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Jack Ye
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Remote materialized-view creation currently expects a REST response containing a registry job ID, while refresh already submits SQL. Route both through the SQL query backend and preserve Python's synchronous and asynchronous return types. ### Query to Job mapping `SqlClient::submit_as_job()` submits an `ExecuteQueryRequest` through the same backend as `execute_query_async()`. Once submission succeeds, it retains the returned `Query` and starts one client-side task that consumes its result reader to completion. `Job::from_query_task()` wraps that task, its cached outcome, a single ID and a cancellation callback. Python exposes this as `Job[T]` or `AsyncJob[T]`. The existing `job_type` property maps SQL-backed handles to the new `"query"` kind. The shared `id` contains that query's UUID string, so Python callers can inspect it with `db.describe_query(job.id)` on the submitting connection. No separate `query_id`, backend enum or `type()` API is added. | Handle | `job_type` | `id` | Available lookup | | --- | --- | --- | --- | | SQL-backed | `"query"`, known at submission | Connection-scoped query UUID string | `describe_query(job.id)` | | Registry-backed | Existing server-reported kind, after `status()` or `refresh()` | Registry job ID string | Registry job APIs, descriptions and events | | Local | `None` | `None` | The current in-process handle | | Job concept | SQL-backed behavior | | --- | --- | | Submission | Await initial SQL submission before returning the handle. Submission errors propagate immediately. | | `status()` | Tracks the adapter task: running during result consumption/finalization, finished after both succeed, failed if either fails, or cancelled after cancellation. | | `wait()` | Returns the operation's typed terminal result. One task consumes the query reader once; repeated waits reuse its cached outcome without resubmitting SQL or reopening the reader. | | `cancel()` | Delegates to `Query.cancel()`. Rejected or unsupported cancellation leaves the task running; an accepted request stops the task. Cancelling a finished operation is a no-op. | | Registry details | Query-backed handles expose no registry description, event history, pause/resume or reattachment after a client restart. `refresh()` updates cached lifecycle state. | The Job tracks command completion rather than exposing SQL batches. A wait timeout does not cancel the query. Query IDs remain scoped to the submitting connection and cannot be used with registry APIs or as durable resume tokens. Python `describe_query()` accepts both UUID objects and the string `id` exposed by query-backed jobs. Rust, Python and TypeScript expose the same classification through their existing job-type accessors. ### Materialized-view operations - **CREATE:** Render `CREATE MATERIALIZED VIEW` with the view namespace and optional auto-refresh settings. After command completion, resolve the job to `None`. Synchronous creation waits on the job, then opens and returns the materialized view. - **REFRESH:** Submit `REFRESH MATERIALIZED VIEW` through the same adapter. After command completion, read the refreshed table's version and row count to construct the existing typed refresh result. Synchronous refresh waits on the same path. Remote creation requires the SQL service. Execution placement remains a server decision. REST endpoints are unchanged. The job guide documents the ID/job-type contract and query-backed limitations. Regression coverage exercises SQL submission, namespaces, refresh options, job-type/ID preservation through typed result mapping, repeated waits, failures and cancellation rejection. No local builds or tests were run.
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
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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.
Contributors
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