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## Why `optimize(cleanup_older_than=timedelta(0))` deletes the data files of every version except the latest. Any other handle still on an older version then fails with a missing-file error, and nothing tells the caller this can happen. #2470 asks for a docs note and a runtime warning. Fixes #2470 ## Scope - `AsyncTable.optimize` emits a `UserWarning` when `cleanup_older_than` rounds to 0 ms or less. `LanceTable.optimize` runs through it, so sync callers get the warning too. - The `cleanup_older_than` docstring on `Table.optimize`, `LanceTable.optimize`, and `AsyncTable.optimize` gets a `.. warning::` block, in the same style as the existing `delete_unverified` one. - Removed the function-local `import warnings` in `AsyncTable.optimize`. `warnings` is already imported at module level, and the local import would shadow it for the new check (`UnboundLocalError`). - `test_optimize` and `test_optimize_delete_unverified` intentionally use a 0 cleanup, so they now assert the warning with `pytest.warns`. Nodejs `cleanupOlderThan` is out of scope, since the issue is filed against the Python SDK. ## Tradeoffs Like the existing `retrain` deprecation warning, the warning is raised inside the async coroutine. For sync callers it is attributed to `asyncio/events.py` rather than the caller's line. Warning from both the sync and async entry points would duplicate the check for a small gain. ## Verification - Repro: a second handle checked out at an older version, then `optimize(cleanup_older_than=timedelta(0))` on the first. The second handle's read fails with `LanceError(IO) ... Not found: .../data/<file>.lance`. Before this change no warning is emitted. After it, the `UserWarning` fires. - New tests `test_optimize_warns_on_zero_cleanup` (async, and checks that `timedelta(days=1)` and the default don't warn) and `test_optimize_warns_on_zero_cleanup_sync` fail on main (`DID NOT WARN`) and pass with the fix. The failing tests are in their own commit before the fix. - `pytest python/tests/test_table.py -m "not slow and not s3_test"`: all pass except `test_merge_insert`. That test's `timeout=timedelta(0)` assertion fails the same way on unmodified main on my machine. - `ruff format --check` and `ruff check` are clean on the changed files. Tests ran against the source tree with the `_lancedb` extension from the matching `0.40.0b11` wheel. The change touches no Rust. This PR was written by an AI coding agent (Claude Code) and reviewed before submission. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Jay Zhou <190699450+jayzhou2309@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
The Multimodal AI Lakehouse
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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⭐ Click here ⭐ to see how fast we're growing!
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 |
Join Us and Contribute
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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