Added support for CSV #489
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Summary of Changes
Hello @liuzicheng1987, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly expands the data format capabilities of the library by integrating robust CSV support. The changes enable seamless conversion of structured C++ data into CSV, leveraging a powerful underlying data processing framework. This enhancement aims to improve data interoperability and provide more flexibility for users working with tabular datasets.
Highlights
- New Feature: CSV Support: Introduced comprehensive support for CSV (Comma Separated Values) serialization and deserialization, allowing users to easily convert C++ structs to and from CSV format.
- Apache Arrow Integration: Leveraged the Apache Arrow library for efficient handling of tabular data, which underpins the new CSV functionality and enhances existing Parquet support.
- Configurable CSV Settings: Added a new
rfl::csv::Settingsstruct, providing extensive options to customize CSV behavior, including delimiters, quoting, null string representation, and more. - Generic Tabular Data Handling Refactor: Refactored the internal tabular data parsing and writing mechanisms to be more generic, enabling a unified approach for CSV and Parquet formats and improving code reusability.
- Comprehensive Documentation and Tests: Included detailed documentation for CSV usage and a wide array of unit tests covering various data types, smart pointers, custom settings, and file operations.
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Summary of Changes
Hello @liuzicheng1987, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly expands the data format capabilities of the library by integrating robust CSV support. It enables seamless serialization and deserialization of tabular data, backed by the high-performance Apache Arrow library. The changes include a new configuration system for CSV operations and a refactoring of the underlying tabular data handling mechanisms to be more flexible and extensible.
Highlights
- New Feature: CSV Support: Introduced comprehensive support for CSV serialization and deserialization, allowing users to easily read and write tabular data.
- Apache Arrow Integration: Leveraged the Apache Arrow library for efficient handling of CSV data, ensuring robust and performant operations.
- Configurable CSV Settings: Added a new
rfl::csv::Settingsstruct, providing extensive options to customize CSV behavior, including delimiters, quoting, and null value representation. - Refactored Tabular Data Handling: Refactored internal components related to tabular data (like
ArrowTypes,ArrowReader,ArrowWriter) to be more generic and support different serialization types (CSV and Parquet). - Enhanced Timestamp Utility: Added a new static
makemethod torfl::Timestamp, improving its usability for creating timestamp objects from strings. - Comprehensive Test Coverage: Included a wide array of new test cases specifically for CSV functionality, covering various data types, smart pointers, and configuration options.
- Updated Documentation: Provided detailed documentation for CSV usage, including code examples and explanations of its capabilities and limitations.
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Code Review
This pull request introduces comprehensive support for CSV serialization and deserialization, leveraging Apache Arrow for high performance. The changes are extensive, including new API endpoints for CSV, build system integration, documentation, and a full suite of tests. A significant part of this work involved refactoring the existing tabular data handling logic (originally for Parquet) to be generic, which is a commendable improvement for maintainability. My review highlights a critical issue with an unsafe type cast that should be addressed, a noexcept violation, and a couple of minor improvements to the new documentation to ensure the examples are correct and clear.
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