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Jack Ye
7e8f0d4982
Simplify experimental materialized views to one output-only storage
layout and one full-rebuild refresh implementation.
## Current layout
Local materialized views currently maintain incremental-refresh state
alongside the materialized values:
- an internal `__source_row_id` column for source provenance;
- `mv.definition` plus `mv.incarnation`;
- source version/timestamp, view version, and last-refresh watermarks;
- optional `mv.staging` state for table-function sources; and
- stable-row-ID requirements on the source table.
Refresh classifies source changes, appends or evicts rows when possible,
and falls back to a rebuild for unsupported deltas. Remote
create/refresh also use separate REST mutation paths.
## New layout
An MV is an ordinary Lance backing table containing only:
- the defining query's output columns;
- its current data fragments; and
- `mv.definition`, encoded as a versioned SQL envelope such as
`{"kind":"query","format":1,"query":"SELECT ..."}`.
There is no provenance column, incarnation, source/view watermark,
staging metadata, or stable-row-ID requirement. Local refresh may still
pin a source version for reproducibility, but it always recomputes the
complete result.
Full refresh streams the new result into uncommitted fragments and then
publishes a single transaction that removes every old fragment and
installs the new set. A shared conflict token prevents concurrent
replacements from both committing. This fragment-replacement primitive
is also used by Sophon's SQL worker.
## New remote create workflow
```text
Connection::create_materialized_view(...)
-> builder renders the defining SELECT
-> remote Database receives a CreateMaterializedViewRequest
-> submit CREATE MATERIALIZED VIEW ... AS ... through Flight SQL
-> retain the SQL Query handle while the Job is running
-> consume the durable SQL-job result
-> open the created table and read mv.definition
-> return MaterializedView
```
Remote refresh similarly submits `REFRESH MATERIALIZED VIEW` through
Flight SQL. Cancelling either wrapper forwards cancellation to the
retained SQL query before aborting the local task, so the server
operation is not silently left running.
## API changes
- remove incremental/no-op modes and the `full` switch;
- remove distributed grouped/partitioned refresh APIs and native
vector-source MV APIs;
- keep full-refresh results across Rust, Python, and TypeScript;
- preserve list/describe/drop as object-management operations while
routing create/refresh mutations through SQL.
The storage contract is experimental and has no compatibility shim;
views using the removed layout must be recreated.
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.
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Key Features:
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- 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.
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Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
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- 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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