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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>
57 lines
2.1 KiB
Plaintext
57 lines
2.1 KiB
Plaintext
---
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title: "Explain"
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sidebarTitle: "Explain"
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description: "EXPLAIN, EXPLAIN ANALYZE and EXPLAIN VERBOSE: reading and measuring a plan from SQL."
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icon: map
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keywords: ["explain", "explain analyze", "explain verbose", "query plan"]
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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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EXPLAIN SELECT * FROM documents WHERE identifier < 1000
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EXPLAIN ANALYZE SELECT * FROM documents WHERE identifier < 1000
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```
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</CodeGroup>
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| Statement | What it returns |
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| --- | --- |
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| `EXPLAIN <query>` | Logical and physical plan, without executing the query. |
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| `EXPLAIN ANALYZE <query>` | Executes the query and annotates each operator with runtime metrics (rows, timing). |
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| `EXPLAIN VERBOSE <query>` | Adds intermediate optimizer plans on top of `EXPLAIN`. |
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The result is a small relation with `plan_type` and `plan` columns, read
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through the same client you use for regular queries. See
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[Connecting](/sql#connecting).
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SQL plans and the SDK's
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[`explain_plan` and `analyze_plan`](/basics/sql#query-plans) output share scan and index
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operators. Use the same approach to inspect index coverage and filter pushdown;
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SQL can also add joins, aggregates and other operators:
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<CardGroup cols={2}>
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<Card title="What it shows" icon="map" href="/basics/sql#inspect-a-query-plan">
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Reading a plan: operators, pushdown, index usage.
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</Card>
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<Card title="What it measures" icon="gauge" href="/basics/sql#analyze-a-query-plan">
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Reading the metrics, and a worked before/after.
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</Card>
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</CardGroup>
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## What to look for {#what-to-look-for}
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| Sign | Means |
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| --- | --- |
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| `ScalarIndexQuery` | the predicate used a scalar index |
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| `LanceScan` with high `bytes_read` | inspect the selected columns, filter pushdown and index use |
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| `FilterExec` above a search | the filter ran after search rather than pushing into it |
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| `ANNSubIndex` / `ANNIvfPartition` | the vector index was used |
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A `LanceScan` feeding a join is the common one worth acting on. See
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[Joins](/sql/dql/join#performance).
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For a table's columns and types without a plan, see
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[`DESCRIBE`](/sql/ddl/table#describe).
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