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Brendan Clement
b218633e36
`view.table()` and `view.definition()` raise TypeError on a synchronous view; both are properties. An async view reads its definition with `await view.definition()`.
101 lines
3.6 KiB
Plaintext
101 lines
3.6 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; a refresh computes
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the rows.
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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. `select`, `where` and `limit` record
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the query -- there is no separate definition step.
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<Warning>
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The source table must have **stable row ids**, and they cannot be enabled after
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a table exists. A table created without them cannot become a view's source
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without being recreated.
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Set `new_table_enable_stable_row_ids` in
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[storage options](/management/database#storage-options) when you create the
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source, not when you create the view.
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</Warning>
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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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Incremental refreshes, and what they cost.
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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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