PyO3 defaults native extension classes to `builtins`, so
mkdocstrings/Griffe could not resolve the newly documented
`lancedb.Session` alias and `Deploy docs to Pages` failed on `main`.
Declare the extension module for the public native types referenced by
the Python API docs so Griffe resolves them through `lancedb._lancedb`
and Pages can build again.
Validated with the docs toolchain used by CI (`griffe==0.49.0`,
`mkdocstrings==0.25.2`, and `mkdocs==1.6.1`); `PYTHONPATH=. mkdocs
build` succeeds.
## What
`LsmWriteSpec::maintained_indexes` becomes `Option<Vec<String>>`:
| value | meaning |
|---|---|
| `None` (new default) | every index the MemWAL supports, resolved when
the spec is installed |
| `Some([])` | maintain nothing — a scan/filter-only WAL table |
| `Some([..])` | exactly these, taken verbatim |
`with_maintained_indexes` keeps its signature;
`with_no_maintained_indexes()` is new. Surfaced through the remote path
(null on the wire), Python, and Node.
## Why
Callers had to state the maintained set by hand every time, which is
both tedious and easy to get wrong — the common case is "maintain what I
already built."
Resolution filters on `IndexConfig::is_memwal_maintainable`, delegating
to lance's `is_maintainable_index_type`. This is load-bearing rather
than cosmetic: lance does **not** skip an index type its memtable cannot
build, it errors when the shard writer opens, so sweeping up a bitmap
index would fail every memtable claim and leave the table unwritable.
The inferred set excludes those, and an explicit list naming one is now
rejected at spec time instead of at claim time.
## Behavior change
A freshly constructed spec used to maintain **nothing**; it now
maintains **everything supported**. This flipped because napi collapses
`undefined` and `null` to `None`, so TypeScript cannot express "absent
means nothing, null means all" — any other choice makes the bindings
disagree with the wire. The error direction also favors it: an unwanted
maintained index costs memory, while a silently unmaintained one
degrades FTS to an unscored scan.
Three existing tests encoded the old default and are updated rather than
worked around.
## Caveat
The resolved set is a snapshot, not a subscription. An index created
after the spec is installed is not maintained until the spec is unset
and set again. `get_lsm_write_spec` therefore always reports a concrete
list — `None` never round-trips.
## Dependency
Needs a lance release carrying `is_maintainable_index_type`
(lance-format/lance#8095) before this builds against the pinned tag.
Draft until then.
## Testing
38 Rust LSM tests and 10 Python tests pass against a local lance build,
including new coverage that a bitmap index is excluded from inference
and rejected when named, and that `[]` stays distinguishable from null
on the wire.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Converge a table's LSM write path into its base table, and inspect it.
`checkpoint_lsm` is `flush` then `compact`, repeated until the fresh
tier is empty — and the loop runs **client-side**. Putting it on the
server would mean a background task, which means a single-flight intent,
an intent that leaks on panic, a bounded-iteration policy, an "is it
done" observable, and a story for every way a client can vanish
mid-operation. None of that exists in this shape: each request does a
bounded unit of work and reports what is left, so completion is *carried
in the responses* rather than inferred from a shared counter that cannot
distinguish "converged" from "hasn't started yet".
Best-effort by construction. Nothing is frozen, so `converged` means L0
was empty as of the last pass. It is idempotent, abandonable at any
point with zero consequence, and safe to run on a cadence — an
already-converged table costs one round trip and zero compaction passes,
because `flush` reports `generations_remaining` and the loop is never
entered.
## The failure taxonomy is the load-bearing part
Five distinct conditions used to arrive at a client as one 503.
`Error::LsmRoute` carries a classification read from the response body's
namespace error code **at the point of receipt** — before any generic
helper folds the body into a string and keeps only the status.
| condition | wire | client action |
|---|---|---|
| contention (latch held / pool saturated) | 429, code 21 | retry with
backoff |
| owning node draining | 503, code 19 `InvalidTableState` | **stop** |
| fenced / no slot / transport | 503, code 17 | retry with backoff |
| registry entry vanished | 404 | re-issue from `flush` (capped) |
| table being dropped / not WAL-backed | 409 / 400 | stop |
Draining is terminal because the drain gate is a one-way latch —
retrying spins until the deadline to report a failure that was knowable
on the first response. Transport retry is disabled on these routes for
the same reason: it treats every 503 alike and would burn its budget
before the classifier ever saw the body.
`get_lsm_stats` returns `Option<LsmStats>`, matching
`get_lsm_write_spec` — `None` only when the table has no LSM write path,
since a struct of zeros would read as measurements.
Python bindings mirror all four, preserving per-bucket detail rather
than flattening to a table-level summary.
## Testing
Six new unit tests against the mocked endpoint, plus the taxonomy
round-trip:
- flush into an empty L0 issues **zero** compact calls (asserts the call
count — `generations_consumed: 0` is also true of a loop that ran a
pointless pass)
- the loop drives compact until the server reports zero remaining
- **contention is not draining**: a 429 retries and converges; asserts
the retry count
- a draining node stops after **exactly one** request, no retries
- stats round-trips fully populated; `include_generation_rows` off by
default
- every `(status, code)` pair classifies correctly, including
unparseable 503 bodies falling back to *retryable* rather than terminal
`cargo test -p lancedb --features remote --lib`: 723 passed.
## Notes for review
- Depends on the sibling lance change returning `SealedGeneration` from
`force_seal_active` only at the *server* level — no lance API is used
here.
- The branch is based on `codex/update-lance-10-0-0-beta-5`, so it
carries one extra commit (`chore: update lance dependency to
v10.0.0-beta.5`) that is not part of this change.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: lancedb automation <robot@lancedb.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
<!-- lance-gatekeeper-fix:v1 agent=5c80c44c083b3b8ad0da595419d468fc
generation=1 -->
## Root cause
The legacy synchronous Python table called `delete` on a shared, mutable
`lance.Dataset`. Concurrent table operations could hold a PyO3 borrow
while delete requested an exclusive borrow, producing `RuntimeError:
Already borrowed`. The current async-backed binding fixes this by
cloning its thread-safe Rust table handle before awaiting, but that
concurrency contract had no regression coverage.
## Fix
- Document why delete must clone the Rust table handle before entering
its async future.
- Add a barrier-synchronized regression test that deletes distinct rows
through one shared table from eight Python threads.
- Verify every delete commits exactly one row, every commit gets a
distinct version, and no rows remain.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo fmt --all -- --check`
- `uv run --extra tests --extra dev ruff format --check
python/tests/test_table.py`
- `uv run --extra tests --extra dev ruff check
python/tests/test_table.py`
- `uv run --extra tests --extra dev pytest
python/tests/test_table.py::test_concurrent_deletes_are_thread_safe
python/tests/test_table.py::test_delete
python/tests/test_table.py::test_delete_expr
python/tests/test_table.py::test_delete_expr_async -q` (4 passed)
- Manual stress reproduction: 100 concurrent deletes on one table
completed at versions 2–101 with zero rows remaining.
Fixes#530
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Table::add_columns now takes no arguments and returns AddColumnsBuilder,
so calls become .add_columns().transform(t).execute().
read_columns was the second positional argument but reaches only one of
the five transform variants. In lance's add_columns_to_fragments only
BatchUDF receives the caller's value: SqlExpressions replaces it with
the columns its expressions reference, Stream and Reader pass None, and
AllNulls reads nothing. So it was mandatory on every call -- all
eighteen call sites here passed None -- and silently discarded four
times out of five. As a builder method it is optional, and setting it
where lance would discard it is now an error, which does reject a call
that previously succeeded while ignoring the argument.
Matches the builders add, update, and merge_insert already use.
## Summary
- Implements Cloud `fetch_blob_files`: returns real seekable `BlobFile`
handles over HTTP Range instead of `NotSupported`.
- Completes the second Cloud blob read verb after #3684 (`fetch_blobs` =
eager whole bytes; this = lazy / partial / sequential reads).
- Same public handle API as local (`read_range`, `read_up_to`, `seek`,
`tell`, `close`), so one code path works for local and Cloud.
Large blobs (video, audio, PDFs) should not require downloading the
whole object to inspect a header or stream a slice. After search,
callers open a handle and read only what they need:
```python
hits = table.search(vec).select(["id", "video"]).limit(5).to_arrow()
with table.fetch_blob_files("video", hits)[0] as f:
header = f.read_range(0, 256)
f.seek(keyframe_offset)
chunk = f.read_up_to(1 << 20)
```
### Behavior
- Handle creation probes size with `bytes=0-0` (bounded concurrency,
input order preserved).
- `204` → null (`None`); `416` with `bytes */0` → valid empty blob;
other `416` → error.
- `read_range` validates `Content-Range` and body length; OOB ranges
fail with `invalid_input` before the request (aligned with Lance).
- `read_up_to` reuses one open-ended Range response across sequential
reads; `seek` drops it.
- Servers older than 0.5.0 get a clear `NotSupported` (does not suggest
`fetch_blobs`, which they also lack).
## Testing
- `cargo test --features remote -p lancedb remote_blob`
- `cargo test --features remote -p lancedb test_blob`
- `cargo clippy --features remote --tests --examples` (no new warnings
from this change)
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
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>
IndexBuilder::execute now returns a Job with wait and cancel methods.
Local tables build the index synchronously and return an already-done
job. Remote tables read the job id the server returns from create_index
and track it through the /v1/jobs API: wait polls describe until the job
reaches a terminal state and cancel posts a cancellation. Servers that
return no job id yield a done job, so behavior against older servers is
unchanged. The job id is not exposed on the handle.
The Python and TypeScript bindings keep their current signatures and
discard the handle; exposing Job there is left to follow-ups.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
## What
Expose custom FTS stop-word lists in the Python and TypeScript public
APIs, including their standalone tokenize helpers and remote index
creation.
This PR supports concrete string lists only. It does not add file or
LanceDB-table stop-word sources.
## Why
Rust already exposes Lance's custom stop-word list option. The Python
and TypeScript APIs did not pass it through, and local index details did
not retain the full tokenizer parameters needed by index-backed
tokenization after reopening a table.
## How
- Add `custom_stop_words` / `customStopWords` to the Python and
TypeScript FTS and tokenize options.
- Preserve `None` / `undefined`, empty lists, and list contents without
normalization.
- Load the persisted FTS segment parameters when returning local index
details.
- Serialize the concrete list in remote create-index requests.
- Keep Python and TypeScript tests thin; behavior, persistence, query
tokenization, and remote JSON coverage live primarily in Rust.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo test --quiet --features remote --tests`
- Python extension rebuild with `uv` and `maturin`
- Targeted Python tests: 4 passed
- Python `ruff format --check` and `ruff check`
- TypeScript build, typecheck, Biome lint, generated docs, and targeted
tests
---------
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
## Summary
Lance can now plan multiple byte ranges for the same blob in one
`read_blob_ranges` operation, but LanceDB users currently cannot expose
a complete set of logical ranges to that planner.
This complements `BlobFile`: file-like consumers such as PyAV can
continue to discover ranges dynamically, while callers that already know
the ranges for a batch can submit them together.
## Motivating example
A training table may store a large video blob together with a small
application-level clip index:
```text
video: blob
clips: [{offset, length}, ...]
```
The caller can select the videos and clips for a batch, obtain their row
IDs from the query, and read all of the selected windows together:
```python
rows = (
table.search()
.select(["clips"])
.with_row_id(True)
.limit(64)
.to_arrow()
.to_pylist()
)
requests = []
for row in rows:
clip = sample_clip(row["clips"])
requests.append(
(row["_rowid"], clip["offset"], clip["length"])
)
chunks = table.fetch_blob_ranges("video", requests)
```
Here, `_rowid` comes from the LanceDB query, while `offset` and `length`
come from the application's clip index and are relative to that row's
video blob. The caller describes only the logical reads; Lance still
handles validation, source grouping, coalescing, scheduling, and byte
backpressure.
Lance v10.0.0-beta.5 returns one logical result per blob selector or
range request and explicitly distinguishes null blobs from valid empty
values. LanceDB consumes that aligned result contract directly and only
adds a cardinality check for unresolved row IDs.
This PR exposes batched blob-range reads on local Rust and Python
tables. Results preserve request identity, duplicates, null slots, and
valid empty ranges while allowing Lance to execute the physical reads
out of order. Scheduler buffer sizing remains an internal Lance concern,
so the LanceDB API does not expose `io_buffer_size`.
Cloud tables continue to report this operation as unsupported until
there is a corresponding remote API.
## What
MemWAL LSM **read** support. When a table has an LSM write spec
(`set_lsm_write_spec`), `merge_insert` upserts live in the MemWAL
active/frozen memtables and flushed SSTables until an external
compaction merges them into the base table, so a normal scan returns
**stale** data. This routes reads through Lance's `LsmScanner` so
queries also surface that in-flight data, deduplicated by primary key
(newest generation wins).
## How
- Adds a **`use_lsm: Option<bool>`** query flag, symmetric with the
`merge_insert` flag:
- **unset** — auto-route through the LSM scanner when the table carries
a write spec
- **`use_lsm(true)`** — force the LSM path; error if there is no spec
- **`use_lsm(false)`** — read the base table only (the escape hatch)
- Plain scan, single-column full-text search, and single-vector ANN all
run through one `LsmScanner` (assembled from on-disk shard manifests
plus the cached writer's in-memory memtables), so a `where` predicate is
honored as a **prefilter** uniformly — including for vector search.
- **Compaction-aware snapshots:** an SSTable generation is dropped only
once it is both compacted into the base table and covered by the arm's
base-index catch-up (`index_catchup`); plain scans use the compaction
watermark alone.
- Query shapes the scanner cannot honor hard-error with guidance to set
`use_lsm(false)`: hybrid, multi/binary vectors, `with_row_id`,
reranking, `order_by`, dynamic/Substrait projection or filters,
`distance_range`, `use_index(false)`, postfilter, take-by-row-id/offset,
reads from a time-traveled version, and an unmaintained or ambiguous
(multiple) FTS/vector index. Namespace-pushdown queries fall back to
local execution when a spec is present; WAL-only writers are handled.
- Exposed across the Rust core and the Python (`use_lsm`) and TypeScript
(`useLsm`) bindings, including `TakeQuery`.
Rebased from Lance `7.2.0-beta.3` to `10.0.0-beta.3`.
## What changed
- add `block_size` to Python FTS configuration and the deprecated
local/remote helpers
- add `blockSize` to the TypeScript FTS options and propagate it through
the NAPI binding
- serialize the value as `block_size` for remote index creation
- document the existing Rust builder API and generate the TypeScript API
reference
- add local, remote, metadata, search, and invalid-value regression
coverage
## Why
Lance supports configuring the number of documents per compressed FTS
posting block, but LanceDB's Python and TypeScript APIs did not expose
the setting. This made the experimental FTS V3 layout unavailable
through those clients and allowed the value to be dropped before index
creation.
## How it works
The default remains `128`. Supported values are `128` and `256`;
selecting `256` uses the experimental FTS V3 format. Invalid values are
rejected by the Lance builder and surfaced as Python or JavaScript
errors.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo +1.94.0 clippy --quiet --features remote --tests --examples --
-D warnings`
- targeted Rust local and remote index tests
- Rust doctests: 34 passed
- Python Ruff checks, doctest, and targeted local/remote tests: 5 passed
- TypeScript build, Biome lint, generated docs, and targeted Jest tests:
9 passed
- `git diff --check`
## Limitations
The Java client remains unchanged because its external remote REST model
does not currently expose `block_size`.
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
This PR adds some support for `diff` / `merge` in the remote client as
for local tables we stay `NotSupported` until
https://github.com/lance-format/lance/issues/7263.
This wires the two review-and-land calls against the remote REST API:
- `POST /v1/table/{id}/branches/diff`
- `POST /v1/table/{id}/branches/merge`
Rust gets typed results (`BranchDiff`, `MergeBranchResult`). Python
returns the wire JSON, same shape as the REST response.
Merge here means promoting a branch's added columns onto `main`.
### Behavior
- Remote only. Local raises `NotSupported`.
- A rejected merge is not an exception. HTTP 409 still returns `Ok` / a
dict with `status="rejected"` and blockers in `diff.mergeBlockers`.
- Unknown blocker / status codes parse as `Unknown` so a newer server
does not break older clients.
- `MergePreview` tolerates missing fields for the same reason.
- Merge requests are not retried. 409 is final and carries the body you
need.
### Example
```python
table = db.open_table("images")
table.branches.create("exp")
exp = table.branches.checkout("exp")
exp.add_columns({"tag": "cast('draft' as string)"})
diff = table.branches.diff("exp")
preview = table.branches.merge("exp", dry_run=True)
result = table.branches.merge("exp", dry_run=False)
if result["status"] == "merged":
print("landed at", result["mainVersionAfter"])
elif result["status"] == "rejected":
print(result["diff"]["mergeBlockers"])
```
### Testing
cargo test -p lancedb --features remote diff_branch
cargo test -p lancedb --features remote merge_branch
---------
Co-authored-by: Cursor <cursoragent@cursor.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>
## Problem
`table.add(dataset)` with a `pyarrow.dataset.Dataset` OOMs the client
during bulk ingestion of wide rows (e.g. embedding columns), even
against a remote table where the upload itself is streaming.
The cause is in `to_scannable`: a `Dataset` is scanned with pyarrow's
default scanner settings (`batch_size=131072` rows,
`batch_readahead=16`, `fragment_readahead=4`). pyarrow's internal
threads prefetch that read-ahead window independently of LanceDB's
backpressure, so for wide rows a large fraction of the dataset is held
in memory. On the remote path this is then multiplied across the
multipart write partitions (one in-flight batch per partition, up to
CPU-core count).
Reproduced on a 10 GB / 1.55M-row dataset with two 768-dim float32
embeddings: peak client RSS ~11.7 GB for the scan alone (6.8 GB after
consuming a *single* batch), ~15.4 GB for the full remote `add()`.
## Fix
`to_scannable` now sizes the scanner from an estimate of bytes-per-row
derived from the schema:
- **Narrow datasets keep pyarrow's defaults** (empty scanner kwargs) —
no throughput regression. The bound only engages above ~410 bytes/row.
- **Wide rows** get a smaller `batch_size` (~16 MiB/batch) and reduced
read-ahead (`batch_readahead=2`, `fragment_readahead=1`) so peak
in-flight memory stays near a ~1 GiB budget. Read-ahead (not just batch
size) has to drop, because pyarrow pins whole row-group buffers.
On the 10 GB dataset this drops peak client RSS to ~1.4 GB, and it stays
flat as the dataset grows. The `Dataset`/`LanceDataset` scannables
remain rescannable (retry-safe).
## Also: expose `write_parallelism` on `add()`
`AddDataBuilder::write_parallelism` already existed in Rust but was not
exposed in Python. This PR forwards it through the async, sync, and
remote `add()` methods, so users can cap the number of parallel write
partitions (each buffers data in flight) to trade throughput for memory
on large uploads.
## Tests
- `test_scannable.py`: bytes-per-row estimation; narrow → defaults; wide
→ bounded; `Dataset` reader streams bounded batches and stays
rescannable.
- `test_table.py`: `write_parallelism` on sync and async `add()`, and
that `write_parallelism=0` is rejected.
Fixes ENT-1883
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
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.
## 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
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>
## Summary
Adds `Table::get_lsm_write_spec` returning `Option<LsmWriteSpec>` — the
read counterpart to the existing `set_lsm_write_spec` /
`unset_lsm_write_spec`. Returns `None` when the MemWAL LSM write path is
not enabled; otherwise reconstructs the spec (mode, shard column,
`num_buckets`, `maintained_indexes`, `writer_config_defaults`) exactly
as installed.
## Changes
- **Rust core (`NativeTable`)** — reconstructs the spec from
`mem_wal_index_details()`, resolving the shard column from its Lance
field id via the dataset schema. This is a raw metadata read, so it is
unaffected by `describe_indices` system-index filtering.
- **Remote (`RemoteTable`)** — reads the `__lance_mem_wal` system index
through `index/list` with `include_system: true` (so the curated
`list_indices` surface stays unchanged), then parses the index `details`
JSON. It matches the index by name and ignores `index_type`, so no
client `IndexType` variant is needed. It uses the **server-resolved
`column` name** from the details (Lance field ids do not travel to the
remote client).
- **Python + TypeScript bindings** — sync and async, mirroring
`set`/`unset`, with round-trip tests (bucket / identity / unsharded,
plus `None` when unset).
## Tests
- Rust: native round-trip unit test + remote mock-endpoint tests
(present + absent). All green (`cargo test --features remote -p
lancedb`).
- Python/TS: round-trip tests added; binding-runtime execution runs in
CI.
## Dependencies for the remote path
The remote path is complete on the client side but depends on two
out-of-repo pieces to work end-to-end:
1. **lance** — emit the server-resolved shard **`column`** name in the
MemWAL index `details` JSON (field ids can't reach the client). See
lance-format/lance#7667.
2. **server** — honor `include_system` on `index/list` so the
`__lance_mem_wal` entry is returned for this read.
Against an older server (no `include_system`), the remote getter
degrades gracefully to `Ok(None)` rather than erroring.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
### **Summary**
Closes#3212
Extends the Python `lit()` helper to natively support three additional
types (`date`, `datetime`, and `Decimal`) and implements reflexive
operators for the `Expr` class.
This implementation specifically addresses the blocking feedback
regarding precision loss, CI discovery, and query engine limitations:
* **Logic Refactoring**: Simplified `lit()` by combining `date` and
`datetime` normalization into ISO-8601 strings, ensuring stable SQL
parsing across different engine locales.
* **Precision Preservation**: `decimal.Decimal` objects are now passed
as high-precision strings to the Rust bridge, bypassing intermediate
float conversions and preserving full 128-bit decimal precision for
DataFusion.
* **Averted CI Failures**: Temporarily deferred `bytes` literal support
to a future PR to resolve a known DataFusion `expr_to_sql` limitation
that was crashing the `Doctest` runner.
* **Reflexive Operators**: Added support for "literal-first" arithmetic
and logical operations (e.g., `10 + col('a')` or `True &
col('active')`). Redundant reflexive comparisons (e.g., `__rlt__`) were
pruned as Python's data model handles them automatically.
* **Integration Verification**: Added dedicated integration tests in the
official test directory to ensure the query engine correctly handles the
new types and preserves bit-perfect fidelity.
### **Changes**
####
[python/python/lancedb/expr.py](file:///c:/Users/Laksh/Documents/lancedb/python/python/lancedb/expr.py)
* Updated `lit()` to handle `date`, `datetime`, and `Decimal` natively.
* Implemented reflexive operators (`__radd__`, `__rand__`, `__rmul__`,
etc.) to support literals on the left-hand side.
* Removed the problematic `bytes` doctest example and `lit()` type
support to unblock CI.
####
[python/src/expr.rs](file:///c:/Users/Laksh/Documents/lancedb/python/src/expr.rs)
* Modified the Rust FFI bridge to extract `Decimal` objects as strings.
* Ensured the `expr_lit` handler is ready to receive normalized temporal
strings.
* Consolidated imports and added missing operator documentation.
####
[python/python/lancedb/_lancedb.pyi](file:///c:/Users/Laksh/Documents/lancedb/python/python/lancedb/_lancedb.pyi)
* Updated type stubs for `expr_lit` to include `Any` (allowing for
`Decimal`).
### **Testing**
Added several new advanced test cases in
[python/python/tests/test_expr.py](file:///c:/Users/Laksh/Documents/lancedb/python/python/tests/test_expr.py)
covering:
* **High-precision Decimal preservation**: Verified against 128-bit
boundaries with a "one point off" test case (`1.234567890123456789 <
1.234567890123456790`).
* **Reflexive operator positioning**: Verified successful query
construction with literals on the left.
* **Timezone-aware normalization**: Confirmed stable behavior for
`datetime` objects.
* **Integration Testing**: Confirmed Date32 and Decimal columns return
the correct Python types and values from the engine during `.to_arrow()`
calls.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
# Elastic Streaming Dataloader
## Motivation
Training large models on LanceDB tables today requires loading the
entire dataset
into memory or writing bespoke batching logic. This PR introduces
`StreamingDataset`, a PyTorch `IterableDataset` that streams directly
from a
LanceDB table with two hard guarantees that are difficult to achieve
together:
**elastic determinism** and **resumability**.
## Goals
### Elastic determinism
The dataset partitions the table into a fixed number of *splits*
(controlled by
`num_splits`, `shuffle_seed`, and `epoch`). Samples are yielded by
round-robining
over splits one sample per split per cycle. Because the split structure
is fixed,
the set of samples that makes up each global training step is identical
regardless
of `world_size` or `num_workers`. You can scale your cluster up or down
between
runs and the model sees the same data in the same order — no
re-sharding, no
gradient variance from topology changes.
### Resumability
`state_dict()` / `load_state_dict()` capture how many samples each split
has
consumed. Because all splits are the same size and the round-robin
design keeps
them in lockstep, the state reduces to a single scalar
(`samples_consumed_per_split`)
that is topology-independent. A checkpoint saved with 8 GPUs can resume
correctly
on 4 GPUs or 16 GPUs without any adjustment.
### PyTorch `IterableDataset` / streaming
`StreamingDataset` implements the standard PyTorch `IterableDataset`
interface, so
it drops into any existing `DataLoader` pipeline without modification.
Data is
fetched lazily from Lance in chunks — only the rows needed for the
current batch are
ever in memory.
Compared to the map dataset this takes more work from pytorch and puts
it into the dataset itself (e.g. shuffling, filtering, etc.). We do this
because we cannot achieve things like elastic determinism or
prefiltering otherwise.
### Multi-worker support
DataLoader workers are automatically assigned contiguous sub-blocks of
splits (the
rank's splits are divided evenly across workers). Each worker is
independent:
no shared state, no inter-process coordination. The only constraint is
that
`num_splits` must be divisible by `world_size * num_workers`.
That being said, multi-worker is highly discouraged as it relies on
multiprocessing which is inefficient. Still, we want to support it.
### Filters as prefilters
Filters are applied at *permutation-build time* via
`PermutationBuilder.filter()`,
not re-evaluated on every fetch. The filtered row IDs are stored in the
permutation
table so that subsequent reads see only the matching rows. This allows
us to avoid loading rows that don't match the filter (which is the
default pytorch behavior)
### Prefetching
Two parameters control the I/O pipeline:
- `read_batch_size` (default 64) — number of rows fetched per
`take_offsets` call.
Larger values amortise per-request overhead, which is critical on object
storage
where a single round-trip can cost ~100 ms.
- `prefetch_batches` (default 4) — number of batches prefetched in
parallel per
split via a `ThreadPoolExecutor`. While the model processes the current
batch,
the next several batches are already in flight, hiding storage latency
behind
compute.
If set correctly then you can get good performance even with
num_workers=0 (unless you are bottlenecked on transform).
### Transform parallelism
The underlying `Permutation` API supports a `with_transform()` callback
for
decoding, augmentation, and format conversion. Unfortunately, this is
not parallelized. Pytorch typically parallelizes this with num_workers
which is multiprocessing which is highly inefficient. For simple
transforms we should be able to utilize multithreading and Rust based
UDFs. For complex python UDFs we could have a dedicated multiprocessing
pipeline for just the transform. Or we could just utilize
multithreading. In both cases we would exclude the I/O stage from the
multiprocessing because that ends up being very memory hungry and
inefficient.
---------
Co-authored-by: Claude Sonnet 4.6 <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.
The server now serializes an index's `created_at` as an RFC 3339 string
(e.g. `"2026-06-18T21:37:36.637Z"`), but the client deserializer only
accepted a unix timestamp in milliseconds. This caused `list_indices` to
fail with:
```
Failed to parse list_indices response: invalid type: string "2026-06-18T21:37:36.637Z", expected a unix timestamp in milliseconds
```
This PR replaces the fixed millisecond deserializer with a custom one
that accepts both an RFC 3339 string (current server) and a
unix-millisecond integer (legacy deployments), so the client works
against any server version.
It also improves the `IndexConfig` repr in the Python bindings.
Previously it printed only three fields (`Index(FTS, columns=["text"],
name="text_idx")`), hiding the metadata that `list_indices` returns. It
now renders every populated field, omitting any that are `None`. Each
value is valid Python — integer counts use `_` thousands separators and
`created_at` uses the `datetime` repr — so values round-trip. The real
repr is a single line; it's wrapped here for readability:
```python
>>> table.list_indices()
[IndexConfig(
name="text_idx",
index_type="FTS",
columns=["text"],
index_uuid="aefd3e00-2f95-4bdc-92ac-06de84442bf1",
type_url="/lance.table.InvertedIndexDetails",
created_at=datetime.datetime(2026, 6, 18, 21, 37, 36, 637000, tzinfo=datetime.timezone.utc),
num_indexed_rows=2,
size_bytes=3_669,
num_segments=1,
index_version=1,
index_details={
'lance_tokenizer': None,
'base_tokenizer': 'simple',
'language': 'English',
'with_position': False,
'max_token_length': 40,
'lower_case': True,
'stem': True,
'remove_stop_words': True,
'custom_stop_words': None,
'ascii_folding': True,
'min_ngram_length': 3,
'max_ngram_length': 3,
'prefix_only': False,
},
)]
```
Fixes#3556🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
## Summary
Surfaces the rich per-index metadata added in #3497 to the Python and
Node.js language bindings. Closes#3495.
New optional fields exposed on `IndexConfig` in both bindings:
- `index_uuid` / `indexUuid` — UUID of the first index segment
- `type_url` / `typeUrl` — protobuf type URL for the index
- `created_at` / `createdAt` — creation timestamp (milliseconds since
Unix epoch)
- `num_indexed_rows` / `numIndexedRows` — rows covered by the index
- `num_unindexed_rows` / `numUnindexedRows` — rows not yet indexed
- `size_bytes` / `sizeBytes` — total index file size in bytes
- `num_segments` / `numSegments` — number of index segments
- `index_version` / `indexVersion` — on-disk format version
- `index_details` / `indexDetails` — type-specific JSON details string
All fields are `None`/`undefined` for remote tables (which don't yet
surface this metadata through the server response).
## Changes
- `python/src/index.rs`: extend `IndexConfig` pyclass; update `From`
impl; update `__getitem__`
- `python/python/lancedb/_lancedb.pyi`: add type hints for new fields
- `python/python/tests/test_table.py`: new `test_index_config_fields`
test
- `nodejs/src/table.rs`: extend `IndexConfig` napi struct; update `From`
impl
- `nodejs/__test__/table.test.ts`: new test; update existing `toEqual`
assertions to `expect.objectContaining` to accommodate new fields
## Test plan
- [x] Python: `uv run --extra tests pytest
python/tests/test_table.py::test_index_config_fields`
- [x] Node.js: `pnpm test __test__/table.test.ts`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Another little pain point as I was working to integrate with
paperless-ngx. The read path of table.search() or table.query() already
accepted an Expr, but write paths Table.delete and
merge_insert(...).when_not_matched_by_source_delete did not. This PR
attempts to close that gap, so writes and reads can both use Expr,
instead of one side needing to build a string.
The `Expr` build already includes a lot of useful filtering options,
`eq, ne, gt/gte, lt/lte, and_, or_, contains, cast`, but is was missing
a membership like `isin`. This PR adds that support, as minimally as
possible, allowing easy filtering for membership in a list, without
needing to be a series of `where` expressions.
I didn't see anything in CONTRIBUTING.md about needing a feature request
or issue first, so I just made the change. My apologies if I missed that
somewhere.
Thanks for the vector store, we're using it now in paperless-ngx.
Adds an FM-Index — a scalar index over string and binary columns that
accelerates substring search (`contains(col, 'needle')`), distinct from
the tokenized `FTS` index — across the Rust core and the Python and
TypeScript bindings.
## Rust
- `Index::Fm(FmIndexBuilder)` and `IndexType::Fm`.
- `make_index_params` maps `Index::Fm` to Lance's
`ScalarIndexParams::for_builtin(BuiltinIndexType::Fm)`.
- `supported_fm_data_type` validates
`Utf8`/`LargeUtf8`/`Binary`/`LargeBinary` columns.
- `list_indices` round-trips the type (`"Fm"` → `IndexType::Fm`); the
remote wire type is `"FM"`.
## Python
Adds `lancedb.index.Fm`, accepted by `create_index`:
```python
from lancedb.index import Fm
await tbl.create_index("text", config=Fm())
```
## TypeScript
Adds the `Index.fm()` factory:
```ts
await tbl.createIndex("text", { config: Index.fm() });
```
### Description
Stacked on #3490. Adds an optional version to branch checkout across the
Rust core and the Python and TypeScript SDKs, so you can open a specific
version on a branch ("version V of branch B"), not just the branch's
latest version
Rust
```rust
// Open version 3 of branch "exp" (a read-only view): check out from an
// existing table, or open it directly from the connection.
let exp_v3 = table.checkout_branch("exp", Some(3)).await?;
let exp_v3 = db.open_table("items").branch("exp").version(3).execute().await?;
// checkout_latest re-attaches to the branch's writable HEAD.
exp_v3.checkout_latest().await?;
// With no branch, a version opens main at that version.
let main_v3 = db.open_table("items").version(3).execute().await?;
```
Python
```python
# Open version 3 of branch "exp" (a read-only view): check out from an
# existing table, or open it directly from the connection.
branch_v3 = await table.branches.checkout("exp", version=3)
branch_v3 = await db.open_table("items", branch="exp", version=3)
# checkout_latest re-attaches to the branch's writable HEAD.
await branch_v3.checkout_latest()
# With no branch, a version opens main at that version.
main_v3 = await db.open_table("items", version=3)
```
TypeScript
```typescript
// Open version 3 of branch "exp" (a read-only view): check out from an
// existing table, or open it directly from the connection.
const branchV3 = await (await table.branches()).checkout("exp", 3);
const opened = await db.openTable("items", undefined, { branch: "exp", version: 3 });
// checkoutLatest re-attaches to the branch's writable HEAD.
await branchV3.checkoutLatest();
// With no branch, a version opens main at that version.
const mainV3 = await db.openTable("items", undefined, { version: 3 });
```
### Testing
- Added unit tests (Rust, Python sync + async, TypeScript):
branch-scoped resolution at a version number shared with `main` and with
another branch, read-only enforcement on a pinned handle,
`checkout_latest` recovery to the branch's HEAD, fork-point reads, and
the nonexistent-version/branch error paths.
- Ran smoke tests against the Python and TypeScript SDKs on local
machine.
### Description
Adds first-class support for table branches across the Rust core and the
Python and TypeScript SDKs.
Rust
```rust
use lance::dataset::refs::Ref;
// Create a branch from main and write to it — main is untouched.
let exp = table.create_branch("exp", Ref::Version(None, None)).await?;
exp.add(batches).await?;
// Reopen the branch later: check out from a table, or open it directly.
let exp = table.checkout_branch("exp").await?;
let exp = db.open_table("items").branch("exp").execute().await?;
let branches = table.list_branches().await?;
table.delete_branch("exp").await?;
```
Python
```python
# Create a branch from main and write to it
branch = await table.branches.create("exp", from_ref="main")
await branch.add(data)
# Reopen the branch later: check out from a table, or open it directly.
branch = await table.branches.checkout("exp")
branch = await db.open_table("items", branch="exp")
await table.branches.list()
await table.branches.delete("exp")
```
TypeScript
```typescript
const branches = await table.branches();
// Create a branch from main and write to it
const branch = await branches.create("exp");
await branch.add(data);
// Reopen the branch later: check out from a table, or open it directly.
const checkedOut = await branches.checkout("exp");
const opened = await db.openTable("items", undefined, { branch: "exp" });
await branches.list();
await branches.delete("exp");
```
### Testing
- Added unit tests
- ran smoke tests against python and typescript sdks on local machine
### Next steps
- Add RemoteTable support
- Add Branch Comparison support
- Merge Branching support
BREAKING CHANGE: direct Rust users lose the `IndexStatistics::loss`
field. Python and Node.js consumers are unaffected in practice for
remote tables (the value was always `None`/absent), but the attribute is
gone for local tables too.
`IndexStatistics::loss` was local-only — LanceDB Cloud never returned
it, so
`RemoteTable::index_stats` always set `loss: None`. It's vestigial; this
removes it.
- Remove `loss` from `IndexStatistics` and the internal `IndexMetadata`
in `rust/lancedb/src/index.rs`, plus the summing logic in
`NativeTable::index_stats`.
- Drop `loss` from the Python and Node.js bindings (and their
tests/docs).
Fixes#3493🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
### Summary
Deprecates the Python replace_field_metadata (on Table and AsyncTable)
in favor of update_field_metadata. Mirrors Lance, which already
deprecated Dataset.replace_field_metadata for update_field_metadata.
Stacked on top of #3482 as this was a follow-up task after adding
update_field_metadata
### Summary
Adds update_field_metadata to the client SDK (Rust core, Python, and
TypeScript) so clients can edit per-field (column) Arrow metadata
(schema.fields[].metadata)
### Testing
- added unit tests
- ran E2E against a local server on both local and remote tables (set →
merge → delete), across Python sync/async and TypeScript
### Next steps
- deprecate replace_field_metadata in the python lancedb favor of this
(typescript didn't have replace_field_metadata method). This matches
Lance's API direction (Lance already deprecated replace_field_metadata
for update_field_metadata)
Xuanwo
Dan Rammer
lancedb-gatefixer[bot]
Wyatt Alt
Drew Gallardo
Yang Cen
Heng Ge
Drew Gallardo
Will Jones
Jack Ye
LanceDB Robot
LAKSH JAIN
Weston Pace
Ryan Green
Trenton H
Brendan Clement