Updated the logic for determining column types in the union_column_types function. Changed the default type from BIGINT to DOUBLE PRECISION for numeric columns without explicit format hints, ensuring better handling of both integer and float values. This adjustment prevents loading failures due to format discrepancies and maintains consistent data processing across various SAS formats.
Implemented a new feature allowing users to specify explicit column type mappings via a `column_types` configuration in both `load_folder.py` and `load_sas.py`. This addition enables users to bypass automatic type inference for specific columns, ensuring correct data types are used when loading datasets. Updated the YAML configuration files to include examples of the new `column_types` option, enhancing usability and flexibility in handling varying data formats across files.
Added memory management optimizations in the _worker_load_append_file function to release unused memory from pyarrow's pool and trigger Python's garbage collection. Implemented explicit memory trimming using glibc's malloc_trim to ensure efficient memory usage during long-running processes. Updated the copy_dataframes function in load_sas.py to release pyarrow's memory pool between chunks, preventing high memory usage in long-lived workers. These changes aim to reduce memory footprint and improve overall performance during large dataset processing.
Introduced a new command-line argument, --chunk-rows, allowing users to specify the number of rows per chunk for pyreadstat streaming and COPY operations. This option overrides the GENERIC_LOADER_CHUNK_ROWS environment variable and auto-scaling behavior when using multiple workers. Enhanced memory management by providing detailed information on peak memory usage based on the specified chunk size, improving performance and usability during large dataset processing.
Added support for numeric date and datetime conversions from SAS formats. Implemented logic to handle float64 representations of dates (days since 1960-01-01) and datetimes (seconds since 1960-01-01), ensuring proper parsing and preventing errors during data copying to Postgres. This enhancement improves compatibility with various SAS date formats.
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.
Included pyarrow as a new dependency in requirements.txt for improved CSV serialization performance. Refactored the _prepare_for_copy function to utilize vectorized operations for date and timestamp conversions, reducing CPU overhead. Introduced a new _serialize_chunk_csv function leveraging pyarrow for faster CSV writing, enhancing efficiency during data copying to Postgres.
Introduced a new set of widening compatible type pairs to allow for accepting narrower inferred types when they fit within wider target types during schema compatibility checks. This change enhances the type inference process by preventing unnecessary mismatches and improving handling of varying integer ranges in cluster loads. Updated warning messages to inform users of accepted type adjustments.
Changed the default setting for TYPE_INFERENCE_SAMPLE_ROWS to None, allowing type and nullability inference to consider all rows in a SAS file. This adjustment ensures accurate handling of null values and integer ranges, addressing issues observed in production with large datasets. Updated documentation to reflect the implications of this change and the risks associated with using an integer cap for sampling.
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.
