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Brendan Clement
a2803e1a02
The note dated the new behavior to "SDK 0.40", but it was verified on the development SDK 0.40.0b12 built from main, and no stable 0.40 has been released. The note now names that build and keeps the comparison with the published 0.39.0, which creates the view empty, has no with_no_data, and writes both matching rows on the first refresh.
129 lines
4.7 KiB
Plaintext
129 lines
4.7 KiB
Plaintext
---
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title: "Refresh a materialized view"
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sidebarTitle: "Materialized View Refresh"
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description: "A lakehouse object: a table defined by a query over another table, kept current incrementally."
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icon: table
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keywords: ["materialized view", "create_materialized_view", "refresh", "incremental"]
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---
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import {
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PyMaterializedViewCreate as MaterializedViewCreate,
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PyMaterializedViewRefresh as MaterializedViewRefresh,
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} from '/snippets/materialized_view.mdx';
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A materialized view is a lakehouse object: a table whose contents are defined by
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a query over another table. The query is recorded in the view's schema;
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creating the view computes its rows, and a refresh brings them up to date.
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The result is an ordinary table. It can be queried, indexed, and searched, and
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it appears in `table_names()` — nothing about reading it is special.
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<Note>
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In SQL this is [`REFRESH MATERIALIZED VIEW`](/sql/refresh#refresh-materialized-view).
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</Note>
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## Create and refresh {#create-and-refresh}
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<CodeBlock filename="Python" language="Python" icon="python">
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{MaterializedViewCreate}
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</CodeBlock>
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Creating the view computes its rows, so it is ready to read. Pass
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`with_no_data=True` to create it empty instead. Later changes to the source
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reach the view through a refresh:
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<CodeBlock filename="Python" language="Python" icon="python">
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{MaterializedViewRefresh}
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</CodeBlock>
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<Note>
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These results were verified with the development SDK 0.40.0b12, built from
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lancedb main. With the published 0.39.0, `create_materialized_view` creates the
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view empty and has no `with_no_data`, so the first `refresh()` writes every
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matching row, two in this example.
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</Note>
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`select` takes column names, `(alias, expression)` pairs, or a dict of the same.
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Omitting it selects every source column, expanded against the schema as it was
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at creation time. `where` filters source rows; `limit` caps the view.
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## The source needs stable row ids {#stable-row-ids}
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<Warning>
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The source table must have been created with stable row ids:
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```python
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db = lancedb.connect(uri, storage_options={"new_table_enable_stable_row_ids": "true"})
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```
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They are what keeps the view's provenance valid when the source is compacted,
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and **they cannot be enabled after a table exists**. A table created without
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them cannot later become the source of a view — it has to be recreated.
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</Warning>
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This is the one decision here that is hard to undo, so it is worth setting the
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option on any table you might later want to derive from.
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## Refreshes are incremental {#incremental}
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`refresh()` reconciles the source's changes into the view — rows added, changed,
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or removed since the last refresh — rather than recomputing from scratch. It
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falls back to a full rebuild when the changes cannot be reconciled.
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Two arguments override that:
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- `full=True` forces a rebuild.
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- `source_version=N` refreshes against that source version instead of the
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latest, which is how you make a view reproduce a known point in time.
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Concurrent refreshes of one view do not duplicate rows. If two plan the same
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source rows, they conflict on commit and the loser raises rather than writing
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them twice — so a retry loop around `refresh()` is safe.
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<Note>
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**Enterprise job type:** `mat_view_refresh`.
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</Note>
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## Against a deployment {#availability}
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<Badge color="red">Enterprise</Badge>
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`create_materialized_view` works on a `db://` connection as well as a local one.
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What changes is where the refresh runs -- across the cluster rather than in your
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process -- and that SQL can drive it:
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```sql
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REFRESH MATERIALIZED VIEW chunks
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```
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`WITH (full = true)` recomputes every row instead of reconciling what changed
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in the source since the last refresh. `SHOW MATERIALIZED VIEWS` lists them, which is
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cheaper than `list_materialized_views` -- that method finds views by reading
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every table's schema, so it costs one open per table.
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<Note>
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`CASCADE` is accepted by the parser but not yet implemented. A view built on
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another view is refreshed by refreshing each in dependency order.
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</Note>
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## Working with the view {#working-with-the-view}
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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 — the select list, predicate, and
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limit — which is how you check what a view actually contains without inferring
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it from the rows. Both are properties; on an async view, the definition is read
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with `await view.definition()`.
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An existing view is reopened with `db.open_materialized_view(name)`.
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
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Derive a column instead of a whole table.
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</Card>
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<Card title="Versioning" icon="clock-rotate-left" href="/management/table#versioning">
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Every refresh commits a table version.
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</Card>
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</CardGroup>
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