A high-performance, concurrent Reddit-like social platform simulator built with Go and the Proto Actor framework. The system models realistic user behavior using Zipf distribution for subreddit popularity and supports large-scale simulations with up to 100K users and 250K actions.
- 🏗️ Actor-Based Architecture: Distributed system using Proto Actor framework
- 📊 Realistic User Modeling: Zipf distribution for authentic subreddit popularity simulation
- ⚡ High Performance: Handles 100K+ users with 250K+ actions in under 3 minutes
- 🔄 Concurrent Operations: Full async/await support for all user interactions
- 👥 Complete Social Features: Posts, comments, voting, direct messaging, and feeds
- 📈 Karma System: Dynamic reputation tracking with upvote/downvote mechanics
- 🔌 Connection Management: User online/offline status simulation
- 📱 Real-time Feed: Personalized content feeds based on subscriptions
- 📊 Analytics: Comprehensive simulation statistics and user action tracking
| Component | Technology |
|---|---|
| Language | Go 1.19+ |
| Actor Framework | Proto Actor |
| Concurrency Model | Actor-based messaging |
| Distribution | Zipf distribution for realistic modeling |
| Architecture | Concurrent, event-driven |
reddit-simulator/
├── main.go # Application entry point and configuration
├── engine.go # Core Reddit engine with all business logic
├── simulator.go # User behavior simulation and Zipf distribution
├── models.go # Data structures for users, posts, comments
├── messages.go # Actor message definitions and protocols
└── README.md # Project documentation
- Go 1.19 or higher
- Proto Actor Go framework
git clone <repository-url>
cd reddit-simulatorgo mod init reddit-simulator
go get github.com/asynkron/protoactor-go/actor
go mod tidy# Default simulation (30 users, 6 subreddits, 200 actions, 5 seconds)
go run .
# Custom parameters
go run . -users 10 -subreddits 3 -actions 100 -time 3
# Large-scale simulation
go run . -users 1000 -subreddits 50 -actions 5000 -time 30┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Main Thread │ │ Actor System │ │ Engine │
│ │────▶│ │────▶│ Actor │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Simulator │ │ Message │ │ Data Models │
│ Actor │◀───│ Passing │────▶│ │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
| Flag | Default | Description |
|---|---|---|
-users |
30 | Maximum number of users to simulate |
-subreddits |
6 | Maximum number of subreddits to create |
-actions |
200 | Total number of simulation actions |
-time |
5 | Simulation duration in seconds |
# Quick test run
go run . -users 5 -subreddits 2 -actions 50 -time 2
# Medium simulation
go run . -users 100 -subreddits 10 -actions 1000 -time 10
# Large-scale performance test
go run . -users 10000 -subreddits 100 -actions 25000 -time 60- User Registration: Dynamic user creation during simulation
- Connection Status: Online/offline state management
- Karma System: Reputation tracking based on community interactions
- Create Subreddits: Dynamic community creation
- Join/Leave: Flexible membership management
- Zipf Distribution: Realistic popularity modeling
- Post Creation: Rich content posting to any subreddit
- Nested Comments: Multi-level comment threading with replies
- Voting System: Upvote/downvote mechanics affecting karma
- Direct Messaging: Private user-to-user communication
- Personalized Feeds: Content from subscribed subreddits
- Real-time Interactions: Concurrent user actions
- Community Building: Organic subreddit growth patterns
The karma system models Reddit's reputation mechanics:
- +1 Karma: Per post creation
- +1 Karma: Per comment on posts
- +1 Karma: Per upvote received on content
- -1 Karma: Per downvote received on content
- Default Upvote: Authors automatically upvote their own posts
Total Karma = Posts Created + Comments Made + (Total Upvotes - Total Downvotes)
The simulator uses Zipf distribution to model realistic user behavior:
// Zipf parameter: 1.07 (slightly skewed distribution)
zipf := rand.NewZipf(r, 1.07, 1, uint64(maxSubreddits))
// Popular subreddits get more users and content
subredditIndex := int(zipf.Uint64())Benefits:
- Realistic Modeling: Mimics real-world social platform dynamics
- Popular Communities: Some subreddits naturally become more active
- Long Tail Effect: Many smaller communities with less activity
Responsibilities:
- Process all user actions and state changes
- Maintain data consistency across the system
- Handle message routing and response generation
Key Methods:
func (e *Engine) Receive(context actor.Context)
func (e *Engine) registerUser(username string)
func (e *Engine) createSubreddit(name, creator string)
func (e *Engine) createPost(postID, subredditName, author, title, content string)
func (e *Engine) vote(postID, userID string, isUpvote bool)Responsibilities:
- Generate realistic user behavior patterns
- Manage simulation lifecycle and timing
- Coordinate with Engine for action execution
Key Methods:
func (s *Simulator) runSimulation(context actor.Context)
func (s *Simulator) simulateAction(context actor.Context)
func (s *Simulator) simulateCreatePost(context actor.Context)
func (s *Simulator) simulateVote(context actor.Context)| Users | Actions | Subreddits | Time | Machine |
|---|---|---|---|---|
| 100 | 500 | 10 | 0:05 | M3 Pro |
| 1,000 | 5,000 | 50 | 0:15 | M3 Pro |
| 10,000 | 25,000 | 100 | 0:45 | M3 Pro |
| 100,000 | 250,000 | 600 | 2:40 | 12-Core M3 Pro, 18GB RAM |
- Linear Scaling: Performance scales predictably with user count
- Memory Efficient: Optimized data structures for large simulations
- Concurrent Processing: Full utilization of multi-core systems
[REGISTER USER] Registered as new user
[CREATE SUB] Subreddit created: r/Sub 1 by User 1
[JOIN SUB] User 2 joined subreddit r/Sub 1
[POST] Post 1 created in r/Sub 1 by User 1: Post Title
[POST Comment] User 2 commented on post Post 1: Comment content
[VOTE] User 3 upvoted post Post 1
[Direct Message] DM sent to User 4: Hello there!
[SHOW FEED] Feed for user User 2 -----
- Action Throughput: Actions processed per second
- User Activity: Active vs. inactive user ratios
- Content Distribution: Posts and comments per subreddit
- Engagement Rates: Voting patterns and participation
- User Karma Distribution: Reputation spread across users
- Subreddit Popularity: Member counts and activity levels
- Content Metrics: Post engagement and comment threading depth
- Performance Stats: Execution time and resource usage
- Fork the repository
- Create feature branch (
git checkout -b feature/actor-enhancement) - Commit changes (
git commit -m 'Add new actor feature') - Push to branch (
git push origin feature/actor-enhancement) - Open a Pull Request
⚡ Built with Go's concurrency power and Proto Actor's distributed magic