SeaweedFS EC Worker Testing Environment

This Docker Compose setup provides a comprehensive testing environment for SeaweedFS Erasure Coding (EC) workers using official SeaweedFS commands.

📂 Directory Structure

The testing environment is located in docker/admin_integration/ and includes:

docker/admin_integration/
├── Makefile                     # Main management interface
├── docker-compose-ec-test.yml   # Docker compose configuration
├── EC-TESTING-README.md         # This documentation
└── run-ec-test.sh              # Quick start script

🏗️ Architecture

The testing environment uses official SeaweedFS commands and includes:

1 Master Server (port 9333) - Coordinates the cluster with 50MB volume size limit
6 Volume Servers (ports 8080-8085) - Distributed across 2 data centers and 3 racks for diversity
1 Filer (port 8888) - Provides file system interface
1 Admin Server (port 23646) - Detects volumes needing EC and manages workers using official admin command
3 EC Workers - Execute erasure coding tasks using official worker command with task-specific working directories
1 Load Generator - Continuously writes and deletes files using SeaweedFS shell commands
1 Monitor - Tracks cluster health and EC progress using shell scripts

✨ New Features

Task-Specific Working Directories

Each worker now creates dedicated subdirectories for different task types:

/work/erasure_coding/ - For EC encoding tasks
/work/vacuum/ - For vacuum cleanup tasks
/work/balance/ - For volume balancing tasks

This provides:

Organization: Each task type gets isolated working space
Debugging: Easy to find files/logs related to specific task types
Cleanup: Can clean up task-specific artifacts easily
Concurrent Safety: Different task types won't interfere with each other's files

🚀 Quick Start

Prerequisites

Docker and Docker Compose installed
GNU Make installed
At least 4GB RAM available for containers
Ports 8080-8085, 8888, 9333, 23646 available

Start the Environment

# Navigate to the admin integration directory
cd docker/admin_integration/

# Show available commands
make help

# Start the complete testing environment
make start

The make start command will:

Start all services using official SeaweedFS images
Configure workers with task-specific working directories
Wait for services to be ready
Display monitoring URLs and run health checks

Alternative Commands

# Quick start aliases
make up              # Same as 'make start'

# Development mode (higher load for faster testing)
make dev-start

# Build images without starting
make build

📋 Available Make Targets

Run make help to see all available targets:

🚀 Main Operations

make start - Start the complete EC testing environment
make stop - Stop all services
make restart - Restart all services
make clean - Complete cleanup (containers, volumes, images)

📊 Monitoring & Status

make health - Check health of all services
make status - Show status of all containers
make urls - Display all monitoring URLs
make monitor - Open monitor dashboard in browser
make monitor-status - Show monitor status via API
make volume-status - Show volume status from master
make admin-status - Show admin server status
make cluster-status - Show complete cluster status

📋 Logs Management

make logs - Show logs from all services
make logs-admin - Show admin server logs
make logs-workers - Show all worker logs
make logs-worker1/2/3 - Show specific worker logs
make logs-load - Show load generator logs
make logs-monitor - Show monitor logs
make backup-logs - Backup all logs to files

⚖️ Scaling & Testing

make scale-workers WORKERS=5 - Scale workers to 5 instances
make scale-load RATE=25 - Increase load generation rate
make test-ec - Run focused EC test scenario

🔧 Development & Debug

make shell-admin - Open shell in admin container
make shell-worker1 - Open shell in worker container
make debug - Show debug information
make troubleshoot - Run troubleshooting checks

📊 Monitoring URLs

Service	URL	Description
Master UI	http://localhost:9333	Cluster status and topology
Filer	http://localhost:8888	File operations
Admin Server	http://localhost:23646/	Task management
Monitor	http://localhost:9999/status	Complete cluster monitoring
Volume Servers	http://localhost:8080-8085/status	Individual volume server stats

Quick access: make urls or make monitor

🔄 How EC Testing Works

1. Continuous Load Generation

Write Rate: 10 files/second (1-5MB each)
Delete Rate: 2 files/second
Target: Fill volumes to 50MB limit quickly

2. Volume Detection

Admin server scans master every 30 seconds
Identifies volumes >40MB (80% of 50MB limit)
Queues EC tasks for eligible volumes

3. EC Worker Assignment

Worker 1: EC specialist (max 2 concurrent tasks)
Worker 2: EC + Vacuum hybrid (max 2 concurrent tasks)
Worker 3: EC + Vacuum hybrid (max 1 concurrent task)

4. Comprehensive EC Process

Each EC task follows 6 phases:

Copy Volume Data (5-15%) - Stream .dat/.idx files locally
Mark Read-Only (20-25%) - Ensure data consistency
Local Encoding (30-60%) - Create 14 shards (10+4 Reed-Solomon)
Calculate Placement (65-70%) - Smart rack-aware distribution
Distribute Shards (75-90%) - Upload to optimal servers
Verify & Cleanup (95-100%) - Validate and clean temporary files

5. Real-Time Monitoring

Volume analysis and EC candidate detection
Worker health and task progress
No data loss verification
Performance metrics

📋 Key Features Tested

✅ EC Implementation Features

Local volume data copying with progress tracking
Local Reed-Solomon encoding (10+4 shards)
Intelligent shard placement with rack awareness
Load balancing across available servers
Backup server selection for redundancy
Detailed step-by-step progress tracking
Comprehensive error handling and recovery

✅ Infrastructure Features

Multi-datacenter topology (dc1, dc2)
Rack diversity (rack1, rack2, rack3)
Volume size limits (50MB)
Worker capability matching
Health monitoring and alerting
Continuous workload simulation

🛠️ Common Usage Patterns

Basic Testing Workflow

# Start environment
make start

# Watch progress
make monitor-status

# Check for EC candidates
make volume-status

# View worker activity
make logs-workers

# Stop when done
make stop

High-Load Testing

# Start with higher load
make dev-start

# Scale up workers and load
make scale-workers WORKERS=5
make scale-load RATE=50

# Monitor intensive EC activity
make logs-admin

Debugging Issues

# Check port conflicts and system state
make troubleshoot

# View specific service logs
make logs-admin
make logs-worker1

# Get shell access for debugging
make shell-admin
make shell-worker1

# Check detailed status
make debug

Development Iteration

# Quick restart after code changes
make restart

# Rebuild and restart
make clean
make start

# Monitor specific components
make logs-monitor

📈 Expected Results

Successful EC Testing Shows:

Volume Growth: Steady increase in volume sizes toward 50MB limit
EC Detection: Admin server identifies volumes >40MB for EC
Task Assignment: Workers receive and execute EC tasks
Shard Distribution: 14 shards distributed across 6 volume servers
No Data Loss: All files remain accessible during and after EC
Performance: EC tasks complete within estimated timeframes

Sample Monitor Output:

# Check current status
make monitor-status

# Output example:
{
  "monitor": {
    "uptime": "15m30s",
    "master_addr": "master:9333",
    "admin_addr": "admin:9900"
  },
  "stats": {
    "VolumeCount": 12,
    "ECTasksDetected": 3,
    "WorkersActive": 3
  }
}

🔧 Configuration

Environment Variables

You can customize the environment by setting variables:

# High load testing
WRITE_RATE=25 DELETE_RATE=5 make start

# Extended test duration
TEST_DURATION=7200 make start  # 2 hours

Scaling Examples

# Scale workers
make scale-workers WORKERS=6

# Increase load generation
make scale-load RATE=30

# Combined scaling
make scale-workers WORKERS=4
make scale-load RATE=40

🧹 Cleanup Options

# Stop services only
make stop

# Remove containers but keep volumes
make down

# Remove data volumes only
make clean-volumes

# Remove built images only
make clean-images

# Complete cleanup (everything)
make clean

🐛 Troubleshooting

Quick Diagnostics

# Run complete troubleshooting
make troubleshoot

# Check specific components
make health
make debug
make status

Common Issues

Services not starting:

# Check port availability
make troubleshoot

# View startup logs
make logs-master
make logs-admin

No EC tasks being created:

# Check volume status
make volume-status

# Increase load to fill volumes faster
make scale-load RATE=30

# Check admin detection
make logs-admin

Workers not responding:

# Check worker registration
make admin-status

# View worker logs
make logs-workers

# Restart workers
make restart

Performance Tuning

For faster testing:

make dev-start           # Higher default load
make scale-load RATE=50  # Very high load

For stress testing:

make scale-workers WORKERS=8
make scale-load RATE=100

📚 Technical Details

Network Architecture

Custom bridge network (172.20.0.0/16)
Service discovery via container names
Health checks for all services

Storage Layout

Each volume server: max 100 volumes
Data centers: dc1, dc2
Racks: rack1, rack2, rack3
Volume limit: 50MB per volume

EC Algorithm

Reed-Solomon RS(10,4)
10 data shards + 4 parity shards
Rack-aware distribution
Backup server redundancy

Make Integration

Color-coded output for better readability
Comprehensive help system (make help)
Parallel execution support
Error handling and cleanup
Cross-platform compatibility

🎯 Quick Reference

# Essential commands
make help              # Show all available targets
make start             # Start complete environment
make health            # Check all services
make monitor           # Open dashboard
make logs-admin        # View admin activity
make clean             # Complete cleanup

# Monitoring
make volume-status     # Check for EC candidates  
make admin-status      # Check task queue
make monitor-status    # Full cluster status

# Scaling & Testing
make test-ec           # Run focused EC test
make scale-load RATE=X # Increase load
make troubleshoot      # Diagnose issues

This environment provides a realistic testing scenario for SeaweedFS EC workers with actual data operations, comprehensive monitoring, and easy management through Make targets.

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