Updated the main function to replace sequential file processing with a threaded approach using ThreadPoolExecutor. This change enhances the efficiency of reading row counts from SAS files, particularly for large datasets, by allowing concurrent I/O operations. Added progress tracking with tqdm for better user feedback during the pre-scan phase.
Refactored the load_cluster function in load_folder.py to support parallel file loading using ProcessPoolExecutor, improving performance during the append phase. Added workers parameter for controlling parallelism and integrated a progress_queue for real-time progress updates. Introduced read_sas_metadata function in load_sas.py to efficiently read metadata from SAS files, optimizing the pre-scan process for global progress tracking.
Updated the pyarrow version in requirements.txt to improve compatibility. Enhanced the _infer_cluster_schema and _stream_file functions in load_folder.py and load_sas.py to return total row counts for better progress tracking during data streaming. Integrated tqdm for visual feedback on row processing, improving user experience during large data loads.
Introduce generate_sample_folder.py to create a test folder with clustered SAS XPORT files, including configurations for schema compatibility checks. Implement load_folder.py to facilitate loading entire directories of SAS files into Postgres, supporting explicit and auto-detect clustering. Update sample_folder_config.yaml for usage examples and configuration structure. Enhance load_sas.py with a public schema compatibility check function for orchestrators.
