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## 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>
The Multimodal AI Lakehouse
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
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How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
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