seaweedfs/test/postgres

SeaweedFS PostgreSQL Protocol Test Suite

This directory contains a comprehensive Docker Compose test setup for the SeaweedFS PostgreSQL wire protocol implementation.

Overview

The test suite includes:

• SeaweedFS Cluster: Full SeaweedFS server with MQ broker and agent
• PostgreSQL Server: SeaweedFS PostgreSQL wire protocol server
• MQ Data Producer: Creates realistic test data across multiple topics and namespaces
• PostgreSQL Test Client: Comprehensive Go client testing all functionality
• Interactive Tools: psql CLI access for manual testing

Quick Start

1. Run Complete Test Suite (Automated)

./run-tests.sh all

This will automatically:

1. Start SeaweedFS and PostgreSQL servers
2. Create test data in multiple MQ topics
3. Run comprehensive PostgreSQL client tests
4. Show results

2. Manual Step-by-Step Testing

# Start the services
./run-tests.sh start

# Create test data
./run-tests.sh produce

# Run automated tests
./run-tests.sh test

# Connect with psql for interactive testing
./run-tests.sh psql

3. Interactive PostgreSQL Testing

# Connect with psql
./run-tests.sh psql

# Inside psql session:
postgres=> SHOW DATABASES;
postgres=> USE analytics;
postgres=> SHOW TABLES;
postgres=> SELECT COUNT(*) FROM user_events;
postgres=> SELECT user_type, COUNT(*) FROM user_events GROUP BY user_type;
postgres=> \q

Test Data Structure

The producer creates realistic test data across multiple namespaces:

Analytics Namespace

• user_events (1000 records): User interaction events

  • Fields: id, user_id, user_type, action, status, amount, timestamp, metadata
  • User types: premium, standard, trial, enterprise
  • Actions: login, logout, purchase, view, search, click, download

• system_logs (500 records): System operation logs

  • Fields: id, level, service, message, error_code, timestamp
  • Levels: debug, info, warning, error, critical
  • Services: auth-service, payment-service, user-service, etc.

• metrics (800 records): System metrics

  • Fields: id, name, value, tags, timestamp
  • Metrics: cpu_usage, memory_usage, disk_usage, request_latency, etc.

E-commerce Namespace

• product_views (1200 records): Product interaction data

  • Fields: id, product_id, user_id, category, price, view_count, timestamp
  • Categories: electronics, books, clothing, home, sports, automotive

• user_events (600 records): E-commerce specific user events

Logs Namespace

• application_logs (2000 records): Application logs
• error_logs (300 records): Error-specific logs with 4xx/5xx error codes

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   PostgreSQL    │    │   PostgreSQL     │    │   SeaweedFS     │
│   Clients       │◄──►│   Wire Protocol  │◄──►│   SQL Engine    │
│   (psql, Go)    │    │   Server         │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │                         │
                              ▼                         ▼
                       ┌──────────────────┐    ┌─────────────────┐
                       │   Session        │    │   MQ Broker     │
                       │   Management     │    │   & Topics      │
                       └──────────────────┘    └─────────────────┘

Services

SeaweedFS Server

• Ports: 9333 (master), 8888 (filer), 8333 (S3), 8085 (volume), 9533 (metrics), 26777→16777 (MQ agent), 27777→17777 (MQ broker)
• Features: Full MQ broker, S3 API, filer, volume server
• Data: Persistent storage in Docker volume
• Health Check: Cluster status endpoint

PostgreSQL Server

• Port: 5432 (standard PostgreSQL port)
• Protocol: Full PostgreSQL 3.0 wire protocol
• Authentication: Trust mode (no password for testing)
• Features: Real-time MQ topic discovery, database context switching

MQ Producer

• Purpose: Creates realistic test data
• Topics: 7 topics across 3 namespaces
• Data Types: JSON messages with varied schemas
• Volume: ~4,400 total records with realistic distributions

Test Client

• Language: Go with standard lib/pq PostgreSQL driver
• Tests: 8 comprehensive test categories
• Coverage: System info, discovery, queries, aggregations, context switching

Available Commands

./run-tests.sh start      # Start services
./run-tests.sh produce    # Create test data  
./run-tests.sh test       # Run client tests
./run-tests.sh psql       # Interactive psql
./run-tests.sh logs       # Show service logs
./run-tests.sh status     # Service status
./run-tests.sh stop       # Stop services
./run-tests.sh clean      # Complete cleanup
./run-tests.sh all        # Full automated test

Test Categories

1. System Information

• PostgreSQL version compatibility
• Current user and database
• Server settings and encoding

2. Database Discovery

• SHOW DATABASES - List MQ namespaces
• Dynamic namespace discovery from filer

3. Table Discovery

• SHOW TABLES - List topics in current namespace
• Real-time topic discovery

4. Data Queries

• Basic SELECT * FROM table queries
• Sample data retrieval and display
• Column information

5. Aggregation Queries

• COUNT(*), SUM(), AVG(), MIN(), MAX()
• GROUP BY operations
• Statistical analysis

6. Database Context Switching

• USE database commands
• Session isolation testing
• Cross-namespace queries

7. System Columns

• _timestamp_ns, _key, _source access
• MQ metadata exposure

8. Complex Queries

• WHERE clauses with comparisons
• ORDER BY and LIMIT
• Multi-condition filtering

Expected Results

After running the complete test suite, you should see:

=== Test Results ===
✅ Test PASSED: System Information
✅ Test PASSED: Database Discovery  
✅ Test PASSED: Table Discovery
✅ Test PASSED: Data Queries
✅ Test PASSED: Aggregation Queries
✅ Test PASSED: Database Context Switching
✅ Test PASSED: System Columns
✅ Test PASSED: Complex Queries

Test Results: 8/8 tests passed
🎉 All tests passed!

Manual Testing Examples

Connect with psql

./run-tests.sh psql

Basic Exploration

-- Check system information
SELECT version();
SELECT current_user, current_database();

-- Discover data structure  
SHOW DATABASES;
USE analytics;
SHOW TABLES;
DESCRIBE user_events;

Data Analysis

-- Basic queries
SELECT COUNT(*) FROM user_events;
SELECT * FROM user_events LIMIT 5;

-- Aggregations
SELECT 
    user_type,
    COUNT(*) as events,
    AVG(amount) as avg_amount
FROM user_events 
WHERE amount IS NOT NULL
GROUP BY user_type
ORDER BY events DESC;

-- Time-based analysis
SELECT 
    action,
    COUNT(*) as count
FROM user_events 
WHERE status = 'active'
GROUP BY action
ORDER BY count DESC;

Cross-Namespace Analysis

-- Switch between namespaces
USE ecommerce;
SELECT category, COUNT(*) FROM product_views GROUP BY category;

USE logs;  
SELECT level, COUNT(*) FROM application_logs GROUP BY level;

Troubleshooting

Services Not Starting

# Check service status
./run-tests.sh status

# View logs
./run-tests.sh logs seaweedfs
./run-tests.sh logs postgres-server

No Test Data

# Recreate test data
./run-tests.sh produce

# Check producer logs
./run-tests.sh logs mq-producer

Connection Issues

# Test PostgreSQL server health
docker-compose exec postgres-server nc -z localhost 5432

# Test SeaweedFS health
curl http://localhost:9333/cluster/status

Clean Restart

# Complete cleanup and restart
./run-tests.sh clean
./run-tests.sh all

Development

Modifying Test Data

Edit producer.go to change:

• Data schemas and volume
• Topic names and namespaces
• Record generation logic

Adding Tests

Edit client.go to add new test functions:

func testNewFeature(db *sql.DB) error {
    // Your test implementation
    return nil
}

// Add to tests slice in main()
{"New Feature", testNewFeature},

Custom Queries

Use the interactive psql session:

./run-tests.sh psql

Production Considerations

This test setup demonstrates:

• Real MQ Integration: Actual topic discovery and data access
• Universal PostgreSQL Compatibility: Works with any PostgreSQL client
• Production-Ready Features: Authentication, session management, error handling
• Scalable Architecture: Direct SQL engine integration, no translation overhead

The test validates that SeaweedFS can serve as a drop-in PostgreSQL replacement for read-only analytics workloads on MQ data.