Composite topics

Composite topics let you query across two or more independent topics in a single result set, sharing dimensions across them and filtering each topic by its own attributes. Each topic is aggregated independently in its own subquery, and the results are stitched together with a FULL OUTER JOIN on the dimensions and views you mark as shared. A composite topic keeps each fact in its own subquery, so every measure is computed against the correct grain. The result is a single analysis surface that pulls fields and filters from every topic.

​

When to use a composite topic

Reach for a composite topic when all the following are true:

• You want measures from two or more fact tables in the same chart, table, or dashboard tile
• The fact tables share at least one dimension you’d join on (date, customer, region, product)

If you only need measures from a single fact table — even if it joins to several dimension tables — a regular topic is the right choice.

​

Common patterns

​

Composite topics vs. XLOOKUP

XLOOKUP also lets you pull values from another query into the current one. For one-off, exploratory work, it’s often the faster choice. Composite topics are the right tool when the cross-fact analysis needs to be reusable, governed, and available to other users.

​

How it works

Each topic computes its measures independently, then results align on shared dimensions. Nothing fans out.

​

Field picker behavior

The workbook field picker organizes a composite topic’s fields into a few groups:

​

Known caveats and bugs

Current as of the latest beta release.

​

Behaviors to be aware of

• Topic-specific dimensions cannot be in SELECT. They can only be used as filters; selecting one returns a query error. This is by design — there’s no outer GROUP BY to make the result deterministic.
• Shared dimensions can map differently-named columns, but the values must align. Use mappings: to point each topic at its own field — the columns don’t need to share a name or SQL definition. They do need to represent the same logical value: if the underlying SQL differs in a way that changes the result (different timezone conversions, different casing, etc.), rows won’t align on the join even though the model validates.
• Shared measures don’t re-aggregate. Each per-topic subquery aggregates first, then the shared measure runs over the joined result row-by-row. Wrapping a field in SUM(...) or setting aggregate_type: won’t do what you expect — see shared_measures for details.
• Filters on shared measures can’t be pushed down. They’re applied as a WHERE on the post-join result. This is correct, but means a filter on a shared measure won’t reduce the size of the per-topic subqueries.
• All topics become active when any shared field is selected. When a user selects a shared dimension, shared view, or shared measure, every topic in the composite runs as a subquery — even if no fields from that topic are otherwise selected. This guarantees the FULL OUTER JOIN includes nulls from all sides.

​

Known bugs

• Flattening a pivot on a shared dimension errors. Building a pivot table on a composite topic where the pivot field is a shared dimension and clicking Flatten returns No such topic <subtopic name>. A fix is in progress.
• Period-over-period can error on composite topics. Adding a period-over-period comparison to a query with composite topic measures can return an error in some cases.

​

Access controls and AI context

Access grants, row-level security, and AI context defined on each included topic continue to apply when that topic is queried as part of a composite. The composite topic itself does not currently support its own required_access_grants; control access by gating the included topics.

​

We want your feedback

Composite topics are in beta — your input shapes GA. We’re especially interested in:

• Use cases that don’t fit — what cross-fact analyses are you trying to do that this doesn’t support?
• YAML ergonomics — is the shared_dimensions / shared_measures / shared_views shape intuitive? Are there parameters you expected to find?
• Workbook UX rough edges — confusing menus, unclear errors, missing affordances
• Performance — slow plans, surprising query shapes, cases where the subquery breakdown produces expensive SQL

​

Roadmap

​

Modeling

• Remove @ from shared_measures syntax.
• Customize timeframe labels on shared dimensions. When a shared dimension wraps a date or timestamp, the timeframe labels (Day, Week, Month, etc.) currently come from the underlying field. We’re adding the ability to override them from the shared_dimensions definition.

​

Workbook

• Consolidate composite topics into the main topic picker. Composite topics appear in their own group rather than mixing into existing topic subfolders alongside regular topics. We plan to surface them together so end users don’t need to distinguish between the two.
• Workbook-side editing for composite topics, similar to the topic editor for regular topics.
• Promote a calculation to a shared measure. If you build a calc in a workbook that combines fields from multiple topics, you’ll be able to promote it directly into the composite topic’s shared_measures block — the same flow as promoting a calc to a regular measure today.
• Choose which fields each sub-topic exposes. Include or exclude specific fields from each included topic, rather than surfacing every field from every sub-topic.
• Column totals. Support for column totals on composite topic results.
• General workbook UI improvements. We plan to invest some thought into cleaning up the design based on feedback, including tightening field indentation in the field browser.

​

Agent support

• Support interaction through Omni Agents. Natural language workflows such as querying or modeling composite topics are not currently supported with composite topics.

If we make breaking changes, we’ll publish a migration note and reach out to teams using the beta directly.

​

Next steps

• Create a composite topic — set one up in the model IDE and refine its shared views, dimensions, and measures.
• Composite topic examples — worked composite topics you can adapt.
• Composite topic parameters — the full parameter reference.
• Curating datasets with topics — overview of regular topics and how they fit into the model.