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Jack Ye
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The function catalogue is now real rather than a pointer. Scalar functions are listed by taxonomy -- math, string, unicode, regex, date/time, array, map, struct, crypto, type -- and the names come from what datafusion-functions 54 actually registers, not from what DataFusion documents on main. Aggregate and window pages gain the same treatment. Two things the audit corrected rather than copied. SHOW TABLES does not work. DataFusion implements it over information_schema, which this session does not enable, so it fails at plan time. It was documented as working here, on the SHOW page and in three Manage tables; all four are fixed, and the SQL overview now lists the absence alongside SHOW COLUMNS. Prepared statements are a Flight SQL protocol feature, not a PREPARE statement. A script carrying PREPARE ... AS SELECT will not run, while the same intent expressed through a client will. The new page says so rather than copying DataFusion's syntax. Every section of the Documentation tab that has a SQL counterpart now links to it -- 27 of 76 non-SQL pages, each pointing at the specific statement rather than at the reference index.
122 lines
4.2 KiB
Plaintext
122 lines
4.2 KiB
Plaintext
---
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title: "Refresh a materialized view"
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sidebarTitle: "Refresh Materialized View"
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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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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; a
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refresh computes the rows.
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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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```python
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import lancedb
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db = lancedb.connect(
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"./.lancedb",
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storage_options={"new_table_enable_stable_row_ids": "true"},
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)
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table = db.create_table("people", [{"name": "ada", "age": 36},
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{"name": "kid", "age": 7}])
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view = db.create_materialized_view(
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"adults",
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"people",
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select=["name", ("shout", "upper(name)")],
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where="age >= 18",
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)
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result = view.refresh()
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result.rows_written
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# 1
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```
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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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## 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 only the rows the source
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has added 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()` returns the underlying table for querying, indexing, or search.
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`view.definition()` returns 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.
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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="/tables/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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