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Brendan Clement
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Databases, namespaces, jobs and data evolution work in both offerings. Materialized views, computed columns and column refresh work in the embedded library and vary on a deployment, which has to enable them. Views and functions exist only on a deployment that enables them, and aggregation, joins, windowing and sampling are SQL, so they need one with SQL enabled. Sections now say where they differ: views have SDK methods on a deployment, dropping a function needs a further setting, a local job handle runs in-process and has no history, create_index and refresh_column behave differently against a deployment, and the overviews no longer call computed columns and materialized views available everywhere.
105 lines
4.1 KiB
Plaintext
105 lines
4.1 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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availability:
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oss: available
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enterprise: varies
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summary: >-
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Materialized views are created and managed in the embedded library. On a
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deployment they are off by default and have to be enabled, and the SDK
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creates them through the deployment's SQL endpoint; see
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[Against a deployment](#deployment).
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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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brings its stored rows up to date with 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", "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. A local source does not need stable
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row ids or any other storage option; an earlier deployment version may require
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stable row ids (see [Against a deployment](#deployment)). `select`, `where` and `limit` record
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the query -- there is no 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` | `CREATE MATERIALIZED VIEW` |
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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. There the SDK creates and refreshes
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the view through SQL, and the work runs on the cluster rather than in your
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process, so the deployment's [SQL endpoint](/basics/sql#sql-is-remote) has to be
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reachable. Materialized views are off by default on a deployment and have to be
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enabled there, and an earlier deployment version may require a view's source to
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have stable row ids; see
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[Against a deployment](/feature-engineering/materialized-views#availability).
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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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How a refresh works, 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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