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Will Jones
5d0a1ef66c
## Problem On the remote (LanceDB Cloud) write path, each write partition is uploaded as a **single** `/insert?upload_id=...` request that stays open until the whole partition has been streamed and the server has written it to object storage. For large bulk ingests a partition can be many GB, so a single request can run longer than the client read timeout (default 300s), surfacing as: ``` lancedb.remote.errors.HttpError: operation timed out ``` The server already supports staging **multiple** parts under one `upload_id` (each `/insert` writes a separate transaction that `complete` merges atomically), but the client never used that — it sent one part per partition. ## Change Split each partition into multiple parts of at most `max_bytes_per_request` (Arrow IPC, LZ4-compressed) bytes, each uploaded as its own `/insert?upload_id=...&upload_part_id=...` request. This bounds how long any single request stays open, independent of total data size or write parallelism. Key properties: - **Still streamed, not buffered.** Each part's body is driven through a bounded channel while the request is in flight (`futures::join!` of a producer + the send), so peak memory stays at a couple of batches per partition regardless of the part size. Backpressure from a slow/throttled server still propagates upstream. - **Correct part accounting.** An empty partition still sends exactly one (schema-only) part so `complete` has a transaction to commit; a size cut landing exactly on the end of input does not emit a trailing empty part. - **Multipart only.** The single-request (non-multipart) path is unchanged. ## Config New `ClientConfig::max_bytes_per_request: Option<usize>`, also settable via the `LANCE_CLIENT_MAX_BYTES_PER_REQUEST` environment variable. **Default 1 GiB** (`Some(0)` disables splitting → one request per partition). Python users pick up the default/env automatically through the remote client. ## Tests - `test_multipart_chunked_splits_into_parts`: a 1-byte budget puts each batch in its own part → N requests, each carrying the shared `upload_id` and a distinct `upload_part_id`. - `test_multipart_single_part_when_under_budget`: a large budget keeps the partition in a single request. - Verified end-to-end against a live remote table: a forced-chunked multipart add (many parts) assembles to the correct row count. Related to ENT-1883. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.8 (1M context) <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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