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Will Jones
7813907eb7
## Problem `table.add(dataset)` with a `pyarrow.dataset.Dataset` OOMs the client during bulk ingestion of wide rows (e.g. embedding columns), even against a remote table where the upload itself is streaming. The cause is in `to_scannable`: a `Dataset` is scanned with pyarrow's default scanner settings (`batch_size=131072` rows, `batch_readahead=16`, `fragment_readahead=4`). pyarrow's internal threads prefetch that read-ahead window independently of LanceDB's backpressure, so for wide rows a large fraction of the dataset is held in memory. On the remote path this is then multiplied across the multipart write partitions (one in-flight batch per partition, up to CPU-core count). Reproduced on a 10 GB / 1.55M-row dataset with two 768-dim float32 embeddings: peak client RSS ~11.7 GB for the scan alone (6.8 GB after consuming a *single* batch), ~15.4 GB for the full remote `add()`. ## Fix `to_scannable` now sizes the scanner from an estimate of bytes-per-row derived from the schema: - **Narrow datasets keep pyarrow's defaults** (empty scanner kwargs) — no throughput regression. The bound only engages above ~410 bytes/row. - **Wide rows** get a smaller `batch_size` (~16 MiB/batch) and reduced read-ahead (`batch_readahead=2`, `fragment_readahead=1`) so peak in-flight memory stays near a ~1 GiB budget. Read-ahead (not just batch size) has to drop, because pyarrow pins whole row-group buffers. On the 10 GB dataset this drops peak client RSS to ~1.4 GB, and it stays flat as the dataset grows. The `Dataset`/`LanceDataset` scannables remain rescannable (retry-safe). ## Also: expose `write_parallelism` on `add()` `AddDataBuilder::write_parallelism` already existed in Rust but was not exposed in Python. This PR forwards it through the async, sync, and remote `add()` methods, so users can cap the number of parallel write partitions (each buffers data in flight) to trade throughput for memory on large uploads. ## Tests - `test_scannable.py`: bytes-per-row estimation; narrow → defaults; wide → bounded; `Dataset` reader streams bounded batches and stays rescannable. - `test_table.py`: `write_parallelism` on sync and async `add()`, and that `write_parallelism=0` is rejected. Fixes 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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