# Database Handlers

This directory contains database handlers for MongoDB and PostgreSQL used in the backend automate services.

## Overview

The database handlers provide a consistent interface for interacting with different database systems. They implement:

- Connection pooling
- Retry mechanisms
- Error handling
- Thread safety
- Context managers for resource management

## MongoDB Handler

The MongoDB handler is implemented as a singleton pattern to ensure efficient connection management across the application. It provides:

- Connection pooling via PyMongo's built-in connection pool
- Automatic retry capabilities for MongoDB operations
- Context manager for MongoDB collections to ensure connections are properly closed
- Thread safety for concurrent operations

### MongoDB Performance

The MongoDB handler has been tested with a concurrent load test:

```
Concurrent Operation Test Results:
Total threads: 100
Passed: 100
Failed: 0
Execution time: 0.73 seconds
Operations per second: 137.61
```

## PostgreSQL Handler

The PostgreSQL handler leverages SQLAlchemy for ORM capabilities and connection management. It provides:

- Connection pooling via SQLAlchemy's connection pool
- ORM models with CRUD operations
- Filter methods for querying data
- Transaction management

### PostgreSQL Performance

The PostgreSQL handler has been tested with a concurrent load test:

```
Concurrent Operation Test Results:
Total threads: 100
Passed: 100
Failed: 0
Execution time: 0.30 seconds
Operations per second: 332.11
```

## Usage Examples

### MongoDB Example

```python
from Controllers.Mongo.database import mongo_handler

# Using the context manager for automatic connection management
with mongo_handler.collection("users") as users_collection:
    # Perform operations
    users_collection.insert_one({"name": "John", "email": "john@example.com"})
    user = users_collection.find_one({"email": "john@example.com"})
```

### PostgreSQL Example

```python
from Controllers.Postgres.schema import EndpointRestriction

# Using the session context manager
with EndpointRestriction.new_session() as db_session:
    # Create a new record
    new_endpoint = EndpointRestriction(
        endpoint_code="TEST_API",
        endpoint_name="Test API",
        endpoint_method="GET",
        endpoint_function="test_function",
        endpoint_desc="Test description",
        is_confirmed=True
    )
    new_endpoint.save(db=db_session)
    
    # Query records
    result = EndpointRestriction.filter_one(
        EndpointRestriction.endpoint_code == "TEST_API",
        db=db_session
    ).data
```

## Configuration

Both handlers are configured via environment variables:

### MongoDB Configuration

- `MONGO_ENGINE`: Database engine (mongodb)
- `MONGO_HOST`: Database host
- `MONGO_PORT`: Database port
- `MONGO_USER`: Database username
- `MONGO_PASSWORD`: Database password
- `MONGO_DB`: Database name
- `MONGO_AUTH_DB`: Authentication database

### PostgreSQL Configuration

- `POSTGRES_ENGINE`: Database engine (postgresql+psycopg2)
- `POSTGRES_HOST`: Database host
- `POSTGRES_PORT`: Database port
- `POSTGRES_USER`: Database username
- `POSTGRES_PASSWORD`: Database password
- `POSTGRES_DB`: Database name
- `POSTGRES_POOL_SIZE`: Connection pool size
- `POSTGRES_POOL_PRE_PING`: Whether to ping the database before using a connection

## Testing

Both handlers include comprehensive test suites that verify:

- Basic CRUD operations
- Complex queries
- Nested documents (MongoDB)
- Array operations (MongoDB)
- Aggregation (MongoDB)
- Index operations
- Concurrent operations

To run the tests:

```bash
# MongoDB tests
python -m Controllers.Mongo.implementations

# PostgreSQL tests
python -m Controllers.Postgres.implementations
```