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Development Roadmap |
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The DuckDB project is governed by the [non-profit DuckDB Foundation]({% link foundation/index.html %}). The Foundation and DuckDB Labs are not funded by external investors (e.g., venture capital). Instead, the Foundation is funded by contributions from its [members]({% link foundation/index.html %}#supporters), while DuckDB Labs' revenue is based on commercial support and feature prioritization services.
This section lists the features that the DuckDB team plans to work on in the coming year.
- Documentation for the C extension API
- Generic ODBC catalog, similarly to the existing PostgreSQL / MySQL / SQLite integrations
- Go and Rust support for extensions
- Improved support for the Iceberg format through the [iceberg extension]({% link docs/stable/extensions/iceberg/overview.md %})
- Improved support for Delta Lake through the [delta extension]({% link docs/stable/extensions/delta.md %})
MATCH RECOGNIZEfor pattern matching- Remote file content caching using buffer manager (e.g., when querying Parquet files on S3)
- Database file encryption
- Distribution of musl libc binaries
This list was compiled by the DuckDB maintainers and is based on the long-term strategic vision for the DuckDB project and general interactions with users in the open-source community (GitHub Issues and Discussions, social media, etc.). For details on how to request features in DuckDB, please refer to the FAQ item [“I would like feature X to be implemented in DuckDB”]({% link faq.md %}#i-would-like-feature-x-to-be-implemented-in-duckdb-how-do-i-proceed).
Please note that there are no guarantees that a particular feature will be released within the next year. Everything on this page is subject to change without notice.
There are several items that we plan to implement at some point in the future. If you would like to expedite the development of these features, please get in touch with DuckDB Labs.
- Time series optimizations
- Partition-aware optimizations
- Sorting-aware optimizations
- Better Filter Cardinality Estimation using automatically maintained table samples
- Parallel Python UDFs
ALTER TABLEsupport for adding foreign keys- Improvements of query profiling (especially for concurrently running queries)
- XML read support
- Materialized views
MERGEstatement- Support for async I/O
- Support for PL/SQL stored procedures