fbf4a53475
Implements `InsertExec` and `RemoteInsertExec` to support running inserts in DataFusion. ## Context In https://github.com/lancedb/lancedb/pull/2929, I've prototyped moving the insert pipeline into DataFusion. This will enable parallelism at two levels: 1. Running preprocessing, such as casting the input schema or computing embeddings 2. Writing out files This PR is just the first part of running the actual writes. In the end, the plans might look like: ``` InsertExec RepartitionExec num_partitions=<write_parallelism> ProjectionExec vector=compute_embedding() RepartitionExec num_partitions=<num_cpus> DataSourceExec ``` where `num_cpus` is used to take advantage of all cores, while `write_parallelism` might be less than `num_cpus` if there are too few rows to want to split writes across `num_cpus` files. Later PRs will move the preprocessing steps into DataFusion, and then hook this up to the `Table::add()` implementations. ## Relation to future SQL work We eventually plan on having the Remote SDK go through a FlightSQL endpoint. Then for most queries we will send just the SQL string to the server, and not run any sort of DataFusion plan on the client. However, I think writes will be a little special, especially bulk writes where we need to upload large streams of data and likely want parallelism. So we'll have different code paths for writes, and I think using DataFusion makes sense, especially as long as we are doing the pre-processing on the client side still.
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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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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