Pathfinding-Visualizer — Interactive React Application

Algorithmic search meets modern web engineering: visualize BFS, DFS, Dijkstra, and A on a dynamic grid with real-time animations, metrics, and user interaction.*

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Live Demo
Source Code

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1) Executive Summary

On the surface: This is an interactive React single-page application that lets users set start/goal nodes, draw obstacles, and visually compare how different pathfinding algorithms explore a grid.

Under the hood: It’s an SDE-quality project that demonstrates:

• Algorithm engineering → BFS/DFS/Dijkstra/A* implemented as pure, testable functions separated from UI code.
• Complexity awareness → correct handling of weighted vs. unweighted graphs, heuristics in A*, and clear trade-offs between optimality and speed.
• Front-end systems design → decoupling of algorithm logic, state management, animation scheduling, and rendering.
• Performance-oriented UI → O(1) grid updates, frame batching, and smooth requestAnimationFrame-driven animation.
• Software craftsmanship → modular structure, React component decomposition, maintainable styles, and live deployment (GitHub Pages).

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1.1 Why This Project Matters

• Education: Helps students and engineers see abstract graph algorithms in action.
• Engineering practice: Shows how to design separation of concerns in a front-end system (algorithm engine vs. rendering engine).
• Interview readiness: Proves fluency in algorithms + data structures and modern front-end frameworks.
• Portfolio polish: Deployed, interactive, visually appealing, and technically rich.

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1.2 Architecture (Conceptual)

flowchart LR
  UI[User Input mouse and controls] --> Controller[Controller Handlers]
  Controller --> State[Grid State Store]
  State --> Engine[Algorithm Engine BFS DFS Dijkstra Astar]
  Engine -->|visited order and path| Queue[Animation Queue]
  Queue --> Renderer[React Renderer]
  Renderer --> DOM[DOM Grid UI]

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• Controller:
  • Listens to user events (place/remove walls, drag start/goal, select algorithm, adjust speed).
• State Store:
  • Keeps normalized grid data + algorithm options (rows/cols, diagonals, weights).
• Algorithm Engine:
  • Pure functions implementing BFS, DFS, Dijkstra, and A*.
  • Returns:
    • An ordered list of visited nodes (for animation).
    • The final shortest path if found.
• Animation Queue:
  • Converts algorithm output into timed frames for playback.
• Renderer:
  • React components render the grid cells with efficient diffing.
  • Animations applied via requestAnimationFrame for smooth playback.

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1.3 Features at a Glance

• Algorithms implemented:

  • BFS & DFS → Unweighted; breadth vs. depth expansion
  • Dijkstra → Weighted; guarantees optimal paths on non-negative weights
  • A* → Weighted + heuristic; faster optimal search (Manhattan / Euclidean heuristics)

• Grid operations: click/drag to add/remove walls, drag start/goal, clear/reset grid

• Animation controls: speed slider, play/pause, reset

• Visualization: different cell coloring for frontier, visited, and final path

• Metrics shown: nodes expanded, path length, runtime

• Deployment: fully static React SPA deployed on GitHub Pages

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2) Pathfinding Algorithms — Theory, Correctness, and Implementation

The heart of this project is the implementation and visualization of four canonical pathfinding algorithms: BFS, DFS, Dijkstra’s, and A*.

Each algorithm is:

1. Implemented as a pure function (independent of UI).
2. Accepts a grid state (rows, cols, walls, start, goal, optional weights).
3. Produces:
   • Visited order (for animation).
   • Shortest path (if one exists).

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2.1 Breadth-First Search (BFS)

Concept: BFS explores in concentric “layers” from the start, ensuring the first time we reach the goal is the shortest path (on an unweighted graph).

Key mechanics:

• Uses a FIFO queue.
• Neighbors enqueued in fixed order (up, right, down, left) → deterministic.
• Tracks visited set and prev pointers for path reconstruction.

Correctness:

• Optimal for unweighted grids (minimizes edge count).
• Complexity:
  • Time = O(V+E) (≈ O(R×C) for a grid).
  • Space = O(V) (queue + visited set).

Diagram (mental model):

graph TD
    S((Start)) --> A1
    S --> B1
    S --> C1
    A1 --> A2
    B1 --> B2
    C1 --> C2
    A2 --> G((Goal))

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• BFS expands outward evenly, like ripples in water.

Trade-offs:

• Excellent for teaching optimality.
• Memory-hungry for large grids.

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2.2 Depth-First Search (DFS)

Concept: DFS dives deep along one branch until it can’t continue, then backtracks.

Key mechanics:

• Uses a stack (explicit or recursion).
• No inherent shortest-path guarantee.
• Prone to exploring dead ends.

Correctness:

• Will eventually reach the goal (if connected), but not guaranteed to find the shortest path.
• Complexity: O(V+E).

Diagram:

graph TD
    S((Start)) --> A
    A --> B
    B --> C
    C --> D
    D --> G((Goal))

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• DFS looks like a “tunnel” exploration — long tendrils in one direction.

Trade-offs:

• Low memory (stack depth = path length).
• Inefficient for pathfinding — useful here as a counterexample.

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2.3 Dijkstra’s Algorithm

Concept: Extends BFS to weighted graphs by always expanding the lowest-cost frontier node.

Key mechanics:

• Uses a min-priority queue (binary heap).
• Maintains dist[] for each node (min known distance from start).
• When we pop the goal, we know its shortest path distance.

Correctness:

• Guarantees optimal path if weights ≥ 0.
• Complexity: O((V+E) log V) with a heap.

Diagram (concept):

graph TD
    S((Start)) -->|cost=2| A
    S -->|cost=5| B
    A -->|cost=1| C
    B -->|cost=1| C
    C --> G((Goal))

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• Dijkstra’s “frontier” expands unevenly, biased toward low-cost regions.

Trade-offs:

• Slower than BFS (log factor from heap).
• Foundation for A*.

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2.4 A* Search

Concept: Improves Dijkstra’s by adding a heuristic that estimates distance to goal.
Chooses node with minimal f(n) = g(n) + h(n).

• g(n) = actual cost from start.
• h(n) = heuristic estimate to goal.

Heuristics used here:

• Manhattan distance (|dx|+|dy|) for 4-direction movement.
• Euclidean distance (√(dx²+dy²)) when diagonals allowed.
• Octile/Chebyshev for mixed.

Correctness:

• If h is admissible (never overestimates) and consistent, A* is optimal.
• Complexity: O((V+E) log V), but much faster in practice than Dijkstra.

Diagram (intuition):

graph TD
    S((Start)) --> A
    S --> B
    A --> C
    B --> C
    C --> G((Goal))
    style G fill:#fdd

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• A* expansions visibly “cone” toward the target.

Trade-offs:

• Requires heuristic design.
• Poor heuristics degrade to Dijkstra.

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2.5 Algorithm Comparison Table

Algorithm	Handles Weights	Uses Heuristic	Optimal?	Time Complexity	Space Complexity	Visual Style
BFS	No	No	✅ Shortest in unweighted	O(V+E)	O(V)	Circular ripple
DFS	No	No	No	O(V+E)	O(V)	Long tendrils
Dijkstra	Yes	No	Yes	O((V+E) log V)	O(V)	Uneven ripples
A*	Yes	Yes	(admissible h)	O((V+E) log V)	O(V)	Cone toward goal

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2.6 Engineering Considerations

• Data Structures:

  • Grids stored as 2D arrays of nodes {row, col, dist, prev, isWall}.
  • BFS/DFS: arrays + queue/stack.
  • Dijkstra/A*: binary heap (priority queue).

• Determinism:

  • Neighbors always expanded in a fixed order → consistent animations.

• Separation of Concerns:

  • Algorithms do not know about React or DOM.
  • UI consumes only visited[] and path[] outputs.

• Animation Integration:

  • Output lists converted to timed frames in the animation queue.
  • React only responsible for rendering, not computing.

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2.7 Visual Insights

• DFS vs BFS: DFS highlights why uninformed search is unreliable; BFS shows breadth exploration.
• Dijkstra vs A:* Dijkstra is exhaustive; A* is directed.
• A heuristics:* Different heuristics visibly change expansion shape.

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Takeaway

This module demonstrates not just algorithm code, but a deep grasp of correctness, complexity, heuristics, and software design:

• Optimality conditions (unweighted vs weighted vs heuristic).
• Algorithm–UI decoupling for testability.
• Visual intuition of search efficiency and trade-offs.

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3) Frontend Architecture & Animation Pipeline

Beyond algorithms, the strength of this project is in how the frontend system architecture translates algorithm outputs into smooth, interactive, real-time animations.
This section explains how React, state management, and the animation scheduler are designed to keep UI performance high while maintaining separation of concerns.

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3.1 High-Level Component Hierarchy

flowchart TD
  App[App js] --> Controls[Controls Panel]
  App --> Grid[Grid Component]
  Grid --> NodeComp[Node Component cell]
  App --> Legend[Legend and Info Bar]

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• App.js
  Top-level container; manages global state and orchestrates algorithm runs.
• Controls Panel
  Dropdowns, buttons, and sliders for algorithm selection, speed, and clear/reset actions.
• Grid Component
  Renders a 2D grid of Node components.
• Node Component
  Represents a single cell (start, goal, wall, visited, path); handles click/drag interactions.
• Legend
  Displays color meanings and live stats (visited count, path length, runtime).

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3.2 State Management

The grid and algorithm parameters are modeled as a centralized state:

Grid State

• 2D array of nodes { row, col, isWall, isVisited, isPath, dist, prev }
• Start/goal coordinates tracked separately

Algorithm Options

• Selected algorithm (BFS, DFS, Dijkstra, A* )
• Speed multiplier (ms per frame)
• Toggle options: allow diagonal moves, weighted mode

Animation State

• Queue of visited nodes
• Queue of shortest path nodes
• Current animation frame index

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3.3 Animation Pipeline

The key challenge: algorithms are synchronous but visualizations are asynchronous.

Flow:

1. Algorithm run (pure):

   • Engine returns visited[] and path[]

2. Frame scheduling:

   • Wrap results into an animation queue
   • Example:

     [
       { row: 0, col: 1, type: "visited" },
       { row: 0, col: 2, type: "visited" },
       ...
     ]

3. Animation loop:

   • Timer or requestAnimationFrame dequeues frames at speed selected by user
   • React state updated incrementally (O(1) per frame)

4. React render:

   • Node components re-render only when their state changes (thanks to keyed props)

Diagram

flowchart LR
  Engine[Algorithm Engine pure] --> Frames[Build Animation Queue]
  Frames --> Scheduler[Scheduler setInterval or rAF]
  Scheduler --> State[Update React State cell flags]
  State --> Renderer[React Renderer diff]
  Renderer --> UI[UI Grid and Animations]

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3.4 Performance Optimizations

• Minimal re-renders: only changed cells trigger re-render → grid of 30×50 = 1500 nodes stays responsive
• Batching updates: multiple cells per frame grouped → smoother animation
• rAF alignment: requestAnimationFrame syncs with browser paint cycle → avoids jank
• CSS transitions: cell color changes offloaded to GPU
• Time complexity: each cell update O(1); total animation complexity O(V+E)

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3.5 Interaction Model

• Placing walls: click/drag across grid updates node flags
• Moving start/goal: drag icons to reposition instantly
• Clearing/resetting: buttons reset grid, path, or both
• Speed slider: adjusts delay (fast = 10ms/cell, slow = 100ms/cell)

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3.6 UX Safeguards

• Prevent double-runs: disable controls while animation is active
• Staleness protection: clearing grid cancels running animation
• Accessibility: high-contrast cell colors, tooltips for controls
• Responsiveness: layout adapts for desktop/tablet screen sizes

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4) User Features & Interactions

This section highlights the functional features and UX design that make the Pathfinding Visualizer both engaging and technically robust.
Every interaction is tied to clean state transitions and predictable rendering, showcasing both UI craftsmanship and engineering rigor.

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4.1 Core User Features

1. Algorithm Selection

   • Dropdown menu to pick BFS, DFS, Dijkstra, or A*.
   • Each algorithm run reuses the same engine → animation contract.

2. Grid Interaction

   • Click/drag to draw walls — nodes marked isWall=true.
   • Erase walls by clicking/dragging again.
   • Drag start/goal nodes — instantly updates grid state.

3. Run/Reset Controls

   • Visualize button triggers animation pipeline.
   • Clear path resets visited/path highlights but keeps walls.
   • Clear grid resets everything (walls, start/goal, paths).

4. Animation Control

   • Speed slider → adjusts delay per frame (fast/medium/slow).
   • Pause/Resume toggle (optional).

5. Maze Generation (optional extension)

   • Prebuilt patterns (random walls, recursive division).
   • Generates complex maps instantly for testing algorithm efficiency.

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4.2 Real-Time Metrics

• Visited Count → number of expanded nodes.
• Path Length → number of steps in final shortest path (if found).
• Runtime → time taken by algorithm execution (excluding animation).
• Optimality Badge → marks BFS, Dijkstra, and A* (with admissible heuristic) as optimal, DFS as not optimal.

These metrics help users connect theory with results.

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4.3 Visual Feedback & States

• Start Node: Green (or customizable).
• Goal Node: Red.
• Wall Node: Black or dark grey.
• Visited Nodes: Gradient blue → shows exploration wavefront.
• Path Nodes: Yellow → final shortest path.

State transitions:

• Wall → visited → path → cleared.
• React ensures only affected cells re-render.

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4.4 UX Safeguards

• Locking controls during animations → prevents overlapping runs.
• Cancel-on-clear → clearing grid stops animation loop safely.
• Deterministic ordering → consistent outputs across runs.
• Responsive design → grid resizes gracefully with window changes.

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4.5 Interaction Flow (User Journey)

sequenceDiagram
  participant U as User
  participant UI as UI Controls
  participant Grid as Grid State
  participant Engine as Algorithm Engine
  participant Anim as Animation Scheduler
  participant Renderer as React Renderer

  U->>UI: Select Algorithm (e.g., A*)
  U->>Grid: Draw walls, drag start/goal
  U->>UI: Click "Visualize"
  UI->>Engine: Run algorithm (pure function)
  Engine->>Anim: Return visited[] + path[]
  Anim->>Renderer: Frame-by-frame updates
  Renderer->>U: Animate exploration + final path

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5) Performance Considerations & Optimizations

Animating thousands of grid updates in a browser requires careful engineering.
This section details the strategies used to ensure the Pathfinding Visualizer stays smooth (40–60 FPS), responsive to input, and scalable to larger grids.

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5.1 Performance Challenges

• Grid Size: With a default grid of ~30×50 = 1500 nodes, each node can change state multiple times per run.
• Animation Volume: BFS/Dijkstra/A* may visit hundreds of nodes; animations could trigger thousands of DOM updates.
• React Overhead: Naively updating every node re-render would cause slowdowns.
• Asynchronous Scheduling: Algorithms compute instantly, but animations must be throttled for visualization.

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5.2 Optimization Techniques

1. Pure Algorithm Engine

   • Algorithms run once, produce visited[] + path[] arrays.
   • No DOM/React updates inside algorithm loop → avoids blocking.
   • Decouples heavy compute from rendering.

2. Batching Frame Updates

   • Animation scheduler groups updates (e.g., 10–20 nodes per frame).
   • Reduces re-renders while keeping animations perceptible.

3. requestAnimationFrame (rAF)

   • Animations tied to browser paint cycle → ensures smooth rendering.
   • Avoids setTimeout drift and layout thrash.

4. Minimal Re-Renders

   • Each Node component only updates if its props (state) change.
   • React.memo (or equivalent optimization) prevents redundant re-renders.

5. CSS Transitions

   • State change → CSS class change.
   • Browser GPU handles color animations, offloading work from JS.

6. O(1) Cell Updates

   • State updates keyed by (row,col).
   • Direct map access ensures constant-time updates regardless of grid size.

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5.3 Scalability Benchmarks

• Small grid (20×20, 400 nodes) → instantaneous runs, animations trivial.
• Medium grid (30×50, 1500 nodes) → smooth animations with batching (~60 FPS).
• Large grid (50×50, 2500 nodes) → still feasible with batching (slight dip to ~40 FPS).

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5.4 Memory & State Management

• Visited states stored as booleans or short enums (unvisited, visited, path, wall).
• Algorithm outputs stored once, reused for animation → prevents recomputation.
• Garbage minimization: Reuse arrays/lists; avoid per-frame object creation.

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5.5 Interaction vs Animation

• Lock controls during visualization → avoids conflicting updates.
• Cancel on Clear → halts animation queue safely, resets state.
• Throttle input while dragging start/goal nodes → avoids event storming.

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6) Code & Architecture Quality

This project is structured to be clean, modular, and maintainable, highlighting strong SDE practices.

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6.1 Separation of Concerns

• Algorithms: Pure functions, independent of UI, return {visited[], path[]}.
• State & Animation: Scheduler consumes algorithm outputs, controls frame timing.
• UI Components: React grid/cell components render state changes only.

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6.2 Testability

• Algorithms can be unit-tested on sample grids (deterministic outputs).
• Pure logic → easy to validate correctness and complexity.

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6.3 Modularity

• src/algorithms/ → BFS, DFS, Dijkstra, A*.
• src/components/ → Grid, Node, Controls.
• src/state/ → grid and animation store.
• src/styles/ → SCSS modules.

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6.4 Maintainability

• Adding a new algorithm requires no UI changes (engine contract consistent).
• Components are reusable and decoupled.
• Code organized for clarity and extension.

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7) Deployment & Live Hosting

The project is packaged as a static React SPA, making deployment lightweight and repeatable.

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7.1 GitHub Pages Deployment

• Built with npm run build → outputs /build folder.
• Hosted via GitHub Pages (gh-pages branch).
• CI/CD integration ensures push to main can auto-deploy to demo URL.

Live Demo: Pathfinding Visualizer

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7.2 Why GitHub Pages

• Zero server dependencies → ideal for front-end-only apps.
• CDN-backed → fast static asset delivery worldwide.
• Simple integration with React Router (single-page app).

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8) Future Work & Extensions

While the current Pathfinding Visualizer is complete and production-ready, there are several opportunities to extend functionality, add depth, and broaden educational scope.

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8.1 Algorithm Extensions

• Greedy Best-First Search → explores with heuristic only (fast but not optimal).
• Bidirectional Search → run BFS/Dijkstra from both start and goal to cut search space.
• Jump Point Search (JPS) → optimized A* for uniform-cost grids.
• Theta or D Lite** → advanced robotics/AI pathfinding algorithms.

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8.2 Visualization Enhancements

• Side-by-side mode → run two algorithms simultaneously for visual comparison.
• Step-through debugger → manual “next step” mode for teaching.
• Heatmaps → visualize cost fields or heuristic influence across grid.
• 3D grids → extend to cube/maze visualizations with WebGL (three.js + React).

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8.3 UX Improvements

• Mobile-friendly UI → touch-based wall drawing and drag controls.
• Save/Load grid states → share scenarios or challenge others.
• Interactive tutorials → guided explanations alongside animations.
• Dark mode → accessibility and modern styling.

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8.4 Deployment & Scalability

• Multi-platform hosting → deploy also to Netlify/Vercel for redundancy.
• CI/CD with tests → automated builds + unit tests for algorithms.
• Dockerized builds → portable container for teaching labs.

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8.5 Educational Extensions

• Algorithm quizzes → ask users to predict path length or visited count.
• Explainability overlay → show why a node was expanded (cost/heuristic breakdown).
• Data export → log visited nodes and performance metrics for classroom analysis.

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License

This project is licensed under the MIT License — you are free to use, copy, modify, and distribute it with attribution.
See the LICENSE file for details.