XuQianJin-Stars opened a new pull request, #2579:
URL: https://github.com/apache/fluss/pull/2579
### Purpose
Linked issue: Addresses user questions about array type support in Lance
integration (community feedback)
This PR adds comprehensive documentation for using array types with Lance
lakehouse storage, particularly focusing on vector embedding scenarios which
are critical for machine learning and AI applications.
### Brief change log
- Updated `website/docs/streaming-lakehouse/integrate-data-lakes/lance.md`:
- Added `ARRAY<t>` to Lance data type mapping table
- Added new "Array Type Support" section with detailed documentation
- Documented array type use cases (vector embeddings, multi-dimensional
features, time series data)
- Provided complete SQL examples for creating tables with array columns
- Included end-to-end vector embedding example for recommendation systems
- Added Python examples demonstrating Lance vector similarity search
capabilities
- Documented best practices for working with array columns in Lance
- Added performance considerations for large embeddings and vector
workloads
### Tests
This is a documentation-only change. No code tests are required.
Documentation can be verified by:
1. Building the documentation website locally: `cd website && npm install &&
npm start`
2. Reviewing the rendered markdown on GitHub
3. Checking that all code examples follow correct SQL and Python syntax
### API and Format
This change does not affect any API or storage format. It only adds
documentation for existing array type support in Lance integration.
### Documentation
This change enhances existing documentation by providing comprehensive
guidance on array type usage with Lance. It does not introduce a new feature
but documents the existing capability of using `ARRAY<t>` data types with Lance
lakehouse storage, which is particularly important for users working with
vector embeddings and machine learning workloads.
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