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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>
107 lines
3.6 KiB
Plaintext
107 lines
3.6 KiB
Plaintext
---
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title: "DEDUPE"
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sidebarTitle: "DEDUPE"
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description: "The dedupe() table function: one representative row per near-duplicate group, scanned over the index."
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icon: layer-group
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keywords: ["dedupe", "vector_dedup", "sql", "deduplication", "near-duplicate", "table 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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`dedupe` scans a vector column's [index](/indexing/vector-index) partitions
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for rows closer than a distance threshold, resolves the overlapping pairs it
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finds into duplicate groups, and returns one representative row per group --
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without a full pairwise scan or a shuffle. See
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[`duplicate_pairs`](/sql/dql/duplicate-pairs) to inspect the pairs themselves
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before committing to this resolution.
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<CodeGroup>
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```sql SQL icon="database"
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SELECT * FROM dedupe(
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<table>,
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<vector_column>,
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distance_threshold => <float>
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[, resolution => 'connected_components' | 'direct_representative']
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)
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```
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</CodeGroup>
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<CodeGroup>
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```sql SQL icon="database"
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SELECT * FROM dedupe(
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'frames',
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'vec',
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distance_threshold => 0.02
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)
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```
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</CodeGroup>
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<Note>
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Earlier releases spelled this `vector_dedup`; that name is rejected on
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current deployments.
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</Note>
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## Input {#input}
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| Argument | Type | Required | Meaning |
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| --- | --- | --- | --- |
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| table name | string literal | yes | the table to scan |
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| vector column | string literal | yes | the indexed vector column to compare |
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| `distance_threshold` | float | yes | maximum distance for a pair to count as close |
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| `resolution` | `'connected_components'` (default) \| `'direct_representative'` | no | how overlapping pairs group together -- see [Resolution](#resolution) |
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`dedupe` always scans every partition and returns original rows -- sampling
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a subset of partitions and `id_column` are pair-query-only options, accepted
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by [`duplicate_pairs`](/sql/dql/duplicate-pairs#input) but refused here.
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## Output {#output}
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One row per duplicate group, **in the table's own schema** -- every column of
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`<table>`, including a BlobV2 column, comes through unchanged on the
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representative row Lance selected for that group. Unlike
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[`duplicate_pairs`](/sql/dql/duplicate-pairs#output), there is no fixed
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`dedupe`-specific schema to document: the output schema is the source
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table's schema.
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Because the result is already shaped like the table, it composes directly
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into a new table or a materialized view:
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<CodeGroup>
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```sql SQL icon="database"
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CREATE MATERIALIZED VIEW frames_deduped AS
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SELECT * FROM dedupe(
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'frames',
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'vec',
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distance_threshold => 0.02
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)
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```
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</CodeGroup>
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## Resolution {#resolution}
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`resolution` chooses how overlapping pairs group together:
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| `resolution` value | How pairs group |
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| --- | --- |
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| `connected_components` (default) | groups transitively: if a~b and b~c are both close, all three resolve together |
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| `direct_representative` | only pairs within the threshold of each other resolve together |
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## Estimating on a sample first {#sampling}
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`dedupe` itself always scans every partition. To estimate a duplicate rate
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on a large table before committing to a full scan, sample partitions with
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[`duplicate_pairs`](/sql/dql/duplicate-pairs#sampling) instead, which
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accepts `selection`, `count` and `seed` for exactly this.
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="DUPLICATE_PAIRS" icon="clone" href="/sql/dql/duplicate-pairs">
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Inspect the close pairs themselves instead of the resolved groups.
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</Card>
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<Card title="Deduplicate near-duplicates" icon="copy" href="/curation/dedupe">
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When to reach for this instead of exact-key deduplication.
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</Card>
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</CardGroup>
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