mirror of
https://github.com/lancedb/lancedb.git
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Brendan Clement
622e6078c1
Main now rebuilds a materialized view completely on every refresh (#4373): there is no incremental mode, no `full` argument and no stable-row-ID requirement on the source. The example ran against the SDK built from this branch (0.40.0b13) failed, because a refresh after adding Bea writes both matching rows, not one. The example now creates its source with the default options and expects two rows written by the refresh, and its generated snippet is regenerated. Separate tests cover `with_no_data` (one matching row on the first refresh) and refreshing to an earlier source version. The refresh page explains the full rebuild and keeps the #incremental and #stable-row-ids anchors for the sections that replace them. Its version note now says what the published 0.39.0 does differently, as observed with that release. The management, table, database, limits, architecture and data-types pages no longer say a view's source needs stable row ids, and the feature-engineering and tutorial summaries no longer call refreshes incremental.
92 lines
3.3 KiB
Plaintext
92 lines
3.3 KiB
Plaintext
---
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title: "Materialized view"
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sidebarTitle: "Materialized View"
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description: "Create, open, list and drop a table whose contents are defined by a query."
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icon: table
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keywords: ["materialized view", "create_materialized_view", "catalog", "ddl"]
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---
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A materialized view is a table whose contents are defined by a query over
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another table. The query is recorded in the view's schema, and a refresh
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computes its stored rows again from the source.
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The result is an ordinary table -- queryable, indexable, searchable, and present
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in `table_names()`. Nothing about reading it is special.
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This page covers creating and managing one. Keeping it current is
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[Refresh a materialized view](/feature-engineering/materialized-views).
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<Note>
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In SQL, see [`REFRESH MATERIALIZED VIEW`](/sql/dml/refresh#refresh-materialized-view) and [`SHOW MATERIALIZED VIEWS`](/sql/ddl/materialized-view#show).
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</Note>
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## Creating one {#create}
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<CodeGroup>
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```python Python icon="python"
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view = db.create_materialized_view(
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"recent_docs",
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source="documents",
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select=["id", "title", "embedding"],
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where="created_at > '2026-01-01'",
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)
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```
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```typescript TypeScript icon="square-js"
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const view = await db.createMaterializedView("recent_docs", {
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source: "documents",
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select: ["id", "title", "embedding"],
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where: "created_at > '2026-01-01'",
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});
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```
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</CodeGroup>
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`source` names the table it derives from; any table can be one, with no special
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storage option. `select`, `where` and `limit` record the query -- there is no
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separate definition step.
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## Opening, listing and dropping {#manage}
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| | Python | TypeScript | SQL |
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| --- | --- | --- | --- |
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| create | `create_materialized_view` | `createMaterializedView` | -- |
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| open | `open_materialized_view` | `openMaterializedView` | -- |
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| list | `list_materialized_views` | `listMaterializedViews` | `SHOW MATERIALIZED VIEWS` |
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| refresh | `refresh` | `refresh` | `REFRESH MATERIALIZED VIEW` |
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| drop | `drop_table` | `dropTable` | `DROP TABLE` |
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<Note>
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`list_materialized_views` finds views by reading every table's schema, so it
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costs one open per table. `SHOW MATERIALIZED VIEWS` asks the server instead and
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is cheaper on a catalog with many tables.
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</Note>
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A view is dropped with `drop_table`, because a materialized view **is** a table.
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That also means dropping one removes stored rows -- unlike dropping a
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[view](/management/view), which removes only a saved query.
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## Working with the view {#working}
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`view.table` is the underlying table, for querying, indexing, or search.
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`view.definition` is the recorded query -- select list, predicate, limit
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-- which is how you check what a view contains without inferring it from the
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rows. Both are properties; on an async view, the definition is read with
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`await view.definition()`.
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## Against a deployment {#deployment}
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<Badge color="red">Enterprise</Badge> `create_materialized_view` works on a
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`db://` connection as well as a local one. What changes is that a refresh runs
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across the cluster rather than in your process, and SQL can drive it.
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="Refresh a materialized view" icon="arrows-rotate" href="/feature-engineering/materialized-views">
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What a refresh recomputes, and what it writes.
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</Card>
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<Card title="Views" icon="eye" href="/management/view">
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The non-materialized counterpart, defined in SQL.
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</Card>
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</CardGroup>
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