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83
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
  Bruno RamirezandClaude Sonnet 5
|
a9ad499781 |
feat: support remote table compaction via SQL OPTIMIZE TABLE statement (#4802)
`Table::optimize()` previously returned `NotSupported` for remote tables. This PR wires it to the LanceDB Enterprise server's `OPTIMIZE TABLE ... REWRITE DATA` SQL statement, submitted over Flight SQL. Only `OptimizeAction::Compact` without a custom `remap_options` or non-default `CompactionOptions` is supported remotely. `OptimizeAction::All`, `::Prune`, and `::Index` all return a clear `NotSupported` error instead of silently doing the wrong thing. A branch-scoped table handle submits a `WITH (branch = '<branch>')` clause; the statement doesn't validate the branch exists itself, so an unknown branch surfaces as an async job failure rather than an error from this call. The server's SQL job adapter doesn't expose compaction metrics, so `OptimizeStats::compaction` is `Some(CompactionMetrics::default())`, a placeholder rather than real numbers — it must still be `Some`, since callers (including the Python/Node bindings) unwrap it unconditionally on success. This mirrors how `refresh_materialized_view_async` already handles the same limitation for its own metrics. The Python and Node `optimize()` convenience methods call a new shared `Table::optimize_full`, which runs `Compact` and then, on a remote table, skips `Prune` and `Index` instead of attempting and failing on them — returning zeroed prune stats rather than submitting a compaction job and then raising. Python's `RemoteTable.optimize()` also now forwards `compaction_options` to the Rust layer, so a non-default value is rejected before submission instead of being silently dropped. --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com> |
||
 Xuanwo
|
f4271b9dcb |
feat: support pinned Hugging Face model declarations (#4821)
Python Function declarations can now pin Hugging Face snapshots with `models=[HuggingFaceModel(repo=..., revision=...)]`. The native Rust registration path carries those declarations into `CREATE FUNCTION`, and create/get/list results retain `image.models`. Functions without declarations preserve their existing payload shape. The API reference includes a model-loading example that initializes once per Function instance. Validation: - The real SQL mock-service regression fails on the previous native implementation and passes after the fix. It covers immediate/deferred execution, requests with/without models, and create/get/list responses. - Rust Function wire tests, strict Clippy, the public-type doctest, and formatting. - Rebuilt Python extension and targeted registration/golden tests. The service-side snapshot preparation and cache are delivered separately for ENT-3483. |
||
|
Xuanwo
|
c0aa5d9d28 |
docs: show how to run Functions and BlobReader locally (#4803)
Running a Function body in a local test or another engine currently requires discovering undocumented APIs. Document direct calls to decorated Functions and classes, including initialization fields, instance reuse, and cleanup, and explain how they differ from registered `FunctionVersion` applications. Add examples of `BlobReader(read_range, size)` backed by an in-memory byte-range callback, including cursor-independent reads. The local examples and rendered API documentation were validated on EC2 at `df6076e1ae605f425a380d282c4f25b845a3165f`; integration with current main leaves these documented call paths unchanged. Fixes ENT-3561. |
||
  TaylorandBrendan Clement
|
9e65bd2c27 |
docs: use Enterprise terminology across website and SDK documentation (#4787)
Use “LanceDB Enterprise” throughout the staged website guides, SDK reference sources, generated API pages, and user-facing SDK messages. A separate branded “LanceDB Distributed” name adds terminology without clarifying behavior; describe where operations run directly. - Update `docs/web`, including the architecture diagram title and alt text. Use Enterprise in headings, table labels, and prose; simplify redundant product/execution-model wording and name the product only once in the diagram alt text. - Update Python docstrings and the Python/Java reference pages, TypeScript comments and native declaration comments, and Rust API documentation. Regenerate the TypeScript reference, including 24 replacements across six pages. - Update SDK warnings/errors and existing test expectations to match, along with contributor guidance and Vale rules. Remove the obsolete rule distinguishing Enterprise from Distributed. - Keep lowercase “distributed” for technical descriptions. API identifiers, anchors, asset paths, and execution behavior are unchanged. No branded-name exceptions are proposed. Validation: - Python and Node native builds succeeded; focused Python tests passed (36 passed, 4 skipped), and focused Node tests passed (14). - TypeDoc generation and the complete MkDocs reference build passed. MkDocs retains an existing unresolved `lancedb.functions.SecretReference` cross-reference. - Rust formatting, Node lint/format, changed-file Python lint/format, 32 Vale tests, and whitespace checks passed. Repository-wide Python lint has 48 existing findings in six untouched files. - Combined documentation assembly passed. Mintlify validation and link/anchor/redirect checks previously passed for both site builds; no code snippets changed. Companion PRs: [saved editions, terminology guide, and shared rules](https://github.com/lancedb/docs/pull/413), [Enterprise overview and writing rules](https://github.com/lancedb/sophon/pull/9511). --------- Co-authored-by: Brendan Clement <brendan.clement@lancedb.com> |
||
|
Xuanwo
|
63aa06ef7d |
feat(python): expose Function GPU classes and placement (#4791)
## Summary - Add sync/async `DBConnection.list_gpu_classes()` returning typed `GpuClass` records for `GET /v1/gpu/classes`. Records include the class name, Kubernetes resource, GPU model and memory, per-worker GPU maximum, and worker CPU/memory/ephemeral-storage limits. - Add `udf(gpu_class=...)` for immutable Function-level GPU placement. It requires `gpu=True`; Function versions expose their registered `gpu_class`. - Add `gpu_class` and `gpu_count` to `refresh_column` / `refresh_column_async` and Rust `RefreshColumnOptions`. Both are sent as top-level `backfill_column` fields and validated before any request. `gpu_count` is the aggregate GPU budget across Function workers. - Validate GPU class names with the server lowercase 63-character DNS-label rule. - Document the discovery-to-registration flow, configured hardware versus free capacity, System Usage permission, and per-refresh GPU placement in the API and web guides. Older servers without `/v1/gpu/classes` produce a clear `NotImplementedError` instead of a bare 404. Part of ENT-3470. The client follows the in-flight Sophon contract on `xuanwo/ent-3470-fix` for GPU class discovery and backfill placement options. ## Breaking change `PythonRuntimeSpec::PythonV2` adds `gpu_class`, and `RefreshColumnOptions` adds `gpu_class` and `gpu_count`. Rust callers constructing either value with all fields explicitly must supply the new fields. ## Validation At `7685c968`: Rust GPU tests (5 passed), Rust/PyO3 clippy, Python extension build, pyright, ruff, and Python Function/runtime, discovery, refresh placement, and refresh regressions (147 passed, 6 skipped). At `b22428fd`: Rust GPU tests (5 passed), rustfmt, ruff, pyright, Function slice 2 (85 passed), and remote GPU/refresh tests (20 passed). The final commit only updates documentation and Rust doc comments. |
||
|
Xuanwo
|
9786f2b63b |
fix(python): surface typed remote resource errors (#4789)
## Summary - Parse structured `error_code` and `resource` fields from remote HTTP error bodies and carry them through Rust/PyO3. - Add `SecretNotFoundError`, `FunctionNotFoundError`, `NamespaceNotFoundError`, and `AlreadyExistsError` as `HttpError` subclasses. - Expose `error_code`, `resource_kind`, `resource_name`, and `namespace_path` on HTTP exceptions while preserving `status_code`, `request_id`, and existing `except HttpError` handlers. - Do not retry structured domain errors that would otherwise become `RetryError` (notably `already_exists` on 409). Older servers that do not provide the structured fields continue to raise a plain `HttpError`. ## Compatibility This consumes the stable resource error contract added in lancedb/sophon#9516 (merge commit `35858561b3`). The existing `TableNotFoundError` behavior is unchanged. Part of ENT-3373. |
||
 lancedb-gatefixer[bot]
|
71b23ed9fb |
fix(python): preserve request metadata for missing remote tables
Fixes #4750 Remote missing-table errors retained HTTP metadata in Rust but the Python binding converted them to a plain `ValueError`, discarding the request ID and status. This raises a `TableNotFoundError` that preserves the original message, request ID, status code, and chained HTTP cause while remaining compatible with existing `ValueError` and `lance_namespace.errors.TableNotFoundError` handlers. Local and non-HTTP errors retain their previous behavior. Validation covers sync and async opens across cached/uncached and root/nested paths, exception pickling, local compatibility, the remote and error test suites, Clippy, formatting, Ruff, and all applicable PR checks including CodeQL. |
||
 Jack Ye
|
784d649380 |
feat: support Hugging Face storage integrations with optional token Secrets (#4775)
Adds `HUGGINGFACE` and an optional token Secret reference to the
existing storage integration methods. The same SQL transport handles
create, describe, list and drop. Only a Secret reference is stored in
integration metadata; mixed-provider credential options are rejected.
```python
from lancedb.storage_integration import SecretReference
info = db.create_storage_integration(
"private_hub", "hf://datasets/acme/private/raw/",
storage_provider="HUGGINGFACE",
huggingface_token_secret=SecretReference(
"hf_token", database="shared", namespace_path=("credentials",),
),
)
print(info.configuration_instructions)
info = db.describe_storage_integration("private_hub")
names = list(db.list_storage_integrations())
db.drop_storage_integration("private_hub")
```
Omit the token Secret for anonymous access at Hugging Face's default
anonymous
rate limits. A tokenless integration does not use a cluster's Hugging
Face token:
```python
info = db.create_storage_integration(
"public_hub", "hf://datasets/acme/public/raw/",
storage_provider="HUGGINGFACE",
)
```
```sql
CREATE STORAGE INTEGRATION public_hub
STORAGE_PROVIDER = 'HUGGINGFACE'
STORAGE_PREFIX = 'hf://datasets/acme/public/raw/';
```
```sql
CREATE STORAGE INTEGRATION private_hub
STORAGE_PROVIDER = 'HUGGINGFACE'
STORAGE_PREFIX = 'hf://datasets/acme/private/raw/'
HUGGINGFACE_TOKEN_SECRET = shared.credentials.hf_token;
DESCRIBE STORAGE INTEGRATION private_hub;
SHOW STORAGE INTEGRATIONS;
SELECT * FROM 'hf://datasets/acme/private/raw/*.parquet' LIMIT 10;
DROP STORAGE INTEGRATION private_hub;
```
```mermaid
flowchart LR
Python[Sync or async Python] --> SQL[Existing SQL transport]
SQL --> Metadata[Integration and token Secret reference]
```
Adds Rust request construction, SQL rendering, typed metadata decoding,
sync/async Flight SQL regressions and public documentation. Local
builds/tests are excluded by request; CI provides execution validation.
|
||
|
Jack Ye
|
1fb2dd1551 |
feat: accept GCP credentials Secret in storage integration clients (#4771)
Adds GCP inputs to the existing database-owned storage integration
methods. GCP takes one Secret reference; the Secret stores the complete
application credentials JSON as an opaque string. The same methods and
SQL transport handle create, describe, list and drop.
```python
info = db.create_storage_integration(
"gcs", "gs://customer-data/raw/", storage_provider="GCP",
gcp_credentials_json_secret=SecretReference(
"gcp_json", database="shared", namespace_path=("credentials",),
),
)
print(info.configuration_instructions)
info = db.describe_storage_integration("gcs")
names = list(db.list_storage_integrations())
db.drop_storage_integration("gcs")
```
```sql
CREATE STORAGE INTEGRATION gcs
STORAGE_PROVIDER = 'GCP'
STORAGE_PREFIX = 'gs://customer-data/raw/'
GCP_CREDENTIALS_JSON_SECRET = shared.credentials.gcp_json;
DESCRIBE STORAGE INTEGRATION gcs;
SHOW STORAGE INTEGRATIONS;
DROP STORAGE INTEGRATION gcs;
```
```mermaid
flowchart LR
Python[Sync or async Python] --> SQL[Existing Flight SQL transport]
SQL --> Config[Configuration and one Secret reference]
```
Adds Rust request construction, SQL rendering and typed metadata
decoding, plus sync/async Flight SQL regressions across the three Arrow
string encodings. Rejects missing or mixed-cloud credential options.
Local builds/tests are excluded by request; validation runs in CI.
|
||
|
Jack Ye
|
b15fa9d6e4 |
feat: accept Azure inputs in storage integration clients (#4759)
Register Azure Blob integrations through Flight SQL using an explicit
storage account and three Secret references for tenant ID, client ID and
client secret. Extends the database-owned integration APIs with
provider-specific inputs while keeping the same create, describe, list
and drop operations.
```python
info = db.create_storage_integration(
"customer_blobs", "az://customer-data/raw/",
storage_provider="AZURE", azure_storage_account="customeraccount",
azure_tenant_id_secret="tenant", azure_client_id_secret="client",
azure_client_secret_secret="credential",
)
print(info.configuration_instructions)
info = db.describe_storage_integration("customer_blobs")
names = list(db.list_storage_integrations())
db.drop_storage_integration("customer_blobs")
```
Sync and async connections accept the same options. Use
`SecretReference` for cross-database or namespace qualification. Values
remain in Secrets; returned metadata includes only references and setup
guidance.
```sql
CREATE STORAGE INTEGRATION customer_blobs
STORAGE_PROVIDER = 'AZURE'
STORAGE_PREFIX = 'az://customer-data/raw/'
STORAGE_AZURE_STORAGE_ACCOUNT = 'customeraccount'
STORAGE_AZURE_TENANT_ID_SECRET = public.tenant
STORAGE_AZURE_CLIENT_ID_SECRET = public.client
STORAGE_AZURE_CLIENT_SECRET_SECRET = public.credential;
DESCRIBE STORAGE INTEGRATION customer_blobs;
SHOW STORAGE INTEGRATIONS;
DROP STORAGE INTEGRATION customer_blobs;
```
```mermaid
flowchart LR
Python[Sync or async Python] --> SQL[Flight SQL]
SQL --> Metadata[Configuration and Secret references]
```
Azure supports service principal client credentials only. Adds SQL
rendering, provider validation and Python Flight SQL regressions across
Utf8, LargeUtf8 and Utf8View results. Local builds and tests are
excluded as requested; validation runs in CI.
|
||
 importcpp
|
e56150568c |
feat(python): fill failed streaming transform rows
Add `on_transform_error="fill"` to `StreamingDataset`, replacing failed
transform rows at their original positions with a deep-copied fill value or
the result of `repair(exc, row_batch)`.
Fill mode preserves split length, elastic step layout, worker accounting, and
checkpoint progress. Repair failures remain fatal, and `rows_filled` is shared
across workers.
Validated with focused fill and adjacent dataloader tests (10 and 6 passed)
and the complete applicable CI run for
|
||
|
Jack Ye
|
29691719fa |
feat: manage property graphs defined over tables
Replaces #4322, keeping only its property graph defined over tables. A property graph maps node labels and relationship types to existing tables of its namespace, in the shape of SQL/PGQ's `CREATE PROPERTY GRAPH`. It stores only that definition: a query reads the tables as nodes and edges through SQL `GRAPH_TABLE`, as they are when it runs. - **Rust:** `Connection::create_property_graph` takes a `PropertyGraphDefinition` of `NodeTable`s and `EdgeTable`s. `describe_property_graph`, `drop_property_graph` and `list_property_graphs` manage it. - **Python:** `lancedb.graph` has `NodeTable`, `EdgeTable` and `Endpoint`. Sync and async connections gain `create_property_graph`, `open_property_graph`, `drop_property_graph` and `list_property_graphs`, and a `PropertyGraph` handle describes its graph. - **Support:** remote connections only. Local connections raise `NotImplementedError` in Python and return `NotSupported` in Rust. ```python import lancedb from lancedb.graph import EdgeTable, Endpoint, NodeTable db = lancedb.connect("db://demo", api_key="...") db.create_property_graph( "social", nodes=[NodeTable("person", key="person_id", label="Person")], edges=[ EdgeTable( "knows", label="KNOWS", source=Endpoint("src_id", references=("person", "person_id")), destination=Endpoint("dst_id", references=("person", "person_id")), ) ], ) db.execute_query(""" SELECT * FROM GRAPH_TABLE (social MATCH (p:Person)-[:KNOWS]->(f:Person) WHERE p.age > 30 COLUMNS (p.name AS person, f.name AS friend)) """).read_all() ``` |
||
|
Jack Ye
|
6d126a3601 |
feat: add database storage integration APIs (#4618)
Adds database-owned S3 storage integrations with Secret-style namespace
paths in a separate keyspace. Use a regular database under the cluster
root for bootstrap integrations and Secrets. `default` is an example
name, with no enforced or automatically provisioned database; `system`
remains read-only. Authentication uses either an IAM role or two
existing Secret references, including cross-database references;
configuration metadata never contains credential values.
### Python experience
```python
catalog = lancedb.connect_catalog("https://my-server.example", api_key="secret")
db = catalog.connect_database("default")
info = db.create_storage_integration(
"raw", "s3://customer-bucket/raw/",
aws_role_arn="arn:aws:iam::123456789012:role/lancedb-access",
aws_region="us-east-1",
)
print(info.configuration_instructions)
info = db.describe_storage_integration("raw")
print(list(db.list_storage_integrations()))
db.drop_storage_integration("raw")
```
For static credentials, replace `aws_role_arn` with
`aws_access_key_id_secret=SecretReference("s3_key_id",
database="default")` and
`aws_secret_access_key_secret=SecretReference("s3_secret_key",
database="default")`, imported from `lancedb.storage_integration`. Pass
`namespace_path` to address nested namespaces. Async database
connections expose the same APIs; listing is lazy. All four client
operations execute SQL through the existing SQL transport, with typed
Arrow result decoding and name-based listing cursors.
### SQL experience
```sql
CREATE STORAGE INTEGRATION default.public.raw
STORAGE_PROVIDER = 'S3'
STORAGE_PREFIX = 's3://customer-bucket/raw/'
STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789012:role/lancedb-access'
STORAGE_AWS_REGION = 'us-east-1';
DESCRIBE STORAGE INTEGRATION default.public.raw;
SHOW STORAGE INTEGRATIONS IN default.public;
DROP STORAGE INTEGRATION IF EXISTS default.public.raw;
CREATE STORAGE INTEGRATION default.public.raw
STORAGE_PROVIDER = 'S3'
STORAGE_PREFIX = 's3://customer-bucket/raw/'
STORAGE_AWS_ACCESS_KEY_ID_SECRET = default.public.s3_key_id
STORAGE_AWS_SECRET_ACCESS_KEY_SECRET = default.public.s3_secret_key;
```
CREATE and DESCRIBE return configuration and a single sentence of setup
guidance. IAM mode also returns the deployment role principal, stable
external ID, customer trust policy, and prefix-scoped permissions
policy. Registration does not verify access. Creation requires namespace
CREATE_STORAGE_INTEGRATION plus parent USAGE; Secret references require
source database, namespace, and Secret USAGE. Describe requires object
USAGE; drop requires OWNERSHIP. SQL supports GRANT/REVOKE for these
objects and their creation privilege.
Includes client documentation and Flight SQL regression coverage; no
dedicated storage-integration REST endpoints are used. Local
builds/tests are excluded by request.
|
||
|
lancedb-gatefixer[bot]
|
a4f52cd205 |
fix(python): add a Blob V2 array construction helper (#4734)
Fixes #4733 PyArrow expects Blob V2 storage structs when constructing a record batch and cannot infer the extension array from raw bytes. LanceDB already converts binary values during ingestion, but batch writers had no supported array constructor. Add `lancedb.schema.blob_array(payloads)` for iterables of inline bytes and nulls, using the same extension type resolver as `blob()`. The helper preserves empty blobs and null blobs and works with both pylance's type and LanceDB's fallback. Document the descriptor fields, exclusive inline/external representations, batch construction, and existing binary conversion in synchronous and asynchronous `create_table` / `add` APIs. Validation: - Reproduced the reported `ArrowTypeError` on the original code; both new regression cases fail against the original schema module. - 256 focused blob tests and schema doctests passed; one optional PyAV test skipped. - Both new regression cases also passed with PyArrow 25, with and without pylance imports. - Python package Ruff checks and formatting passed. - MkDocs build passed, with an existing unrelated `SecretReference` cross-reference warning. Repository-wide Ruff reports 48 existing errors in `docs/web-tests`. <!-- lance-gatekeeper-fix:v1 agent=20d1a296d76ff5eb914c0848428a747a generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> |
||
 BHAWESHBHASKAR
|
891774343e |
feat(python): add CombinedFieldsQuery for BM25F full text search
Add a typed Python `CombinedFieldsQuery` for BM25F search across multiple columns. The query mirrors Lance's combined-fields semantics with ordered per-column boosts and AND/OR term matching. Convert it in both directions at the PyO3 boundary, including nested Boolean queries, and document the Python typed convenience entry. Closes #4652. |
||
 Will JonesandClaude Opus 5.5
|
4d7a64712f |
docs: adopt Embedded/Distributed terminology across docs and SDKs (#4713)
## Summary Adopts the product terminology guide across the docs site pages, SDK docstrings and error messages. "LanceDB OSS" and local mode become **LanceDB Embedded**. "Remote" as the execution model becomes **LanceDB Distributed**, which LanceDB Enterprise provides. Where free vs paid is the point, pages contrast "LanceDB Embedded (open source)" with "LanceDB Enterprise". This is a first trial of the Embedded name, so reviewers can judge it in context. - **Embedded vs Distributed** for where execution runs (`optimize()`, consistency, branching, maintenance). Main pages define it once: "LanceDB Enterprise runs LanceDB Distributed: …" - **LanceDB Cloud** is kept only for legacy-Cloud specifics (`db://` region, the brute-force KNN limit) and marked legacy. "Cloud and Enterprise" becomes Enterprise or Distributed. - **Plan executor** becomes "executor" or the fleet name. - **Error messages** change, e.g. `optimize is not supported for remote tables.` → `... in LanceDB Distributed.` Tests that match on them are updated. - **Vale rules** (`EmbeddedName`, `OSSProduct`, `RemoteExecution`, `DistributedEnterprise`, `LegacyCloud`, `InternalNames`) enforce the terms in prose. Unchanged: identifiers (`availability.oss`, the `remote` module and feature, `RemoteTable`, `db://`), heading anchors, internal code comments, and Geneva. ## Evidence - Rust lib tests for the changed messages and the Python tests in `test_remote_db.py`, `test_materialized_views.py` and `test_table.py` pass locally. - Not run locally: - The Node tests: `remote.test.ts` and `table.test.ts` have updated assertions. - Vale. - The `experimental_functions`-marked materialized-view tests. - `docs/src` API reference was hand-edited to match the docstrings rather than regenerated. ## Merge Risk **Door:** two-way; prose and message text only. **Blast Radius:** `docs`, `error-messages`: callers matching on old error strings would break. Companion PRs: lancedb/docs (assembler labels, STYLE.md) and sophon (Enterprise pages). 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> |
||
|
lancedb-gatefixer[bot]andXuanwo
|
c167b99fa0 |
fix: read legacy blob columns through blob APIs (#4649)
Legacy blob v1 columns were rejected by LanceDB's blob validation and omitted from `blob_columns()`, even though the pinned Lance reader already supports them. Accept both blob versions in read validation and discovery so `fetch_blobs`, `fetch_blob_files`, and `fetch_blob_ranges` can read legacy payloads through the existing native reader. Keep list-nested blobs excluded from row-based reads and document how to fetch legacy blobs using explicit row IDs. Regression coverage checks sync and async APIs, independently written Lance datasets, ordering and duplicates across fragments, null and empty blobs, lazy seeking, ranges, and deleted rows. Fixes #4644 Validation: - Both new Python regressions failed on the original implementation with the reported legacy-column rejection and empty discovery result, then passed with the fix. - Python blob suite: 202 passed, 1 skipped. - Remote blob tests: 29 passed. - Focused Rust blob unit tests with `remote`: 13 passed. - Rust blob integration suite with `remote`: 59 passed, including legacy storage 2.0 and 2.1. - Rust compile and Clippy checks with `remote`, tests, and examples passed. - Python package Ruff lint and format checks passed; Rust formatting passed. - Repository-wide `ruff check .` reports 49 existing errors in unchanged `docs/web-tests` examples; the Python package is clean. <!-- lance-gatekeeper-fix:v1 agent=02b7edcbfa8ff0e7fad4615159249b3e generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io> |
||
|
Xuanwo
|
8a4effa06a |
fix: gate experimental local functions behind an opt-in feature (#4688)
Published SDK builds currently expose local SQL computed columns and materialized-view execution that are not ready for release. Add the opt-in `experimental-functions` Cargo feature to the Rust core and forward it through the Python and Node bindings; published packages leave it disabled. Local declarations and refreshes now return `NotSupported` before mutating tables or spawning jobs, including prepared materialized-view creation. Remote Cloud/Enterprise client operations, Python UDF packaging, ordinary eager `add_columns`, and access to existing data remain available. Stored metadata and write protections stay active, and shared server commit helpers remain usable. CI covers both feature configurations. Existing local execution tests run with the feature enabled; default-build regressions cover early rejection, unchanged table versions, existing metadata protections, and normal eager transforms. API and source-build documentation explain the opt-in. Validation: Rust library tests pass with the feature disabled (1,869 tests) and enabled (1,947 tests), plus three default-build integration regressions. Python and TypeScript bindings were rebuilt and tested in both configurations, including mocked remote requests; Rust formatting, Python package lint, TypeScript lint/build, and generated docs were checked. Repository-wide Ruff still reports 48 pre-existing errors in unchanged `docs/web-tests` files. |
||
|
lancedb-gatefixer[bot]andXuanwo
|
3352bf49df |
fix(python): expose optimize compaction options (#3870)
## Summary - expose typed compaction options on synchronous and asynchronous table optimization - translate validated Python options into native Lance compaction settings - document the row-based steady-state limit and first-pass/source-budget caveats - carry the Lance beta.15 fragment-exclusion option through the typed Python API ## Root cause The native Python optimize binding always constructed default compaction settings, and the public AsyncTable API offered no way to lower the fragment row target. Wide-row fragments therefore remained compaction candidates on every later optimize call, repeatedly rewriting the accumulated table. ## Limitations This is an explicit row-based mitigation, not byte-native task sizing or a hard memory cap. Callers must derive target_rows_per_fragment from expected row size. Existing oversized fragments are rewritten whole on the first pass; max_bytes_per_file limits output file size rather than compaction memory; and source budgets admit whole planned tasks, so a budget below the first task performs no compaction. Splitting tasks by a byte budget or returning a planner error requires a Lance-side change. ## Validation - cargo check --release -p lancedb-python - cargo clippy --quiet -p lancedb-python - cargo fmt --all --check - ruff format --check python tests - ruff check python tests - uv run --no-sync pytest python/tests/test_table.py -k optimize_compaction -q (16 passed) Fixes #2325 <!-- lance-gatekeeper-fix:v1 agent=9b58eee0c79040b19540e259af0e676c generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io> |
||
 KarthikSandXuanwo
|
180eaaf68e |
fix!: reject unsupported remote connection and table options (#4486)
Fixes #4399. Remote connections currently discard local-only settings, including table write options that determine storage format. The synchronous Python client also binds the fourth positional `create_table` argument to `on_bad_vectors`, so an intended overwrite is sent as a create. This change aligns the remote Python signature with `DBConnection.create_table`, preserves the legacy remote timeout keywords, and raises `ValueError` for other unknown connection keywords. The shared Rust client rejects unsupported connection, create, and open options before sending HTTP requests; open validation also runs before a table-cache hit. Python reports these as `NotImplementedError`, including explicit `enable_v2_manifest_paths=False`. Supported Enterprise connection-level storage options remain available. This implements option 2 from the issue: explicit rejection. It deliberately does not add unverified server write parameters. Calls that previously supplied ignored options must omit them to use server-managed storage and caching. Database URI prefixes are already preserved on current main and are unchanged here. Regression tests cover Python sync/async, TypeScript create/open calls, and the Rust remote client, including empty-table creation, overwrite mode, and cached table handles. Python API documentation describes the behavior and corrected positional order. Reconciled with main at `0ea932da`, preserving upstream blob and error-handling tests and updating the Python test that previously expected open options to be ignored. Validation on the merged source: - Rebuilt the Python native extension; 304 tests passed across remote options, database, remote database, and session suites. - Rust remote database suite: 135 passed. Rebuilt the Node native extension; TypeScript remote suite: 62 passed. TypeScript compilation and documentation generation passed. - Full Python SDK lint and formatting pass (159 files); full TypeScript lint and formatting pass (66 files). Rust formatting and `git diff --check` pass. - Repository-wide Python lint/format reports existing issues in unchanged `docs/web-tests` files (49 lint findings and 11 formatting files). Environment: Ubuntu under WSL2, Linux x86-64, CPython 3.12.13, Rust 1.97.0, and Node.js 22.16.0. Locked dependencies and the repository `ci` build profile. Remote tests use HTTP fixtures; this does not claim validation against the original ARM64 single-node deployment. Documentation review follow-up: added a draft Enterprise migration note in `docs/web/basics/sdk.mdx`. It explains that `db://` connections reject local session/cache and per-table storage/write options, while supported connection-level Enterprise storage options remain available. Following the repository's release conventions, the note stays in an MDX comment below the migration list until the behavior is released. Vale 3.24.0 with the pinned LanceDB style package reports no findings; `git diff --check` passes. This follow-up changes documentation only. BREAKING CHANGE: Remote connections now reject unsupported local session/cache and per-table storage/write options instead of silently ignoring them. Callers must omit these options and use server-managed storage and caching; supported Enterprise connection-level storage options remain available. Integration with current main preserves its synchronous API signatures, including the remote `create_table(mode=None)` default, and the table compatibility defaults. The positional-order repair is already on main via #4608; this PR retains the distinct unsupported-option rejection behavior. CI follow-up: the inherited IVF search-effort test occasionally measured recall 0.375 against its unchanged 0.5 threshold after random index training. A matching local reproducer observed this once in 30 builds. The fixture now places the eight exact neighbors together, preserving the 16-partition setup, two writes, all probe/precedence assertions, and the recall threshold. The adjusted fixture returned recall 1.0 in 100 builds; the full query and remote suites passed (102 tests). This stabilizes test data and does not change search behavior or promise production recall. --------- Co-authored-by: Xuanwo <github@xuanwo.io> |
||
|
KarthikSandXuanwo
|
3dd579785e |
fix(python)!: align synchronous table method signatures (#4498)
Closes #4396. Calls written against `Table` can fail or bind different arguments when used with `RemoteTable`. Align the affected sync signatures, positional order, and defaults; preserve renamed remote keywords as deprecated aliases. Share legacy vector configuration and tokenizer inference, forward index tuning and timeout options, and accept `fast_search` consistently. Preserve current upstream conversion behavior: local `to_polars(batch_size=...)` returns a LazyFrame; remote tables accept the same argument signature but raise the documented unsupported-operation error and direct callers to `table.search().to_polars()`. The abstract `Table` method retains its unsupported-operation error, with the implementation confined to `LanceTable`. Regression coverage checks argument binding, deprecated aliases, option forwarding, native behavior through both wrappers, HTTP index requests, and remote conversion rejection. The merge preserves current tokenizer defaults, remote query defaults, HNSW-PQ support, and unsupported-operation handling. Runtime validation at `c4fb01f2` (Linux x86_64, uv-managed Python 3.12.15): - Focused Ruff lint and formatting passed for all four changed Python files. - Rebuilt and installed the current native extension successfully with the repository's `ci` Cargo profile. - Seven regression suites passed: `test_errors.py`, `test_table_signatures.py`, `test_remote_db.py`, `test_table.py`, `test_index.py`, `test_fts.py`, and `test_query.py`: **874 passed, 1 deselected**, with slow and S3 tests excluded. - CI caught an abstract-base conversion regression in the initial merge. The existing error-contract test reproduced it locally (1 failed, 7 passed); restoring the implementation to `LanceTable` fixes it while preserving signature parity. - Full-root Ruff reports 49 lint errors and 11 files needing formatting, all in unchanged upstream `docs/web-tests` files. The four changed Python files pass both checks. - Validated against upstream `197403e8`, using Lance 15.0.0-beta.1 and LanceDB 0.41.0-beta.14 bindings. Historical validation before this merge: 618 tests passed and one was deselected. That result does not establish correctness of the current merge. The original client-side reproduction produced nine argument-binding errors without contacting a server; the initial regression suite failed 32 cases before the fix and passed 39 after it. Documentation review follow-up: restored the `batch_size` parameter description on the abstract `Table.to_polars` method so it appears in the public API reference. This adds six docstring lines and changes no runtime behavior. After this edit, all 47 focused error-contract and signature tests pass; changed-file Ruff lint/format and `git diff --check` pass. Repository-wide Ruff still reports the same 49 findings and 11 formatting files in unchanged `docs/web-tests`. The full documentation site and seven-suite runtime run were not repeated for this documentation-only follow-up. The reported ARM64 production-server deployment has not been recreated. HTTP integration tests use the repository's local mock server. BREAKING CHANGE: Remote synchronous table methods now use the positional argument order and defaults documented by `Table`. Migrate calls using the former remote-only positional order to named arguments, particularly `create_index` and `search`; pass `index_type` by keyword to `create_scalar_index`. Remote scalar and vector index creation now default to `replace=True`; specify `replace=False` to retain rejection of an existing index. Renamed remote keywords remain supported as deprecated aliases, as documented above. --------- Co-authored-by: Xuanwo <github@xuanwo.io> |
||
|
Jack Ye
|
dcfef7c5ab |
feat(python): open lazy blob files from SQL descriptors (#4668)
SQL blob descriptors need pinned source identity to reopen a value after
the query finishes. Adds `open_blob_file(descriptor)` and
`open_blob_files(descriptors)` so Python can read the original blob
incrementally without materializing it in the SQL result.
### Python experience
```python
rows = db.execute_query("""
SELECT f.timestamp, blob_descriptor(v.video) AS video
FROM videos v JOIN frames f ON v.id = f.video_id
""").read_all()
videos = db.open_table("videos")
for row in rows.to_pylist():
if row["video"] is not None:
with videos.open_blob_file(row["video"]) as video:
frame = extract_frame(video, row["timestamp"])
```
The singular API accepts the descriptor dictionary or an Arrow struct
scalar directly and returns a seekable `BlobFile`.
`videos.open_blob_files(rows["video"])` accepts an Arrow array/chunked
array or another iterable, opens handles as iteration advances, and
preserves order, duplicates, and nulls as `None`. Async tables provide
`await videos.open_blob_file(...)` and `async for video in
videos.open_blob_files(...)`.
The iterable API can be used directly with the Arrow result column. Zip
it with the rows to keep each frame timestamp:
```python
for row, video in zip(rows.to_pylist(), videos.open_blob_files(rows["video"])):
if video is not None:
with video:
frame = extract_frame(video, row["timestamp"])
```
Each iteration opens one handle. Null descriptors yield `None`; handles
preserve input order and duplicates, and the `with` block closes each
non-null file.
For an async table:
```python
async for video in async_videos.open_blob_files(rows["video"]):
if video is not None:
with video:
payload = await video.aread()
```
### Execution
The descriptor retains the canonical `kind`, `position`, `size`,
`blob_id`, and `blob_uri` fields and carries a `source` struct with its
dataset URI, version, commit timestamp, branch, column, row reference,
and whether that reference is a physical address. Native opening
resolves that exact snapshot without changing the table handle's
checkout and rejects a descriptor belonging to another table. Remote
handles send authenticated byte-range requests with the captured
version/branch, commit timestamp, and source URI, so an old physical
address is never interpreted against the latest table.
Known-size remote descriptors open without a payload request. External
blobs with an unspecified size use a metadata-only HEAD request. Payload
bytes load only when the file is read; full reads still allocate the
full value. Existing row-ID-based `fetch_blob_files` remains available
when a descriptor is not already present.
Coverage includes native snapshot preservation after overwrite, both row
IDs and addresses, dictionary/Arrow inputs, null/empty values, reordered
and duplicate descriptors, lazy iterable behavior, async iteration, and
remote range pinning for all six storage kinds.
Descriptor reads isolate their pinned snapshot from the live table
handle's freshness state. Both native and remote reads verify the source
commit timestamp to reject a replaced snapshot after table recreation.
|
||
|
Jack Ye
|
24c2669a50 |
feat(sql): follow adaptive streaming and polling responses (#4664)
SQL execution now starts with one Flight call. The server can stream
rows immediately or return a polling continuation, which the SDK follows
through the existing query handle and reader APIs. The same behavior
applies with and without parameters.
Both initial transports share direct-stream lifecycle handling. Handoffs
are explicitly negotiated; existing servers can continue returning
ordinary streams. Add coverage for streaming, deferred results,
cancellation, malformed decisions, and legacy responses.
```mermaid
flowchart TD
A[execute_query or execute_query_async] --> B{Parameters supplied?}
B -->|No| C[DoGet: SQL command ticket]
B -->|Yes| D[DoExchange: SQL and Arrow parameter row]
C --> E[Server chooses execution path]
D --> E
E -->|Stream| F[Initial response: streaming decision and Arrow batches]
E -->|Defer| G[Initial response: PollInfo continuation]
G --> H[SDK follows PollFlightInfo]
H -->|Still running| H
H -->|Result endpoints available| I[SDK fetches tickets with DoGet]
F --> J[Query reader yields Arrow batches]
I --> J
```
`execute_query` opens the reader; `execute_query_async` returns its
query handle. The caller does not select the transport or execution
path.
|
||
|
Jack Ye
|
6b6778e684 |
feat: infer lazy blob reader Function inputs (#4663)
Infer Blob V2 input access from the Python Function signature. `video:
BlobReader` requests a lazy, seekable reader; `video: bytes` requests
the complete payload. Users do not need a separate `blob_inputs`
registration option.
## Python experience
For a video table joined to a table of frame timestamps, the Function
can pass its reader directly to a media decoder:
```python
import io
import av
from lancedb import BlobReader, udf
@udf(pip=["av", "Pillow"])
def extract_frame(video: BlobReader, timestamp_s: float) -> bytes:
with av.open(video) as container:
stream = container.streams.video[0]
container.seek(int(timestamp_s / stream.time_base), stream=stream)
for frame in container.decode(stream):
if frame.time is not None and frame.time >= timestamp_s:
output = io.BytesIO()
frame.to_image().save(output, format="JPEG")
return output.getvalue()
raise ValueError("No frame at or after the requested timestamp")
```
After registering `extract_frame`, the query keeps the usual Function
call:
```sql
SELECT f.video_id, f.timestamp_s,
extract_frame(v.video, f.timestamp_s) AS image
FROM videos AS v
JOIN frames AS f ON v.id = f.video_id
```
The returned JPEG can feed a subsequent image-model step. The reader
provides `read`, `readinto`, `seek`, `tell`, `size()`, and
`read_range(offset, length)`; range reads leave the sequential cursor
unchanged. Each row invocation owns its reader, which closes after the
call. Decoder seeks can request the parts of the MP4 needed to locate
and decode the requested frame; the amount read depends on the file
layout and codec.
Functions that need the whole value can keep a bytes signature:
```python
@udf
def video_length(video: bytes) -> int:
return len(video)
```
## Execution contract
- Signature inference maps `BlobReader` to a Blob V2 field and records
`blob_access="reader"`. `bytes` keeps its binary Arrow type and records
`blob_access="bytes"`; it can also bind to a Blob V2 source. Explicit
schemas support the same choice, including a Blob V2 field annotated as
`bytes`.
- Reader execution carries a compact blob reference into the Function
worker. Payload reads happen when Python calls the reader, with requests
split into at most 1 MiB ranges. The full video does not need to enter
the Arrow input batch just to invoke the Function.
- Bytes execution materializes the full source before the Function runs
and remains subject to eager-input memory admission. Calling
`reader.read()` without a size also materializes the remaining contents
in Python; lazy access depends on how the Function consumes it.
- Input access metadata survives Python/Rust Function serialization and
registration. Packaged Functions import the worker-provided reader
implementation. This PR defines the SDK contract; execution requires
corresponding server support.
- This changes blob inputs. The example still returns ordinary image
bytes; it does not add streamed outputs or run an image model.
Adds coverage for signature inference, explicit schemas, nullable
readers, signature round trips, seeking, bounded range reads, buffering,
and invalid responses.
|
||
 Colin Patrick McCabe
|
631ea41a63 |
feat!: return lazy iterators for table and namespace listings (#4599)
Make table and namespace listings consistent with other resource
listings across Rust, Python, and TypeScript. Callers can iterate over
individual names across all pages, with configurable page sizes and
continuation tokens.
In order to avoid breaking existing Python users, this change adds some
compatibility methods to the returned Python iterators. Code such as:
```
for t in db.list_tables().tables:
print(t)
```
will continue to work as expected because the iterator has a `tables`
field and a `page_token` field, which function as expected to reveal the
first page of results. In other words, it is never necessary to use the
iterator __as an iterator__. It may be used as if it were an old-style
response object. (As the iterator progresses, the `tables` list may
switch to the second, third, etc. page of results.) Similar
compatibility code allows the async python API to keep working as well.
|
||
|
Jack Ye
|
f9680806c9 |
feat(python): expose materialized view auto-refresh creation options (#4621)
Expose optional `auto_refresh_enabled` and
`auto_refresh_interval_seconds` when creating materialized views from
Python and Rust. Omitted intervals remain unset: the server determines
when to refresh.
```python
view = db.create_materialized_view(
"recent_orders", "orders",
auto_refresh_enabled=True,
auto_refresh_interval_seconds=300,
)
```
## Architecture and workflow
- Python synchronous, asynchronous, job-returning and namespace wrappers
forward both optional values through PyO3 to the Rust creation builder.
Both default to `None`; no client interval is selected.
- Remote creation calls `POST /v1/materialized_view/{id}/create` with
the defining query and only the supplied refresh properties. A `202
Accepted` response must contain a job ID. Existing job tracking waits
for the server's ordinary SQL CREATE job before synchronous creation
opens the view.
- The server validates and authorizes the CREATE, then uses the existing
SQL worker to populate the view and persist its refresh settings. The
Lance agent subsequently uses those settings to submit REFRESH SQL jobs.
- Explicit positive finite fractional seconds are preserved. An interval
alone does not enable refresh. Embedded connections retain manual
refresh, accept explicit disabled state, and reject requests for
automatic scheduling rather than ignore them.
Includes API documentation and coverage for omission, false,
fractional/invalid intervals, namespace forwarding, server job IDs and
invalid acceptance responses.
Requires the server REST-create endpoint to be deployed before SDK
release. Existing remote `with_no_data=True` remains unsupported.
|
||
 Dan RammerandClaude Opus 5.5
|
92825f6f13 |
refactor!: rename MemWAL APIs to RTE (realtime engine) (#4598)
## What Renames the user-facing MemWAL surface to **RTE** (realtime engine) across Rust, Python, TypeScript, and Java, including the REST wire format. This follows the LSM → MemWAL rename in #4075. | Before | After | |---|---| | `MemWalWriteSpec` / `MemWalStats` | `RTEWriteSpec` / `RTEStats` | | `enable_memwal` / `disable_memwal` / `get_memwal_spec` | `enable_rte` / `disable_rte` / `get_rte_spec` | | `flush_memwal` / `compact_memwal` / `checkpoint_memwal` | `flush_rte` / `compact_rte` / `checkpoint_rte` | | `get_memwal_stats` / `close_memwal_writers` | `get_rte_stats` / `close_rte_writers` | | `use_memwal` (query, merge insert) | `use_rte` | | TS/Java `enableMemWal`, `useMemWal`, ... | `enableRTE`, `useRTE`, ... (napi `js_name` set explicitly) | | Java `LanceDbTableMemWal` | `LanceDbTableRTE` | | `table/memwal_stats.rs` | `table/rte_stats.rs` | Docs and comments now say "realtime engine (RTE)". Text about Lance's underlying mechanism keeps "MemWAL" ("Lance's MemWAL", "MemWAL index"). **Left alone:** Lance-owned names (`MemWalIndexDetails`, `DatasetMemWalExt`, `InitializeMemWal*`, `mem_wal` paths), internal `Lsm*` names and modules, and the `does not support with_row_id` refusal text that the hybrid fallback matches on. ## Compatibility Breaking, for an experimental API. The wire format changes, so this must roll out together with the matching sophon change (lancedb/sophon#8870): - Routes: `/enable_memwal/` → `/enable_rte/`, `/disable_memwal/` → `/disable_rte/`, `/get_memwal_spec/` → `/get_rte_spec/`, `/get_memwal_stats/` → `/get_rte_stats/`, `/flush_memwal/` → `/flush_rte/`, `/compact_memwal/` → `/compact_rte/` - Response keys: `memwal_spec` → `rte_spec`, `memwal_stats` → `rte_stats` - Request field: `use_memwal` → `use_rte` on query and merge-insert bodies The SQL keyword becomes `ALTER TABLE ... SET RTE` server-side. The guides under `docs/web` will be updated separately. ## Validation - Rust: `cargo clippy --features remote --tests --examples` clean; `cargo test --features remote -p lancedb --lib`: 1533 passed - Java: `mvn test`: 37 passed (Spotless applied with JDK 11) - Python: `ruff format` / `ruff check` clean; targeted tests (`test_rte_spec`, `test_merge_insert_lsm`, `test_remote_db`, `test_wal_hybrid`, `test_query`, `test_permutation`, `test_table`): 708 passed - TypeScript: `pnpm build`; `table`, `query` tests: 606 passed; `biome check` clean; `docs/src/js` regenerated with `pnpm run docs` 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com> |
||
|
Dan RammerandClaude Opus 5
|
fe34eb3549 |
refactor!: rename LSM APIs to MemWAL and align SSTable/table shard naming (#4075)
## What Renames the MemWAL surface in two passes, across Rust, Python, TypeScript, and Java, including the wire format. ### 1. MemWAL APIs drop "LSM" The write path these APIs drive is Lance's MemWAL; "LSM" described its shape rather than naming it. Turning it on and off now reads as enabling a feature rather than installing a spec. | Before | After | |---|---| | `set_lsm_write_spec` | `enable_memwal` | | `unset_lsm_write_spec` | `disable_memwal` | | `get_lsm_write_spec` | `get_memwal_spec` | | `flush_lsm` / `compact_lsm` / `checkpoint_lsm` | `flush_memwal` / `compact_memwal` / `checkpoint_memwal` | | `get_lsm_stats` | `get_memwal_stats` | | `close_lsm_writers` | `close_memwal_writers` | | `use_lsm` (query, merge insert) | `use_memwal` | | `LsmWriteSpec` / `LsmStats` | `MemWalWriteSpec` / `MemWalStats` | | Java `LanceDbTableLsm` | `LanceDbTableMemWal` | TypeScript and Java use camelCase (`enableMemWal`, `getMemWalStats`, ...); the napi methods set `js_name` so the generated names keep the `MemWal` casing. flush/compact/checkpoint keep a `_memwal` suffix so `compact_memwal` is not confused with `compact_files` / `optimize`, which compact the base table. Internal names (`LsmMode`, `LsmScanner`, the `merge::lsm` / `query::lsm` modules) are unchanged. ### 2. Stats use SSTable and table shard Aligns the stats surface with the MemWAL naming settled upstream in lance-format/lance#7943 and #7957, where the persisted unit became an **SSTable**. **Generation → SSTable.** A `GenerationStats` describes one flushed MemTable, which is an SSTable. The generation *number* is kept — an SSTable is identified by its generation — so only the noun moved. **Bucket → table shard.** Each entry is one MemWAL shard. "Bucket" names only the hash sharding *transform*, so it was already wrong for `identity` and `year` sharding, which produce shards and no buckets at all. | Before | After | |---|---| | `GenerationStats` | `SsTableStats` | | `BucketStats` | `TableShardStats` | | `stats.buckets` | `stats.table_shards` | | `BucketStats.generations` | `TableShardStats.sstables` | | `include_generation_rows` | `include_sstable_rows` | | `newest_generation` | `newest_sstable_generation` | | `outstanding_generations` | `outstanding_sstables` | Docs and comments follow suit: "seal" → "freeze", "L0" / "flushed generations" → SSTables, "bucket" (meaning a shard) → table shard. `MemWalWriteSpec::bucket`, `num_buckets`, and the `"Sealed"` shard status value are **unchanged** — those name the sharding transform or a server value, not the shard. ## Compatibility Breaking, for an experimental API. The wire format changes with the SDKs, so this must roll out together with the matching server change: - Routes: `/set_lsm_write_spec/` → `/enable_memwal/`, `/unset_lsm_write_spec/` → `/disable_memwal/`, `/get_lsm_write_spec/` → `/get_memwal_spec/`, `/flush_lsm/` → `/flush_memwal/`, `/compact_lsm/` → `/compact_memwal/`, `/get_lsm_stats/` → `/get_memwal_stats/` - Response keys: `lsm_write_spec` → `memwal_spec`, `lsm_stats` → `memwal_stats`, `buckets` → `table_shards`, `generations` → `sstables` - Request fields: `include_generation_rows` → `include_sstable_rows`; `use_lsm` → `use_memwal` on query and merge-insert bodies ## Validation - Rust: `cargo test --features remote -p lancedb --lib --tests` — 1467 lib tests and all integration test targets pass; `cargo clippy` clean - Python: rebuilt the extension; `test_remote_db`, `test_memwal_spec`, `test_merge_insert_lsm`, `test_query`, `test_permutation`, `test_table` — 655 passed; `ruff format` / `ruff check` clean - TypeScript: `pnpm build`; `table`, `merge`, `query` tests — 593 passed; `biome check` clean; `docs/src/js` regenerated with `npm run docs` - Java: `mvn test` — 37 passed (Spotless applied with JDK 11; the pinned google-java-format does not run on JDK 21) 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
lancedb-gatefixer[bot]
|
6487503cff | fix: omit unfetchable list blobs from column discovery (#4312) | ||
|
Colin Patrick McCabe
|
6cc4919c3c |
feat: add an authorization API (#4363)
Add an authorization API to the LanceDB catalog. This is an API that can be used to bind access control lists (ACLs) to individual catalog objects. It can also be used to manage server-side roles, and introspect principals and groups. The new API is implemented in both the Rust, Python, and async Python styles and includes list_acls, add_acl, delete_acl, list_role_bindings, add_role_binding, delete_role_binding, get_principal, and get_group. This API implements the design principle of forwards-compatibility. So unknown privilege, object, and subject types do not cause errors, but are be supported as best as possible in the library. |
||
|
lancedb-gatefixer[bot]
|
0212cfd3d6 | fix(python): normalize blob row ID inputs and errors (#4293) | ||
|
Jack Ye
|
4487b1108c |
feat(sql): bind query parameters over Flight DoExchange (#4345)
`execute_query` / `execute_query_async` can now bind values to `$1` /
`$name` placeholders instead of splicing them into the SQL text. The
values travel as Arrow, so a float32 keeps its exact bits and type and a
vector stays a compact `FixedSizeList` (3 KB for 768 dims rather than ~9
KB of decimal literals parsed back as Float64).
```rust
db.execute_query_async("SELECT id FROM docs ORDER BY distance(vector, $vector) LIMIT $k")
.parameters(batch) // one-row RecordBatch
.execute()
.await?;
```
```python
db.execute_query(sql, parameters={"vector": np.float32_vector, "k": 10})
```
- A statement with parameters is sent on one `DoExchange`: the first
message carries the `CommandStatementQuery` descriptor and the parameter
schema, the second the one-row batch, and the result rows stream back on
the same call. Statements without parameters keep `PollFlightInfo` +
`DoGet` unchanged.
- Column `i` binds `$<i+1>`; a column whose name is not a number also
binds `$<name>`. Python accepts a list (positional), a dict (named), or
a one-row `RecordBatch` / `Table`; 1-D numpy / pyarrow arrays become a
`FixedSizeList` of their element type.
- A parameterized query runs on its call: `cancel()`, dropping the
handle before reading, or dropping the reader ends the call and stops
the server-side statement. It is still registered for `describe_query`.
- `Database::execute_query_async` now takes an `ExecuteQueryRequest`
options struct.
Server side: lancedb/sophon#8256.
|
||
|
Colin Patrick McCabe
|
de005f1d61 |
refactor!: implement lazy iterators for several APIs (#4459)
Implement lazy iterators for: - secrets - views - jobs - functions - materialized views |
||
|
Jack Ye
|
7e8f0d4982 |
refactor: always fully refresh materialized views (#4373)
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.
|
||
|
Colin Patrick McCabe
|
d864e7198f |
feat!: list_databases returns an iterator (#4442)
CatalogConnection.list_databases should return an iterator so that users don't have to manually re-implement pagination. For those who do want more control over pagination, add num_page_results and page_token to the Iterator object, and also allow the iterator object to be created at a specific page token. |
||
  
|
0327618a62 |
feat(sql): add native vector dedup and partitioned mv refresh (#4303)
## Summary
Materialize a source-preserving dedup result from an indexed, pinned
snapshot with a short declaration. The default greedy policy keeps the
smallest unassigned snapshot row ID and removes only its directly
matching neighbors: For A < B < C, an A–B–C chain without an A–C edge
keeps A and C.
```python
from lancedb import vector_dedup
view = db.create_materialized_view(
"images_clean",
vector_dedup("images", column="phash", distance_threshold=4),
)
cleaned = view.table
```
The Python source expression captures the source version once at
creation and
waits for the materialized result by default. Use `dataset_version=...`
for an
explicit snapshot, `with_no_data=True` for declaration only, or
`create_materialized_view_async(...)` for a job handle. AsyncConnection
supports
the same expression. It uses the existing MV REST endpoints and registry
jobs,
without opening a SQL query connection. Source names are exact
root-namespace
table names. Additional select/where/limit options are rejected for this
source.
The SQL declaration remains available:
```sql
CREATE MATERIALIZED VIEW images_clean AS
SELECT * FROM vector_dedup('images', 7, 'phash', 4);
REFRESH MATERIALIZED VIEW images_clean;
```
This is the host-facing SQL entry point. The MV builder now accepts a
typed native vector source; the Rust SDK also supports the same
definition through `MaterializedViewDefinition::from_sql` and
`prepare_definition`; `view.refresh().execute()` executes it locally.
Hosts distribute its units using their existing job infrastructure. The
raw `vector_duplicate_pairs` source remains available separately when
callers want the pair relation.
## Implementation and contract
- Use actual Rust Lance `13.0.0-beta.15` (`db53dcf367`); the native
partition primitive computes pairs from index representations without
source-vector aggregation or a Python pair loop.
- Extend partitioned MV refresh with two dependent waves. Physical
segment/partition tasks stream pairs through a bounded external sort,
select direct representatives and write per-source-fragment bitmap
artifacts. Source-fragment tasks start after every selection receipt
completes and stream retained original rows, including blob payloads,
into uncommitted output fragments.
- Reuse normal blob write coercion and row-mask scans, including stable
row-ID translation after compaction. Null vectors, isolated rows and
source deletions are preserved correctly. Conflicting representative
decisions across overlapping index scopes fail before publication.
- Expose `dependency_units()` and `write_refresh_partition_with_inputs`
alongside the existing plan/commit API. Receipts bind
snapshot/configuration/policy/destination generation. Only a complete
accepted set can publish; unfinished units restart, completed units can
be reused. There is no pair-position checkpoint.
- Use MV format 4 for dedup results and format 3 for native pairs;
Python and TypeScript metadata readers recognize both. No source
deletion or source row-ID migration occurs.
Only same-segment/same-partition edges are covered. Quantized scores use
symmetric represented-vector distances. Result materialization requires
V2 data storage and is initially a native MV source, not an ordinary
SELECT table function. Compressed vertex masks stay in memory; edge
sorting spills to local disk. Unreferenced artifacts use normal Lance
retention/cleanup and must be retained for the job's recovery window.
## Validation
AWS Linux validation:
- Native pair/result integration matrix passed: multiple
segments/partitions, exact thresholds, empty output, cross-batch data,
single-process pair parity, source preservation, lost receipts and
publication guards.
- Blob/null-vector/pinned-deletion tests passed with both row-ID modes,
including compaction that changes physical row addresses.
- Representative policy, forced external-sort spill and
conflicting/corrupt artifact tests passed.
- Full MV regression suite: 153 passed, 2 pre-existing ignored. Rust
formatting and Clippy passed.
- Python MV suite: 47 passed; TypeScript MV suite: 11 passed. Native
bindings were rebuilt in project-local environments; formatting, lint
and TypeScript docs passed.
- Rust declaration doctests: 3 passed, including the complete local
dedup example.
- Companion host validation: 41 MV job tests passed (1 pre-existing
ignored), full workspace Clippy passed, and the complete process-level
CLI E2E suite passed (3 tests). Recovery exercises both selection and
materialization waves.
- A real CIFAR-10 pHash run used five actual registry-worker owners,
completed 32 selection + 24 materialization tasks, and retained 49,846
of 50,000 rows with identical original values and image payloads.
Host workflow performance measurements are recorded in the companion
host PR. Cross-platform CI is still running. The previous remote-test
bootstrap failed before Cargo tests with `401 Bad credentials`; the
workflow fix described below is being validated by CI. No deployment or
merge is performed.
## Python source validation
AWS tests cover automatic and explicit snapshots, physical/stable row
IDs, local
and remote creation, sync/job/async-connection APIs, delayed refresh,
strict
options and identifier quoting. The native Python extension was rebuilt
through
the project's uv/maturin backend. Ruff, Pyright and the full MkDocs
API-reference
build passed; both new public symbols are rendered.
A real 50,000-image FEv2 run disabled both Python SQL entry points and
supplied an
unreachable SQL endpoint. The new API still materialized the same 49,846
retained
rows and original image payloads. Three recorded creation/refresh jobs
completed
56 tasks each on all five registry-worker owners, and remained on source
snapshot
3 after source latest advanced to 4. No worker or server wire-protocol
changes
were needed for this client entry point.
Previous CI authentication failure:
https://github.com/lancedb/lancedb/actions/runs/36334679276/job/108663151265.
## Review and CI fixes
- Validate all JSON merge source fields before projection, rejecting
unknown and duplicate names at every struct level. Missing/reordered
fields and ordinary JSON merges remain supported. A checked-in 12-case
integration matrix covers root fields, structs, lists, large lists,
fixed-size lists and map values; rejected writes leave the table version
and row count unchanged.
- Match namespace connection MV method signatures to
`MaterializedViewSource` in all four sync/async methods, fixing the
Python 3.10 import failure from runtime override checks.
- Restore the remote-test script's encrypted-CLI path: use the Actions
token for artifact download and pass `SOPHON_READ_TOKEN` under the
variable the decryption script consumes. The previous workflow omitted
that variable and attempted the private release path, failing with HTTP
401. On the final head, remote CI passes: 1,464 Rust unit tests, the
integration targets and doctests pass (3 unit tests ignored).
AWS validation for these fixes: both review reproducers failed before
their fixes and pass afterward; 55 merge tests, 29 cast tests and the
standalone 7-case admission regression pass. Python 3.10.21 imports
successfully; 90 MV/namespace tests, 14 JSON tests and four real
namespace dedup cases pass. After `794b97a0`, the native binding was
rebuilt again and the JSON suite rerun. Rust formatting/Clippy, Ruff and
Pyright pass. Sophon's locked test compilation passes against the final
SDK source for all seven affected crates. Extended runners, logs and
benchmark artifacts remain local.
Gatekeeper recommends approval on `794b97a0`, and the `breaking-change`
label is set. Final CI: **49 checks passed, 3 expected skips, no
failures or pending checks**, including remote tests, Python 3.10,
minimum dependencies and Windows ARM Rust tests. All wheel builds pass
after retrying the Linux matrix affected by transient AlmaLinux
repository-metadata 404s. All code-review threads are resolved. The PR
remains open for maintainer review and merge.
---------
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
Co-authored-by: Lance Release <lance-dev@lancedb.com>
|
||
|
Taylor
|
a6dcbba49e |
feat(python): add opt-in request batching to TypeSafeReranker (#4316)
Adds opt-in `batch_size=40`: 80 non-null candidates use **2 requests instead of 80**. Default `batch_size=1` preserves the existing payload. Each batched question sees only its document and the shared query; instructions/criteria stay unchanged. Prompts referencing `state.document` need adaptation. Concurrency still limits requests; retries remain SDK-managed. Batch-local IDs and ordered results preserve duplicate documents; invalid IDs/probabilities raise without fallback. Validation: **124 automated tests passed**, plus live search integrations in both modes. A **100-query live subset test of the initial implementation** (6,851 candidate pairs; `jev-1.13.0`, SDK 0.7.1, concurrency 32) passed: unbatched/batched calls **6,851/199**, median **877/351 ms**, p95 **9,446/598 ms**. Hybrid Hit@5/Hit@10 was **83%/89% vs 84%/90%**; vector/FTS results and exact configuration are in the [test report](https://github.com/lancedb/lancedb/blob/typesafe-request-batching/python/benchmarks/typesafe_batching.md). Ruff and MkDocs passed. The simplified batching flow also passed a fresh four-query live check: 292 candidate pairs, with 292 unbatched versus 8 batched calls. Builds on #4209; motivated by [research #7](https://github.com/lancedb/research/pull/7). |
||
|
Jack Ye
|
8541d6d9df |
feat: add view CRUD APIs (#4236)
A view is a named query a database stores and plans on every read. It
holds no rows, which is the whole difference from a materialized view.
## API
| Verb | Route |
| --- | --- |
| `create_view(name, query, namespace_path)` | `POST
/v1/view/{id}/create` |
| `describe_view(name, namespace_path)` | `POST /v1/view/{id}/describe`
|
| `drop_view(name, namespace_path)` | `POST /v1/view/{id}/drop` |
| `list_views(namespace_path)` | `GET /v1/namespace/{id}/view/list` |
On `Connection` and the `Database` trait, with the remote client, Python
(sync and async) and Node bindings. Local databases return
`NotSupported`: the server side is Sophon's, where a view is an object
of the database manifest.
`ViewDescription` carries the defining query, the database *and
namespace path* unqualified names in it resolve against, and the schema
the query resolved to. `create_view` returns one, so a caller has the
schema without a second call.
Both defaults travel with the view because it outlives the session that
declared it: the server re-plans the stored query on every read, so a
reader resolving an unqualified name against its own defaults would read
a different table. `default_namespace_path` crosses the wire as
`default_namespace`, a path like `namespace`, absent for the root.
There is no replace: a name already taken is an error, and changing a
view is a drop followed by a create, each authorized against what it
actually touches.
Querying a view stays SQL's job. There are no rows behind a view, so
there is no `open_view` returning a `Table`.
|
||
 Wyatt Alt
|
5231d37f8d |
feat: list a table's per-row Function errors from the client (#4208)
A refresh running under a skip policy records each row it skipped, with the failing input and the error, but the client could not read that store: the server exposes it over SQL and, since recently, a REST route. A user who hit per-row failures still had to open a SQL session. `Table::function_errors` calls the route. The listing is table-addressed with optional job and column filters, the same addressing the SQL surface uses, so the two cannot disagree about what a table's errors are. The two non-record signals come back as their own fields rather than as rows: capped-fragment summaries, and whether the listing stopped at its limit. Local tables refuse rather than answer with an empty list. Python and Node expose the same call, with the same optional filters. |
||
 Jonathan HsiehandClaude Opus 5
|
60a1b4c219 |
feat(secrets): named Secrets, bindings, and namespace addressing (#4150)
Adds the client half of database-scoped named Secrets: a Secret is a
name and
an opaque value stored by the service, and a Function binds one to the
environment variable its library already reads. Secrets are addressed by
a
namespace path plus a name.
The UDF body is unchanged and stays portable — it reads `OPENAI_API_KEY`
the
way it always did, and the binding is what puts a value there:
```python
db.create_secret("openai-prod", os.environ["OPENAI_API_KEY"])
function = db.create_function(
analyze_caption,
secrets=[
EnvVarSecret(secret_name="openai-prod", env_variable="OPENAI_API_KEY")
],
)
function.secret_bindings # the Secret's name, never its value
```
- `create_secret` / `alter_secret` / `list_secrets` / `describe_secret`
/
`drop_secret` on sync, async and remote connections, with the pyo3
binding
and the Rust client behind them. Each takes `namespace_path`
keyword-only,
defaulting to the root.
- **There is no read API, by construction rather than by policy** — no
code
path returns a stored credential, and `describe_secret` answers with
metadata only.
- `EnvVarSecret` is a pure local constructor: it contacts no server, so
it
cannot fail on a Secret that does not exist. It exists so that a bare
string
in that position — which would be a credential — is a `TypeError` rather
than a plausible-looking mistake that reads identically in a diff.
- `create_function(..., secrets=[...])` carries the bindings as
`secret_bindings`: a list of `SecretBinding` tagged by `kind`, so a
later
delivery mode is a variant rather than a sibling field. The value never
travels — it is resolved by the service when the Function runs, which is
what
lets a rotation reach columns already pinned to an older
FunctionVersion.
- A binding names its Secret as a `SecretReference` of `{name,
namespace_path}`
rather than one joined string, so no delimiter has to be excluded from
every
name and segment forever, and `ClientConfig.id_delimiter` cannot
contradict
an identity built on a fixed separator.
- A root namespace is omitted from the request body rather than sent
empty, so
a root request is byte-identical to one from a client that predates
namespaces. Tests pin it.
This is the client surface the design's §4 describes; the service side
lives in
sophon.
**Previously split across two PRs.** Namespace addressing was #4151,
stacked on
this one; it is folded in here so the Secret identity contract — name,
namespace path, and the binding that carries both — is reviewable as one
piece
rather than as a shape introduced and then replaced.
## Identifier safety, merged from #4189
**#4189 is merged into this branch**, so the client half of Secrets and
the
guards on the identity it puts in the URL are one PR. What it added:
- Components are checked where the identifier is built, before a request
is
constructed. `create_secret("../jobs", value)` no longer resolves to
`/v1/jobs/create` and delivers a credential-bearing body to a route with
none
of this one's body suppression.
- Each component is percent-encoded and joined by the delimiter, so
nothing
inside a component can end the path segment or add one.
- A component may not be empty, a relative segment (`.`, `..`, and their
`%2e`
spellings), or the delimiter itself — the three ways a component erases
a
boundary the split has to recover. `["prod", ""]` joined to `prod$`,
which
reads back as `["prod"]`.
- `$` is the only accepted `id_delimiter`, refused at client
construction.
`ClientConfig.id_delimiter` remains, since the identifier grammar comes
from
the Lance REST catalog standard, but a value that would produce
identifiers no
service splits the caller's way is now an error where it was written.
- One `build_object_identifier` and one character set serve tables,
namespaces,
Secrets, Functions and materialized views.
Components are checked for *addressability*, not a character set: the
name's
own grammar stays each object's own, so a catalog database keeps the `/`
that
`RemoteCatalog::validate_name` allows.
## Known shortcoming
`secret_bindings` is omitted from a registration body when empty, so a
client
that binds nothing sends what a client without bindings sends. When a
client
does bind a Secret and the service does not know the field, the field is
ignored: registration succeeds, the returned version carries no
bindings, and
the Function fails at execution with the variable unset, far from the
call that
asked for it.
`ServerVersion` is how this codebase refuses a feature the service is
too old
for, and it gates five features already. It does not gate this one: it
is held
per table, and registering a Function is a database-level call. Noted at
the
field in `remote/db.rs`; wiring the gate is follow-up work.
**Tests:** lancedb lib 1340 passed, `first_class_function_slice1` 9,
`first_class_function_slice2` 3, plus Python tests across both slices.
Rebased onto `main` after #4176 (OCI Function identity), #4191 (`.`/`..`
table
names) and #4195 (remote catalogs).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
https://claude.ai/code/session_01UfmeJ533rQDnPBkMtjerV6
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
|
||
|
Jack YeandXuanwo
|
6a07f88980 |
feat: add remote catalogs and Python and TypeScript bindings (#4195)
Add a `Catalog` trait and `RemoteCatalog` for managing databases,
exposed through Rust, synchronous/asynchronous Python, and TypeScript. A
remote catalog represents the server's root namespace, and each database
is one child namespace. Create/connect return ordinary LanceDB
connections, so existing table APIs work unchanged.
## Rust API
`Catalog` is an object-safe async trait with `create_database`,
`connect_database`, `list_databases`, and `drop_database`. Backend
create/connect methods return `Arc<dyn Database>`; the public
`CatalogConnection` wraps them as `Connection` values and shares its
embedding registry with those connections. `RemoteCatalog` implements
the trait; `connect_catalog` is the convenience builder, available with
the `remote` feature.
```rust
use lancedb::catalog::{
CreateDatabaseRequest, DropDatabaseRequest, ListDatabasesRequest,
};
let catalog = lancedb::connect_catalog("https://my-server.example")
.api_key("my-api-key")
.execute()
.await?;
let db = catalog.create_database(
CreateDatabaseRequest::new("analytics").exist_ok(true),
).await?;
let connected = catalog.connect_database("analytics").await?;
let page = catalog.list_databases(
ListDatabasesRequest::default().limit(20),
).await?;
// page.databases: Vec<String>; page.page_token: Option<String>
catalog.drop_database(
DropDatabaseRequest::new("analytics").ignore_missing(true),
).await?;
```
Create/drop also accept a plain name for default behavior, e.g.
`catalog.create_database("analytics").await?`. Existing names fail
creation unless `exist_ok` is enabled; missing names fail drop unless
`ignore_missing` is enabled. Drop always requires an empty database.
## Python API
```python
import lancedb
catalog = lancedb.connect_catalog(
"https://my-server.example", api_key="my-api-key"
)
db = catalog.create_database("analytics", exist_ok=True)
connected = catalog.connect_database("analytics")
page = catalog.list_databases(limit=20)
# page.databases: list[str]; page.page_token: Optional[str]
if page.page_token is not None:
next_page = catalog.list_databases(limit=20, page_token=page.page_token)
catalog.drop_database("analytics", ignore_missing=True)
```
`connect_catalog` returns `Catalog`; create/connect return the existing
`DBConnection` API. The async equivalent is `catalog = await
lancedb.connect_catalog_async(...)`, returning `AsyncCatalog`; await
each of the same four methods, with create/connect returning
`AsyncConnection`.
## TypeScript API
```typescript
import { connectCatalog } from "@lancedb/lancedb";
const catalog = await connectCatalog("https://my-server.example", {
apiKey: "my-api-key",
});
const db = await catalog.createDatabase("analytics", { existOk: true });
const connected = await catalog.connectDatabase("analytics");
const page = await catalog.listDatabases({ limit: 20 });
// page.databases: string[]; page.pageToken?: string
if (page.pageToken !== undefined) {
const nextPage = await catalog.listDatabases({
limit: 20, pageToken: page.pageToken,
});
}
await catalog.dropDatabase("analytics", { ignoreMissing: true });
```
Create/connect return the existing `Connection` API. All four methods
are asynchronous.
## REST mapping
All paths below are relative to the catalog endpoint. `{name}` is the
logical database name encoded as one URL path component. The default
namespace delimiter is `$`, so the root identifier is encoded as `%24`.
| Catalog operation | Existing REST route | Request |
| --- | --- | --- |
| `create_database(name)` | `POST /v1/namespace/{name}/create` |
`{"mode":"Create"}`; `exist_ok=true` sends `{"mode":"ExistOk"}` |
| `connect_database(name)` | `POST /v1/namespace/{name}/describe` |
`{}`; verifies existence before returning a scoped connection |
| `list_databases(...)` | `GET /v1/namespace/%24/list` | Optional
`limit` and `page_token` query parameters |
| `drop_database(name)` | `POST /v1/namespace/{name}/drop` |
`{"mode":"Fail","behavior":"Restrict"}`; `ignore_missing=true` changes
mode to `"Skip"` |
For example, database `team/search` uses
`/v1/namespace/team%2Fsearch/create`. A paginated root listing can use
`/v1/namespace/%24/list?limit=20&page_token=a%2Fb`. The list response
retains the existing namespace wire shape,
`{"namespaces":["analytics"],"page_token":"next"}`; the SDK exposes
`namespaces` as `databases` and preserves the opaque continuation token.
An absent or empty token ends pagination. Page limits must be between 1
and 2147483647. Create/drop accept a namespace JSON response or HTTP
204.
Catalog management requests omit both `x-lancedb-database` and
`x-lancedb-database-prefix`, including values supplied through static or
dynamic headers. Returned database connections set `x-lancedb-database`
to the exact logical name and keep independent scope. API keys, OAuth or
dynamic authentication, client settings, table read consistency
settings, and an optional SQL endpoint override carry over to those
connections. OAuth cannot be combined with an API key or a custom header
provider.
For SQL through an HTTPS catalog, configure the existing SQL endpoint
contract with Rust
`.sql_host_override("grpc+tls://sql.example.com:10026")` or Python
`sql_host_override="grpc+tls://sql.example.com:10026"`. TypeScript
catalog options expose the same setting as `sqlHostOverride`. It is
inherited by created/connected databases, retained by Python connection
serialization, and initialized lazily when SQL is executed.
Create HTTP 409 maps to `DatabaseAlreadyExists`; connect/drop HTTP 404
maps to `DatabaseNotFound`, except that `ignore_missing` suppresses a
missing-database drop. Other server errors propagate. The server
enforces restricted deletion; the client never requests cascading
deletion.
Database names preserve literal slashes as part of one name. They must
be nonempty ASCII, with no control characters, surrounding whitespace,
or configured namespace delimiter, and cannot be `.` or `..`. Endpoints
must be HTTP(S) URLs without embedded credentials, query parameters, or
fragments.
## Scope
This PR adds the client API and reuses existing namespace endpoints.
Local catalogs, `__catalog` storage, location generation/sanitization,
and `__manifest` lifecycle support remain deferred; the Lance dependency
is unchanged.
The PR also runs macOS Node tests serially to avoid existing
resource-contention timeouts reproduced across recent main runs.
---------
Co-authored-by: Xuanwo <github@xuanwo.io>
|
||
|
3b37ea2c7a |
feat: represent registered Functions by OCI image identity (#4176)
Function versions identify independent Function objects and their numeric revisions. Rust and Python expose the object ID, location, canonical decimal version, metadata, and availability separately from the OCI image digest. Computed-column applications carry the complete object reference, so existing bindings retain their identity after a name is removed and reused. Source authoring keeps the existing create_function/create_function_async, job wait, and column-binding APIs. The server coordinates baking followed by registration; users do not have to manage manifest digests to create a Function. The previous stored Function representation is intentionally unsupported. Existing contract tests and shared wire fixtures are migrated to the new model. This SDK change accompanies the final integration layer https://github.com/lancedb/sophon/pull/7887 in the Sophon stack: https://github.com/lancedb/sophon/pull/7885 → https://github.com/lancedb/sophon/pull/7886 → https://github.com/lancedb/sophon/pull/7887. A metadata-only revision can retain the same executable image; Function version numbers must not be used as image cache keys. --------- Co-authored-by: lancedb automation <robot@lancedb.com> Co-authored-by: Yang Cen <bubble-cal@outlook.com> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> |
||
|
Jack Ye
|
21f11b4463 |
feat!: replace get_job/job_history with describe_job/query_job_events (#4130)
A 1M-row column refresh over 200 fragments produced no visible result,
and the client could only ever say `"running"`. Everything needed to
diagnose it already existed server-side — the job registry records a
`claim`/`claim_complete` pair per fragment carrying `rows_processed` —
but none of it was reachable.
## Before
Four ways to ask about a job, none of which told you much.
```python
job = table.refresh_column_async("embedding")
job.status() # "running". That was the entire debug surface.
db.get_job(job_id) # state, and a spec. No result, no progress.
db.job_history(job_id) # raw record batches, no limit, no filter
db.job(job_id) # a handle that knew nothing
```
## After
Open a job the way you open a table; the handle answers everything.
```python
job = db.open_job(job_id) # raises JobNotFoundError if there is no such job
```
```python
>>> print(job)
Job(
id='job-1',
state='failed',
job_type='refresh_column',
creation_ms=1757000000000,
spec={
"column": "embedding",
"num_workers": 4
},
failure=JobFailureInfo(phase='execute', message='worker died', retryable=True),
)
```
Individual fields are there too — `job.state`, `job.job_type`,
`job.creation_ms`, `job.spec`, `job.result`, `job.failure` — and
`job.result` carries `rows_assigned` / `rows_failed` as soon as the job
succeeds, with no `wait()` required.
Per-fragment progress *while it is still running*:
```python
done = job.events(filter="state = 'claim_complete'", limit=10_000)
done.column("rows_processed").to_pylist() # [5000, 5000, ...]
```
The handle an async action returns is the same object, one `refresh()`
away:
```python
job = table.refresh_column_async("embedding")
job.refresh()
job.state, job.result
```
TypeScript is the same experience, down to `console.log`:
```ts
const job = await db.openJob(jobId); // rejects if there is no such job
console.log(job); // same multi-line layout
job.state; job.jobType; job.spec; job.result; job.failure;
const done = await job.events({ filter: "state = 'claim_complete'", limit: 10_000 });
```
## Why each piece matters
- **A result without waiting.** `rows_assigned` / `rows_failed` used to
live only on the terminal result, so a job that never terminated
reported nothing at all.
- **`limit`.** The server caps event rows at 1000 and truncates without
saying so, which silently hid most of a 200-fragment job's history.
- **`filter`.** `claim_complete` rows carry per-claim `rows_processed` —
the only progress signal that exists mid-flight.
- **Events outlive the worker.** They live in the job registry, not in
pod logs that vanish with the pod.
- **One place to ask.** `open_job` replaces `describe_job`,
`query_job_events` and `job`, so a question about a job has one answer
instead of one per calling location.
- **A missing job is an error, not a `None`.** The common case is a job
id copied out of a log, where absence is the surprise worth raising —
and it matches `open_table`.
- **Printing is the debug surface.** Every field on its own line, JSON
payloads keeping their structure. An unrefreshed handle stays on one
line, because there is nothing to lay out.
- **In-process jobs say so.** A local refresh reports `state` and leaves
the rest null rather than inventing fields it has no record for.
`list_jobs` and `cancel_job` stay as they were: one lists, the other is
a one-shot action that should not need a describe first.
## Breaking
All shipped in 0.38.0. No deprecated aliases.
| Was | Now |
| --- | --- |
| `Connection.get_job` → `describe_job` | `Connection.open_job` returns
a populated `Job`, or raises |
| `Connection.job_history` → `query_job_events` | `job.events(...)` |
| `Connection.job` | `Connection.open_job` |
| Python events → `List[pa.RecordBatch]` | `pa.Table` |
| `JobDescription.spec_json` / `.result_json` | internal; use `job.spec`
/ `job.result` |
Node's `Job` is now a TypeScript class wrapping the native handle, so it
returns an Arrow table and parsed values like Python does. New
`Error::JobNotFound` / `JobNotFoundError`; the three job exceptions are
now in the Python API reference.
|
||
|
Jack Ye
|
aab23eb39e |
feat: support nullable named function outputs (#4123)
Supports fully nullable named Function outputs while preserving the
distinction between a valid all-null struct and a null/unassigned
result.
## Concrete example
This UDF contract is now valid:
```python
@udf(
input_schema=pa.schema([
pa.field("text", pa.string(), nullable=False),
]),
output_schema=pa.schema([
pa.field(
"embedding",
pa.list_(pa.float32(), list_size=1024),
nullable=True,
),
pa.field("embedding_failure_reason", pa.string(), nullable=True),
pa.field("embedding_failure_code", pa.int32(), nullable=True),
]),
)
def embed(text):
...
```
A successful row can return:
```text
embedding = [0.12, ...]
embedding_failure_reason = NULL
embedding_failure_code = NULL
```
If remote inference still fails after retries, it can return:
```text
embedding = NULL
embedding_failure_reason = "HTTP 429: rate limited"
embedding_failure_code = 429
```
An all-null but valid result struct is also assigned; it is not mistaken
for unfinished work.
## Binding shapes
- Mapping the result to one output column stores the `StructArray`
directly, including its parent validity bitmap.
- Flattening the result into top-level columns stores the parent
validity in a reserved internal nullable Boolean assignment column that
is not part of the UDF result mapping.
- An outer null struct remains unassigned/skipped. A valid struct
remains assigned regardless of which child fields are null.
- Scalar Function outputs remain non-nullable.
The contract is preserved through Python registration, Rust application
planning, persisted `FunctionBinding` metadata, schema revalidation, and
Enterprise execution.
|
||
|
Jack Ye
|
e639b1b650 |
feat: add asynchronous remote SQL queries (#4070)
## Summary
Add SQL execution to remote LanceDB connections. On the standard
synchronous connection, `execute_query` waits for the initial result
stream and returns its Arrow reader. `execute_query_async` is called
without Python `await` and immediately returns a query handle for status
inspection, streaming, or cancellation. Local databases report that SQL
is not supported.
The transport and query lifecycle live in Rust. Python exposes
native-backed synchronous and asynchronous connection methods and query
wrappers; it does not use PyArrow's Flight client.
## User experience
The standard synchronous connection supports both direct reads and
background query execution:
```python
db = lancedb.connect(
"db://analytics",
api_key="ldb_...",
sql_host_override="grpc+tls://sql.example.com:10026",
)
# Direct execution waits only until the initial result stream is available.
# Later batches continue streaming as the query progresses.
reader = db.execute_query(
"SELECT * FROM events",
default_namespace_path=["production"],
)
for batch in reader:
print(batch.num_rows)
# Background execution returns a query handle immediately. Despite the
# `_async` suffix, no Python `await` is needed on a synchronous connection.
query = db.execute_query_async("SELECT * FROM events")
print(query.id)
description = db.describe_query(query.id)
print(description.status)
print(description.progress)
print(description.expires_at)
# Start reading as soon as the service advertises partial results. The reader
# continues polling and yields newly available record batches until the query
# and all result endpoints are complete.
reader = query.reader()
for batch in reader:
print(batch.num_rows)
# Or cancel a different still-running query. Its status becomes "cancelling"
# while the server is still working, then "cancelled" once confirmed.
cancelled_query = db.execute_query_async("SELECT * FROM large_events")
cancelled_query.cancel()
```
The less commonly used asynchronous connection exposes the same
operations as coroutines:
```python
async_db = await lancedb.connect_async(
"db://analytics",
api_key="ldb_...",
sql_host_override="grpc+tls://sql.example.com:10026",
)
query = await async_db.execute_query_async("SELECT * FROM events")
async for batch in await query.reader():
print(batch.num_rows)
```
The UUIDv7 query id is scoped to the connection that submitted it. The
connection retains lightweight shared query state used by
`query.describe()` and `db.describe_query(query.id)`; the id does not
encode SQL or a Flight continuation token and is not a cross-connection
resume token. Abandoned state has bounded retention, and terminal state
remains available briefly.
Unqualified table names use the connected database and the `public`
namespace by default. `default_namespace_path` accepts a list such as
`["production", "events"]`. SQL can still use qualified names to
reference other databases and namespaces available to the deployment.
## Design
- Uses Arrow Flight `PollFlightInfo` for submission and long polling,
`DoGet` for results, and `CancelFlightInfo` for cancellation. Each
`PollInfo.info` is treated as the cumulative set of currently available
endpoints, so advertised tickets are consumed once and batches can be
delivered before execution is complete.
- Serializes result completion and cancellation into one lifecycle. A
server-accepted request reports `cancelling` and wakes blocked
status/result work; a later retry can confirm `cancelled`. Result
retrieval is rejected after cancellation is accepted, while cancellation
after a result was already delivered is a no-op.
- Assigns a time-ordered UUIDv7 connection-scoped query id and retains
only shared evolving lifecycle state, keeping SQL, Flight continuation
tokens, and Arrow result data out of public ids and the registry.
- Leaves admission control to the server while honoring server
expiration and a local fallback retention window for abandoned entries.
- Retains terminal ids for five minutes so they remain available for
connection-level description.
- Keeps one lazily initialized SQL client on each remote database
connection and attaches fresh authentication, routing, namespace, and
request metadata to every operation.
- Applies the configured overall timeout to each execution, description,
reader, and cancellation operation. A result reader carries one absolute
deadline from `reader()` through the end of streaming; connect and read
timeouts continue to bound their individual phases.
- Returns a bounded, backpressured, single-consumer Arrow stream rather
than collecting the full result in memory. Dropping the reader stops
downloading but does not implicitly cancel the server query.
- Preserves typed schemas for empty result sets through the stream
schema.
- Accepts Flight result messages up to 1 GiB so a valid row containing a
large blob, string, or vector is not rejected by tonic's 4 MiB default
receive limit.
- Supports the Python client first while keeping the authoritative
implementation in the Rust core.
|
||
 Drew
|
83cff3ab93 | fix(python): use one blobv2 type and coerce blob writes by metadata (#4065) | ||
|
Xuanwo
|
b78f2a5044 |
feat: expose list-element FTS document granularity (#4050)
## Summary LanceDB could not request Lance's list-element FTS document granularity through Python or Remote APIs, and generic nested-field resolution exposed Arrow's internal `item` segment instead of the public field path. This exposes typed `row | list_element` configuration for Python FTS index creation and match/phrase queries, preserves `_doc_index`, and keeps nested FTS paths public (for example, `docs.content`). Remote list-element requests require server API version 0.6.0 so older servers cannot silently execute them with row semantics; explicit row requests remain compatible. ## Compatibility Omitted index and query parameters retain row granularity. Remote row index creation omits the new wire field. ## Tracking [ENT-2342](https://linear.app/lancedb/issue/ENT-2342/expose-list-element-fts-document-granularity-end-to-end) |
||
|
lancedb-gatefixer[bot]andXuanwo
|
105fd73bc6 |
fix(python): commit streaming worker checkpoints on consumption (#4023)
## Summary - add `StreamingDataLoader`, which transports worker snapshots with prefetched batches and commits them to the parent dataset only when the trainer receives each batch - preserve exact non-uniform per-split progress and resume lagging splits without replaying already-consumed rows - reject stale parent checkpoints after a standard multi-process `DataLoader` has started, with guidance to use the consumer-aware loader - document the new public loader and merge non-uniform state across ranks ## Root cause PyTorch runs `StreamingDataset.__iter__` in private worker-process copies, while callers invoke `state_dict()` on the parent dataset. Sharing producer counters would still be incorrect because DataLoader prefetch can advance workers beyond batches returned to the trainer. ## Validation - `uv run --extra tests pytest python/tests/test_elastic_dataloader.py -q` (154 passed) - focused non-uniform merge regression (1 passed) - `uv run --project python --extra tests --extra dev ruff format .` - `uv run --project python --extra tests --extra dev ruff check .` - `cd docs && PYTHONPATH=. ../python/.venv/bin/mkdocs build` Fixes #3967 <!-- lance-gatekeeper-fix:v1 agent=572be272619660b97e87fd5c85188341 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io> |
||
|
Wyatt Alt
|
851fa16b47 |
feat(python): materialized view bindings (#3933)
Exposes materialized views to Python in both the async and sync clients: create_materialized_view / open_materialized_view / list_materialized_views on the connections, and MaterializedView / AsyncMaterializedView handles carrying the parsed definition and refresh(full=, source_version=), which returns the typed refresh result. select accepts column names, (alias, expression) pairs, or a dict of the same; the definition reads back off the stored schema, so a reopened handle needs no side channel. Remote connections raise NotImplementedError up front rather than failing deep in a request, matching the computed-column convention. <sub>Stack created with <a href="https://github.com/github/gh-stack">GitHub Stacks CLI</a> • <a href="https://gh.io/stacks-feedback">Give Feedback 💬</a></sub> |
||
|
Xuanwo
|
a588208de6 |
feat: add scalar function authoring and catalog client (#3991)
## Problem The canonical Function wire values and typed remote Job contract do not yet provide a Python authoring surface or catalog client, so users cannot package a scalar callable, register it, or reopen the exact immutable Function version. ## Behavior This adds scalar-only `@udf` authoring with deterministic annotation or explicit Arrow schema validation, content-addressed Python artifacts, and an internal scalar-to-Arrow-batch adapter descriptor. Registration payloads model non-secret environment values and secret names only. Remote connections can submit `create_function_async` and receive a typed `Job<FunctionVersion>`, then reopen that exact version by name and version ID. Synchronous connections can call `create_function` to submit and wait for the immutable version in one operation. Local Function catalog operations return a stable `NotSupported` error. Shared Rust/Python golden payloads and mocked catalog responses freeze the request, typed terminal result, and exact lookup contract. ## Validation - Rust formatting, remote check, clippy, and focused LDB-1/LDB-2 tests - Python formatting, lint, and focused LDB-1/LDB-2 tests - Python API documentation build |