Index Industrial Time-Series Data for Fast Analytics with PyFlink and Apache Iceberg

"""
Production implementation for indexing industrial time-series data using PyFlink and Apache Iceberg.
Provides secure and scalable operations for fast analytics.
"""
from typing import Dict, Any, List
import os
import logging
from time import sleep
from pyflink.table import EnvironmentSettings, TableEnvironment

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

class Config:
    iceberg_table: str = os.getenv('ICEBERG_TABLE', 'default.database.table')
    batch_size: int = int(os.getenv('BATCH_SIZE', 1000))

def validate_input_data(data: Dict[str, Any]) -> None:
    """Validate the input data for required fields.
    
    Args:
        data: Input time-series data to validate.
    Raises:
        ValueError: If validation fails.
    """
    if 'timestamp' not in data or 'value' not in data:
        raise ValueError('Input data must contain timestamp and value fields.')
    logger.debug('Input data validation passed.')

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize.
    Returns:
        Sanitized data.
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.debug('Sanitized fields: %s', sanitized_data)
    return sanitized_data

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize the input data for consistent formatting.
    
    Args:
        data: Input data to normalize.
    Returns:
        Normalized data.
    """
    data['timestamp'] = pd.to_datetime(data['timestamp'])
    logger.debug('Normalized data: %s', data)
    return data

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Returns:
        List of time-series data records.
    """
    # Placeholder for fetching data logic
    logger.info('Fetching data...')
    # Simulated data fetch
    return [{'timestamp': '2023-01-01T00:00:00Z', 'value': 100}]

def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save processed records to Iceberg table.
    
    Args:
        records: List of records to save.
    """
    logger.info('Saving %d records to Iceberg table: %s', len(records), Config.iceberg_table)
    # Logic for saving records to Iceberg

def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of records with validation and normalization.
    
    Args:
        batch: List of records to process.
    """
    for record in batch:
        try:
            validate_input_data(record)
            sanitized_record = sanitize_fields(record)
            normalized_record = normalize_data(sanitized_record)
            save_to_db([normalized_record])
        except ValueError as e:
            logger.error('Error processing record %s: %s', record, str(e))

def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from the processed records.
    
    Args:
        records: List of records to aggregate.
    Returns:
        Aggregated metrics.
    """
    total_value = sum(record['value'] for record in records)
    avg_value = total_value / len(records) if records else 0
    logger.debug('Aggregated metrics: total=%d, avg=%f', total_value, avg_value)
    return {'total': total_value, 'average': avg_value}

class TimeSeriesIndexer:
    """Main class to index time-series data using PyFlink and Iceberg.
    """

    def __init__(self) -> None:
        self.env = TableEnvironment.create(
            EnvironmentSettings.new_instance().use_blink_planner().in_batch_mode().build()
        )
        logger.info('Flink Table Environment initialized.')

    def run(self) -> None:
        logger.info('Starting the indexing process...')
        data = fetch_data()
        for i in range(0, len(data), Config.batch_size):
            batch = data[i:i + Config.batch_size]
            process_batch(batch)
            sleep(1)  # Simulate processing time

if __name__ == '__main__':
    indexer = TimeSeriesIndexer()  # Create indexer instance
    indexer.run()  # Execute indexing process