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lancedb-gatefixer[bot]andXuanwo
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## Summary - expose typed compaction options on synchronous and asynchronous table optimization - translate validated Python options into native Lance compaction settings - document the row-based steady-state limit and first-pass/source-budget caveats - carry the Lance beta.15 fragment-exclusion option through the typed Python API ## Root cause The native Python optimize binding always constructed default compaction settings, and the public AsyncTable API offered no way to lower the fragment row target. Wide-row fragments therefore remained compaction candidates on every later optimize call, repeatedly rewriting the accumulated table. ## Limitations This is an explicit row-based mitigation, not byte-native task sizing or a hard memory cap. Callers must derive target_rows_per_fragment from expected row size. Existing oversized fragments are rewritten whole on the first pass; max_bytes_per_file limits output file size rather than compaction memory; and source budgets admit whole planned tasks, so a budget below the first task performs no compaction. Splitting tasks by a byte budget or returning a planner error requires a Lance-side change. ## Validation - cargo check --release -p lancedb-python - cargo clippy --quiet -p lancedb-python - cargo fmt --all --check - ruff format --check python tests - ruff check python tests - uv run --no-sync pytest python/tests/test_table.py -k optimize_compaction -q (16 passed) Fixes #2325 <!-- lance-gatekeeper-fix:v1 agent=9b58eee0c79040b19540e259af0e676c generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>
The Multimodal Lakehouse for AI
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
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We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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