mirror of
https://github.com/lancedb/lancedb.git
synced 2026-11-13 22:26:53 +00:00
Xuanwo
10f89b3c32
Remote Blob reads retain and concatenate intermediate IPC buffers across batches and HTTP requests, increasing peak memory. Decode fragmented IPC bodies using their declared length, assemble Blob values once across batches/requests, and collect large-object Range responses into known-size buffers. Impossible IPC/Range allocations return errors instead of panicking. Closing a remote Blob file releases cached bytes immediately, including while a cross-window read is waiting for the network. Rust and Python expose configurable Blob read buffers. Python sync/async `fetch_blob_files` and `open_blob_file(s)` accept `buffer_size`; the default is 512 KiB and zero disables prefetch. Explicit ranges bypass prefetch. New PE batch-endpoint adoption is separate. Malformed-length and retained-cache regressions fail on the previous implementation and pass after the correction. Real HTTP tests cover buffer propagation, close during blocked input, batch/request aggregation, and large-object fallback. The final extension was rebuilt before Python validation. Non-Blob Arrow arrays may still require alignment copies; a contiguous multi-batch result still requires its final allocation. ## Benchmark Peak client RSS increment in KiB, using `python/benchmarks/bench_fetch_blobs_rss.py`, independent clients and real loopback HTTP. Both extensions were rebuilt from clean, unchanged source with the same toolchain/configuration; loaded artifact hashes were checked. Baseline `42f11d13a2755e44b63876b5a5edd93901291150`, after `0a8a76de5286949d80368f95a5c529adf422c187`. | Equivalent fetch workload | Payload | Before | After | | --- | ---: | ---: | ---: | | Single response, one batch | 48 MiB | 63,812 | 63,896 | | Multiple responses, one batch per response | 144 MiB | 423,104 | 195,628 | | Single response, 12 rows per batch | 48 MiB | 130,520 | 71,348 | | Multiple responses, 12 rows per batch | 144 MiB | 363,144 | 172,748 | | One oversized row, Range fallback | 70 MiB | 182,816 | 85,548 | These are one observation per workload and build, not a statistical distribution or memory upper bound. The single-batch workload shows no improvement. Cloud/Sophon deployment, credentialed S3 and long-running stress have not been exercised.
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
Star LanceDB to get updates!
⭐ 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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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.
Contributors
Stay in Touch With Us