Version Dataset Snapshots for Reproducible Digital Twin Training Runs with LakeFS and MLflow

"""
Production implementation for version dataset snapshots.
Provides reproducible digital twin training runs using LakeFS and MLflow.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.exc import SQLAlchemyError

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    lakefs_url: str = os.getenv('LAKEFS_URL')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')
    database_url: str = os.getenv('DATABASE_URL')

engine = create_engine(Config.database_url)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'dataset_id' not in data:
        raise ValueError('Missing dataset_id in input data')
    if 'version' not in data:
        raise ValueError('Missing version in input data')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized input data
    """
    return {key: str(value).strip() for key, value in data.items()}

async def fetch_data(dataset_id: str, version: str) -> Dict[str, Any]:
    """Fetch dataset version from LakeFS.
    
    Args:
        dataset_id: ID of the dataset
        version: Version of the dataset
    Returns:
        Dataset version data
    Raises:
        HTTPError: If fetch fails
    """
    response = requests.get(f'{Config.lakefs_url}/repositories/my_repo/objects/{dataset_id}/{version}')
    response.raise_for_status()  # Raise an error on a bad response
    return response.json()

async def transform_records(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Transform fetched records for processing.
    
    Args:
        data: Raw data from LakeFS
    Returns:
        List of transformed records
    """
    # Implement transformation logic here
    transformed = []  # Placeholder for transformed data
    return transformed

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save processed records to the database.
    
    Args:
        records: List of records to save
    Raises:
        SQLAlchemyError: If saving fails
    """
    with engine.connect() as connection:
        for record in records:
            stmt = text('INSERT INTO dataset (column1, column2) VALUES (:val1, :val2)')
            connection.execute(stmt, val1=record['field1'], val2=record['field2'])
    logger.info('Records saved successfully')

async def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    metrics = {'count': len(records), 'average': sum(record['value'] for record in records) / len(records)}
    return metrics

async def process_batch(data: Dict[str, Any]) -> None:
    """Process a batch of data for MLflow.
    
    Args:
        data: Input data to process
    Raises:
        Exception: If processing fails
    """
    try:
        await validate_input(data)
        sanitized_data = await sanitize_fields(data)
        raw_data = await fetch_data(sanitized_data['dataset_id'], sanitized_data['version'])
        transformed_records = await transform_records(raw_data)
        await save_to_db(transformed_records)
        logger.info('Batch processed successfully')
    except ValueError as ve:
        logger.error(f'Validation error: {ve}')
        raise
    except HTTPError as he:
        logger.error(f'HTTP error occurred: {he}')
        raise
    except SQLAlchemyError as se:
        logger.error(f'Database error occurred: {se}')
        raise
    except Exception as e:
        logger.error(f'General error: {e}')
        raise

async def retry_with_backoff(func, *args, retries: int = 3, delay: float = 1.0) -> None:
    """Retry a function with exponential backoff.
    
    Args:
        func: Function to call
        args: Arguments for the function
        retries: Number of retries
        delay: Initial delay in seconds
    Raises:
        Exception: If all retries fail
    """
    for attempt in range(retries):
        try:
            await func(*args)
            return
        except Exception as e:
            logger.warning(f'Attempt {attempt + 1} failed: {e}')
            time.sleep(delay)
            delay *= 2  # Exponential backoff
    raise Exception('Max retries exceeded')

class DatasetProcessor:
    """Main class for processing datasets.
    
    This orchestrates the workflow for fetching, transforming, and saving dataset snapshots.
    """

    async def run(self, data: Dict[str, Any]) -> None:
        """Run the dataset processing workflow.
        
        Args:
            data: Input data to process
        """
        await retry_with_backoff(process_batch, data)

if __name__ == '__main__':
    # Example usage
    import asyncio
    data = {'dataset_id': 'my_dataset', 'version': 'v1.0'}
    processor = DatasetProcessor()
    asyncio.run(processor.run(data))