GitHub user dosubot[bot] added a comment to the discussion: RLS condition 
inconsistently applied inside dataset metric subqueries

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the superset team.

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Here's what I found about how RLS injection works in Superset and the behavior 
you're experiencing:

**How Superset decides where to inject RLS:**

Superset uses sqlglot's AST (Abstract Syntax Tree) transformation to 
recursively detect **all tables** in a query—including those inside subqueries 
and metric definitions—and applies RLS predicates to each one 
[[1]](https://github.com/apache/superset/blob/829e4d92d91ceae4b43b1ed3b063ffe45377799c/superset/sql/parse.py).
 The injection method depends on the database engine's capabilities 
[[2]](https://github.com/apache/superset/blob/829e4d92d91ceae4b43b1ed3b063ffe45377799c/superset/db_engine_specs/base.py#L440-L452):

- **AS_PREDICATE**: Appends RLS directly to WHERE/ON clauses
- **AS_SUBQUERY**: Wraps each table in a filtered subquery (used when the DB 
supports subqueries and aliases)

**Why metric subqueries are sometimes affected:**

When the `ALLOW_ADHOC_SUBQUERY` feature flag is enabled, Superset's 
`validate_adhoc_subquery()` function parses ad-hoc SQL expressions (including 
metric definitions) and calls `apply_rls()` on any subqueries it detects 
[[3]](https://github.com/apache/superset/blob/829e4d92d91ceae4b43b1ed3b063ffe45377799c/superset/models/helpers.py#L108-L139).
 This means RLS gets injected into tables at **all nesting levels**. The 
inconsistency you're seeing likely depends on whether a given metric's SQL 
contains explicit table references that sqlglot can detect vs. simple column 
references.

**Can you control RLS scope?**

Unfortunately, **there is no mechanism to selectively exclude specific 
subqueries or metrics from RLS injection** 
[[4]](https://github.com/apache/superset/issues/37695). The recursive table 
detection applies RLS to every table it finds. Your only levers are:

- `RLS_IN_SQLLAB` feature flag (controls whether RLS applies in SQL Lab) 
[[5]](https://github.com/apache/superset/blob/829e4d92d91ceae4b43b1ed3b063ffe45377799c/superset/config.py#L517-L519)
- Role-based filter types (`REGULAR` vs `BASE`) to control which roles get the 
filter
- `group_key` to OR filters together within a group 
[[6]](https://github.com/apache/superset/blob/829e4d92d91ceae4b43b1ed3b063ffe45377799c/superset/connectors/sqla/models.py#L740-L753)

**Recommended workarounds for your use case:**

1. **Restructure metrics** so they don't reference tables directly in 
subqueries—instead, rely on the dataset's main query to pre-filter rows before 
aggregation.
2. **Use physical datasets** where possible, as virtual datasets with JOINs can 
trigger double-application of RLS (a known regression in Superset 6) 
[[7]](https://github.com/apache/superset/pull/38601).
3. **Design your RLS clause** to be safe when applied at any level—e.g., ensure 
`warehouse_id` exists on all referenced tables, or make the clause a no-op on 
tables without that column by using conditions that always evaluate to true on 
irrelevant tables.
4. **Specify explicit dataset IDs** in RLS rules rather than applying them 
globally, so the clause only targets datasets where it makes sense.

This is a known architectural limitation. There's ongoing work related to RLS 
double-application in virtual datasets ([PR 
#37395](https://github.com/apache/superset/pull/37395)), but no fine-grained 
scope control for RLS injection is currently planned.


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GitHub link: 
https://github.com/apache/superset/discussions/40400#discussioncomment-17039190

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