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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>
93 lines
3.0 KiB
Plaintext
93 lines
3.0 KiB
Plaintext
---
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title: "Joins"
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sidebarTitle: "Joins"
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description: "Combining rows from more than one table, including across namespaces."
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icon: link
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keywords: ["join", "inner", "left", "outer", "cross", "namespace"]
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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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Use SQL joins to combine rows from two or more tables.
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## The join types {#types}
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<CodeGroup>
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```sql SQL icon="database"
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SELECT d.title, c.name
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FROM documents AS d
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JOIN categories AS c ON c.id = d.category_id
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```
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</CodeGroup>
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| | What it keeps |
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| --- | --- |
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| `[INNER] JOIN` | rows matching on both sides |
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| `LEFT [OUTER] JOIN` | every left row, nulls where the right has no match |
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| `RIGHT [OUTER] JOIN` | every right row |
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| `FULL [OUTER] JOIN` | every row from both |
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| `CROSS JOIN` | every combination (the product of the two) |
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`USING (col)` is shorthand for an equality join on a shared column name, and
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avoids repeating it in the select list.
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## Across namespaces {#namespaces}
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Tables in different [namespaces](/management/namespace) join like any other table, each
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addressed by its full `database.schema.table` identifier. The catalog is one
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catalog, so there is nothing to federate:
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<CodeGroup>
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```sql SQL icon="database"
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SELECT c.id, u.email
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FROM prod.events.clicks AS c
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JOIN prod.reference.users AS u ON u.id = c.user_id
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```
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</CodeGroup>
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## Joining a search result {#search}
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[`fts()`](/sql/dql/fts) is a table function, so its result is a relation
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and joins like any other. This example assumes `my_docs` has `text` and
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`category_id` columns, a full-text index on `text`, and a `categories` table
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with `id` and `name` columns:
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<CodeGroup>
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```sql SQL icon="database"
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SELECT c.name, count(*) AS hits
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FROM fts(
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'my_docs',
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'{"match":{"column":"text","terms":"puppy"}}'
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) AS m
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JOIN categories AS c ON c.id = m.category_id
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GROUP BY c.name
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ORDER BY hits DESC
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```
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</CodeGroup>
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That composition is the reason full-text search is a relation rather than a
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predicate: a `WHERE` clause has nowhere to put the relevance score.
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## Performance {#performance}
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A [scalar index](/indexing/scalar-index) can accelerate supported filters on
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the input tables. An index on a join key does not by itself guarantee an
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index lookup join. Use SQL [`EXPLAIN`](/sql/dql/explain) to inspect the scans,
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filters, and join chosen for the complete statement.
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<Warning>
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Avoid selecting blob or vector columns you do not need on either side of a join.
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The join multiplies rows before projection is applied in some plans, so a wide
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column can be materialized many more times than the result has rows.
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</Warning>
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## Late materialization for wide columns {#late-materialization}
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The planner can avoid the multiplication above: when a join narrows down to
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a key column before a wide one is needed, it can defer reading a BlobV2 or
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vector column until after the join has already settled which rows survive.
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See [Blob Late Materialization](/curation/blob-late-materialization) for
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the eligibility conditions, the `RemoteTake` operator, and how to confirm
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a specific query got the deferred read with [`EXPLAIN`](/sql/dql/explain).
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