## Summary
Adds progress reporting for `table.add()` so users can track large write
operations. The progress callback is available in Rust, Python (sync and
async), and through the PyO3 bindings.
### Usage
Pass `progress=True` to get an automatic tqdm bar:
```python
table.add(data, progress=True)
# 100%|██████████| 1000000/1000000 [00:12<00:00, 82345 rows/s, 45.2 MB/s | 4/4 workers]
```
Or pass a tqdm bar for more control:
```python
from tqdm import tqdm
with tqdm(unit=" rows") as pbar:
table.add(data, progress=pbar)
```
Or use a callback for custom progress handling:
```python
def on_progress(p):
print(f"{p['output_rows']}/{p['total_rows']} rows, "
f"{p['active_tasks']}/{p['total_tasks']} workers, "
f"done={p['done']}")
table.add(data, progress=on_progress)
```
In Rust:
```rust
table.add(data)
.progress(|p| println!("{}/{:?} rows", p.output_rows(), p.total_rows()))
.execute()
.await?;
```
### Details
- `WriteProgress` struct in Rust with getters for `elapsed`,
`output_rows`, `output_bytes`, `total_rows`, `active_tasks`,
`total_tasks`, and `done`. Fields are private behind getters so new
fields can be added without breaking changes.
- `WriteProgressTracker` tracks progress across parallel write tasks
using a mutex for row/byte counts and atomics for active task counts.
- Active task tracking uses an RAII guard pattern (`ActiveTaskGuard`)
that increments on creation and decrements on drop.
- For remote writes, `output_bytes` reflects IPC wire bytes rather than
in-memory Arrow size. For local writes it uses in-memory Arrow size as a
proxy (see TODO below).
- tqdm postfix displays throughput (MB/s) and worker utilization
(active/total).
- The `done` callback always fires, even on error (via `FinishOnDrop`),
so progress bars are always finalized.
### TODO
- Track actual bytes written to disk for local tables. This requires
Lance to expose a progress callback from its write path. See
lance-format/lance#6247.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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>
Realized our MSRV check was inert because `rust-toolchain.toml` was
overriding the Rust version. We set the `RUSTUP_TOOLCHAIN` environment
variable, which overrides that.
Also needed to update to MSRV 1.88 (due to dependencies like Lance and
DataFusion) and fix some clippy warnings.
BREAKING CHANGE: removes `aws`, `dynamodb`, `azure`, `gcs`, `oss`,
`huggingface` from default Rust features. They can be enabled by users
as needed.
They are still enabled for Python and NodeJS, since those users don't
control the compilation of artifacts.
Closes#2911
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.
