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Jack Ye
ddb318567a
## Summary - Complete the SQL reference: `ATTACH DATABASE`, the `CHANGES` clause, and `dedupe()`/`duplicate_pairs()` (verified against current sophon source; these were recently renamed from `vector_dedup`/`vector_duplicate_pairs`). Fix the blob-column default-projection claim and add the previously undocumented `blob_descriptor()`. - Enrich the gated Functions draft (still `.mintignore`'d, pre-release): chained Functions over blob outputs, GPU-produced blobs, Secrets, multi-input/output binding, `code=[module]` packaging. - Upgrade the materialized-view and computed-column auto-refresh drafts with precise, verified catch-up semantics, and add the missing `ALTER MATERIALIZED VIEW` / `CREATE ... WITH(...)` sections (also gated — the automatic-refresh scheduler is still dev/staging-only). - Thread one running example (video frames → GPU extract/caption → dedup → auto-refreshing materialized view) through Functions, materialized views, and the LeRobotDataset integration guide instead of a separate tutorial. - Rename "LanceDB OSS"/"OSS" to "LanceDB Embedded"/"Embedded" throughout (52 files); activate the existing availability-comparison mechanism on `basics/offerings.mdx` for a full feature-by-feature comparison table.
102 lines
2.7 KiB
Plaintext
102 lines
2.7 KiB
Plaintext
---
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title: "Window functions"
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sidebarTitle: "Window Functions"
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description: "Compute over a set of rows related to the current one, without collapsing them."
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icon: chart-simple
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keywords: ["window", "over", "partition by", "row_number", "rank", "lag", "lead"]
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availability:
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embedded: unavailable
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enterprise: available
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---
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A window function computes across a set of rows related to the current one and
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returns a value **per row**. That is the difference from
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[aggregation](/sql/dql/aggregation), which collapses the rows it covers.
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```sql
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SELECT
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title,
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score,
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row_number() OVER (PARTITION BY category ORDER BY score DESC) AS rank
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FROM documents
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```
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Every row survives; each one gains its rank within its category.
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## The window {#window}
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```sql
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OVER (
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[PARTITION BY expr [, ...]]
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[ORDER BY expr [, ...]]
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[ROWS | RANGE BETWEEN frame_start AND frame_end]
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)
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```
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`PARTITION BY` splits the rows into independent groups. `ORDER BY` orders them
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inside each partition. Supply an ordering for useful ranks, and a stable
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tie-breaker when selecting one row from several equal values. The frame clause narrows the window further, which is
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how running totals and moving averages are expressed:
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```sql
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SELECT
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created_at,
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sum(amount) OVER (ORDER BY created_at ROWS UNBOUNDED PRECEDING) AS running_total,
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avg(amount) OVER (ORDER BY created_at ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS trailing_7
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FROM transactions
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```
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A `WINDOW` clause names a definition when several functions share one:
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```sql
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SELECT
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row_number() OVER w,
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lag(score) OVER w
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FROM documents
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WINDOW w AS (PARTITION BY category ORDER BY score DESC)
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```
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## The functions {#functions}
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Registered in DataFusion 54.
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**Ranking**
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```text
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row_number rank dense_rank percent_rank cume_dist ntile
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```
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**Offset and positional**
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```text
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lag lead first_value last_value nth_value
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```
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**Aggregates**
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Any [aggregate function](/sql/dql/aggregation#functions) used with `OVER` becomes a
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window function. This is how running totals and moving averages are written.
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## Deduplicating with a window {#dedup}
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The common practical use: keep one row per key, chosen by some ordering. A
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window ranks, and an outer query filters:
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```sql
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WITH ranked AS (
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SELECT *, row_number() OVER (PARTITION BY doc_id ORDER BY version DESC) AS rn
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FROM revisions
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)
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SELECT * FROM ranked WHERE rn = 1
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```
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The outer query is needed because a window function cannot appear in `WHERE`:
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windows are computed after filtering, so the predicate would have nothing to
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read.
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<Note>
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Window functions sort within each partition, so on a large table they are among
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the more expensive things you can ask for. Filter first, and check the cost with
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[Explain Analyze](/search/explain).
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</Note>
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