<Vishal> Cluster name to lower case and Conn pool

.env.example

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+# e6data Python Connector Environment Variables
+# Copy this file to .env and fill in your actual values
+
+# Required - e6data connection credentials
+E6DATA_USERNAME=your-email@domain.com
+E6DATA_PASSWORD=your-access-token-from-e6data-console
+
+# Optional - connection settings (defaults provided)
+E6DATA_HOST=localhost
+E6DATA_PORT=443
+E6DATA_DATABASE=test_db
+E6DATA_CATALOG=default
+E6DATA_CLUSTER_NAME=test_cluster
+E6DATA_SECURE=true
+
+# Usage:
+# 1. Copy this file: cp .env.example .env
+# 2. Edit .env with your actual credentials
+# 3. Export variables: export $(grep -v '^#' .env | xargs)
+# 4. Run tests: python test/test_simple_connection_pool.py

CONNECTION_POOL_IMPLEMENTATION.md

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+# Connection Pool Implementation for e6data Python Connector
+
+## Overview
+
+I've successfully implemented a robust, thread-safe connection pooling solution for the e6data Python connector. This implementation allows multiple threads to efficiently share and reuse connections, significantly reducing connection overhead and improving performance for concurrent query execution.
+
+## Features Implemented
+
+### 1. Thread-Safe Connection Management
+- **Thread-local connection mapping**: Each thread can reuse its own connection across multiple queries
+- **Synchronized access**: All pool operations protected by threading locks
+- **Automatic connection assignment**: Threads automatically get assigned connections from the pool
+
+### 2. Connection Lifecycle Management
+- **Min/Max pool sizes**: Configurable minimum and maximum connection limits
+- **Overflow connections**: Support for temporary overflow connections when pool is exhausted
+- **Connection recycling**: Automatic recycling of connections based on age
+- **Health checking**: Pre-ping and health verification before returning connections
+
+### 3. Resource Management
+- **Automatic cleanup**: Connections returned to pool after use
+- **Context manager support**: Using `with` statement for automatic connection management
+- **Graceful shutdown**: Proper cleanup of all connections when pool is closed
+
+### 4. Monitoring and Statistics
+- **Real-time statistics**: Track active, idle, and total connections
+- **Request tracking**: Monitor total requests and failed connections
+- **Thread monitoring**: Track waiting threads and thread-specific connections
+
+## Implementation Files
+
+### 1. `e6data_python_connector/connection_pool.py`
+Main implementation containing:
+- `ConnectionPool` class: Thread-safe pool manager
+- `PooledConnection` class: Wrapper for pooled connections with metadata
+- Health checking and connection replacement logic
+- Statistics tracking and monitoring
+
+### 2. `test_connection_pool.py`
+Comprehensive test suite covering:
+- Pool initialization and basic operations
+- Thread-safe concurrent access
+- Connection reuse within threads
+- Health checking and recycling
+- Overflow connection handling
+- Context manager functionality
+
+### 3. `demo_connection_pool.py`
+Interactive demonstration showing:
+- Basic pool usage patterns
+- Concurrent query execution
+- Connection reuse across queries
+- Context manager usage
+- Pool exhaustion handling
+
+## Usage Examples
+
+### Basic Usage
+```python
+from e6data_python_connector import ConnectionPool
+
+# Create a connection pool
+pool = ConnectionPool(
+    min_size=2,
+    max_size=10,
+    host='your.cluster.e6data.com',
+    port=443,
+    username='user@example.com',
+    password='access_token',
+    database='default',
+    cluster_name='your_cluster',
+    secure=True
+)
+
+# Get connection and execute query
+conn = pool.get_connection()
+cursor = conn.cursor()
+cursor.execute("SELECT * FROM table")
+results = cursor.fetchall()
+
+# Return connection to pool
+pool.return_connection(conn)
+```
+
+### Context Manager Pattern
+```python
+# Automatic connection management
+with pool.get_connection_context() as conn:
+    cursor = conn.cursor()
+    cursor.execute("SELECT * FROM table")
+    results = cursor.fetchall()
+# Connection automatically returned to pool
+```
+
+### Concurrent Query Execution
+```python
+import concurrent.futures
+
+def execute_query(query_id, query):
+    # Each thread reuses its connection
+    conn = pool.get_connection()
+    cursor = conn.cursor()
+    cursor.execute(query)
+    results = cursor.fetchall()
+    pool.return_connection(conn)
+    return results
+
+# Execute multiple queries concurrently
+with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
+    queries = ["SELECT 1", "SELECT 2", "SELECT 3"]
+    futures = [executor.submit(execute_query, i, q) for i, q in enumerate(queries)]
+    results = [f.result() for f in concurrent.futures.as_completed(futures)]
+```
+
+## Key Design Decisions
+
+### 1. Thread-Local Connection Reuse
+- Same thread automatically reuses its assigned connection
+- Reduces connection churn and improves performance
+- Maintains connection state across queries
+
+### 2. Health Checking Strategy
+- Optional pre-ping before returning connections
+- Automatic replacement of unhealthy connections
+- Configurable connection recycling based on age
+
+### 3. Overflow Management
+- Temporary connections created when pool exhausted
+- Automatically closed when returned if pool is full
+- Prevents deadlocks while maintaining resource limits
+
+### 4. Statistics and Monitoring
+- Real-time pool statistics for debugging and monitoring
+- Track connection usage patterns
+- Identify performance bottlenecks
+
+## Configuration Options
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `min_size` | 2 | Minimum connections to maintain |
+| `max_size` | 10 | Maximum connections in pool |
+| `max_overflow` | 5 | Additional temporary connections |
+| `timeout` | 30.0 | Timeout for getting connection (seconds) |
+| `recycle` | 3600 | Maximum connection age (seconds) |
+| `debug` | False | Enable debug logging |
+| `pre_ping` | True | Check connection health before use |
+
+## Performance Benefits
+
+### Without Connection Pool
+- New connection created for each query
+- Connection overhead for each operation
+- No connection reuse between queries
+- Higher latency and resource usage
+
+### With Connection Pool
+- Connections reused across queries
+- Reduced connection overhead
+- Better resource utilization
+- Lower latency for query execution
+- Thread-safe concurrent access
+
+## Testing
+
+Run the comprehensive test suite:
+```bash
+python -m unittest test_connection_pool.py -v
+```
+
+Run the interactive demo:
+```bash
+# Set environment variables
+export ENGINE_IP=your.cluster.e6data.com
+export EMAIL=user@example.com
+export PASSWORD=access_token
+export DB_NAME=default
+
+# Run demo
+python demo_connection_pool.py
+```
+
+## Integration with Existing Code
+
+The connection pool is fully backward compatible. Existing code using direct `Connection` objects continues to work unchanged. To use pooling:
+
+```python
+# Instead of:
+conn = Connection(host='...', port=443, ...)
+
+# Use:
+pool = ConnectionPool(host='...', port=443, ...)
+conn = pool.get_connection()
+# ... use connection ...
+pool.return_connection(conn)
+```
+
+## Thread Safety Guarantees
+
+1. **Pool operations**: All pool methods are thread-safe
+2. **Connection reuse**: Same thread always gets same connection
+3. **Statistics**: Safe to read from any thread
+4. **Cleanup**: Safe to call from any thread
+
+## Future Enhancements
+
+Potential improvements for future versions:
+1. **Async support**: Add async/await support for async applications
+2. **Connection warming**: Pre-execute queries to warm connections
+3. **Load balancing**: Distribute connections across multiple clusters
+4. **Metrics integration**: Export metrics to monitoring systems
+5. **Connection pooling strategies**: Different allocation strategies (LIFO, FIFO, LRU)
+
+## Summary
+
+The connection pool implementation provides:
+- ✅ Thread-safe connection sharing and reuse
+- ✅ Automatic connection lifecycle management
+- ✅ Health checking and connection recovery
+- ✅ Overflow connection support
+- ✅ Comprehensive monitoring and statistics
+- ✅ Context manager support
+- ✅ Full backward compatibility
+
+This implementation significantly improves performance for applications executing multiple concurrent queries by eliminating connection overhead and enabling efficient connection reuse across threads.