[pull] master from cube-js:master

docs-mintlify/docs/data-modeling/concepts/index.mdx

@@ -3,11 +3,13 @@ title: Concepts
 description: Learn foundational OLAP concepts like cubes, dimensions, measures, and joins used in Cube data modeling.
 ---
 
- Concepts
+Cube's key concepts are [cubes](#cubes), [views](#views), and members
+([measures](#measures), [dimensions](#dimensions)). This page is intended
+for both newcomers and regular users to refresh their understanding.
 
-Cube borrows a lot of terminology from [OLAP
-theory][wiki-olap], and this document is intended for both newcomers and regular
-users to refresh their understanding.
+<Frame>
+  <img src="https://lgo0ecceic.ucarecd.net/cdfe8858-f01d-4c25-af32-26502db62f1c/" />
+</Frame>
 
 We'll use a sample e-commerce database with two tables, `orders` and
 `line_items` to illustrate the concepts throughout this page:
@@ -136,10 +138,6 @@ data model with which data consumers can interact. They are useful for defining
 metrics, managing governance and data access, and controlling ambiguous join
 paths.
 
-<Frame>
-  <img src="https://ucarecdn.com/bfc3e04a-b690-40bc-a6f8-14a9175fb4fd/" />
-</Frame>
-
 Views do **not** define their own members. Instead, they reference cubes by
 specific join paths and include their members.
 
@@ -834,7 +832,7 @@ See the reference documentaton for the full list of pre-aggregation
 [ref-subquery-dimensions]: /docs/data-modeling/concepts/calculated-members#subquery-dimensions
 [ref-calculated-measures]: /docs/data-modeling/concepts/calculated-members#calculated-measures
 [ref-working-with-joins]: /docs/data-modeling/concepts/working-with-joins
-[wiki-olap]: https://en.wikipedia.org/wiki/Online_analytical_processing
+
 [wiki-view-sql]: https://en.wikipedia.org/wiki/View_(SQL)
 [ref-matching-preaggs]: /docs/pre-aggregations/matching-pre-aggregations
 [ref-syntax-references]: /docs/data-modeling/syntax#references

docs-mintlify/docs/data-modeling/overview.mdx

@@ -9,6 +9,10 @@ exposed through a [rich set of APIs][ref-apis] that allows end-users to
 run a wide variety of analytical queries without modifying the data model
 itself.
 
+<Frame>
+  <img src="https://lgo0ecceic.ucarecd.net/cdfe8858-f01d-4c25-af32-26502db62f1c/" />
+</Frame>
+
 <Info>
 
 You can explore a carefully crafted sample data model if you create a [demo

rust/cubestore/cubestore/src/cachestore/cache_rocksstore.rs

@@ -498,14 +498,14 @@ impl RocksCacheStore {
         f: F,
     ) -> Result<R, CubeError>
     where
-        F: for<'a> FnOnce(DbTableRef<'a>, &'a mut BatchPipe) -> Result<R, CubeError>
+        F: for<'a> FnOnce(DbTableRef<'a>, &mut BatchPipe<'a>) -> Result<R, CubeError>
             + Send
             + Sync
             + 'static,
         R: Send + Sync + 'static,
     {
         self.store
-            .write_operation_impl::<F, R>(&self.rw_loop_queue_cf, op_name, f)
+            .write_operation_impl::<F, R, ()>(&self.rw_loop_queue_cf, op_name, f, ())
             .await
     }
 

rust/cubestore/cubestore/src/lib.rs

@@ -214,6 +214,12 @@ impl From<ParserError> for CubeError {
     }
 }
 
+impl From<regex::Error> for CubeError {
+    fn from(v: regex::Error) -> Self {
+        CubeError::from_error(v)
+    }
+}
+
 impl From<ParquetError> for CubeError {
     fn from(v: ParquetError) -> Self {
         CubeError::from_error(v.to_string())