afv-library/skills/data-cloud-bdt-expert/references/bdt-window-functions.md

Window Functions — for computeRelative nodes

Last synced: 2026-04-23 from the SFSQL window-functions reference and the BDT canonical schema. Consult this file whenever narrating a computeRelative node or explaining a window-function expression.

When this applies

computeRelative nodes evaluate a window function over rows. The parameters:

{
  "partitionBy": ["ssot__AccountId__c"],              // → SQL `PARTITION BY`
  "orderBy": [                                         // → SQL `ORDER BY`
    {"fieldName": "ssot__CreatedDate__c", "direction": "ASC"}
  ],
  "expressionType": "SQL",                             // or "DCSQL"
  "fields": [
    {
      "name": "OrderRank__c",
      "formulaExpression": "row_number()",             // the window function call
      "type": "NUMBER", "businessType": "Number",
      "precision": 18, "scale": 0
    }
  ]
}

The formulaExpression names the window function; partitioning and ordering come from the top-level partitionBy and orderBy. A computeRelative node may include at most one compute-relative function per expression (per upstream BDT docs).

Available window functions

Function	Returns	What it does
row_number()	NUMBER	1, 2, 3… for each row in its partition, in the given order. Non-deterministic when sort keys tie.
rank()	NUMBER	Like row_number but peers share a rank; next rank after N peers is N+1 (gaps).
dense_rank()	NUMBER	Like rank but no gaps — consecutive integers even with ties.
percent_rank()	NUMBER	(rank - 1) / (partition_rows - 1) — relative rank within partition, 0 to 1.
cume_dist()	NUMBER	Cumulative distribution: fraction of partition rows at or before current.
ntile(n)	NUMBER	Bucket number 1..n, dividing partition rows as evenly as possible.
lag(value) / lag(value, offset) / lag(value, offset, default)	same as value	Value at offset rows before current (default offset=1; default if no such row is NULL unless a default is supplied).
lead(value) / lead(value, offset) / lead(value, offset, default)	same as value	Symmetric with lag but looks forward.
first_value(value)	same as value	Value at the first row of the current window frame.
last_value(value)	same as value	Value at the last row of the frame. Default frame ends at "current + peers", which is often not what users want.
nth_value(value, n)	same as value	Value at the nth row of the frame (counting from 1). NULL if no such row.
Any aggregate with OVER(...)	depends	Runs the aggregate over the window (running sum, etc.).

How this maps to BDT JSON

• partitionBy is the SQL PARTITION BY — the columns that group rows into windows.
• orderBy is the SQL ORDER BY — the ordering within each partition.
• Peers are rows with identical sort keys.
• Default frame (when not otherwise specified): rows from the first row of the partition through the current row's last peer. For last_value and nth_value this is often not the user's intent — narrate accordingly.

Common narration patterns

• row_number() partitioned by X → "numbers each row within the same X, in the order given by orderBy."
• rank() partitioned by X order by Y → "ranks rows within each X group by Y; ties share a rank and the next rank has gaps."
• case when row_number()=1 then VAL else 0 end → "keeps VAL only on the first ranked row per partition; everything else is 0. This is the canonical 'first-occurrence extract' idiom."

Sources

• SFSQL window-functions reference (internal Data Cloud / SDB SFSQL docs).
• BDT canonical schema: ComputeRelativeParametersInputRepresentation, ComputeRelativeSortParametersInputRepresentation.