e081708cce
When a table has a read consistency interval, queries within the
interval skip the version check. Once the interval expires, a list call
checks for new versions. If the version hasn't changed, the timer should
reset so the next interval begins, but it didn't. The timer stayed
expired, so every query after that triggered a list call, even though
nothing changed.
This affects all read operations (queries, schema lookups, searches) on
tables with read_consistency_interval set. Each operation adds a
list("_versions/") call to object storage, adding latency proportional
to the store's list performance. For high-QPS workloads, this can
saturate object store list throughput and significantly degrade query
latency.
Bug flow:
1. Every read operation (query, schema, search) calls
ensure_up_to_date()
2. ensure_up_to_date() calls is_up_to_date(), which compares
last_consistency_check.elapsed() against
read_consistency_interval
3. If the interval has expired, it calls reload()
4. reload() calls need_reload(), which calls latest_version_id() — this
is the list IOP
(list("_versions/"))
5. If no new version, reload() returns early without resetting
last_consistency_check
6. On the next query, step 2 sees the stale timer again → step 3 → step
4 → another list IOP
7. This repeats on every query forever
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
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:
- 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
Stay in Touch With Us