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Ten merges and renames, all in one direction: fewer pages, each covering a whole subject. Table absorbs schema evolution and versioning. Vector search absorbs multi-vector. Explain absorbs explain analyze. Reranking was seven pages -- an overview and six rerankers of a few dozen lines each -- and is now one. The vector index page absorbs quantization, which was only ever a parameter of it. Indexing moves to Manage, where the other catalog objects are. The operational half stays in Operate as one Indexing page, absorbing GPU indexing -- which was filed in sophon despite importing lancedb's snippets and being mostly open-source content with one Enterprise-badged section. Performance was a single page aggregating tips for four unrelated operations, which is hard to keep current. Each section now sits with the operation it is about: ingestion tips on Insert, index tips on Indexing, compaction tips on Compaction, query tips on Search. Data Types leaves Concepts. The type reference belongs in the SQL reference, which already described the same types from the query side; Table now carries a short summary that points there. The multimodal material it had absorbed is its own page again, since storing blobs is a task rather than a type definition. Query becomes Semantic Search, Branches becomes Branch, and the Enterprise Security page becomes Compliance. Redirects cover every moved URL, and 30 files were repointed at the merged anchors.
118 lines
4.3 KiB
Plaintext
118 lines
4.3 KiB
Plaintext
---
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title: "Data evolution"
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sidebarTitle: "Data Evolution"
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description: "Derive new columns from data already in the table, without rewriting what is already there."
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icon: wand-magic-sparkles
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keywords: ["data evolution", "derived columns", "add_columns", "zero copy", "feature engineering"]
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---
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import {
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PySchemaAddSetup as SchemaAddSetup,
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TsSchemaAddSetup as TsSchemaAddSetup,
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RsSchemaAddSetup as RsSchemaAddSetup,
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PyAddColumnsCalculated as AddColumnsCalculated,
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TsAddColumnsCalculated as TsAddColumnsCalculated,
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RsAddColumnsCalculated as RsAddColumnsCalculated,
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PyAddFeatureColumnsSql as AddFeatureColumnsSql,
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TsAddFeatureColumnsSql as TsAddFeatureColumnsSql,
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RsAddFeatureColumnsSql as RsAddFeatureColumnsSql,
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} from '/snippets/tables.mdx';
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Data evolution is adding **values** derived from what a table already holds.
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[Schema evolution](/management/table#schema) changes the table's shape; this fills the
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shape with something new.
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The reason it is cheap is the format. Adding a derived column writes only that
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column -- the existing data, which is the expensive part on a multimodal table,
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is never rewritten. See [why that works](/basics/lance#evolution).
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<Note>
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In SQL this is [`ALTER TABLE ... ADD COLUMN ... AS`](/sql/ddl#computed-column).
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</Note>
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## Set up the example table {#set-up-the-example-table}
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{SchemaAddSetup}
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</CodeBlock>
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<CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
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{TsSchemaAddSetup}
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</CodeBlock>
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<CodeBlock filename="Rust" language="Rust" icon="rust">
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{RsSchemaAddSetup}
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</CodeBlock>
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</CodeGroup>
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## Add derived columns {#add-derived-columns}
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You can add new columns that are derived from existing data using SQL expressions.
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For feature engineering on large existing tables, group related derived features into
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one `add_columns` operation instead of running many separate writes. This creates one
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new table version for the schema change and computes the new columns from the existing
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rows, which avoids growing the table's version history with many small updates.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{AddColumnsCalculated}
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</CodeBlock>
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<CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
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{TsAddColumnsCalculated}
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</CodeBlock>
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<CodeBlock filename="Rust" language="Rust" icon="rust">
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{RsAddColumnsCalculated}
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</CodeBlock>
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</CodeGroup>
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The same call can add multiple derived columns at once. For example, if you are
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building several lightweight features from existing product fields, pass all of the
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new column expressions together:
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{AddFeatureColumnsSql}
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</CodeBlock>
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<CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
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{TsAddFeatureColumnsSql}
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</CodeBlock>
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<CodeBlock filename="Rust" language="Rust" icon="rust">
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{RsAddFeatureColumnsSql}
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</CodeBlock>
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</CodeGroup>
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<Info>
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LanceDB `add_columns` does not currently accept Python callables, batch UDFs, or
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PyArrow `RecordBatch` iterators for populating new columns. New column values must be
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defined with SQL expressions, or added as NULL columns from an Arrow field or schema.
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If your transformation cannot be expressed in SQL, compute the values outside
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`add_columns` before writing them back through another workflow.
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</Info>
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## When SQL is not enough {#beyond-sql}
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The expressions above are evaluated once, as part of the call. Two other shapes
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exist when that does not fit:
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- A [computed column](/feature-engineering/add-computed-column) declares the
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expression and leaves the column empty, so you choose when the work happens.
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That is the right default on a large table.
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- A [function](/management/function) fills a column with Python, for values SQL
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cannot express -- an embedding, a model call, an image decode.
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## What to read 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 now, fill later.
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</Card>
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<Card title="Schema evolution" icon="boxes-stacked" href="/management/table#schema">
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Change the table's shape instead of its contents.
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</Card>
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</CardGroup>
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