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Jack Ye
734eb39dc3
Six groups were verbs -- Manage, Ingest, Train, Operate, Secure, Deploy -- and now name the thing rather than the action. Four more were sentence case. The page labels that were verb phrases follow: Add Computed Column becomes Computed Columns, Refresh Column becomes Column Refresh, Configuring Storage becomes Configuration, and Explain becomes Query Plans, which is also more accurate now that the page covers analyze as well. SQL statement pages take the keyword -- SELECT, DDL, DML, REFRESH, SHOW, EXPLAIN -- because a statement name is a noun and lowercase Select and Show read as instructions. Concept pages there keep noun phrases: Joins, Window Functions, Subqueries, Operators. Two duplicate labels are resolved on the way. Reranking sat in Semantic Search labelled Overview despite being the only page on its subject, and the Enterprise client page sat in SQL Reference labelled SQL next to the reference Overview; it is Client Connection now. Title case leaves prepositions and articles lowercase, so Needle in a Haystack and Hybrid Search on BEIR are already correct and unchanged.
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: "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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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="/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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