Updated error handling in the _worker_load_append_file function to include full tracebacks in exception messages, improving context for failures during file loading. Additionally, modified the _safe_numeric_to_datetime function to provide detailed warnings when conversion errors occur, ensuring users are informed of potential data issues. These changes aim to facilitate easier debugging and enhance the robustness of the data loading process.
Introduced an `all_nullable` boolean option in both `load_folder.py` and `load_sas.py`, allowing users to specify whether all columns should be treated as nullable during schema inference. This feature addresses scenarios where the data sampling may incorrectly suggest that columns are non-nullable, preventing potential errors during data loading. Updated YAML configuration files to include examples of this new option, enhancing usability and providing clearer documentation for users.
Implemented the _safe_numeric_to_datetime function to convert numeric SAS-epoch series to datetime64[ns] while managing potential overflow and non-finite values. This enhancement improves error handling during data processing by masking invalid entries before conversion, ensuring robust handling of SAS date formats in the _prepare_for_copy function.
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 an `--abort-on-first-failure` option in the `_load_remaining_files_parallel` function, allowing users to cancel all pending tasks immediately upon the first worker failure. This change improves user experience by providing real-time feedback on errors through stderr, ensuring that users are promptly informed of issues without waiting for all tasks to complete. Additionally, refined error reporting to maintain accurate summaries of successes and failures, even during interruptions.
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.
Updated the load_cluster function to enhance parallel processing by committing the table creation before dispatching all files to worker processes. This change allows for more efficient handling of large datasets by reducing the serial workload and ensuring schema compatibility checks can access the committed table. The logic for streaming files has been clarified, maintaining progress tracking throughout the loading process.
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.
Introduced a new command-line argument, --no-prescan, allowing users to bypass the per-file metadata scan during the loading process. This enhancement is particularly useful for large folders where the pre-scan may be time-consuming. The progress bar will still display rows loaded, rate, and elapsed time, but without an estimated time of arrival (ETA) for completion. Updated the main function to handle this new option and adjusted the progress tracking accordingly.
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.
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.
Updated the regular expression for partition name patterns to improve matching accuracy for state-related columns. The new pattern captures variations like `state`, `state_code`, and `statecode` while avoiding false positives from unrelated terms. This change enhances the precision of partition candidate selection.
Added a new constant for profiling chunk size to optimize memory usage during profiling operations. Refactored the update method in the _ColumnStats class to improve efficiency in handling missing values and calculating statistics for numeric and string data types. This update includes vectorized operations for better performance and clarity in the implementation.
Updated the partition candidate selection process to restrict candidates to columns matching specific name patterns, improving accuracy and reducing noise. Removed outdated distinct value constraints and clarified documentation for partitioning behavior. Enhanced handling of pre-sharded columns and refined the classification logic for better performance.
Introduced a new script `sas_profiler.py` that profiles local SAS files and generates an Excel report with recommendations for drops, partitions, and indexes, along with type-inference warnings. The utility supports command-line overrides for configuration and is compatible with Python 3.10+. This addition enhances the existing tools for SAS file management.
Introduced a new script `s3_download.py` for downloading files from S3 based on a YAML configuration. The script supports recursive listing, file clustering, and customizable download behavior. Also added a sample configuration file `sample_s3_download_config.yaml` to demonstrate usage.
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.
