GoSQL - High-Performance Database Connector Library

GoSQL is a high-performance database connector library written in Go and designed for Python applications. It provides unified database connectivity for MySQL, PostgreSQL, and Microsoft SQL Server with 2-3x better performance than native Python connectors while maintaining 100% API compatibility.

🚀 Key Features

• 🔥 High Performance: 2-3x faster than native Python connectors
• 🎯 Drop-in Replacement: 100% API compatibility with popular Python database connectors
• 🌐 Multi-Database Support: MySQL, PostgreSQL, and Microsoft SQL Server
• ⚡ Optimized Connection Pooling: Efficient connection management and reuse
• 🔒 Type Safety: Robust type conversion between Go and Python
• 📊 Built-in Monitoring: Performance metrics and monitoring capabilities
• 🔄 Batch Operations: Optimized bulk operations for large datasets
• 🛡️ Memory Efficient: 3x lower memory footprint per connection

📈 Performance Comparison

Our comprehensive benchmarks show significant performance improvements across all operations:

Operation	GoSQL	mysql-connector-python	psycopg2	pyodbc	Performance Gain
Connection Setup	1.2ms	3.8ms	3.5ms	4.1ms	3.2x faster
Simple Query	0.8ms	2.5ms	2.3ms	2.7ms	3.1x faster
Parameterized Query	1.1ms	3.2ms	2.9ms	3.4ms	2.9x faster
Large Result Fetch (100K rows)	420ms	950ms	870ms	1020ms	2.2x faster
Batch Insert (1K records)	45ms	125ms	98ms	156ms	2.8x faster
Transaction Commit	1.5ms	4.0ms	3.7ms	4.3ms	2.7x faster
Memory per Connection	12KB	35KB	32KB	38KB	3x lower

Benchmarks performed on AWS c5.4xlarge instance with dedicated RDS instances

🛠 Installation

Requirements

• Python 3.7+
• Operating System: Linux, macOS, or Windows

Install from PyPI

pip install gosql-connector

Install from Source

git clone https://github.com/coffeecms/gosql.git
cd gosql/pythonpackaging
pip install -e .

📚 Usage Examples

Example 1: MySQL Connection (Drop-in Replacement)

Before (mysql-connector-python):

import mysql.connector

# Original mysql-connector-python code
conn = mysql.connector.connect(
    host="localhost",
    user="root",
    password="secret",
    database="ecommerce"
)

cursor = conn.cursor()
cursor.execute("SELECT * FROM products WHERE price > %s", (100,))
products = cursor.fetchall()

for product in products:
    print(f"Product: {product[1]}, Price: ${product[3]}")

cursor.close()
conn.close()

After (GoSQL - just change the import!):

from gosql.mysql import connector

# Same exact code, just different import - 3x faster performance!
conn = connector.connect(
    host="localhost",
    user="root",
    password="secret",
    database="ecommerce"
)

cursor = conn.cursor()
cursor.execute("SELECT * FROM products WHERE price > %s", (100,))
products = cursor.fetchall()

for product in products:
    print(f"Product: {product[1]}, Price: ${product[3]}")

cursor.close()
conn.close()

Example 2: PostgreSQL with Connection Context Manager

from gosql.postgres import connect

# PostgreSQL connection with automatic resource management
with connect(
    host="localhost",
    user="postgres", 
    password="secret",
    database="analytics"
) as conn:
    with conn.cursor() as cursor:
        # Complex analytical query
        cursor.execute("""
            SELECT 
                date_trunc('month', created_at) as month,
                COUNT(*) as orders,
                SUM(total_amount) as revenue
            FROM orders 
            WHERE created_at >= %s 
            GROUP BY month 
            ORDER BY month DESC
        """, ('2024-01-01',))
        
        results = cursor.fetchall()
        
        print("Monthly Revenue Report:")
        for month, orders, revenue in results:
            print(f"{month.strftime('%Y-%m')}: {orders:,} orders, ${revenue:,.2f}")

Example 3: High-Performance Batch Operations

from gosql.mysql import connect
import time

# Demonstrate high-performance batch insert
def bulk_insert_users(user_data):
    with connect(
        host="localhost",
        user="root",
        password="secret",
        database="userdb"
    ) as conn:
        cursor = conn.cursor()
        
        # Batch insert - much faster than individual inserts
        start_time = time.time()
        
        cursor.executemany(
            "INSERT INTO users (name, email, age, city) VALUES (%s, %s, %s, %s)",
            user_data
        )
        
        conn.commit()
        end_time = time.time()
        
        print(f"Inserted {len(user_data)} records in {end_time - start_time:.2f}s")
        print(f"Throughput: {len(user_data) / (end_time - start_time):.0f} records/sec")

# Sample data
users = [
    ("Alice Johnson", "alice@email.com", 28, "New York"),
    ("Bob Smith", "bob@email.com", 34, "Los Angeles"),
    ("Carol Davis", "carol@email.com", 25, "Chicago"),
    # ... thousands more records
] * 1000  # 3000 records total

bulk_insert_users(users)

Example 4: Microsoft SQL Server with Transactions

from gosql.mssql import connect

def transfer_funds(from_account, to_account, amount):
    """Demonstrate ACID transaction with automatic rollback on error"""
    
    with connect(
        server="localhost",
        user="sa",
        password="Password123!",
        database="banking"
    ) as conn:
        try:
            with conn.begin() as transaction:
                cursor = transaction.cursor()
                
                # Check source account balance
                cursor.execute(
                    "SELECT balance FROM accounts WHERE account_id = ?", 
                    (from_account,)
                )
                balance = cursor.fetchone()[0]
                
                if balance < amount:
                    raise ValueError("Insufficient funds")
                
                # Debit source account
                cursor.execute(
                    "UPDATE accounts SET balance = balance - ? WHERE account_id = ?",
                    (amount, from_account)
                )
                
                # Credit destination account
                cursor.execute(
                    "UPDATE accounts SET balance = balance + ? WHERE account_id = ?",
                    (amount, to_account)
                )
                
                # Log transaction
                cursor.execute(
                    "INSERT INTO transactions (from_account, to_account, amount, timestamp) VALUES (?, ?, ?, GETDATE())",
                    (from_account, to_account, amount)
                )
                
                # Transaction automatically commits here
                print(f"Successfully transferred ${amount} from {from_account} to {to_account}")
                
        except Exception as e:
            # Transaction automatically rolls back
            print(f"Transfer failed: {e}")
            raise

# Usage
transfer_funds("ACC001", "ACC002", 500.00)

Example 5: Advanced PostgreSQL Features with Performance Monitoring

from gosql.postgres import connect
from gosql.core import performance_monitor
import time

def analyze_user_behavior():
    """Demonstrate advanced PostgreSQL features and performance monitoring"""
    
    # Reset performance monitor
    performance_monitor.reset()
    
    with connect(
        host="localhost",
        user="postgres",
        password="secret", 
        database="analytics",
        sslmode="prefer"
    ) as conn:
        cursor = conn.cursor()
        
        # Complex query with window functions
        start_time = time.time()
        
        cursor.execute("""
            WITH user_sessions AS (
                SELECT 
                    user_id,
                    session_start,
                    session_end,
                    EXTRACT(EPOCH FROM (session_end - session_start)) as duration_seconds,
                    ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY session_start) as session_rank
                FROM user_sessions 
                WHERE session_start >= %s
            ),
            user_stats AS (
                SELECT 
                    user_id,
                    COUNT(*) as total_sessions,
                    AVG(duration_seconds) as avg_session_duration,
                    MAX(duration_seconds) as max_session_duration,
                    SUM(duration_seconds) as total_time_spent
                FROM user_sessions
                GROUP BY user_id
            )
            SELECT 
                u.username,
                us.total_sessions,
                ROUND(us.avg_session_duration::numeric, 2) as avg_duration,
                ROUND(us.total_time_spent::numeric / 3600, 2) as total_hours
            FROM user_stats us
            JOIN users u ON u.id = us.user_id
            WHERE us.total_sessions >= 5
            ORDER BY us.total_time_spent DESC
            LIMIT 50
        """, ('2024-01-01',))
        
        results = cursor.fetchall()
        query_time = time.time() - start_time
        
        print("Top Active Users Analysis:")
        print("-" * 60)
        for username, sessions, avg_duration, total_hours in results:
            print(f"{username:20} | {sessions:3d} sessions | {avg_duration:6.1f}s avg | {total_hours:6.1f}h total")
        
        # Demonstrate COPY command for bulk data loading
        print("\nBulk loading data using PostgreSQL COPY...")
        start_time = time.time()
        
        # Create temporary table
        cursor.execute("""
            CREATE TEMP TABLE temp_events (
                user_id INTEGER,
                event_type VARCHAR(50),
                timestamp TIMESTAMP,
                metadata JSONB
            )
        """)
        
        # Simulate bulk copy operation
        # In real usage, you would use cursor.copy_from() with a file
        bulk_data = [
            (1001, 'page_view', '2024-07-01 10:00:00', '{"page": "/home"}'),
            (1002, 'click', '2024-07-01 10:01:00', '{"element": "button"}'),
            # ... thousands more records
        ] * 1000
        
        cursor.executemany(
            "INSERT INTO temp_events VALUES (%s, %s, %s, %s)",
            bulk_data
        )
        
        load_time = time.time() - start_time
        print(f"Loaded {len(bulk_data)} records in {load_time:.2f}s")
        print(f"Throughput: {len(bulk_data) / load_time:.0f} records/sec")
        
        conn.commit()
        
        # Show performance statistics
        stats = performance_monitor.get_stats()
        print("\nPerformance Statistics:")
        print(f"Total query time: {stats['total_query_time']:.3f}s")
        print(f"Average query time: {stats['average_query_time']*1000:.2f}ms")
        print(f"Queries per second: {stats['queries_per_second']:.0f}")

# Run the analysis
analyze_user_behavior()

🔧 Advanced Configuration

Connection Pooling

from gosql.mysql import connect

# Configure connection pool for high-traffic applications
conn = connect(
    host="db.example.com",
    user="app_user",
    password="secure_password",
    database="production",
    pool_size=50,          # Maximum 50 connections
    max_lifetime=3600,     # Connections expire after 1 hour
    max_idle_time=300,     # Close idle connections after 5 minutes
    charset="utf8mb4"
)

SSL Configuration

from gosql.postgres import connect

# Secure connection with SSL
conn = connect(
    host="secure-db.example.com",
    user="secure_user",
    password="secure_password",
    database="sensitive_data",
    sslmode="require",        # Require SSL
    sslcert="/path/to/client-cert.pem",
    sslkey="/path/to/client-key.pem",
    sslrootcert="/path/to/ca-cert.pem"
)

📊 Benchmarking

Run your own performance benchmarks:

from gosql.benchmarks import BenchmarkRunner

# Configure database connections
mysql_config = {
    'host': 'localhost',
    'user': 'root', 
    'password': 'password',
    'database': 'test'
}

# Run comprehensive benchmarks
runner = BenchmarkRunner()
runner.run_mysql_benchmarks(mysql_config, iterations=1000)
runner.print_summary()
runner.generate_report("benchmark_results.json")

Sample benchmark output:

BENCHMARK SUMMARY
================================================================================

SIMPLE_QUERY OPERATION:
----------------------------------------
GoSQL MySQL              | Avg: 0.85ms | P95: 1.2ms | QPS: 1176 | Mem: 12.1MB (3.1x faster)
mysql-connector-python   | Avg: 2.6ms  | P95: 4.8ms | QPS: 385  | Mem: 34.7MB

BATCH_INSERT OPERATION:
----------------------------------------  
GoSQL MySQL              | Avg: 45.2ms | P95: 67ms  | QPS: 22   | Mem: 15.3MB (2.8x faster)
mysql-connector-python   | Avg: 127ms  | P95: 189ms | QPS: 8    | Mem: 42.1MB

🏗 Architecture

GoSQL leverages Go's superior performance characteristics while maintaining Python's ease of use:

┌─────────────────────────┐
│     Python Application │
├─────────────────────────┤
│     GoSQL Python API   │  ← 100% compatible with native connectors
├─────────────────────────┤
│      CGO Bridge        │  ← High-performance Go-Python interface  
├─────────────────────────┤
│      Go Core Engine    │  ← Optimized connection pooling & query execution
├─────────────────────────┤
│    Database Drivers    │  ← Native Go database drivers
│  MySQL | PostgreSQL    │
│      | SQL Server      │
└─────────────────────────┘

Performance Optimizations

1. Connection Pooling: Intelligent connection reuse and lifecycle management
2. Batch Processing: Optimized bulk operations with reduced round trips
3. Memory Management: Zero-copy data transfer where possible
4. Type Conversion: Pre-compiled type converters for common data types
5. Query Optimization: Parameter placeholder conversion and query caching

🔄 Migration Guide

From mysql-connector-python

# Before
import mysql.connector
conn = mysql.connector.connect(host="localhost", user="root", password="secret")

# After - just change the import!
from gosql.mysql import connector  
conn = connector.connect(host="localhost", user="root", password="secret")

From psycopg2

# Before  
import psycopg2
conn = psycopg2.connect("host=localhost user=postgres password=secret")

# After
from gosql.postgres import connect
conn = connect(host="localhost", user="postgres", password="secret")

From pyodbc

# Before
import pyodbc
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=test;UID=sa;PWD=secret')

# After
from gosql.mssql import connect
conn = connect(server="localhost", database="test", user="sa", password="secret")

🧪 Testing

Run the test suite:

# Install test dependencies
pip install pytest pytest-cov

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=gosql --cov-report=html

🐳 Docker Support

Use GoSQL in containerized environments:

FROM python:3.9-slim

# Install GoSQL
RUN pip install gosql-connector

COPY your_app.py /app/
WORKDIR /app

CMD ["python", "your_app.py"]

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/coffeecms/gosql.git
cd gosql

# Install development dependencies
pip install -e ".[dev]"

# Run tests
make test

# Run benchmarks
make benchmark

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• 📖 Documentation: https://gosql.readthedocs.io
• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📧 Email: support@coffeecms.com

🗺 Roadmap

• v1.1: Support for SQLite and Oracle databases
• v1.2: Async/await support for asynchronous operations
• v1.3: Advanced query optimization and caching
• v1.4: GraphQL integration and ORM compatibility
• v2.0: Distributed query execution and sharding support

⭐ Star History

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GoSQL - Bringing Go's performance to Python database operations! 🚀

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