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CRDT Chaos Visualizer 🐵🤖

An interactive visualization tool for exploring Conflict-free Replicated Data Types (CRDTs) and their behavior under chaotic network conditions.

Live Demo

Visit the live demo at: https://YOUR-USERNAME.github.io/crdt/

Features

  • G-Counter CRDT Implementation: A grow-only counter that demonstrates basic CRDT principles
  • Chaos Controls 🐵:
    • Drop Rate: Simulate packet loss
    • Delays: Add network latency
    • Duplicates: Test idempotency
    • Network Partitions: Isolate nodes completely
  • Auto-Increment 🤖: Automated testing with configurable intervals
  • Real-time Convergence Graph: Visualize how nodes converge over time
  • Interactive Nodes: Three independent nodes (A, B, C) that sync their state

What are CRDTs?

CRDTs are data structures that can be replicated across multiple nodes in a distributed system, where each replica can be updated independently and concurrently without coordination. Despite network failures, delays, or partitions, CRDTs guarantee eventual consistency - all nodes will converge to the same state once they can communicate.

How to Use

  1. Manual Testing: Click "Increment" on any node to increase its counter
  2. Automated Testing: Use "Start Auto-Increment" to make nodes increment automatically
  3. Add Chaos: Adjust the chaos controls to simulate real network conditions
  4. Create Partitions: Isolate nodes to simulate network splits
  5. Watch Convergence: The graph shows how nodes diverge and reconverge

Experiments to Try

  1. Perfect Network: Start with no chaos - watch perfect synchronization
  2. Packet Loss: Set 50% drop rate - see how nodes handle missing updates
  3. Network Partition: Isolate Node A, let others continue, then reconnect
  4. Chaos Storm: Max out all chaos controls and watch CRDTs maintain consistency
  5. Partition Healing: Create divergence, then remove all chaos to see convergence

Technical Details

This visualizer implements a G-Counter (Grow-only Counter) CRDT using:

  • State-based replication (nodes share their full state)
  • Vector clock-like structure (each node tracks its own count)
  • Merge operation using max() for convergence

The implementation demonstrates key CRDT properties:

  • Commutativity: Order of merges doesn't matter
  • Associativity: Grouping of merges doesn't matter
  • Idempotency: Merging the same state multiple times is safe

Local Development

Simply open chaos-counter.html in a web browser. No build process or dependencies required!

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