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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>
78 lines
2.9 KiB
Plaintext
78 lines
2.9 KiB
Plaintext
---
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title: "Create a view"
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sidebarTitle: "Create View"
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description: "Define a derived dataset as a saved query, with no storage or refresh of its own."
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icon: eye
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keywords: ["view", "create view", "feature engineering", "saved query"]
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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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A view is the lightest way to derive a feature-engineered dataset: it stores
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a **query**, not rows, so there is nothing to refresh and nothing that can
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fall out of date. Reach for one before
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[a computed column](/feature-engineering/add-computed-column) or
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[a materialized view](/feature-engineering/materialized-views) when the
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underlying query is cheap enough to simply re-run on every read.
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## Define a view {#create}
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A view over a products table, selecting and deriving a few columns:
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<CodeGroup>
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```sql SQL icon="database"
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CREATE VIEW "discounted_products" AS
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SELECT id, name, price, price * 0.9 AS sale_price
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FROM "products"
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WHERE in_stock = true
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```
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</CodeGroup>
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The statement resolves the view's schema from the query at creation time.
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Anything the [SQL dialect](/sql/dql/functions) supports can define one: joins,
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predicates, aggregates, window functions -- the same expressiveness as
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[a computed column's](/feature-engineering/add-computed-column#expressions)
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SQL expression, just over a whole derived table instead of one column.
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## Read it like a table {#reading}
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<CodeGroup>
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```sql SQL icon="database"
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SELECT * FROM "discounted_products" WHERE sale_price < 20
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```
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</CodeGroup>
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Every read runs the saved query against current data, so `sale_price`
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reflects whatever `price` is right now -- there is no staleness window to
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reason about, unlike a materialized view between refreshes. A predicate
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applied when reading the view composes with the query inside it, rather than
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filtering the view's output afterward, so a selective filter does not pay
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for rows the view would have produced and then discarded.
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## When to use a view instead {#choosing}
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| | View | Computed column / materialized view |
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| --- | --- | --- |
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| Stores | the query | derived values |
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| Freshness | always current | as of the last refresh |
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| Read cost | the full query, every read | a column or table scan |
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| Write cost | none | a refresh |
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Reach for a view when the query is cheap, or when reading a stale value would
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be wrong for the workload. Reach for a computed column or a materialized view
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when the query is expensive and the data changes less often than it is read.
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See [View](/sql/ddl/view#choosing) for the full SQL reference comparison,
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including the separate privileges each one needs.
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="View" icon="eye" href="/sql/ddl/view">
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CREATE VIEW, DROP VIEW, and listing views alongside tables.
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</Card>
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<Card title="Create a materialized view" icon="table" href="/feature-engineering/materialized-views">
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The stored-rows counterpart, and when a refresh is worth paying for.
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</Card>
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</CardGroup>
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