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Jack Ye
52684d50b9
OSS docs now own the full usage content for features that only run against an Enterprise deployment, since the client interface itself lives here; Enterprise docs keep only the parts specific to its own behavior. Sophon's overlay for these five pages is dropped since the OSS versions are now the complete reference. SQL Reference is restructured to match an object-type SQL manual (DDL by object, then DML, then DQL, then Expressions) instead of the old flat, loosely-ordered page list, absorbing the SQL client-connection guide and FTS-via-SQL examples into it.
115 lines
5.1 KiB
Plaintext
115 lines
5.1 KiB
Plaintext
---
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title: "Feature engineering"
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sidebarTitle: "Overview"
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description: "Derive columns and tables from data already in the lakehouse, without moving it somewhere else to compute."
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icon: layer-group
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keywords: ["feature engineering", "computed columns", "materialized views", "functions", "lakehouse", "jobs"]
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---
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Feature engineering happens where the data already is. Rather than exporting a
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table, computing somewhere else and writing the result back, you declare what a
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column or a table should contain and let LanceDB fill it.
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[Functions](/management/function) and
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[materialized views](/feature-engineering/materialized-views) are ordinary
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[catalog objects](/basics/catalog), alongside tables and indexes -- created,
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versioned, and refreshed like anything else, not a separate system bolted
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alongside.
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<Note>
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Every operation here has a SQL form -- see [DDL](/sql/ddl/table#alter) to declare a column and [REFRESH](/sql/dml/refresh) to fill it.
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</Note>
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## The shape everything shares {#the-shape}
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Every mechanism here separates **declaring** from **filling**, and that
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separation is the point:
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- Declaring is cheap and immediate. It records intent in the schema and commits
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a new table version, with no data written. Declaring a column on a
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billion-row table costs what declaring one on an empty table costs.
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- Filling is a separate call you control, can retry, and -- against a deployment
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-- can hand to the server as a [job](/basics/jobs) rather than waiting on.
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**Filling never revisits work it has already done.** A refresh gives values to
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rows that lack them and leaves the rest alone, so calling it twice is not twice
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the work, and appending rows means the next refresh handles only the new ones.
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The corollary is worth stating plainly: **a filled value is not recomputed when
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its inputs change.** If a column derives from `price` and a row's `price` is
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later updated, the derived value stays as it was computed. Recomputing means
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dropping the column and declaring it again. That is a deliberate trade -- it is
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what makes refreshes idempotent and cheap -- but it will surprise you if you
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expect a spreadsheet.
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## Which one to reach for {#which-one}
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| | What it produces | Filled by | Available |
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| --- | --- | --- | --- |
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| [Computed column](/feature-engineering/add-computed-column) | a new column on an existing table | a SQL expression | everywhere |
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| [Materialized view](/feature-engineering/materialized-views) | a new table derived by a query | a refresh | everywhere |
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| [Function](/management/function) | a computed column filled by Python | registered code | <Badge color="red">Enterprise</Badge> |
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**Computed columns** are the common case: a column declared with a SQL
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expression, filled by [a refresh](/feature-engineering/refresh-column).
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**Materialized views** derive a whole table -- a filtered, projected, or
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aggregated view of a source -- and keep it current incrementally. The result is
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an ordinary table: queryable, indexable, searchable.
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**Functions** run Python where the data is, for work SQL cannot express:
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embedding a column, calling a model, decoding an image. Registered once,
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versioned, then applied as a column.
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They compose. A materialized view can select computed columns, and a computed
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column can be filled by a registered function.
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## Where the work runs {#where}
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<Badge color="red">Enterprise</Badge>
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Locally, a refresh runs in your process. Against a deployment it runs across the
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cluster, and the API reflects that: `refresh_column` raises, and
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`refresh_column_async` returns a [job](/basics/jobs) handle instead.
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```python
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job = table.refresh_column_async("embedding")
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result = job.wait()
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result.rows_assigned
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```
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A backfill over a large table can therefore be started, left, and checked on
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from a different process -- the connection does not have to stay open.
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That relocation is the main thing to understand about Enterprise feature
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engineering. The API is the same; what changes is that the code runs somewhere
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else, which is also why a function must declare the environment it needs.
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## Failure is resumable {#failures}
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A function that raises fails the job. There is no per-row retry or skip policy
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in the function definition, so a function calling a flaky external service
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should handle that itself, inside the function body.
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The alternative is to let the job fail and run the refresh again. Since a
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refresh never revisits a filled row, re-running after a partial failure costs
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only the rows that did not complete -- **a failed backfill is resumed, not
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restarted.**
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## Where to go next {#next}
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<CardGroup cols={2}>
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<Card title="Add a computed column" icon="table-columns" href="/feature-engineering/add-computed-column">
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Declare a column from a SQL expression.
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</Card>
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<Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
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Fill the rows that lack values, locally or as a job.
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</Card>
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<Card title="Refresh a materialized view" icon="table" href="/feature-engineering/materialized-views">
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Bring a derived table up to date with its source.
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</Card>
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<Card title="Functions" icon="code" href="/management/function">
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Register Python to fill a column with.
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</Card>
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</CardGroup>
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