Geneva Table.load_columns() parity on the REST-only client. Fills existing
columns from an external Parquet/Lance/IPC source by primary-key join.
- BaseTable::load_columns default (NotSupported) + public Table::load_columns,
taking a LoadColumnsRequest (source uris/format/storage_options, target/source
key, (target, source?) column mappings, on_missing, worker/batch/commit knobs).
- Remote impl POSTs to /v1/table/{id}/load_columns with the matching body;
mock test asserts the request shape.
- PyO3 binding + Python remote Table.load_columns(source, pk, columns, *,
source_format, source_pk, on_missing, ...) accepting a column list or
{target: source} dict.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
View.refresh(full=True) (sync + async) now works -- it previously raised
NotImplementedError. Thread the flag through the client: RefreshMaterialized-
ViewRequest.full -> the REST body (RemoteRefreshMaterializedViewRequest.full);
pyo3 refresh_materialized_view(full=...); Connection.refresh_materialized_view(
name, full=) sync + async. A full refresh forces a recompute-and-replace and
preserves the view's indexes (reindexed by the distributed indexer).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add View.create_index / create_scalar_index / create_fts_index / search
as pass-throughs to open_table(name). A materialized view is a real Lance
dataset; these let it be indexed and searched like any other table,
closing the parity gap with Geneva (whose create_materialized_view returns
a first-class Table).
The server-side create_index handler records indexes declared on a view so
they survive a full refresh (which overwrites the dataset, dropping its
indices); that re-apply is wired in the sophon engine.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Thread priority (Kueue tier) through refresh_column at every layer (Python sync+async
+ RemoteTable -> pyo3 -> Rust client trait/public/remote -> REST body), mirroring
num_workers/batch_size. The function keeps its priority as a default; the per-refresh
value overrides. Also adds the previously-missed batch_size to RemoteTable.refresh_column
(the REST sync path). cargo check (lancedb --features remote --tests, lancedb-python) +
ruff clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
batch_size / num_workers / max_workers are invocation concerns (how to schedule THIS
refresh), so expose batch_size on refresh_column through every layer (Python sync+async
-> pyo3 -> Rust client -> the REST RefreshColumnRequest.batch_size, which the handler
already forwards into the backfill). num_workers/max_workers were already invocation-
placed; batch_size was the gap. The function may still carry a default; the refresh
override wins (extends the batch_size_override model). Both crates cargo-check clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A computed column is an expression over a registered function applied to input
columns, not a UDF coupled to a column. fn("data") already returned the expression
string "fn(data)"; make it a ColumnExpr (a str subclass) that also carries the
function's return type, so add_columns(computed={"vec": embed("data")}) declares the
column with no hand-written type. _normalize_computed handles the new form (and tuple
keys for STRUCT fan-out) and keeps the legacy {col: (sql_type, expression)} tuple.
add_computed_column is deprecated (delegates, with a DeprecationWarning). The function
stays decoupled from columns -- register once, apply anywhere.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Thread an optional partition_by through the client: CreateMaterializedViewRequest
-> REST body -> pyo3 binding -> Python create_materialized_view/create_view
kwarg (sync + async). The server partitions the view's table function by the
named source column -- by IVF index clusters if the column is indexed
(image-dedup), else by distinct value. Unifies Geneva's partition_by +
partition_by_indexed_column into one knob.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Mirrors the sync ergonomics on the async surface: AsyncConnection
create_function(udf, replace=)/create_view/job; AsyncTable.add_computed_column;
AsyncView + AsyncJobHandle (await + asyncio.sleep; shared submission-prefix
matcher with the sync JobHandle). Decorator + REST routes are shared/already
validated; this is the async wrapper layer. Exported from the package root.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
db.job(id) gets the submission id the refresh/backfill endpoints return,
but list_jobs / cancel report the agent's manifest id
(<table>-<type>-<first 8 of submission id>). JobHandle now matches that
(exact id or submission prefix) so wait()/progress() truly track, and
cancel() cancels by the resolved canonical id instead of the unusable
submission uuid.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Brings the @udf/@table_udf decorator + type inference into lancedb as
lancedb.udf (Apache-2.0), and adds the ergonomic glue to the existing
connection/table so there's no separate object model:
- create_function() accepts a Udf (and a replace= flag)
- Table.add_computed_column(column, udf)
- create_view(name, source, select, ...) -> View (assembles the SELECT)
- Connection.job(job_id) -> JobHandle
- View / JobHandle are thin references over a connection
Exports udf/table_udf/Udf/JobHandle/View from the package root. The
operations stay the existing remote-only methods (enterprise/cloud); the
decorator works locally.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Exposes the existing server-side CANCEL JOB (CoordinatorCatalog::cancel_job)
as a REST-backed SDK method: Database trait default NotSupported,
RemoteDatabase POSTs /v1/job/{id}/cancel, pyo3 binding, sync+async python
wrappers. Best-effort: a missing job returns false, not an error. Mock-HTTP
unit test in test_derived_compute_routes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Per the interface design: computed columns are parameters on the
existing add_columns operation, not a separate method.
- BaseTable::add_computed_columns((name, sql_type) pairs + a f(args)
expression) -- default NotSupported; RemoteTable posts 'computed'
entries to the existing /v1/table/{id}/add_columns route.
- python add_columns gains computed= on LanceTable, RemoteTable, and
AsyncTable: tbl.add_columns(computed={'doubled': ('FLOAT',
'double_it(val)')}); grouped by expression so struct-returning
functions' columns land adjacently.
Adds the derived-compute interface to the SDK:
- Database trait: create/list/drop_function, create/refresh/alter/
drop/list_materialized_view, list_jobs -- default implementations
return Error::NotSupported (NotImplementedError in python), so
existing Database impls are unaffected; local single-node
implementations are planned. BaseTable gains refresh_column with
the same default.
- RemoteDatabase/RemoteTable implement them against the server REST
routes (/v1/function/*, /v1/materialized_view/*, /v1/job/list,
/v1/table/{id}/refresh_column), with mock-HTTP unit tests.
- Connection/Table public methods, pyo3 bindings (FunctionInfo,
MaterializedViewInfo, JobInfo pyclasses), and python wrappers:
sync on the DBConnection base (shared by local and remote
connections), async on AsyncConnection; refresh_column on
LanceTable, RemoteTable, and AsyncTable.
BREAKING CHANGE: splits generated by the permutation data loader will
not be the same, due to a change in hash function.
Updates the Lance dependencies and Java lance-core to
[v9.0.0-rc.1](https://github.com/lance-format/lance/releases/tag/v9.0.0-rc.1).
Includes the required DataFusion 54 and Lance file-reader compatibility
updates.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds client-side support for analyze_plan distributed metrics modes
across Rust, Python, and TypeScript clients. Defaults to aggregate for
backward compatibility and sends the remote distributed_metrics
parameter only when a non-default mode is requested.
Fixes#3174
Also fixes#3645
Empty record batches now append correctly typed empty embedding arrays
without invoking embedding providers. This avoids OpenAI requests with
an invalid empty input while preserving source-column validation and
the non-empty execution paths.
As a small cleanup, the single- and multi-embedding code paths now share
a single upfront lookup of their source columns ("input_columns")
instead
of each path looking them up independently. Also moves `lance-testing`
from regular dependencies to dev-dependencies where it belongs.
Tests run:
- `cargo fmt --all -- --check`
- `cargo test --quiet -p lancedb --lib
empty_batch_skips_embedding_functions`
- `cargo test --quiet -p lancedb --lib
empty_batch_still_validates_source_column`
- `cargo test --quiet -p lancedb --lib
test_create_empty_table_with_embeddings`
- `cargo check --quiet -p lancedb --features remote --tests --examples`
- `cargo clippy --quiet -p lancedb --features remote --tests --examples`
- `cargo test --quiet -p lancedb --lib`
- `cargo test --quiet --features remote --tests`
## Summary
Fix `on_bad_vectors="fill"` so it replaces only invalid or missing
vector values instead of replacing the entire vector row.
Fixes#3026.
## Reasoning
The old Python sanitizer detected whether a vector row was bad at row
granularity. For `fill`, it then used that row-level flag to replace the
whole vector with `[fill_value] * dim`. That meant an input like `[1.0,
NaN, 3.0]` became `[0.0, 0.0, 0.0]`, even though the documented and more
useful behavior is to preserve valid values and fill only the bad
element.
I checked whether this should be a Rust-side fix so TypeScript users
would benefit too. Today, Rust core exposes `NaNVectorBehavior::{Error,
Keep}` for rejecting or keeping NaN vectors, while the Python
`on_bad_vectors` API (`error`, `drop`, `fill`, `null`) is implemented in
the Python ingestion sanitizer before data reaches Rust. TypeScript does
not expose the Python `on_bad_vectors="fill"` behavior today. Moving
this exact behavior to Rust would be a broader cross-language API
change, so this PR keeps the fix scoped to the currently affected Python
API.
## What changed
- Added a small helper that fills bad vector rows by preserving valid
elements, replacing NaN elements with `fill_value`, truncating vectors
longer than the expected dimension, and padding short vectors with
`fill_value`.
- Kept the existing fast path unchanged: the helper only runs after bad
vectors are detected and `on_bad_vectors="fill"` is selected.
- Updated sanitizer and table tests to assert element-wise NaN
replacement and short-vector padding for both `create_table` and `add`.
## Validation
- `uv run ruff format .`
- `uv run ruff check .`
- `cd python && uv run --no-sync pytest
python/tests/test_util.py::test_handle_bad_vectors_jagged
python/tests/test_util.py::test_handle_bad_vectors_nan
python/tests/test_table.py::test_create_with_nans
python/tests/test_table.py::test_add_with_nans -vv`
Targeted pytest result: `10 passed`.
## Why this fix is Python-side (and not Rust)
The problematic behavior lives in Python’s `on_bad_vectors` sanitizer,
before data is handed off to Rust. Rust currently only exposes
`NaNVectorBehavior::{Error, Keep}` for add operations, while Python has
the richer `on_bad_vectors={"error","drop","fill","null"}` API.
TypeScript does not currently expose the Python-style fill behavior, so
moving this exact fix into Rust would require designing a broader
cross-language bad-vector handling API.
This PR keeps the change scoped to the existing affected surface:
Python’s `on_bad_vectors="fill"` path. This way, Python users
immediately benefit.
## What the new agent skill covers
We want to help users _easily_ write LanceDB pipelines to bring their
data in from other places, no matter whether they use LanceDB OSS or
Enterprise.
The `lancedb` set of skills contains guidance for agents on the
following:
- Distinguishes local and remote table capabilities.
- Promotes bounded reads using `select()` and `limit()`.
- Prevents accidental full-table materialization.
- Documents correct Python sync/async scan APIs.
- Recommends validated Python schemas and batched ingestion.
- Provides indexing, query-tuning, diagnostics, and maintenance
guidance.
- Documents the Enterprise table-name cache issue: avoid immediately
reusing a dropped or overwritten table name; write to a fresh name and
rename after propagation.
- Adds Python and TypeScript API, pattern, and performance references.
- Adds a heuristic scanner for potentially unsafe Python and TypeScript
materialization patterns.
This change only adds agent documentation and tooling: no LanceDB
runtime code, Rust code, SDK APIs, dependencies, or CI configuration are
modified.
## Context
The LanceDB agent skill was accidentally pushed directly to `main` in
`8ea78e3fbcb26718112ab4ddec55a91804b869d3`, bypassing the normal review
workflow. That commit was reverted on `main` by `c12a6dce` so the
protected branch is back to its prior content.
## Summary
- add table-level FTS query tokenization returning token text and
position
- use the native index tokenizer for local tables and remote index
metadata for remote tables
- expose sync and async Python table wrappers with focused coverage
`Dataset::index_statistics()` loads index files and does meaningful CPU
work to serialize low-level info. Most fields
`NativeTable::index_stats()` needs are available from manifest metadata
via `Dataset::describe_indices()`, which is much cheaper.
`NativeTable::index_stats()` now:
- Calls `describe_indices()` filtered by name; returns `Ok(None)` if no
match.
- Parses `distance_type` from `description.details()` JSON (the
`VectorIndexDetails` proto stored in the manifest by recent Lance
versions).
- Falls back to `index_statistics()` only for vector indices where
`details()` returns no `distance_type` — this handles older Lance
datasets that didn't write `VectorIndexDetails`.
- `Unknown` index types (e.g. Lance's internal `FragReuseIndex`) are
explicitly filtered out of `list_indices` rather than erroring.
## Test plan
- [x] `test_create_scalar_index` — asserts `index_type`,
`distance_type`, and `num_unindexed_rows > 0` after adding rows
post-index
- [x] `test_create_fm_index`, `test_create_bitmap_index`,
`test_create_label_list_index` — added `index_stats` assertions
- [x] IvfPq, IvfHnswPq, IvfHnswSq, IvfHnswFlat tests assert
`distance_type == Some(L2)`
- [x] `test_list_indices_skip_frag_reuse` — FragReuseIndex is filtered
by the Unknown guard in `list_indices`
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- serialize sync phrase queries consistently for execution and query
plans
- restore the documented no-argument hybrid `phrase_query()` behavior
- keep reranker input as the original user text without mutating the
builder
Fixes#3653.
## Testing
- `python/.venv/bin/python -m pytest <8 focused test nodes> -q` (`8
passed`)
- `python/.venv/bin/python -m ruff format --check
python/python/lancedb/query.py python/python/tests/test_fts.py
python/python/tests/test_hybrid_query.py`
- `python/.venv/bin/python -m ruff check .`
- `git diff --check origin/main...HEAD`
The complete hybrid module and the real native FTS phrase test were not
completed
in the current PyO3 runtime environment: both stalled in the native
`lancedb.connect()` fixture and were interrupted without an assertion
failure.
The CODEOWNERS file added in #3312 automatically requests reviewers on
every PR — the `*` default owner routes all changes to two reviewers.
This is mostly noise for contributors, and we prefer a single requested
reviewer per PR.
Remove the file.
Reverts #3312.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Carrying over from #2915, this patch introduces:
* Single-API call batching support for Gemini embeddings (up to 100 at a
time, the API limit)
* A versioned user agent header for Gemini API calls
* Support for [variable embedding dimension
size](https://ai.google.dev/gemini-api/docs/embeddings#control-embedding-size)
(Gemini is MRL trained)
## Summary
- preserve explicit `0.0` distance bounds in synchronous hybrid search
- distinguish omitted `None` endpoints from zero-valued endpoints when
configuring the vector child query
- add a public end-to-end regression test for a zero upper bound
## Testing
- `cd python && uv run --extra tests pytest
python/tests/test_hybrid_query.py -q`
- `uv run --project python ruff format --check
python/python/lancedb/query.py python/python/tests/test_hybrid_query.py`
- `uv run --project python ruff check .`
Fixes#3651
The `build - aarch64-pc-windows-msvc` node build job (and, marginally,
the x86_64 one) had started hitting `rustc-LLVM ERROR: out of memory`
while linking the `lancedb-nodejs` cdylib — most recently surfaced by
#3526, which adds the goosefs backend (and its tonic/prost gRPC subtree)
to the default node binary.
The peak-memory step is the fat-LTO codegen (`lto=fat`,
`codegen-units=1` from `.cargo/config.toml`), which merges the whole
crate graph into a single LLVM module and runs single-threaded. It
therefore neither parallelizes across cores nor fits in the 16 GB of the
standard `windows-latest` runner as the dependency graph grows.
This PR:
- Moves both `*-pc-windows-msvc` node build jobs to
`windows-2025-8x-x64` (more memory + cores).
- Overrides the release profile to ThinLTO for just these jobs, via
`CARGO_PROFILE_RELEASE_LTO=thin` /
`CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16` in `pre_build`. ThinLTO
parallelizes the cross-module optimization across the runner's cores and
keeps peak memory well under the limit. Scoped so Python wheels and Rust
release builds keep fat LTO.
The larger runner alone would clear the OOM but waste the added cores on
the single-threaded fat-LTO tail; ThinLTO is what makes the extra cores
actually reduce wall-clock and gives durable memory headroom for future
dependency growth.
Tradeoff: ThinLTO can leave a small runtime-perf gap vs fat LTO for the
node native binary, but it recovers most of it and is a common release
configuration.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Bridges Lance's internal `metrics`-crate instrumentation (object store
request counts, bytes, latency, errors, and throttles) into
OpenTelemetry, in both the Python and Node bindings, with a shared
adapter in the Rust core. This is the LanceDB counterpart to
lance-format/lance#7537.
## Rust core (`rust/lancedb`)
Two new, **off-by-default** features:
- `metrics` — re-exports the [`metrics`](https://docs.rs/metrics) crate
as `lancedb::metrics` and turns on Lance's object-store instrumentation.
Install any `metrics`-compatible recorder to collect them.
- `metrics-otel` — adds `lancedb::metrics_otel`, a pull-based adapter
that installs a process-global recorder aggregating into lock-free
cumulative storage and exposes a snapshot/catalog API
(`register_metrics_recorder`, `metrics_catalog`, `snapshot_metrics`,
`MetricPoint`/`MetricValue`/`MetricKind`/`MetricDescription`). Both
bindings build on this.
## Python
`lancedb.otel.instrument_lancedb_metrics()` registers each metric as an
OpenTelemetry observable instrument on the given (or global)
`MeterProvider`. Available via the `otel` extra (`pip install
lancedb[otel]`), which pulls in only `opentelemetry-api` — the
application supplies and configures the SDK.
## Node
`instrumentLanceDbMetrics()` provides the equivalent wiring against
`@opentelemetry/api`. This is the only public entry point; the
underlying recorder/catalog/snapshot functions stay internal.
Because OpenTelemetry has no asynchronous histogram instrument,
histograms are exported Prometheus-style as `<name>_bucket` (with an
`le` attribute), `<name>_count`, and `<name>_sum`. Only `_sum` carries
the histogram's unit; `_bucket` and `_count` observe cumulative counts
and are unitless. The adapter is enabled by default in the Python and
Node builds, and off by default in the Rust crate.
## Notes
- Requires Lance ≥ `v9.0.0-beta.19`, which ships the object-store
metrics APIs (upstream lance-format/lance#7537, now merged). `main` is
already on beta.19, so this is a single feature commit with no
dependency bump.
- Tests: 8 Rust unit tests, 3 Python tests, 2 Node tests, all covering
the end-to-end object-store-metrics → OpenTelemetry path.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Bumps Lance to v9.0.0-beta.19, which includes lance-format/lance#7687
for side-effect-free DirectoryNamespace read paths.
This fixes root-level read-only table opens that previously could
trigger `__manifest` creation through directory namespace construction,
including Hugging Face bucket reads with read-only tokens. A LanceDB
regression test now covers root listing operations without creating
`__manifest`.
Fixes#3633.
### Summary
`flatten_columns` raises `ValueError` when called with `flatten=False`,
even though `False` should mean "do not flatten". This is reachable from
the public API — `Query.to_pandas(flatten=...)` and
`to_batches(flatten=...)` type their `flatten` param as
`Optional[Union[int, bool]]` and pass it straight to `flatten_columns`.
### Cause
`bool` is a subclass of `int`, so `isinstance(False, int)` is `True`.
`flatten=False` skips the `flatten is True` check, falls into the
integer branch, and `False <= 0` evaluates to `True`, raising:
```
ValueError: Please specify a positive integer for flatten or the boolean value `True`
```
### Reproduction
```python
import lancedb
db = lancedb.connect("/tmp/db")
t = db.create_table("t", data=[{"id": 1, "vector": [0.1, 0.2]}])
t.search([0.1, 0.2]).to_pandas(flatten=False) # -> ValueError
```
### Fix
Guard the integer branch with `not isinstance(flatten, bool)` so that
`flatten=False` (and `None`) mean "do not flatten". Behavior is
otherwise unchanged:
- `flatten=True` → flatten all nested levels
- positive `int` → flatten to that depth
- non-positive `int` (e.g. `0`) → still rejected with `ValueError`
Added a regression test in `tests/test_util.py` covering `None`,
`False`, `True`, a positive depth, and `0`.
This PR fixes a serialization error when using Ollama embeddings in
`create_table`.
The use of `@cached_property` for the Ollama client was causing issues
during serialization/pickling, which is required by certain LanceDB
operations (like when using multiprocessing or certain storage
backends). Switching to a standard `@property` ensures the client is
instantiated when needed without being stored in a way that breaks
serialization.
Verified with the following script:
```python
import lancedb
from lancedb.embeddings import get_registry
import pickle
registry = get_registry().get(\"ollama\")
model = registry(name=\"llama3\")
# This would fail before the fix
pickled = pickle.dumps(model)
unpickled = pickle.loads(pickled)
```
Fixes#2629 (or similar serialization issues reported).
---------
Co-authored-by: Unmilan Mukherjee <Missing-Identity@users.noreply.github.com>
## Summary
Closes#3525
This PR wires up two new optional object-store backends at the LanceDB
layer, exposing capabilities that already exist upstream in `lance` /
`lance-io`:
| Backend | Cargo feature | Default in Rust crate | Default in Python
wheel | Default in Node binding |
| --- | --- | --- | --- | --- |
| **Tencent COS** | `cos` | ❌ off | ✅ on | ❌ off |
| **GooseFS** | `goosefs` | ❌ off | ✅ on | ✅ on |
Both backends are additive and do not affect existing users who don't
opt in.
## Motivation
- **Tencent COS** is the dominant object storage in the China region.
Tencent Cloud users currently need an S3-compatible proxy or a private
fork to use LanceDB against COS buckets.
- **GooseFS** is Tencent Cloud's distributed cache acceleration layer
that sits in front of COS/S3, a common pattern for vector search / AI
training where the same hot dataset is read repeatedly.
- This brings COS / GooseFS to feature parity with the existing
first-class backends (`aws`, `gcs`, `azure`, `oss`, `huggingface`).
See the linked issue #3525 for the full discussion.
## Changes
### `rust/lancedb/Cargo.toml`
Add two new optional features that pull through the corresponding
upstream feature flags:
```toml
cos = ["lance/tencent", "lance-io/tencent"]
goosefs = [
"lance/goosefs",
"lance-io/goosefs",
"lance-namespace-impls/dir-goosefs",
]
```
### `python/Cargo.toml`
Enable both `cos` and `goosefs` by default for the Python wheels, so
`pip install lancedb` works against COS / GooseFS out of the box
(consistent with how `aws` / `gcs` / `azure` / `oss` are bundled today):
```diff
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/cos", "lancedb/goosefs"]
```
### `nodejs/Cargo.toml`
Enable `goosefs` by default for the Node binding (COS kept opt-in to
limit the default native binary size; can be revisited based on demand):
```diff
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/goosefs"]
```
### `Cargo.lock`
Regenerated to reflect the transitive dependencies brought in by the new
upstream features. No manual edits.
## Example Usage
### Rust
```toml
# Cargo.toml
lancedb = { version = "0.30", features = ["cos", "goosefs"] }
```
```rust
// Tencent COS
let db = lancedb::connect("cos://my-bucket/my-db").execute().await?;
// GooseFS
let db = lancedb::connect("goosefs://my-namespace/my-db").execute().await?;
```
### Python
```python
import lancedb
db = lancedb.connect(
"cos://my-bucket/my-db",
storage_options={
"secret_id": "...",
"secret_key": "...",
"region": "ap-guangzhou",
},
)
```
## Backwards Compatibility
- All new features are **opt-in** at the Rust crate level (`default =
[]` for `lancedb` itself is unchanged).
- The Python wheel gains both backends by default, increasing wheel size
slightly but matching the existing pattern of bundling all major cloud
backends.
- Node binding only adds `goosefs` to defaults; existing users see no
behavior change.
## Testing
- `cargo check --all-features` ✅
- `cargo check -p lancedb --features cos` ✅
- `cargo check -p lancedb --features goosefs` ✅
- End-to-end COS / GooseFS smoke tests require Tencent Cloud credentials
and are intentionally not added to CI in this PR (same approach used for
`s3-test`). Happy to add a gated test feature in a follow-up if
reviewers prefer.
## Checklist
- [x] Added `cos` and `goosefs` features to `rust/lancedb/Cargo.toml`
- [x] Updated `python/Cargo.toml` default features
- [x] Updated `nodejs/Cargo.toml` default features
- [x] Regenerated `Cargo.lock`
- [x] Verified build with `--all-features`
- [ ] Documentation update (can be done in a follow-up PR once API
stabilizes)
## Related
- Issue: #3525
- Upstream support:
[`lance/tencent`](https://github.com/lance-format/lance),
[`lance/goosefs`](https://github.com/lance-format/lance)
Wyatt Alt
Jack Ye
lancedb automation
Dan Tasse
Lance Release
dependabot[bot]
kid
Prashanth Rao
Will Jones
Weston Pace
Mateusz Szewczyk
Mark McDonald
Drew Gallardo
Xuanwo
Pranav Achar
Unmilan Mukherjee
ForwardXu