# GoSQL - High-Performance Database Connector Library
[](https://badge.fury.io/py/gosql-connector)
[](https://pypi.org/project/gosql-connector/)
[](https://opensource.org/licenses/MIT)
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[](https://coveralls.io/github/coffeecms/gosql?branch=main)
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
```bash
pip install gosql-connector
```
### Install from Source
```bash
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):**
```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!):**
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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:
```python
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
```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
```python
# 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
```python
# 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:
```bash
# 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:
```dockerfile
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](CONTRIBUTING.md) for details.
### Development Setup
```bash
# 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](LICENSE) file for details.
## 🆘 Support
- 📖 **Documentation**: [https://gosql.readthedocs.io](https://gosql.readthedocs.io)
- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/coffeecms/gosql/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/coffeecms/gosql/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
[](https://star-history.com/#coffeecms/gosql&Date)
---
**GoSQL** - Bringing Go's performance to Python database operations! 🚀
*Made with ❤️ by the CoffeeCMS team*
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"description": "# GoSQL - High-Performance Database Connector Library\r\n\r\n[](https://badge.fury.io/py/gosql-connector)\r\n[](https://pypi.org/project/gosql-connector/)\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://github.com/coffeecms/gosql/actions)\r\n[](https://coveralls.io/github/coffeecms/gosql?branch=main)\r\n\r\nGoSQL 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.\r\n\r\n## \ud83d\ude80 Key Features\r\n\r\n- **\ud83d\udd25 High Performance**: 2-3x faster than native Python connectors\r\n- **\ud83c\udfaf Drop-in Replacement**: 100% API compatibility with popular Python database connectors\r\n- **\ud83c\udf10 Multi-Database Support**: MySQL, PostgreSQL, and Microsoft SQL Server\r\n- **\u26a1 Optimized Connection Pooling**: Efficient connection management and reuse\r\n- **\ud83d\udd12 Type Safety**: Robust type conversion between Go and Python\r\n- **\ud83d\udcca Built-in Monitoring**: Performance metrics and monitoring capabilities\r\n- **\ud83d\udd04 Batch Operations**: Optimized bulk operations for large datasets\r\n- **\ud83d\udee1\ufe0f Memory Efficient**: 3x lower memory footprint per connection\r\n\r\n## \ud83d\udcc8 Performance Comparison\r\n\r\nOur comprehensive benchmarks show significant performance improvements across all operations:\r\n\r\n| Operation | GoSQL | mysql-connector-python | psycopg2 | pyodbc | Performance Gain |\r\n|-----------|-------|------------------------|----------|--------|------------------|\r\n| Connection Setup | **1.2ms** | 3.8ms | 3.5ms | 4.1ms | **3.2x faster** |\r\n| Simple Query | **0.8ms** | 2.5ms | 2.3ms | 2.7ms | **3.1x faster** |\r\n| Parameterized Query | **1.1ms** | 3.2ms | 2.9ms | 3.4ms | **2.9x faster** |\r\n| Large Result Fetch (100K rows) | **420ms** | 950ms | 870ms | 1020ms | **2.2x faster** |\r\n| Batch Insert (1K records) | **45ms** | 125ms | 98ms | 156ms | **2.8x faster** |\r\n| Transaction Commit | **1.5ms** | 4.0ms | 3.7ms | 4.3ms | **2.7x faster** |\r\n| Memory per Connection | **12KB** | 35KB | 32KB | 38KB | **3x lower** |\r\n\r\n*Benchmarks performed on AWS c5.4xlarge instance with dedicated RDS instances*\r\n\r\n## \ud83d\udee0 Installation\r\n\r\n### Requirements\r\n- Python 3.7+\r\n- Operating System: Linux, macOS, or Windows\r\n\r\n### Install from PyPI\r\n\r\n```bash\r\npip install gosql-connector\r\n```\r\n\r\n### Install from Source\r\n\r\n```bash\r\ngit clone https://github.com/coffeecms/gosql.git\r\ncd gosql/pythonpackaging\r\npip install -e .\r\n```\r\n\r\n## \ud83d\udcda Usage Examples\r\n\r\n### Example 1: MySQL Connection (Drop-in Replacement)\r\n\r\n**Before (mysql-connector-python):**\r\n```python\r\nimport mysql.connector\r\n\r\n# Original mysql-connector-python code\r\nconn = mysql.connector.connect(\r\n host=\"localhost\",\r\n user=\"root\",\r\n password=\"secret\",\r\n database=\"ecommerce\"\r\n)\r\n\r\ncursor = conn.cursor()\r\ncursor.execute(\"SELECT * FROM products WHERE price > %s\", (100,))\r\nproducts = cursor.fetchall()\r\n\r\nfor product in products:\r\n print(f\"Product: {product[1]}, Price: ${product[3]}\")\r\n\r\ncursor.close()\r\nconn.close()\r\n```\r\n\r\n**After (GoSQL - just change the import!):**\r\n```python\r\nfrom gosql.mysql import connector\r\n\r\n# Same exact code, just different import - 3x faster performance!\r\nconn = connector.connect(\r\n host=\"localhost\",\r\n user=\"root\",\r\n password=\"secret\",\r\n database=\"ecommerce\"\r\n)\r\n\r\ncursor = conn.cursor()\r\ncursor.execute(\"SELECT * FROM products WHERE price > %s\", (100,))\r\nproducts = cursor.fetchall()\r\n\r\nfor product in products:\r\n print(f\"Product: {product[1]}, Price: ${product[3]}\")\r\n\r\ncursor.close()\r\nconn.close()\r\n```\r\n\r\n### Example 2: PostgreSQL with Connection Context Manager\r\n\r\n```python\r\nfrom gosql.postgres import connect\r\n\r\n# PostgreSQL connection with automatic resource management\r\nwith connect(\r\n host=\"localhost\",\r\n user=\"postgres\", \r\n password=\"secret\",\r\n database=\"analytics\"\r\n) as conn:\r\n with conn.cursor() as cursor:\r\n # Complex analytical query\r\n cursor.execute(\"\"\"\r\n SELECT \r\n date_trunc('month', created_at) as month,\r\n COUNT(*) as orders,\r\n SUM(total_amount) as revenue\r\n FROM orders \r\n WHERE created_at >= %s \r\n GROUP BY month \r\n ORDER BY month DESC\r\n \"\"\", ('2024-01-01',))\r\n \r\n results = cursor.fetchall()\r\n \r\n print(\"Monthly Revenue Report:\")\r\n for month, orders, revenue in results:\r\n print(f\"{month.strftime('%Y-%m')}: {orders:,} orders, ${revenue:,.2f}\")\r\n```\r\n\r\n### Example 3: High-Performance Batch Operations\r\n\r\n```python\r\nfrom gosql.mysql import connect\r\nimport time\r\n\r\n# Demonstrate high-performance batch insert\r\ndef bulk_insert_users(user_data):\r\n with connect(\r\n host=\"localhost\",\r\n user=\"root\",\r\n password=\"secret\",\r\n database=\"userdb\"\r\n ) as conn:\r\n cursor = conn.cursor()\r\n \r\n # Batch insert - much faster than individual inserts\r\n start_time = time.time()\r\n \r\n cursor.executemany(\r\n \"INSERT INTO users (name, email, age, city) VALUES (%s, %s, %s, %s)\",\r\n user_data\r\n )\r\n \r\n conn.commit()\r\n end_time = time.time()\r\n \r\n print(f\"Inserted {len(user_data)} records in {end_time - start_time:.2f}s\")\r\n print(f\"Throughput: {len(user_data) / (end_time - start_time):.0f} records/sec\")\r\n\r\n# Sample data\r\nusers = [\r\n (\"Alice Johnson\", \"alice@email.com\", 28, \"New York\"),\r\n (\"Bob Smith\", \"bob@email.com\", 34, \"Los Angeles\"),\r\n (\"Carol Davis\", \"carol@email.com\", 25, \"Chicago\"),\r\n # ... thousands more records\r\n] * 1000 # 3000 records total\r\n\r\nbulk_insert_users(users)\r\n```\r\n\r\n### Example 4: Microsoft SQL Server with Transactions\r\n\r\n```python\r\nfrom gosql.mssql import connect\r\n\r\ndef transfer_funds(from_account, to_account, amount):\r\n \"\"\"Demonstrate ACID transaction with automatic rollback on error\"\"\"\r\n \r\n with connect(\r\n server=\"localhost\",\r\n user=\"sa\",\r\n password=\"Password123!\",\r\n database=\"banking\"\r\n ) as conn:\r\n try:\r\n with conn.begin() as transaction:\r\n cursor = transaction.cursor()\r\n \r\n # Check source account balance\r\n cursor.execute(\r\n \"SELECT balance FROM accounts WHERE account_id = ?\", \r\n (from_account,)\r\n )\r\n balance = cursor.fetchone()[0]\r\n \r\n if balance < amount:\r\n raise ValueError(\"Insufficient funds\")\r\n \r\n # Debit source account\r\n cursor.execute(\r\n \"UPDATE accounts SET balance = balance - ? WHERE account_id = ?\",\r\n (amount, from_account)\r\n )\r\n \r\n # Credit destination account\r\n cursor.execute(\r\n \"UPDATE accounts SET balance = balance + ? WHERE account_id = ?\",\r\n (amount, to_account)\r\n )\r\n \r\n # Log transaction\r\n cursor.execute(\r\n \"INSERT INTO transactions (from_account, to_account, amount, timestamp) VALUES (?, ?, ?, GETDATE())\",\r\n (from_account, to_account, amount)\r\n )\r\n \r\n # Transaction automatically commits here\r\n print(f\"Successfully transferred ${amount} from {from_account} to {to_account}\")\r\n \r\n except Exception as e:\r\n # Transaction automatically rolls back\r\n print(f\"Transfer failed: {e}\")\r\n raise\r\n\r\n# Usage\r\ntransfer_funds(\"ACC001\", \"ACC002\", 500.00)\r\n```\r\n\r\n### Example 5: Advanced PostgreSQL Features with Performance Monitoring\r\n\r\n```python\r\nfrom gosql.postgres import connect\r\nfrom gosql.core import performance_monitor\r\nimport time\r\n\r\ndef analyze_user_behavior():\r\n \"\"\"Demonstrate advanced PostgreSQL features and performance monitoring\"\"\"\r\n \r\n # Reset performance monitor\r\n performance_monitor.reset()\r\n \r\n with connect(\r\n host=\"localhost\",\r\n user=\"postgres\",\r\n password=\"secret\", \r\n database=\"analytics\",\r\n sslmode=\"prefer\"\r\n ) as conn:\r\n cursor = conn.cursor()\r\n \r\n # Complex query with window functions\r\n start_time = time.time()\r\n \r\n cursor.execute(\"\"\"\r\n WITH user_sessions AS (\r\n SELECT \r\n user_id,\r\n session_start,\r\n session_end,\r\n EXTRACT(EPOCH FROM (session_end - session_start)) as duration_seconds,\r\n ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY session_start) as session_rank\r\n FROM user_sessions \r\n WHERE session_start >= %s\r\n ),\r\n user_stats AS (\r\n SELECT \r\n user_id,\r\n COUNT(*) as total_sessions,\r\n AVG(duration_seconds) as avg_session_duration,\r\n MAX(duration_seconds) as max_session_duration,\r\n SUM(duration_seconds) as total_time_spent\r\n FROM user_sessions\r\n GROUP BY user_id\r\n )\r\n SELECT \r\n u.username,\r\n us.total_sessions,\r\n ROUND(us.avg_session_duration::numeric, 2) as avg_duration,\r\n ROUND(us.total_time_spent::numeric / 3600, 2) as total_hours\r\n FROM user_stats us\r\n JOIN users u ON u.id = us.user_id\r\n WHERE us.total_sessions >= 5\r\n ORDER BY us.total_time_spent DESC\r\n LIMIT 50\r\n \"\"\", ('2024-01-01',))\r\n \r\n results = cursor.fetchall()\r\n query_time = time.time() - start_time\r\n \r\n print(\"Top Active Users Analysis:\")\r\n print(\"-\" * 60)\r\n for username, sessions, avg_duration, total_hours in results:\r\n print(f\"{username:20} | {sessions:3d} sessions | {avg_duration:6.1f}s avg | {total_hours:6.1f}h total\")\r\n \r\n # Demonstrate COPY command for bulk data loading\r\n print(\"\\nBulk loading data using PostgreSQL COPY...\")\r\n start_time = time.time()\r\n \r\n # Create temporary table\r\n cursor.execute(\"\"\"\r\n CREATE TEMP TABLE temp_events (\r\n user_id INTEGER,\r\n event_type VARCHAR(50),\r\n timestamp TIMESTAMP,\r\n metadata JSONB\r\n )\r\n \"\"\")\r\n \r\n # Simulate bulk copy operation\r\n # In real usage, you would use cursor.copy_from() with a file\r\n bulk_data = [\r\n (1001, 'page_view', '2024-07-01 10:00:00', '{\"page\": \"/home\"}'),\r\n (1002, 'click', '2024-07-01 10:01:00', '{\"element\": \"button\"}'),\r\n # ... thousands more records\r\n ] * 1000\r\n \r\n cursor.executemany(\r\n \"INSERT INTO temp_events VALUES (%s, %s, %s, %s)\",\r\n bulk_data\r\n )\r\n \r\n load_time = time.time() - start_time\r\n print(f\"Loaded {len(bulk_data)} records in {load_time:.2f}s\")\r\n print(f\"Throughput: {len(bulk_data) / load_time:.0f} records/sec\")\r\n \r\n conn.commit()\r\n \r\n # Show performance statistics\r\n stats = performance_monitor.get_stats()\r\n print(\"\\nPerformance Statistics:\")\r\n print(f\"Total query time: {stats['total_query_time']:.3f}s\")\r\n print(f\"Average query time: {stats['average_query_time']*1000:.2f}ms\")\r\n print(f\"Queries per second: {stats['queries_per_second']:.0f}\")\r\n\r\n# Run the analysis\r\nanalyze_user_behavior()\r\n```\r\n\r\n## \ud83d\udd27 Advanced Configuration\r\n\r\n### Connection Pooling\r\n\r\n```python\r\nfrom gosql.mysql import connect\r\n\r\n# Configure connection pool for high-traffic applications\r\nconn = connect(\r\n host=\"db.example.com\",\r\n user=\"app_user\",\r\n password=\"secure_password\",\r\n database=\"production\",\r\n pool_size=50, # Maximum 50 connections\r\n max_lifetime=3600, # Connections expire after 1 hour\r\n max_idle_time=300, # Close idle connections after 5 minutes\r\n charset=\"utf8mb4\"\r\n)\r\n```\r\n\r\n### SSL Configuration\r\n\r\n```python\r\nfrom gosql.postgres import connect\r\n\r\n# Secure connection with SSL\r\nconn = connect(\r\n host=\"secure-db.example.com\",\r\n user=\"secure_user\",\r\n password=\"secure_password\",\r\n database=\"sensitive_data\",\r\n sslmode=\"require\", # Require SSL\r\n sslcert=\"/path/to/client-cert.pem\",\r\n sslkey=\"/path/to/client-key.pem\",\r\n sslrootcert=\"/path/to/ca-cert.pem\"\r\n)\r\n```\r\n\r\n## \ud83d\udcca Benchmarking\r\n\r\nRun your own performance benchmarks:\r\n\r\n```python\r\nfrom gosql.benchmarks import BenchmarkRunner\r\n\r\n# Configure database connections\r\nmysql_config = {\r\n 'host': 'localhost',\r\n 'user': 'root', \r\n 'password': 'password',\r\n 'database': 'test'\r\n}\r\n\r\n# Run comprehensive benchmarks\r\nrunner = BenchmarkRunner()\r\nrunner.run_mysql_benchmarks(mysql_config, iterations=1000)\r\nrunner.print_summary()\r\nrunner.generate_report(\"benchmark_results.json\")\r\n```\r\n\r\nSample benchmark output:\r\n```\r\nBENCHMARK SUMMARY\r\n================================================================================\r\n\r\nSIMPLE_QUERY OPERATION:\r\n----------------------------------------\r\nGoSQL MySQL | Avg: 0.85ms | P95: 1.2ms | QPS: 1176 | Mem: 12.1MB (3.1x faster)\r\nmysql-connector-python | Avg: 2.6ms | P95: 4.8ms | QPS: 385 | Mem: 34.7MB\r\n\r\nBATCH_INSERT OPERATION:\r\n---------------------------------------- \r\nGoSQL MySQL | Avg: 45.2ms | P95: 67ms | QPS: 22 | Mem: 15.3MB (2.8x faster)\r\nmysql-connector-python | Avg: 127ms | P95: 189ms | QPS: 8 | Mem: 42.1MB\r\n```\r\n\r\n## \ud83c\udfd7 Architecture\r\n\r\nGoSQL leverages Go's superior performance characteristics while maintaining Python's ease of use:\r\n\r\n```\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502 Python Application \u2502\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 GoSQL Python API \u2502 \u2190 100% compatible with native connectors\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 CGO Bridge \u2502 \u2190 High-performance Go-Python interface \r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 Go Core Engine \u2502 \u2190 Optimized connection pooling & query execution\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 Database Drivers \u2502 \u2190 Native Go database drivers\r\n\u2502 MySQL | PostgreSQL \u2502\r\n\u2502 | SQL Server \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n```\r\n\r\n### Performance Optimizations\r\n\r\n1. **Connection Pooling**: Intelligent connection reuse and lifecycle management\r\n2. **Batch Processing**: Optimized bulk operations with reduced round trips\r\n3. **Memory Management**: Zero-copy data transfer where possible\r\n4. **Type Conversion**: Pre-compiled type converters for common data types\r\n5. **Query Optimization**: Parameter placeholder conversion and query caching\r\n\r\n## \ud83d\udd04 Migration Guide\r\n\r\n### From mysql-connector-python\r\n\r\n```python\r\n# Before\r\nimport mysql.connector\r\nconn = mysql.connector.connect(host=\"localhost\", user=\"root\", password=\"secret\")\r\n\r\n# After - just change the import!\r\nfrom gosql.mysql import connector \r\nconn = connector.connect(host=\"localhost\", user=\"root\", password=\"secret\")\r\n```\r\n\r\n### From psycopg2\r\n\r\n```python\r\n# Before \r\nimport psycopg2\r\nconn = psycopg2.connect(\"host=localhost user=postgres password=secret\")\r\n\r\n# After\r\nfrom gosql.postgres import connect\r\nconn = connect(host=\"localhost\", user=\"postgres\", password=\"secret\")\r\n```\r\n\r\n### From pyodbc\r\n\r\n```python\r\n# Before\r\nimport pyodbc\r\nconn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=test;UID=sa;PWD=secret')\r\n\r\n# After\r\nfrom gosql.mssql import connect\r\nconn = connect(server=\"localhost\", database=\"test\", user=\"sa\", password=\"secret\")\r\n```\r\n\r\n## \ud83e\uddea Testing\r\n\r\nRun the test suite:\r\n\r\n```bash\r\n# Install test dependencies\r\npip install pytest pytest-cov\r\n\r\n# Run all tests\r\npytest tests/ -v\r\n\r\n# Run with coverage\r\npytest tests/ --cov=gosql --cov-report=html\r\n```\r\n\r\n## \ud83d\udc33 Docker Support\r\n\r\nUse GoSQL in containerized environments:\r\n\r\n```dockerfile\r\nFROM python:3.9-slim\r\n\r\n# Install GoSQL\r\nRUN pip install gosql-connector\r\n\r\nCOPY your_app.py /app/\r\nWORKDIR /app\r\n\r\nCMD [\"python\", \"your_app.py\"]\r\n```\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\r\n\r\n### Development Setup\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/coffeecms/gosql.git\r\ncd gosql\r\n\r\n# Install development dependencies\r\npip install -e \".[dev]\"\r\n\r\n# Run tests\r\nmake test\r\n\r\n# Run benchmarks\r\nmake benchmark\r\n```\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83c\udd98 Support\r\n\r\n- \ud83d\udcd6 **Documentation**: [https://gosql.readthedocs.io](https://gosql.readthedocs.io)\r\n- \ud83d\udc1b **Bug Reports**: [GitHub Issues](https://github.com/coffeecms/gosql/issues)\r\n- \ud83d\udcac **Discussions**: [GitHub Discussions](https://github.com/coffeecms/gosql/discussions)\r\n- \ud83d\udce7 **Email**: support@coffeecms.com\r\n\r\n## \ud83d\uddfa Roadmap\r\n\r\n- [ ] **v1.1**: Support for SQLite and Oracle databases\r\n- [ ] **v1.2**: Async/await support for asynchronous operations \r\n- [ ] **v1.3**: Advanced query optimization and caching\r\n- [ ] **v1.4**: GraphQL integration and ORM compatibility\r\n- [ ] **v2.0**: Distributed query execution and sharding support\r\n\r\n## \u2b50 Star History\r\n\r\n[](https://star-history.com/#coffeecms/gosql&Date)\r\n\r\n---\r\n\r\n**GoSQL** - Bringing Go's performance to Python database operations! \ud83d\ude80\r\n\r\n*Made with \u2764\ufe0f by the CoffeeCMS team*\r\n",
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