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https://issues.apache.org/jira/browse/SPARK-60399?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-60399:
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Labels: pull-request-available (was: )
> Add a Metric Views programming guide and a CREATE METRIC VIEW reference page
> ----------------------------------------------------------------------------
>
> Key: SPARK-60399
> URL: https://issues.apache.org/jira/browse/SPARK-60399
> Project: Spark
> Issue Type: Documentation
> Components: Documentation, SQL
> Affects Versions: 4.2.0, 4.3.0
> Reporter: Lisa Cao
> Priority: Major
> Labels: pull-request-available
>
> Spark 4.2.0 added metric views (SPARK-53524 and follow-ups): {{CREATE VIEW
> ... WITH METRICS LANGUAGE YAML AS $$ ... $$}} and the {{MEASURE()}} aggregate
> function. The docs have no page for either. The SQL reference has no entry
> for the statement or the YAML definition format, and the only mention of
> {{MEASURE}} is the generated built-in function entry.
> This adds two pages:
> * {{docs/metric-views-programming-guide.md}}, listed in the Programming
> Guides menu next to Declarative Pipelines: what a metric view is and why a
> measure is evaluated at the grain of the query that asks for it, a quick
> start, the key concepts (source, dimensions, measures, filter, grain),
> defining metric views in SQL, querying them from SQL and from the DataFrame
> API, modeling patterns (ratios as single measures, date dimensions, derived
> dimensions in a SQL source, a metric view over a metric view), lifecycle, and
> considerations.
> * {{docs/sql-ref-syntax-ddl-create-metric-view.md}}, in the shape of the
> other DDL reference pages (Description, Syntax, Parameters, Examples, Related
> Statements): the statement and its clauses, the v0.1 YAML definition, how
> column types are derived, what {{DESCRIBE EXTENDED}} shows, and the querying
> rules.
> It also links the reference page from the DDL list in {{sql-ref-syntax.md}},
> {{CREATE VIEW}}, {{ALTER VIEW}} and {{SHOW CREATE TABLE}}, and adds a
> {{MEASURE}} section to the aggregate function page.
> Every statement on both pages was run against Spark 4.3.0 and the output
> shown is what it produced.
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