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Jack YeandClaude Sonnet 5
de95ed3f6d
A broad documentation pass across the areas LanceDB Enterprise and Embedded diverge on most: **Data Curation** -- GPU Function execution, Slurm scheduling (and what Slurm is, for readers new to it), blob late materialization as its own page, joins, aggregation/profiling, sampling, near-duplicate detection, chunking, distributed shuffle, and IVF-partition clustering. **Feature Engineering** -- computed columns and materialized views, restructured to lead with the Function-based path and show the SQL form side by side with Python throughout; a single Auto Refresh page instead of duplicating scheduling details on every refresh page; adaptive execution; checkpointing; GPU and Slurm placement for refresh workers; a Training Cache page for the PyTorch data loader's automatic cache benefit. **Security & Governance** -- an Audit Log reference (schema, access, grants), Secrets promoted to an ordinary catalog object with their own SQL DDL and a vending/rotation guide, and privilege-table fixes verified against the authorization source. **Deployment & Maintenance** -- Horizontal Scaling and OpenTelemetry moved into a real Deployment area; Automated Cleanup folded into Intelligent Auto-Maintenance (every other maintenance decision already lived there); RTE Compaction and Advanced Conflict Resolution documented in depth. **Landing page** -- the Enterprise feature comparison table reorganized (several redundant or stale rows removed, several new areas added), and the "why Enterprise" bullets replaced with ones tied to a concrete page (Performance, Scale, Fault tolerance, Multicloud deployment, Simplify operations) instead of vague promises. Plus a full pass addressing lancedb-gatekeeper's review: a real test-safety bug (a quickstart test could silently overwrite and delete a deployment's own `frames` table), a fabricated SQL statement with no backing implementation, an actual dedup-guarantee bug in `take_row_ids`, and dozens of documentation-accuracy fixes verified against source rather than taken on faith -- each either fixed, or left with an explicit note on why it needs a feature or deployment owner's sign-off instead of being guessed at. 🤖 Generated with [Claude Code](https://claude.com/claude-code) https://claude.ai/code/session_018PXMCbRH6i5w47TsjeEUMT --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
43 lines
1.4 KiB
Plaintext
43 lines
1.4 KiB
Plaintext
---
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title: "BLOB_DESCRIPTOR"
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sidebarTitle: "BLOB_DESCRIPTOR"
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description: "The blob_descriptor() scalar function: a BlobV2 column's payload location, without reading it."
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icon: file-binary
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keywords: ["blob_descriptor", "sql", "blob", "large binary", "scalar function"]
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availability:
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embedded: unavailable
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enterprise: available
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---
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<CodeGroup>
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```sql SQL icon="database"
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SELECT id, blob_descriptor(video) AS descriptor FROM videos
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```
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</CodeGroup>
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A [BlobV2](/management/table#managed-blobs) column's bytes are not fetched by
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a plain `SELECT`: that already returns `large_binary`, the same as any other
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binary column. `blob_descriptor` instead returns a small struct describing
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where the payload lives, without reading it. Pass one row's descriptor to
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the SDK's blob file reader to open that payload directly:
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<CodeGroup>
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```python Python icon="python"
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rows = db.execute_query("SELECT id, blob_descriptor(video) AS descriptor FROM videos").read_all().to_pylist()
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with table.open_blob_file(rows[0]["descriptor"]) as reader:
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payload = reader.read()
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```
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</CodeGroup>
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A descriptor is pinned to the table version it was read at: it keeps
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resolving correctly even after the table is appended to and checked out to a
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newer version.
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="Managed blobs" icon="file-binary" href="/management/table#managed-blobs">
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The BlobV2 column type this function describes.
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</Card>
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</CardGroup>
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