Adds namespace addressing to Functions, matching what Secrets already
have (#4150).
## API
`create_function` / `create_function_async`, `get_function`,
`list_functions`, `drop_function` / `drop_function_async` take
`namespace_path` — keyword-only in Python (sync, async and remote
connections) and a trailing `&[String]` in Rust — defaulting to the
root. The namespace is addressed in the route identifier
(`/v1/function/analytics$features$embed/describe`,
`/v1/namespace/analytics$features/function/list`); no body carries it.
`FunctionVersion` and `FunctionVersionRef` name their Function **by
parts** — a short `name` plus `namespace_path` — the same shape as
`SecretReference`, so a returned version reopens from its own fields:
```python
fv = db.create_function(embed, namespace_path=["analytics", "features"])
fv.name, fv.namespace_path # "embed", ("analytics", "features")
db.get_function(fv.name, version=fv.version, namespace_path=list(fv.namespace_path))
```
An empty path is omitted from the wire, so root documents keep their
shape. `namespace_path` is passed straight to the binding, so a bare
string (`namespace_path="prod"`) is a `TypeError` rather than four
one-character segments; segments are checked for addressability before a
request is built.
The binding and application allow-lists now share one field list,
`FUNCTION_VERSION_REF_FIELDS`, which admits `namespace_path` — without
it a namespaced binding or application read as coming from a newer
contract.
## Service side
The Sophon half serves and persists FunctionVersion / FunctionVersionRef
documents in this split form, keeping the joined id as its internal
catalog key. It lands in sophon with the submodule bump.
## Tests
- Rust: every Function verb addresses a namespace in the path; an
unaddressable segment never reaches the transport; `FunctionVersion`
round-trips a namespace and `reference()` carries it; a namespaced
binding and application are known shapes.
- Python: sync and async verbs address the namespace; a created Function
reopens from its own `name`/`namespace_path`; a bare-string path is
refused before any request.
Draft: compiles, `cargo fmt --check` and clippy `-D warnings` clean;
lancedb lib Function tests (116) and `first_class_function_slice2` pass
on this base.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
## 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>
Cancellation is the only job control the SDK exposes, and it is
terminal, so a long-running server-side job cannot be parked and picked
up again. This adds `pause_job` and `resume_job` to the Rust core
connection and the Python bindings (sync and async), posting to the
server's `/v1/jobs/pause` and `/v1/jobs/resume` endpoints.
A pause parks the job until it is resumed: its workers drain and stop.
The outcome strings mirror the server's answers — a job finalizing its
results reports `"committing"` and cannot be parked, and a resume before
the drain is confirmed reports `"still_pausing"`; both are retried
rather than failed. Resuming re-queues the job and its workers pick
their work back up from checkpoints. Local connections report the
operations as unsupported, like the rest of the jobs API.
Dropping a view unbinds its name and leaves the definition dataset to a
server-side cleanup job, so the two are separate events a caller may
want to wait on.
`drop_view_async` returns that job — the same shape
`drop_materialized_view_async` and `drop_function_async` already use: a
`202` carries the job id, a `200` (nothing was bound to the name) yields
an already-finished job with no id, and any other success status is an
error rather than a silent no-op.
## `drop_view` waits
`drop_view` now awaits the job before returning, so a caller who does
not want to think about cleanup gets the stronger guarantee: when it
returns, the definition really is deleted.
That is deliberately **different** from `drop_materialized_view` and
`drop_function`, which return as soon as the name is unbound and
document that content may still be deleting. The view API is the newer
one, and waiting is the semantic worth having; the other two are left
alone rather than changing behaviour already released.
## Surfaces
`Database` trait, the remote client, `Connection`, and the Python and
Node bindings — matching where `drop_materialized_view_async` is already
exposed.
Four client tests cover the accepted case reporting its job id, the
nothing-bound case reporting a finished job, a `202` without a usable
`job_id`, and an unexpected success status.
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`.
Dropping a Function can leave its content to a server-side cleanup job,
so the name drop and the content deletion become separate events a
caller may want to wait on.
`drop_function_async` returns the unbind result alongside a `Job` for
the cleanup, the same shape `drop_table_async` and
`drop_materialized_view_async` already use: a `202` carries the job id,
and a `200` — an inline deletion, or a name that was not bound — yields
an already-finished job with no id. A `202` without a usable job id is
rejected rather than silently reported as finished.
`drop_function` keeps its `bool` result and now delegates, so nothing
changes for callers that do not care when the content goes.
Available on `Connection` in Rust and on both the sync and asyncio
Python connections.
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>
## Summary
Align the experimental materialized-view HTTP transport with the
equivalent Table API shape and add remote materialized-view support
across Rust, Python, and TypeScript. This is an intentional breaking
change to the experimental materialized-view surface.
Materialized-view creation performs an initial refresh by default. The
create endpoint returns `202 Accepted` with `{ "job_id": "..." }`;
blocking SDK creation waits for that job before returning a populated
view. `with_no_data` / `withNoData` explicitly creates only the
definition and empty backing table.
## Route comparison
| Operation | Materialized-view API | Equivalent Table API |
| --- | --- | --- |
| Create | `POST /v1/materialized_view/{id}/create` | `POST
/v1/table/{id}/create` |
| Describe/open | `POST /v1/materialized_view/{id}/describe` | `POST
/v1/table/{id}/describe` |
| List | `GET /v1/namespace/{id}/materialized_view/list` | `GET
/v1/namespace/{id}/table/list` |
| Refresh | `POST /v1/materialized_view/{id}/refresh` | asynchronous
Table mutation pattern |
| Drop | `POST /v1/materialized_view/{id}/drop` | `POST
/v1/table/{id}/drop` |
Create, describe, refresh, and drop identify the target in the singular
item path instead of duplicating it in the request body. Create and drop
require `202 Accepted` with a valid job ID. List is a namespace-scoped
GET with opaque pagination tokens. The Rust list API now returns view
names, matching Table listing and the existing Python and TypeScript
APIs.
## Python API changes
| Operation | Synchronous API | Asynchronous API | Table/job pattern |
| --- | --- | --- | --- |
| Create and wait | `DBConnection.create_materialized_view(...)` |
`await AsyncConnection.create_materialized_view(...)` | Returns a
materialized-view handle after its initial-population job finishes |
| Submit create | `DBConnection.create_materialized_view_async(...) ->
Job[None]` | `await AsyncConnection.create_materialized_view_async(...)
-> AsyncJob[None]` | Matches job-returning Table mutations such as
`create_index_async` |
| Open | `DBConnection.open_materialized_view(...)` | `await
AsyncConnection.open_materialized_view(...)` | Opens the backing Table
plus its definition |
| List | `DBConnection.list_materialized_views()` | `await
AsyncConnection.list_materialized_views()` | Returns names like Table
listing |
| Refresh and wait | `MaterializedView.refresh(...)` | `await
AsyncMaterializedView.refresh(...)` | Returns the typed refresh result
after the job finishes |
| Submit refresh | `MaterializedView.refresh_async(...) ->
Job[RefreshMaterializedViewResult]` | `await
AsyncMaterializedView.refresh_async(...) ->
AsyncJob[RefreshMaterializedViewResult]` | Matches
`Table.refresh_column_async`; remote job handles expose the server job
ID |
| Drop | `DBConnection.drop_materialized_view(...)` | `await
AsyncConnection.drop_materialized_view(...)` | Matches blocking
`drop_table` |
| Submit drop | `DBConnection.drop_materialized_view_async(...) ->
Job[None]` | `await AsyncConnection.drop_materialized_view_async(...) ->
AsyncJob[None]` | Matches `drop_table_async`; remote handles expose the
server cleanup job ID |
The materialized-view handle exposes its backing Table through `.table`,
so normal Table query, search, and index APIs apply. Definition lookup
and refresh are backend-aware rather than depending on local schema
metadata. TypeScript exposes the equivalent blocking/job drop pair as
`dropMaterializedView` and `dropMaterializedViewAsync`.
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.
## 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.
Function registration and exact lookup are exposed through the SDK, but
clients cannot discover published versions even though the server
provides `POST /v1/functions/list`.
Add Rust and Python sync/async `list_functions()` APIs that return typed
`FunctionVersion` values. The remote client requests canonical
definitions and follows opaque page tokens until the listing is
complete, including empty intermediate pages, while preserving the
server's name/version ordering. Local databases retain the existing
Function-catalog unsupported error.
The SDK consumes protocol pagination internally so callers receive the
complete catalog rather than handling server-specific page tokens.
## Problem
`refresh_column_async` returned a unit-result job even though durable
refresh jobs carry a canonical terminal result. Python callers could not
obtain row counts or source and published versions through the public
`Job` API, and local and remote refresh jobs exposed different result
semantics.
## Behavior
`refresh_column_async` now returns `Job[RefreshColumnResult]` for local
and remote tables. The general typed-job bridge binds each endpoint to
its public result model while preserving unit-result jobs and existing
status, wait, cancel, and timeout behavior. A local no-op refresh
reports no published version.
The Node.js API continues to resolve `wait()` as `void`; its binding
erases the Rust result type internally to preserve the existing public
contract.
## Ownership and integration boundary
LanceDB owns the language-neutral `Job<T>` contract and language-binding
decode. Sophon owns production and durable persistence of terminal
payloads. Sophon #7348 and #7378 now publish the canonical refresh
result for Function-backed and expression-backed refresh jobs,
respectively. The remote client fixture matches the merged server
schema; live deployment and end-to-end demo acceptance remain separate
rollout checks.
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>
## 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
## Summary
- add `drop_table_async` and return a job handle while preserving
`drop_table`
- consume remote 202 responses with cleanup job IDs and retain
older-server compatibility
- expose the API through Python and TypeScript connection wrappers
Adds job operations to the connection surface, building on the Job
handle from #3742: job(id), list_jobs, get_job, cancel_job, and
job_history, plus a non-blocking Job.status(). Implemented on the
Database trait (defaulting to NotSupported), the remote backend
(/v1/jobs), and the Python and Node bindings; job_history returns Arrow
batches.
errors() and progress() are not included.
Tested with mocked endpoints in all three languages.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
## Problem
On the remote (LanceDB Cloud) write path, each write partition is
uploaded as a **single** `/insert?upload_id=...` request that stays open
until the whole partition has been streamed and the server has written
it to object storage. For large bulk ingests a partition can be many GB,
so a single request can run longer than the client read timeout (default
300s), surfacing as:
```
lancedb.remote.errors.HttpError: operation timed out
```
The server already supports staging **multiple** parts under one
`upload_id` (each `/insert` writes a separate transaction that
`complete` merges atomically), but the client never used that — it sent
one part per partition.
## Change
Split each partition into multiple parts of at most
`max_bytes_per_request` (Arrow IPC, LZ4-compressed) bytes, each uploaded
as its own `/insert?upload_id=...&upload_part_id=...` request. This
bounds how long any single request stays open, independent of total data
size or write parallelism.
Key properties:
- **Still streamed, not buffered.** Each part's body is driven through a
bounded channel while the request is in flight (`futures::join!` of a
producer + the send), so peak memory stays at a couple of batches per
partition regardless of the part size. Backpressure from a
slow/throttled server still propagates upstream.
- **Correct part accounting.** An empty partition still sends exactly
one (schema-only) part so `complete` has a transaction to commit; a size
cut landing exactly on the end of input does not emit a trailing empty
part.
- **Multipart only.** The single-request (non-multipart) path is
unchanged.
## Config
New `ClientConfig::max_bytes_per_request: Option<usize>`, also settable
via the `LANCE_CLIENT_MAX_BYTES_PER_REQUEST` environment variable.
**Default 1 GiB** (`Some(0)` disables splitting → one request per
partition). Python users pick up the default/env automatically through
the remote client.
## Tests
- `test_multipart_chunked_splits_into_parts`: a 1-byte budget puts each
batch in its own part → N requests, each carrying the shared `upload_id`
and a distinct `upload_part_id`.
- `test_multipart_single_part_when_under_budget`: a large budget keeps
the partition in a single request.
- Verified end-to-end against a live remote table: a forced-chunked
multipart add (many parts) assembles to the correct row count.
Related to ENT-1883.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Summary:
- Route built-in sync namespace connections through the Rust namespace
connector.
- Keep custom namespace clients on the existing Python fallback.
- Preserve namespace-backed to_lance compatibility with lazy Python
client construction and add regressions.
Expose the merged Rust OAuth header provider through the Python async
connection path.
Includes:
- Python OAuthConfig and OAuthFlowType public config objects
- PyO3 conversion into the Rust OAuthConfig
- connect_async(oauth_config=...) plumbing
- repr redaction coverage for client_secret
Local validation: cargo fmt --all; ruff format/check on touched Python
files.
By default the read freshness provider was not included in the namespace
client, preventing the read freshness headers from being included in the
request. This prevents checkout_latest() from working as expected when
using the namespace client.
This fix ensures the provided is built into the client when the
namespace impl and properties are provided.
Follow-up to #3371 , which added runtime validation for namespace `mode`
and `behavior` parameters in the NodeJS SDK. Bringing the same fix to
Python for cross-SDK consistency.
**Before:** unrecognized values were silently dropped to `None`, so
`db.create_namespace(["x"], mode="foobar")` would quietly fall through
to the server's default mode and hide caller typos.
**After:** raises `ValueError` listing the valid values.
## Summary
PyTorch's `DataLoader` uses fork-based multiprocessing by default on
Linux, but threads do not survive `fork()`. LanceDB's Python bindings
drive async work through two threaded layers, both of which become inert
in a forked child:
- `BackgroundEventLoop` runs an asyncio loop on a Python
`threading.Thread`.
- `pyo3-async-runtimes::tokio` holds a global multi-threaded tokio
runtime whose worker threads also die on fork — and its runtime lives in
a `OnceLock` that cannot be replaced after first use.
As a result, any `Permutation` (or other async API) used inside a
fork-based `DataLoader` worker hangs indefinitely. This PR makes both
layers fork-safe so `Permutation` works as a `torch.utils.data.Dataset`
with `num_workers > 0`.
## Approach
### Rust — new `python/src/runtime.rs`
Mirrors the pattern used in [Lance's Python
bindings](https://github.com/lance-format/lance/blob/456198cd6f42be07f99617a6d7e39d6209cdf3cc/python/src/lib.rs#L139),
adapted for the async-bridge use case.
- `LanceRuntime` implements `pyo3_async_runtimes::generic::Runtime +
ContextExt`, backed by an `AtomicPtr<tokio::runtime::Runtime>` we own
(sidestepping `pyo3-async-runtimes`'s frozen `OnceLock` global).
- A `pthread_atfork(after_in_child)` handler nulls the pointer; the next
`spawn` rebuilds the runtime in the child. The previous runtime is
intentionally **leaked** — calling `Drop` would try to join now-dead
worker threads and hang.
- `runtime::future_into_py` is a drop-in for
`pyo3_async_runtimes::tokio::future_into_py`. All ~80 call sites in
`arrow.rs` / `connection.rs` / `permutation.rs` / `query.rs` /
`table.rs` are updated to route through it.
- `python/Cargo.toml` adds `libc = "0.2"` and the tokio
`rt-multi-thread` feature.
### Python — `lancedb/background_loop.py`
- Refactors `BackgroundEventLoop.__init__` to a reusable `_start()`
method.
- An `os.register_at_fork(after_in_child=…)` hook calls `LOOP._start()`
to give the singleton a fresh asyncio loop and thread **in place**. This
matters because the rest of the codebase imports `LOOP` via `from
.background_loop import LOOP` — rebinding the module attribute would
leave those references holding the dead loop.
### Python — `lancedb/__init__.py`
Removes the `__warn_on_fork` pre-fork warning (and the now-unused
`import warnings`). Fork is supported.
## Test plan
- [x] New `test_permutation_dataloader_fork_workers` in
`python/tests/test_torch.py`: runs a `Permutation` through
`torch.utils.data.DataLoader(num_workers=2,
multiprocessing_context="fork")` inside a spawn-isolated child with a
30s hang detector. **Pre-fix**: timed out at 36s. **Post-fix**: passes
in ~3.6s.
- [x] New `test_remote_connection_after_fork` in
`python/tests/test_remote_db.py`: forks a child that creates a fresh
`lancedb.connect(...)` against a mock HTTP server and calls
`table_names()`; passes in <1s, validates the runtime reset is
sufficient for fresh remote clients.
- [x] All 62 tests in `test_torch.py` + `test_permutation.py` pass.
- [x] All 35 tests in `test_remote_db.py` pass.
- [x] `test_table.py` (87) + `test_db.py` + `test_query.py` (157, minus
one unrelated `sentence_transformers` import skip) — 244 passing.
- [x] `cargo clippy -p lancedb-python --tests` clean.
- [x] `cargo fmt`, `ruff check`, `ruff format` all clean.
## Known limitation (follow-up)
This PR makes a **freshly-built** `lancedb.connect(...)` work in a
forked child. An **inherited** `Connection` from the parent still
carries an inherited `reqwest::Client` whose hyper connection pool
references socket FDs and TCP/TLS state shared with the parent — using
it from the child after fork is unsafe (especially with HTTP/1.1
keep-alive). The recommended pattern for fork-based `DataLoader` workers
that hit a remote DB is to construct a new connection inside the worker.
Auto-clearing inherited HTTP client pools on fork would require tracking
live `Connection` instances in `lancedb` core and is left for a
follow-up PR.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds manifest_enabled for local/native connections so directory
namespace manifests can be the source of truth, including migration from
directory listing and Azure credential vending feature wiring. Also
exposes the option through Rust, Python, and Node bindings with focused
validation.
So far, I have been using a hacky approach that creates and opens
namespace-backed table, by getting its location and use a temporary
lancedb connection to create or open it. This was working for features
like credentials vending but is no longer fully working for the managed
versioning feature, recently geneva tests have been failing here and
there and various patches are not addressing the root cause. This PR
fully fixes this and implements proper rust binding for it.
Specifically:
- build a real Rust namespace-backed connection from the Python
namespace client
- route namespace table create/open through that connection instead of
resolved-location temp connections
- keep namespace client naming consistent in the Rust bridge and
preserve federated namespace + DuckDB behavior
## Summary
- Add a `user_id` field to `ClientConfig` that allows users to identify
themselves to LanceDB Cloud/Enterprise
- The user_id is sent as the `x-lancedb-user-id` HTTP header in all
requests
- Supports three configuration methods:
- Direct assignment via `ClientConfig.user_id`
- Environment variable `LANCEDB_USER_ID`
- Indirect env var lookup via `LANCEDB_USER_ID_ENV_KEY`
Closes#3230🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
1. Refactored every client (Rust core, Python, Node/TypeScript) so
“namespace” usage is explicit: code now keeps namespace paths
(namespace_path) separate from namespace clients (namespace_client).
Connections propagate the client, table creation routes through it, and
managed versioning defaults are resolved from namespace metadata. Python
gained LanceNamespaceDBConnection/async counterparts, and the
namespace-focused tests were rewritten to match the clarified API
surface.
2. Synchronized the workspace with Lance 5.0.0-beta.3 (see
https://github.com/lance-format/lance/pull/6186 for the upstream
namespace refactor), updating Cargo/uv lockfiles and ensuring all
bindings align with the new namespace semantics.
3. Added a namespace-backed code path to lancedb.connect() via new
keyword arguments (namespace_client_impl, namespace_client_properties,
plus the existing pushdown-ops flag). When those kwargs are supplied,
connect() delegates to connect_namespace, so users can opt into
namespace clients without changing APIs. (The async helper will gain
parity in a later change)
Without this fix, if user directly use the native table to do operations
like `add_columns`, even if it is configured to use namespace db
connection, it is not really propagated through.
The fix is to bring lancedb's python binding up to date and do a similar
implementation as https://github.com/lance-format/lance/pull/5968, and
make sure the namespace is fully propagated through all the related
calls.
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This PR migrates all Rust crates in the workspace to Rust 2024 edition
and addresses the resulting compatibility updates. It also fixes all
clippy warnings surfaced by the workspace checks so the codebase remains
warning-free under the current lint configuration.
Context:
- Scope: workspace edition bump (`2021` -> `2024`) plus follow-up
refactors required by new edition and clippy rules.
- Validation: `cargo fmt --all` and `cargo clippy --quiet --features
remote --tests --examples -- -D warnings` both pass.
BREAKING CHANGE: Arbitrary `impl RecordBatchReader` is no longer
accepted, it must be made into `Box<dyn RecordBatchReader>`.
This PR replaces `IntoArrow` with a new trait `Scannable` to define
input row data. This provides the following advantages:
1. **We can implement `Scannable` for more types than `IntoArrow`, such
as `RecordBatch` and `Vec<RecordBatch>`.** The `IntoArrow` trait was
implemented for arbitrary `T: RecordBatchReader`, and the Rust compiler
would prevent us from implementing it for foreign types like
`RecordBatch` because (theoretically) those types might implement
`RecordBatchReader` in the future. That's why we implement `Scannable`
for `Box<dyn RecordBatchReader>` instead; since it's a concrete type it
doesn't block implementing for other foreign types.
2. **We can potentially replay `Scannable` values**. Previously, we had
to choose between buffering all data in memory and supporting retries of
writes. But because `Scannable` things can optionally support
re-scanning, we now have a way of supporting retries while also
streaming.
3. **`Scannable` can provide hints like `num_rows`, which can be used to
schedule parallel writers.** Without knowing the total number of rows,
it's difficult to know whether it's worth writing multiple files in
parallel.
We don't yet fully take advantage of (2) and (3) yet, but will in future
PRs. For (2), in order to be ready to leverage this, we need to hook the
`Scannable` implementation up to Python and NodeJS bindings. Right now
they always pass down a stream, but we want to make sure they support
retries when possible. And for (3), this will need to be hooked up to
#2939 and to a pipeline for running pre-processing steps (like embedding
generation).
## Other changes
* Moved `create_table` and `add_data` into their own modules. I've
created a follow up issue to split up `table.rs` further, as it's by far
the largest file: https://github.com/lancedb/lancedb/issues/2949
* Eliminated the `HAS_DATA` generic for `CreateTableBuilder`. I didn't
see any public-facing places where we differentiated methods, which is
why I felt this simplification was okay.
* Added an `Error::External` variant and integrated some conversions to
allow certain errors to pass through transparently. This will fully work
once we upgrade Lance and get to take advantage of changes in
https://github.com/lance-format/lance/pull/5606
* Added LZ4 compression support for write requests to remote endpoints.
I checked and this has been supported on the server for > 1 year.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
1. Use generated models in lance-namespace for request response models
to avoid multiple layers of conversions
2. Make sure the API is consistent with the namespace spec
3. Deprecate the table_names API in favor of the list_tables API in
namespace that allows full pagination support without the need to have
sorted table names
4. Add describe_namespace API which was a miss in the original
implementation
Based on https://github.com/lancedb/lance/pull/4984
1. Bump to 1.0.0-beta.2
2. Use DirectoryNamespace in lance to perform all testing in python and
rust for much better coverage
3. Refactor `ListingDatabase` to be able to accept location and
namespace. This is because we have to leverage listing database (local
lancedb connection) for using namespace, namespace only resolves the
location and storage options but we don't want to bind all the way to
rust since user will plug-in namespace from python side. And thus
`ListingDatabase` needs to be able to accept location and namespace that
are created from namespace connection.
4. For credentials vending, we also pass storage options provider all
the way to rust layer, and the rust layer calls back to the python
function to fetch next storage option. This is exactly the same thing we
did in pylance.
I'm working on a lancedb version of pytorch data loading (and hopefully
addressing https://github.com/lancedb/lance/issues/3727).
However, rather than rely on pytorch for everything I'm moving some of
the things that pytorch does into rust. This gives us more control over
data loading (e.g. using shards or a hash-based split) and it allows
permutations to be persistent. In particular I hope to be able to:
* Create a persistent permutation
* This permutation can handle splits, filtering, shuffling, and sharding
* Create a rust data loader that can read a permutation (one or more
splits), or a subset of a permutation (for DDP)
* Create a python data loader that delegates to the rust data loader
Eventually create integrations for other data loading libraries,
including rust & node
Support shallow cloning a dataset at a specific location to create a new
dataset, using the shallow_clone feature in Lance. Also introduce remote
`clone` API for remote tables for this functionality.
## Summary
This PR introduces a `HeaderProvider` which is called for all remote
HTTP calls to get the latest headers to inject. This is useful for
features like adding the latest auth tokens where the header provider
can auto-refresh tokens internally and each request always set the
refreshed token.
---------
Co-authored-by: Claude <noreply@anthropic.com>
This PR adds mTLS (mutual TLS) configuration support for the LanceDB
remote HTTP client, allowing users to authenticate with client
certificates and configure custom CA certificates for server
verification.
---------
Co-authored-by: Claude <noreply@anthropic.com>
This PR adds support of multi-level namespace in a LanceDB database,
according to the Lance Namespace spec.
This allows users to create namespace inside a database connection,
perform create, drop, list, list_tables in a namespace. (other
operations like update, describe will be in a follow-up PR)
The 3 types of database connections behave like the following:
1 Local database connections will continue to have just a flat list of
tables for backwards compatibility.
2. Remote database connections will make REST API calls according to the
APIs in the Lance Namespace spec.
3. Lance Namespace connections will invoke the corresponding operations
against the specific namespace implementation which could have different
behaviors regarding these APIs.
All the table APIs now take identifier instead of name, for example
`/v1/table/{name}/create` is now `/v1/table/{id}/create`. If a table is
directly in the root namespace, the API call is identical. If the table
is in a namespace, then the full table ID should be used, with `$` as
the default delimiter (`.` is a special character and creates issues
with URL parsing so `$` is used), for example
`/v1/table/ns1$table1/create`. If a different parameter needs to be
passed in, user can configure the `id_delimiter` in client config and
that becomes a query parameter, for example
`/v1/table/ns1__table1/create?delimiter=__`
The Python and Typescript APIs are kept backwards compatible, but the
following Rust APIs are not:
1. `Connection::drop_table(&self, name: impl AsRef<str>) -> Result<()>`
is now `Connection::drop_table(&self, name: impl AsRef<str>, namespace:
&[String]) -> Result<()>`
2. `Connection::drop_all_tables(&self) -> Result<()>` is now
`Connection::drop_all_tables(&self, name: impl AsRef<str>) ->
Result<()>`
## Summary
- Adds an overall `timeout` parameter to `TimeoutConfig` that limits the
total time for the entire request
- Can be set via config or `LANCE_CLIENT_TIMEOUT` environment variable
- Exposed in Python and Node.js bindings
- Includes comprehensive tests
## Test plan
- [x] Unit tests for Rust TimeoutConfig
- [x] Integration tests for Python bindings
- [x] Integration tests for Node.js bindings
- [x] All existing tests pass
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
- Exposes `Session` in Python and Typescript so users can set the
`index_cache_size_bytes` and `metadata_cache_size_bytes`
* The `Session` is attached to the `Connection`, and thus shared across
all tables in that connection.
- Adds deprecation warnings for table-level cache configuration
🤖 Generated with [Claude Code](https://claude.ai/code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
This reverts commit a547c523c2 or #2281
The current implementation can cause panics and performance degradation.
I will bring this back with more testing in
https://github.com/lancedb/lancedb/pull/2311
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Documentation**
- Enhanced clarity on read consistency settings with updated
descriptions and default behavior.
- Removed outdated warnings about eventual consistency from the
troubleshooting guide.
- **Refactor**
- Streamlined the handling of the read consistency interval across
integrations, now defaulting to "None" for improved performance.
- Simplified internal logic to offer a more consistent experience.
- **Tests**
- Updated test expectations to reflect the new default representation
for the read consistency interval.
- Removed redundant tests related to "no consistency" settings for
streamlined testing.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Previously, when we loaded the next version of the table, we would block
all reads with a write lock. Now, we only do that if
`read_consistency_interval=0`. Otherwise, we load the next version
asynchronously in the background. This should mean that
`read_consistency_interval > 0` won't have a meaningful impact on
latency.
Along with this change, I felt it was safe to change the default
consistency interval to 5 seconds. The current default is `None`, which
means we will **never** check for a new version by default. I think that
default is contrary to most users expectations.
If we start supporting external catalogs then "drop database" may be
misleading (and not possible). We should be more clear that this is a
utility method to drop all tables. This is also a nice chance for some
consistency cleanup as it was `drop_db` in rust, `drop_database` in
python, and non-existent in typescript.
This PR also adds a public accessor to get the database trait from a
connection.
BREAKING CHANGE: the `drop_database` / `drop_db` methods are now
deprecated.
Jonathan HsiehandClaude Opus 5.5
Wyatt Alt
Jack Ye
Xuanwo
Will JonesandClaude Opus 4.8
Ryan Green
Brendan Clement
Weston PaceandClaude Opus 4.7
LanceDB RobotandJack Ye
