import { Job, HourJob } from '../models/job';
import { getDataForHour } from './userDataProvider';
import { saveFile } from './fileWriters';
import { uploadFile } from './uploadProviders';
import { Config } from '../types/config';
import { getLogger } from './logger';
import { maskSensitiveData } from './dataMasking';

export async function processAndUploadFile(job: Job, hourJob: HourJob, config: Config) {
  const logger = getLogger();
  
  logger.info(`Processing file for ${job.date} ${hourJob.hour_range}`);

  try {
    // Get data for the hour (now with memory protection)
    const rawData = await getDataForHour(job.date, hourJob.hour_range);
    const dataCount = Array.isArray(rawData) ? rawData.length : 0;
    logger.debug(`Retrieved ${dataCount} records for ${hourJob.hour_range}`);

    // Memory usage monitoring
    const memUsage = process.memoryUsage();
    const memUsedMB = Math.round(memUsage.heapUsed / 1024 / 1024);
    logger.info(`💾 Memory usage: ${memUsedMB}MB (processing ${dataCount} logs)`);

    // Apply sensitive data masking if enabled
    const data = config.dataMasking?.enabled 
      ? maskSensitiveData(rawData, config.dataMasking)
      : rawData;

    // Memory protection: Check if data is too large
    if (dataCount > 1000) {
      logger.warn(`⚠️  Large dataset detected (${dataCount} logs). Consider implementing chunking for better performance.`);
    }

    // Save file locally first
    const filePath = await saveFile(job, hourJob, data, config.fileFormat || 'json', config);
    logger.debug(`File saved locally: ${filePath}`);

    // Upload to configured provider
    const uploadedPath = await uploadFile(filePath, config.uploadProvider, job, hourJob, config);
    logger.info(`File uploaded successfully: ${uploadedPath}`);

    // Update hour job with the uploaded path
    hourJob.file_path = uploadedPath;

    // Final memory check
    const finalMemUsage = process.memoryUsage();
    const finalMemUsedMB = Math.round(finalMemUsage.heapUsed / 1024 / 1024);
    logger.info(`💾 Final memory usage: ${finalMemUsedMB}MB (processed ${dataCount} logs)`);

    return uploadedPath;
  } catch (error) {
    logger.error(`Error processing file for ${job.date} ${hourJob.hour_range}`, { error });
    
    // Log memory usage on error for debugging
    const memUsage = process.memoryUsage();
    const memUsedMB = Math.round(memUsage.heapUsed / 1024 / 1024);
    logger.error(`💾 Memory usage at error: ${memUsedMB}MB`);
    
    throw error;
  }
}