning.
- Career growth depends on it: Interviews and senior roles demand system-level thinking, not just feature delivery.
Feedback is rare and messy.
- Most practice is abstract: Slide decks and whiteboards rarely show what actually breaks under load.
- Feedback is inconsistent: You often get opinions instead of repeatable signals from the system itself.
- Real experiments are expensive: Running production-scale tests is risky, slow, and hard to replicate.
- Trade-offs stay invisible: Without metrics, it is hard to see how a choice shifts bottlenecks or cost.
This is how the system design simulation closes the gaps.
Each pain point in learning system design has a direct response in the product workflow.
A repeatable loop for learning system design
Run the same loop as many times as you need. Each run exposes new trade-offs and new ideas to test.
Start with business context, goals, constraints, and actors that frame the system you need to design.
Use the component catalogue, system boundaries, and role bindings to build the diagram.
Simulate the diagram to surface bottlenecks, latency paths, lint findings, and capacity limits.
Playback story chapters, inspect acceptance criteria, and read the production readiness review.
Pick a brief, run the simulation, and let the system show you where the design bends or breaks.