DuckDB 1.2.0
DuckDB version 1.2.0, codenamed "Histrionicus," introduces enhancements like improved CSV and Parquet support, primary key addition, a new C API, and extensive community contributions with over 5,000 commits.
Read original articleDuckDB has announced the release of version 1.2.0, codenamed "Histrionicus," which introduces numerous enhancements and features. Key updates include a new random function with a larger state, changes to the map function, and the ability to add primary keys to existing tables. The CSV reader now supports Latin-1 and UTF-16 encodings, multi-byte delimiters, and strict parsing according to RFC 4180. Performance improvements have been made to the CSV parser, and the limitation on row length has been lifted. Parquet file support has been expanded to include dictionary encoding and Bloom filters, along with new compression methods. The command line interface (CLI) has been improved with a safe mode and better autocomplete features. Additionally, a new C API for extensions has been introduced, allowing for easier integration with various programming languages. The release includes over 5,000 commits from more than 70 contributors, reflecting significant community involvement. For detailed information, users can refer to the full release notes available on GitHub.
- DuckDB 1.2.0 introduces significant features and improvements, including enhanced CSV and Parquet support.
- The new version allows adding primary keys to existing tables and improves the random function.
- The CLI now includes a safe mode and better autocomplete functionality.
- A new C API for extensions facilitates easier integration with other programming languages.
- The release reflects extensive community contributions, with over 5,000 commits from more than 70 contributors.
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- Push dynamically generated join filters through UNION, UNNEST and AGGREGATE
- Fix arrow table filters
- [Arrow] Fix scan of an object providing the PyCapsuleInterface when projection pushdown is possible.
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- [Python] Use an ArrowQueryResult in FetchArrowTable when possible.
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Paul Gross prefers DuckDB for data analysis over Polars and Pandas, citing its intuitive SQL syntax, ease of use for data manipulation, and automatic date parsing as significant advantages.
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