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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>
58 lines
2.4 KiB
Plaintext
58 lines
2.4 KiB
Plaintext
---
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title: "Functions"
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sidebarTitle: "Functions"
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description: "Where to find each kind of function, and the dialect version they come from."
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icon: function
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keywords: ["functions", "datafusion", "scalar", "aggregate", "window", "fts", "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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LanceDB executes SQL with [Apache DataFusion](https://datafusion.apache.org),
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currently **version 54**. The function set is DataFusion's default registration
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plus what Lance adds.
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| --- | --- |
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| [Scalar functions](/sql/dql/scalar-functions) | per row: math, string, regex, date/time, array, struct, JSON |
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| [Aggregate functions](/sql/dql/aggregation#functions) | per group: `count`, `sum`, statistical, approximate |
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| [Window functions](/sql/dql/window#functions) | per window: ranking, offset, running aggregates |
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| [Table functions](#table-functions) | produce a relation rather than a value |
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<Note>
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Function availability is version-specific. DataFusion adds functions in most
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releases, so one documented on `main` may not exist in 54. When a name is not
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recognised, check it against the reference for that version before assuming a
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bug.
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</Note>
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## Table functions {#table-functions}
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A table function appears in `FROM` and produces a relation, which is what lets
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it return columns a scalar function has nowhere to put. Each has its own
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reference page:
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| --- | --- |
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| [`fts(table, query)`](/sql/dql/fts) | full-text search, with a relevance score alongside the matching rows |
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| [`dedupe(table, column, ...)`](/sql/dql/dedupe) | one representative row per near-duplicate group |
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| [`duplicate_pairs(table, column, ...)`](/sql/dql/duplicate-pairs) | every close pair, before resolving into groups |
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| [`generate_series(start, stop)`](/sql/dql/generate-series) | rows from a range |
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`_score`, the relevance pseudo-column `fts()` produces, is not a function;
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see [Data types](/sql/data-types#pseudo-columns).
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## What Lance accelerates {#accelerated}
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Two function families use an index rather than scanning, which is the difference
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between a fast predicate and a slow one:
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| | Index used |
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| --- | --- |
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| [`contains_tokens`](/sql/dql/contains-tokens) | full-text index |
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| [`json_get`, typed variants, `json_extract`](/sql/dql/json-functions) | JSON scalar index |
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Confirm with [`EXPLAIN`](/sql/dql/explain): an accelerated predicate shows an
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index node rather than a `LanceScan` followed by a filter.
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