Version and Audit Manufacturing Data Lake Commits with LakeFS and Apache Spark

"""
Production implementation for Version and Audit Manufacturing Data Lake Commits with LakeFS and Apache Spark.
Provides secure, scalable operations for managing manufacturing data lake commits.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import json
import requests
from pyspark.sql import SparkSession
from pyspark.sql import DataFrame
from time import sleep

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

class Config:
    lakefs_endpoint: str = os.getenv('LAKEFS_ENDPOINT')
    api_key: str = os.getenv('LAKEFS_API_KEY')
    max_retries: int = 5
    retry_delay: int = 2  # seconds

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

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

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for processing.
    
    Args:
        data: Raw input data
    Returns:
        Normalized data
    """
    # Example normalization logic
    data['timestamp'] = data.get('timestamp', '')  # Ensure timestamp is present
    return data

async def fetch_data(commit_id: str) -> Dict[str, Any]:
    """Fetch data from LakeFS for a given commit ID.
    
    Args:
        commit_id: ID of the commit
    Returns:
        Fetched data
    Raises:
        RuntimeError: If data fetch fails
    """
    url = f'{Config.lakefs_endpoint}/commits/{commit_id}'
    headers = {'Authorization': f'Bearer {Config.api_key}'}
    response = requests.get(url, headers=headers)
    if response.status_code != 200:
        raise RuntimeError(f'Failed to fetch data: {response.text}')  # Log the error
    return response.json()

async def process_batch(data: List[Dict[str, Any]]) -> DataFrame:
    """Process a batch of data and return a DataFrame.
    
    Args:
        data: List of data records
    Returns:
        Spark DataFrame
    """
    spark = SparkSession.builder.appName('DataLakeProcessing').getOrCreate()  # Create Spark session
    df = spark.createDataFrame(data)
    return df

async def aggregate_metrics(df: DataFrame) -> Dict[str, Any]:
    """Aggregate metrics from the DataFrame.
    
    Args:
        df: Spark DataFrame
    Returns:
        Aggregated metrics
    """
    # Example aggregation logic
    metrics = df.groupBy('category').count().collect()  # Aggregate by category
    return {row['category']: row['count'] for row in metrics}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        RuntimeError: If saving fails
    """
    logger.info('Saving data to DB')  # Log the save operation
    # Database save logic goes here
    # Simulate save with a sleep
    sleep(1)

async def log_audit(commit_id: str, metrics: Dict[str, Any]) -> None:
    """Log audit information for the commit.
    
    Args:
        commit_id: ID of the commit
        metrics: Metrics to log
    """
    logger.info(f'Audit log for commit {commit_id}: {json.dumps(metrics)}')  # Log audit information

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Function to wrap
    """
    async def wrapper(*args, **kwargs):
        for attempt in range(Config.max_retries):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                logger.error(f'Error: {e}')  # Log the error
                sleep(Config.retry_delay)
        raise RuntimeError('Max retries exceeded')  # Log final failure
    return wrapper

class DataLakeManager:
    """Orchestrator for managing data lake commits.
    """

    @handle_errors
    async def process_commit(self, commit_id: str, data: Dict[str, Any]) -> None:
        """Process a single commit.
        
        Args:
            commit_id: ID of the commit
            data: Input data for the commit
        """
        await validate_input(data)  # Validate input data
        sanitized_data = await sanitize_fields(data)  # Sanitize fields
        normalized_data = await normalize_data(sanitized_data)  # Normalize data
        commit_data = await fetch_data(commit_id)  # Fetch commit data
        processed_df = await process_batch(commit_data['records'])  # Process batch data
        metrics = await aggregate_metrics(processed_df)  # Aggregate metrics
        await save_to_db(metrics)  # Save to database
        await log_audit(commit_id, metrics)  # Log audit

if __name__ == '__main__':
    # Example usage
    manager = DataLakeManager()
    commit_example = {'commit_id': '1234', 'data': {'key': 'value'}}
    import asyncio
    asyncio.run(manager.process_commit(commit_example['commit_id'], commit_example['data']))